

'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
  from huggingface_hub import snapshot_download
  from pyctcdecode import BeamSearchDecoderCTC

  from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
  from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
  from transformers import WavaVecaForCTC







@require_pyctcdecode
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
      __SCREAMING_SNAKE_CASE      = dict(zip(__SCREAMING_SNAKE_CASE   ,				range(len(__SCREAMING_SNAKE_CASE     )     )     )     )

      __SCREAMING_SNAKE_CASE      = {
          """unk_token""": """<unk>""",
          """bos_token""": """<s>""",
          """eos_token""": """</s>""",
      }
      __SCREAMING_SNAKE_CASE      = {
          """feature_size""": 1,
          """padding_value""": 0.0,
          """sampling_rate""": 16_000,
          """return_attention_mask""": False,
          """do_normalize""": True,
      }

      __SCREAMING_SNAKE_CASE      = tempfile.mkdtemp()
      __SCREAMING_SNAKE_CASE      = os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["""vocab_file"""]     )
      __SCREAMING_SNAKE_CASE      = os.path.join(self.tmpdirname   ,				__SCREAMING_SNAKE_CASE     )
      with open(self.vocab_file   ,				"""w"""   ,				encoding="""utf-8"""     ) as fp:
           fp.write(json.dumps(__SCREAMING_SNAKE_CASE     ) + """\n"""     )

      with open(self.feature_extraction_file   ,				"""w"""   ,				encoding="""utf-8"""     ) as fp:
           fp.write(json.dumps(__SCREAMING_SNAKE_CASE     ) + """\n"""     )

      # load decoder from hub
      __SCREAMING_SNAKE_CASE      = """hf-internal-testing/ngram-beam-search-decoder"""
 def  UpperCAmelCase__						(						self   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.add_kwargs_tokens_map.copy()
      kwargs.update(__SCREAMING_SNAKE_CASE     )
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					Any:


      """simple docstring"""




      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			Any     )    ->					List[Any]:


      """simple docstring"""




      return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Optional[int]:


      """simple docstring"""




      shutil.rmtree(self.tmpdirname     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_feature_extractor()
      __SCREAMING_SNAKE_CASE      = self.get_decoder()

      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE   ,				feature_extractor=__SCREAMING_SNAKE_CASE   ,				decoder=__SCREAMING_SNAKE_CASE     )

      processor.save_pretrained(self.tmpdirname     )
      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname     )

      # tokenizer
      self.assertEqual(processor.tokenizer.get_vocab()   ,				tokenizer.get_vocab()     )
      self.assertIsInstance(processor.tokenizer   ,				__SCREAMING_SNAKE_CASE     )

      # feature extractor
      self.assertEqual(processor.feature_extractor.to_json_string()   ,				feature_extractor.to_json_string()     )
      self.assertIsInstance(processor.feature_extractor   ,				__SCREAMING_SNAKE_CASE     )

      # decoder
      self.assertEqual(processor.decoder._alphabet.labels   ,				decoder._alphabet.labels     )
      self.assertEqual(
          processor.decoder.model_container[decoder._model_key]._unigram_set   ,				decoder.model_container[decoder._model_key]._unigram_set   ,				)
      self.assertIsInstance(processor.decoder   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM(
          tokenizer=self.get_tokenizer()   ,				feature_extractor=self.get_feature_extractor()   ,				decoder=self.get_decoder()     )
      processor.save_pretrained(self.tmpdirname     )

      # make sure that error is thrown when decoder alphabet doesn't match
      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM.from_pretrained(
          self.tmpdirname   ,				alpha=5.0   ,				beta=3.0   ,				score_boundary=-7.0   ,				unk_score_offset=3     )

      # decoder
      self.assertEqual(processor.language_model.alpha   ,				5.0     )
      self.assertEqual(processor.language_model.beta   ,				3.0     )
      self.assertEqual(processor.language_model.score_boundary   ,				-7.0     )
      self.assertEqual(processor.language_model.unk_score_offset   ,				3     )
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      # add token to trigger raise
      tokenizer.add_tokens(["""xx"""]     )
      with self.assertRaisesRegex(__SCREAMING_SNAKE_CASE   ,				"""include"""     ):
           WavaVecaProcessorWithLM(
               tokenizer=__SCREAMING_SNAKE_CASE   ,				feature_extractor=self.get_feature_extractor()   ,				decoder=self.get_decoder()     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_feature_extractor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_decoder()

      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE   ,				feature_extractor=__SCREAMING_SNAKE_CASE   ,				decoder=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = floats_list((3, 1_000)     )

      __SCREAMING_SNAKE_CASE      = feature_extractor(__SCREAMING_SNAKE_CASE   ,				return_tensors="""np"""     )
      __SCREAMING_SNAKE_CASE      = processor(__SCREAMING_SNAKE_CASE   ,				return_tensors="""np"""     )

      for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()   ,				input_processor[key].sum()   ,				delta=1E-2     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_feature_extractor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_decoder()

      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE   ,				feature_extractor=__SCREAMING_SNAKE_CASE   ,				decoder=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = """This is a test string"""

      __SCREAMING_SNAKE_CASE      = processor(text=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = tokenizer(__SCREAMING_SNAKE_CASE     )

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]   ,				encoded_processor[key]     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=(2, 10, 16)   ,				__SCREAMING_SNAKE_CASE   :			str=77     )    ->					str:


      """simple docstring"""




      np.random.seed(__SCREAMING_SNAKE_CASE     )
      return np.random.rand(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_feature_extractor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_decoder()

      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE   ,				feature_extractor=__SCREAMING_SNAKE_CASE   ,				decoder=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self._get_dummy_logits(shape=(10, 16)   ,				seed=13     )

      __SCREAMING_SNAKE_CASE      = processor.decode(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = decoder.decode_beams(__SCREAMING_SNAKE_CASE     )[0]

      self.assertEqual(decoded_decoder[0]   ,				decoded_processor.text     )
      self.assertEqual("""</s> <s> </s>"""   ,				decoded_processor.text     )
      self.assertEqual(decoded_decoder[-2]   ,				decoded_processor.logit_score     )
      self.assertEqual(decoded_decoder[-1]   ,				decoded_processor.lm_score     )
 @parameterized.expand([[None], ["""fork"""], ["""spawn"""]]     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_feature_extractor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_decoder()

      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE   ,				feature_extractor=__SCREAMING_SNAKE_CASE   ,				decoder=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self._get_dummy_logits()

      # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
      #       otherwise, the LM won't be available to the pool's sub-processes.
      # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
      if pool_context is None:
           __SCREAMING_SNAKE_CASE      = processor.batch_decode(__SCREAMING_SNAKE_CASE     )
      else:
           with get_context(__SCREAMING_SNAKE_CASE     ).Pool() as pool:
                __SCREAMING_SNAKE_CASE      = processor.batch_decode(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = list(__SCREAMING_SNAKE_CASE     )

      with get_context("""fork"""     ).Pool() as p:
           __SCREAMING_SNAKE_CASE      = decoder.decode_beams_batch(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = [], [], []
      for beams in decoded_beams:
           texts_decoder.append(beams[0][0]     )
           logit_scores_decoder.append(beams[0][-2]     )
           lm_scores_decoder.append(beams[0][-1]     )

      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				decoded_processor.text     )
      self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]   ,				decoded_processor.text     )
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				decoded_processor.logit_score     )
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				decoded_processor.lm_score     )
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_feature_extractor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_decoder()

      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE   ,				feature_extractor=__SCREAMING_SNAKE_CASE   ,				decoder=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self._get_dummy_logits()

      __SCREAMING_SNAKE_CASE      = 15
      __SCREAMING_SNAKE_CASE      = -20.0
      __SCREAMING_SNAKE_CASE      = -4.0

      __SCREAMING_SNAKE_CASE      = processor.batch_decode(
          __SCREAMING_SNAKE_CASE   ,				beam_width=__SCREAMING_SNAKE_CASE   ,				beam_prune_logp=__SCREAMING_SNAKE_CASE   ,				token_min_logp=__SCREAMING_SNAKE_CASE   ,				)
      __SCREAMING_SNAKE_CASE      = decoded_processor_out.text

      __SCREAMING_SNAKE_CASE      = list(__SCREAMING_SNAKE_CASE     )

      with get_context("""fork"""     ).Pool() as pool:
           __SCREAMING_SNAKE_CASE      = decoder.decode_beams_batch(
               __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				beam_width=__SCREAMING_SNAKE_CASE   ,				beam_prune_logp=__SCREAMING_SNAKE_CASE   ,				token_min_logp=__SCREAMING_SNAKE_CASE   ,				)

      __SCREAMING_SNAKE_CASE      = [d[0][0] for d in decoded_decoder_out]
      __SCREAMING_SNAKE_CASE      = [d[0][2] for d in decoded_decoder_out]
      __SCREAMING_SNAKE_CASE      = [d[0][3] for d in decoded_decoder_out]

      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]   ,				__SCREAMING_SNAKE_CASE     )

      self.assertTrue(np.array_equal(__SCREAMING_SNAKE_CASE   ,				decoded_processor_out.logit_score     )     )
      self.assertTrue(np.allclose([-20.054, -18.447]   ,				__SCREAMING_SNAKE_CASE   ,				atol=1E-3     )     )

      self.assertTrue(np.array_equal(__SCREAMING_SNAKE_CASE   ,				decoded_processor_out.lm_score     )     )
      self.assertTrue(np.allclose([-15.554, -13.9474]   ,				__SCREAMING_SNAKE_CASE   ,				atol=1E-3     )     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_feature_extractor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_decoder()

      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE   ,				feature_extractor=__SCREAMING_SNAKE_CASE   ,				decoder=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self._get_dummy_logits()

      __SCREAMING_SNAKE_CASE      = 2.0
      __SCREAMING_SNAKE_CASE      = 5.0
      __SCREAMING_SNAKE_CASE      = -20.0
      __SCREAMING_SNAKE_CASE      = True

      __SCREAMING_SNAKE_CASE      = processor.batch_decode(
          __SCREAMING_SNAKE_CASE   ,				alpha=__SCREAMING_SNAKE_CASE   ,				beta=__SCREAMING_SNAKE_CASE   ,				unk_score_offset=__SCREAMING_SNAKE_CASE   ,				lm_score_boundary=__SCREAMING_SNAKE_CASE   ,				)
      __SCREAMING_SNAKE_CASE      = decoded_processor_out.text

      __SCREAMING_SNAKE_CASE      = list(__SCREAMING_SNAKE_CASE     )
      decoder.reset_params(
          alpha=__SCREAMING_SNAKE_CASE   ,				beta=__SCREAMING_SNAKE_CASE   ,				unk_score_offset=__SCREAMING_SNAKE_CASE   ,				lm_score_boundary=__SCREAMING_SNAKE_CASE   ,				)

      with get_context("""fork"""     ).Pool() as pool:
           __SCREAMING_SNAKE_CASE      = decoder.decode_beams_batch(
               __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				)

      __SCREAMING_SNAKE_CASE      = [d[0][0] for d in decoded_decoder_out]

      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = processor.decoder.model_container[processor.decoder._model_key]
      self.assertEqual(lm_model.alpha   ,				2.0     )
      self.assertEqual(lm_model.beta   ,				5.0     )
      self.assertEqual(lm_model.unk_score_offset   ,				-20.0     )
      self.assertEqual(lm_model.score_boundary   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""     )

      __SCREAMING_SNAKE_CASE      = processor.decoder.model_container[processor.decoder._model_key]
      __SCREAMING_SNAKE_CASE      = Path(language_model._kenlm_model.path.decode("""utf-8"""     )     ).parent.parent.absolute()

      __SCREAMING_SNAKE_CASE      = os.listdir(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = ["""alphabet.json""", """language_model"""]

      downloaded_decoder_files.sort()
      expected_decoder_files.sort()

      # test that only decoder relevant files from
      # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
      # are downloaded and none of the rest (e.g. README.md, ...)
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = snapshot_download("""hf-internal-testing/processor_with_lm"""     )

      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM.from_pretrained(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = processor.decoder.model_container[processor.decoder._model_key]
      __SCREAMING_SNAKE_CASE      = Path(language_model._kenlm_model.path.decode("""utf-8"""     )     ).parent.parent.absolute()

      __SCREAMING_SNAKE_CASE      = os.listdir(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = os.listdir(__SCREAMING_SNAKE_CASE     )

      local_decoder_files.sort()
      expected_decoder_files.sort()

      # test that both decoder form hub and local files in cache are the same
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""     )
      __SCREAMING_SNAKE_CASE      = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""     )

      __SCREAMING_SNAKE_CASE      = floats_list((3, 1_000)     )

      __SCREAMING_SNAKE_CASE      = processor_wavaveca(__SCREAMING_SNAKE_CASE   ,				return_tensors="""np"""     )
      __SCREAMING_SNAKE_CASE      = processor_auto(__SCREAMING_SNAKE_CASE   ,				return_tensors="""np"""     )

      for key in input_wavaveca.keys():
           self.assertAlmostEqual(input_wavaveca[key].sum()   ,				input_auto[key].sum()   ,				delta=1E-2     )

      __SCREAMING_SNAKE_CASE      = self._get_dummy_logits()

      __SCREAMING_SNAKE_CASE      = processor_wavaveca.batch_decode(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = processor_auto.batch_decode(__SCREAMING_SNAKE_CASE     )

      self.assertListEqual(decoded_wavaveca.text   ,				decoded_auto.text     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.get_feature_extractor()
      __SCREAMING_SNAKE_CASE      = self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      = self.get_decoder()

      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM(tokenizer=__SCREAMING_SNAKE_CASE   ,				feature_extractor=__SCREAMING_SNAKE_CASE   ,				decoder=__SCREAMING_SNAKE_CASE     )

      self.assertListEqual(
          processor.model_input_names   ,				feature_extractor.model_input_names   ,				msg="""`processor` and `feature_extractor` model input names do not match"""   ,				)
 @staticmethod
 def  UpperCAmelCase__						(						__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = [d[key] for d in offsets]
      return retrieved_list
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""     )
      __SCREAMING_SNAKE_CASE      = self._get_dummy_logits()[0]

      __SCREAMING_SNAKE_CASE      = processor.decode(__SCREAMING_SNAKE_CASE   ,				output_word_offsets=__SCREAMING_SNAKE_CASE     )
      # check Wav2Vec2CTCTokenizerOutput keys for word
      self.assertEqual(len(outputs.keys()     )   ,				4     )
      self.assertTrue("""text""" in outputs     )
      self.assertTrue("""word_offsets""" in outputs     )
      self.assertTrue(isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )     )

      self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""]   ,				"""word"""     )     )   ,				outputs.text     )
      self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]   ,				"""word"""     )   ,				["""<s>""", """<s>""", """</s>"""]     )
      self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]   ,				"""start_offset"""     )   ,				[0, 2, 4]     )
      self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]   ,				"""end_offset"""     )   ,				[1, 3, 5]     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""     )
      __SCREAMING_SNAKE_CASE      = self._get_dummy_logits()

      __SCREAMING_SNAKE_CASE      = processor.batch_decode(__SCREAMING_SNAKE_CASE   ,				output_word_offsets=__SCREAMING_SNAKE_CASE     )

      # check Wav2Vec2CTCTokenizerOutput keys for word
      self.assertEqual(len(outputs.keys()     )   ,				4     )
      self.assertTrue("""text""" in outputs     )
      self.assertTrue("""word_offsets""" in outputs     )
      self.assertTrue(isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )     )

      self.assertListEqual(
          [""" """.join(self.get_from_offsets(__SCREAMING_SNAKE_CASE   ,				"""word"""     )     ) for o in outputs["""word_offsets"""]]   ,				outputs.text     )
      self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]   ,				"""word"""     )   ,				["""<s>""", """<s>""", """</s>"""]     )
      self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]   ,				"""start_offset"""     )   ,				[0, 2, 4]     )
      self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]   ,				"""end_offset"""     )   ,				[1, 3, 5]     )


 @slow
 @require_torch
 @require_torchaudio
 def  UpperCAmelCase__						(						self   :			int     )    ->					Any:


      """simple docstring"""




      import torch

      __SCREAMING_SNAKE_CASE      = load_dataset("""common_voice"""   ,				"""en"""   ,				split="""train"""   ,				streaming=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = ds.cast_column("""audio"""   ,				datasets.Audio(sampling_rate=16_000     )     )
      __SCREAMING_SNAKE_CASE      = iter(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = next(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""     )
      __SCREAMING_SNAKE_CASE      = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""     )

      # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
      __SCREAMING_SNAKE_CASE      = processor(sample["""audio"""]["""array"""]   ,				return_tensors="""pt"""     ).input_values

      with torch.no_grad():
           __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE     ).logits.cpu().numpy()

      __SCREAMING_SNAKE_CASE      = processor.decode(logits[0]   ,				output_word_offsets=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
      __SCREAMING_SNAKE_CASE      = [
          {
              """start_time""": d["""start_offset"""] * time_offset,
              """end_time""": d["""end_offset"""] * time_offset,
              """word""": d["""word"""],
          }
          for d in output["""word_offsets"""]
      ]

      __SCREAMING_SNAKE_CASE      = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""

      # output words
      self.assertEqual(""" """.join(self.get_from_offsets(__SCREAMING_SNAKE_CASE   ,				"""word"""     )     )   ,				__SCREAMING_SNAKE_CASE     )
      self.assertEqual(""" """.join(self.get_from_offsets(__SCREAMING_SNAKE_CASE   ,				"""word"""     )     )   ,				output.text     )

      # output times
      __SCREAMING_SNAKE_CASE      = torch.tensor(self.get_from_offsets(__SCREAMING_SNAKE_CASE   ,				"""start_time"""     )     )
      __SCREAMING_SNAKE_CASE      = torch.tensor(self.get_from_offsets(__SCREAMING_SNAKE_CASE   ,				"""end_time"""     )     )

      # fmt: off
      __SCREAMING_SNAKE_CASE      = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]     )
      __SCREAMING_SNAKE_CASE      = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]     )
      # fmt: on

      self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				atol=0.01     )     )
      self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				atol=0.01     )     )









'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase					:			str       =	logging.get_logger(__name__)

UpperCAmelCase					:			Optional[Any]       =	{
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "blenderbot-small"
 lowerCAmelCase__       = ["past_key_values"]
 lowerCAmelCase__       = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
 def __init__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=50_265   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			Tuple=8   ,				__SCREAMING_SNAKE_CASE   :			str=2_048   ,				__SCREAMING_SNAKE_CASE   :			Tuple=16   ,				__SCREAMING_SNAKE_CASE   :			Dict=8   ,				__SCREAMING_SNAKE_CASE   :			Dict=2_048   ,				__SCREAMING_SNAKE_CASE   :			str=16   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Any=1   ,				__SCREAMING_SNAKE_CASE   :			Any=False   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=2   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = d_model
      __SCREAMING_SNAKE_CASE      = encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      = encoder_layers
      __SCREAMING_SNAKE_CASE      = encoder_attention_heads
      __SCREAMING_SNAKE_CASE      = decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      = decoder_layers
      __SCREAMING_SNAKE_CASE      = decoder_attention_heads
      __SCREAMING_SNAKE_CASE      = dropout
      __SCREAMING_SNAKE_CASE      = attention_dropout
      __SCREAMING_SNAKE_CASE      = activation_dropout
      __SCREAMING_SNAKE_CASE      = activation_function
      __SCREAMING_SNAKE_CASE      = init_std
      __SCREAMING_SNAKE_CASE      = encoder_layerdrop
      __SCREAMING_SNAKE_CASE      = decoder_layerdrop
      __SCREAMING_SNAKE_CASE      = use_cache
      __SCREAMING_SNAKE_CASE      = encoder_layers
      __SCREAMING_SNAKE_CASE      = scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE   ,				decoder_start_token_id=__SCREAMING_SNAKE_CASE   ,				forced_eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE   ,				)







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]     )

           if self.use_past:
                __SCREAMING_SNAKE_CASE      = {0: """batch"""}
                __SCREAMING_SNAKE_CASE      = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                __SCREAMING_SNAKE_CASE      = {0: """batch""", 1: """decoder_sequence"""}
                __SCREAMING_SNAKE_CASE      = {0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE   ,				direction="""inputs"""     )
      elif self.task == "causal-lm":
           # TODO: figure this case out.
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]     )
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                   ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
               ]     )

      return common_inputs
 @property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = super().outputs
      else:
           __SCREAMING_SNAKE_CASE      = super(__SCREAMING_SNAKE_CASE   ,				self     ).outputs
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      return common_outputs
 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      # Generate decoder inputs
      __SCREAMING_SNAKE_CASE      = seq_length if not self.use_past else 1
      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
      __SCREAMING_SNAKE_CASE      = dict(**__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if self.use_past:
           if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
           else:
                import torch
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = common_inputs["""input_ids"""].shape
           __SCREAMING_SNAKE_CASE      = common_inputs["""decoder_input_ids"""].shape[1]
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_attention_heads
           __SCREAMING_SNAKE_CASE      = (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           __SCREAMING_SNAKE_CASE      = decoder_seq_length + 3
           __SCREAMING_SNAKE_CASE      = (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           __SCREAMING_SNAKE_CASE      = torch.cat(
               [common_inputs["""decoder_attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )]   ,				dim=1     )

           __SCREAMING_SNAKE_CASE      = []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
           __SCREAMING_SNAKE_CASE      = min(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = max(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ) - min_num_layers
           __SCREAMING_SNAKE_CASE      = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

           for _ in range(__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                    )     )
           # TODO: test this.
           __SCREAMING_SNAKE_CASE      = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
           for _ in range(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append((torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     ))     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      if self.use_past:
           if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
           else:
                import torch
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = common_inputs["""input_ids"""].shape
           # Not using the same length for past_key_values
           __SCREAMING_SNAKE_CASE      = seqlen + 2
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_attention_heads
           __SCREAMING_SNAKE_CASE      = (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           __SCREAMING_SNAKE_CASE      = common_inputs["""attention_mask"""].dtype
           __SCREAMING_SNAKE_CASE      = torch.cat(
               [common_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				dtype=__SCREAMING_SNAKE_CASE     )]   ,				dim=1     )
           __SCREAMING_SNAKE_CASE      = [
               (torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     )) for _ in range(__SCREAMING_SNAKE_CASE     )
           ]
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				fixed_dimension=OnnxConfig.default_fixed_batch   ,				num_token_to_add=0     )

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      __SCREAMING_SNAKE_CASE      = tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				fixed_dimension=OnnxConfig.default_fixed_sequence   ,				num_token_to_add=__SCREAMING_SNAKE_CASE     )

      # Generate dummy inputs according to compute batch and sequence
      __SCREAMING_SNAKE_CASE      = [""" """.join([tokenizer.unk_token]     ) * seq_length] * batch_size
      __SCREAMING_SNAKE_CASE      = dict(tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				seq_length=__SCREAMING_SNAKE_CASE   ,				is_pair=__SCREAMING_SNAKE_CASE   ,				framework=__SCREAMING_SNAKE_CASE     )

      elif self.task == "causal-lm":
           __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_causal_lm(
               __SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				seq_length=__SCREAMING_SNAKE_CASE   ,				is_pair=__SCREAMING_SNAKE_CASE   ,				framework=__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				seq_length=__SCREAMING_SNAKE_CASE   ,				is_pair=__SCREAMING_SNAKE_CASE   ,				framework=__SCREAMING_SNAKE_CASE     )

      return common_inputs


 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]     )    ->					Optional[Any]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = super(__SCREAMING_SNAKE_CASE   ,				self     )._flatten_past_key_values_(
               __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )






