

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 		a_       (							unittest.TestCase     ):



      """simple docstring"""







      def        _lowerCAmelCase     (      self     :							List[Any]				):
           SCREAMING_SNAKE_CASE      						='| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
           SCREAMING_SNAKE_CASE      						=dict(zip(snake_case			,range(len(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': 16000,
               '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			,snake_case				)
           with open(self.vocab_file			,'w'			,encoding='utf-8'				) as fp:
                fp.write(json.dumps(snake_case				) + '\n'				)

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

           # load decoder from hub
           SCREAMING_SNAKE_CASE      						='hf-internal-testing/ngram-beam-search-decoder'
      def        _lowerCAmelCase     (      self     :							Optional[int]			,**snake_case     :							Optional[Any]				):
           SCREAMING_SNAKE_CASE      						=self.add_kwargs_tokens_map.copy()
           kwargs.update(snake_case				)
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,**snake_case				)
      def        _lowerCAmelCase     (      self     :							Dict			,**snake_case     :							int				):
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,**snake_case				)
      def        _lowerCAmelCase     (      self     :							List[str]			,**snake_case     :							Dict				):
           return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name			,**snake_case				)
      def        _lowerCAmelCase     (      self     :							List[Any]				):
           shutil.rmtree(self.tmpdirname				)
      def        _lowerCAmelCase     (      self     :							List[Any]				):
           SCREAMING_SNAKE_CASE      						=self.get_tokenizer()
           SCREAMING_SNAKE_CASE      						=self.get_feature_extractor()
           SCREAMING_SNAKE_CASE      						=self.get_decoder()

           SCREAMING_SNAKE_CASE      						=WavaVecaProcessorWithLM(tokenizer=snake_case			,feature_extractor=snake_case			,decoder=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			,snake_case				)

           # feature extractor
           self.assertEqual(processor.feature_extractor.to_json_string()			,feature_extractor.to_json_string()				)
           self.assertIsInstance(processor.feature_extractor			,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			,snake_case				)
      def        _lowerCAmelCase     (      self     :							Any				):
           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        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           SCREAMING_SNAKE_CASE      						=self.get_tokenizer()
           # add token to trigger raise
           tokenizer.add_tokens(['xx']				)
           with self.assertRaisesRegex(snake_case			,'include'				):
                WavaVecaProcessorWithLM(
                    tokenizer=snake_case			,feature_extractor=self.get_feature_extractor()			,decoder=self.get_decoder()				)
      def        _lowerCAmelCase     (      self     :							Tuple				):
           SCREAMING_SNAKE_CASE      						=self.get_feature_extractor()
           SCREAMING_SNAKE_CASE      						=self.get_tokenizer()
           SCREAMING_SNAKE_CASE      						=self.get_decoder()

           SCREAMING_SNAKE_CASE      						=WavaVecaProcessorWithLM(tokenizer=snake_case			,feature_extractor=snake_case			,decoder=snake_case				)

           SCREAMING_SNAKE_CASE      						=floats_list((3, 1000)				)

           SCREAMING_SNAKE_CASE      						=feature_extractor(snake_case			,return_tensors='np'				)
           SCREAMING_SNAKE_CASE      						=processor(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        _lowerCAmelCase     (      self     :							str				):
           SCREAMING_SNAKE_CASE      						=self.get_feature_extractor()
           SCREAMING_SNAKE_CASE      						=self.get_tokenizer()
           SCREAMING_SNAKE_CASE      						=self.get_decoder()

           SCREAMING_SNAKE_CASE      						=WavaVecaProcessorWithLM(tokenizer=snake_case			,feature_extractor=snake_case			,decoder=snake_case				)

           SCREAMING_SNAKE_CASE      						='This is a test string'

           SCREAMING_SNAKE_CASE      						=processor(text=snake_case				)

           SCREAMING_SNAKE_CASE      						=tokenizer(snake_case				)

           for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]			,encoded_processor[key]				)
      def        _lowerCAmelCase     (      self     :							Union[str, Any]			,snake_case     :							List[Any]=(2, 10, 16)			,snake_case     :							Any=77				):
           np.random.seed(snake_case				)
           return np.random.rand(*snake_case				)
      def        _lowerCAmelCase     (      self     :							Tuple				):
           SCREAMING_SNAKE_CASE      						=self.get_feature_extractor()
           SCREAMING_SNAKE_CASE      						=self.get_tokenizer()
           SCREAMING_SNAKE_CASE      						=self.get_decoder()

           SCREAMING_SNAKE_CASE      						=WavaVecaProcessorWithLM(tokenizer=snake_case			,feature_extractor=snake_case			,decoder=snake_case				)

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

           SCREAMING_SNAKE_CASE      						=processor.decode(snake_case				)

           SCREAMING_SNAKE_CASE      						=decoder.decode_beams(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        _lowerCAmelCase     (      self     :							Dict			,snake_case     :							Any				):
           SCREAMING_SNAKE_CASE      						=self.get_feature_extractor()
           SCREAMING_SNAKE_CASE      						=self.get_tokenizer()
           SCREAMING_SNAKE_CASE      						=self.get_decoder()

           SCREAMING_SNAKE_CASE      						=WavaVecaProcessorWithLM(tokenizer=snake_case			,feature_extractor=snake_case			,decoder=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(snake_case				)
           else:
                with get_context(snake_case				).Pool() as pool:
                     SCREAMING_SNAKE_CASE      						=processor.batch_decode(snake_case			,snake_case				)

           SCREAMING_SNAKE_CASE      						=list(snake_case				)

           with get_context('fork'				).Pool() as p:
                SCREAMING_SNAKE_CASE      						=decoder.decode_beams_batch(snake_case			,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(snake_case			,decoded_processor.text				)
           self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']			,decoded_processor.text				)
           self.assertListEqual(snake_case			,decoded_processor.logit_score				)
           self.assertListEqual(snake_case			,decoded_processor.lm_score				)
      def        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           SCREAMING_SNAKE_CASE      						=self.get_feature_extractor()
           SCREAMING_SNAKE_CASE      						=self.get_tokenizer()
           SCREAMING_SNAKE_CASE      						=self.get_decoder()

           SCREAMING_SNAKE_CASE      						=WavaVecaProcessorWithLM(tokenizer=snake_case			,feature_extractor=snake_case			,decoder=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(
               snake_case			,beam_width=snake_case			,beam_prune_logp=snake_case			,token_min_logp=snake_case			,)
           SCREAMING_SNAKE_CASE      						=decoded_processor_out.text

           SCREAMING_SNAKE_CASE      						=list(snake_case				)

           with get_context('fork'				).Pool() as pool:
                SCREAMING_SNAKE_CASE      						=decoder.decode_beams_batch(
                    snake_case			,snake_case			,beam_width=snake_case			,beam_prune_logp=snake_case			,token_min_logp=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(snake_case			,snake_case				)
           self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']			,snake_case				)

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

           self.assertTrue(np.array_equal(snake_case			,decoded_processor_out.lm_score				)				)
           self.assertTrue(np.allclose([-15.554, -13.9_474]			,snake_case			,atol=1e-3				)				)
      def        _lowerCAmelCase     (      self     :							Dict				):
           SCREAMING_SNAKE_CASE      						=self.get_feature_extractor()
           SCREAMING_SNAKE_CASE      						=self.get_tokenizer()
           SCREAMING_SNAKE_CASE      						=self.get_decoder()

           SCREAMING_SNAKE_CASE      						=WavaVecaProcessorWithLM(tokenizer=snake_case			,feature_extractor=snake_case			,decoder=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(
               snake_case			,alpha=snake_case			,beta=snake_case			,unk_score_offset=snake_case			,lm_score_boundary=snake_case			,)
           SCREAMING_SNAKE_CASE      						=decoded_processor_out.text

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

           with get_context('fork'				).Pool() as pool:
                SCREAMING_SNAKE_CASE      						=decoder.decode_beams_batch(
                    snake_case			,snake_case			,)

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

           self.assertListEqual(snake_case			,snake_case				)
           self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']			,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			,snake_case				)
      def        _lowerCAmelCase     (      self     :							Dict				):
           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(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(snake_case			,snake_case				)
      def        _lowerCAmelCase     (      self     :							Any				):
           SCREAMING_SNAKE_CASE      						=snapshot_download('hf-internal-testing/processor_with_lm'				)

           SCREAMING_SNAKE_CASE      						=WavaVecaProcessorWithLM.from_pretrained(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(snake_case				)
           SCREAMING_SNAKE_CASE      						=os.listdir(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(snake_case			,snake_case				)
      def        _lowerCAmelCase     (      self     :							Dict				):
           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, 1000)				)

           SCREAMING_SNAKE_CASE      						=processor_wavaveca(snake_case			,return_tensors='np'				)
           SCREAMING_SNAKE_CASE      						=processor_auto(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(snake_case				)
           SCREAMING_SNAKE_CASE      						=processor_auto.batch_decode(snake_case				)

           self.assertListEqual(decoded_wavaveca.text			,decoded_auto.text				)
      def        _lowerCAmelCase     (      self     :							Optional[Any]				):
           SCREAMING_SNAKE_CASE      						=self.get_feature_extractor()
           SCREAMING_SNAKE_CASE      						=self.get_tokenizer()
           SCREAMING_SNAKE_CASE      						=self.get_decoder()

           SCREAMING_SNAKE_CASE      						=WavaVecaProcessorWithLM(tokenizer=snake_case			,feature_extractor=snake_case			,decoder=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        _lowerCAmelCase     (      snake_case     :							Optional[int]			,snake_case     :							Any				):
           SCREAMING_SNAKE_CASE      						=[d[key] for d in offsets]
           return retrieved_list
      def        _lowerCAmelCase     (      self     :							Union[str, Any]				):
           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(snake_case			,output_word_offsets=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(snake_case			,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        _lowerCAmelCase     (      self     :							Optional[int]				):
           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(snake_case			,output_word_offsets=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(snake_case			,snake_case				)				)

           self.assertListEqual(
               [' '.join(self.get_from_offsets(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        _lowerCAmelCase     (      self     :							Tuple				):
           import torch

           SCREAMING_SNAKE_CASE      						=load_dataset('common_voice'			,'en'			,split='train'			,streaming=snake_case				)
           SCREAMING_SNAKE_CASE      						=ds.cast_column('audio'			,datasets.Audio(sampling_rate=16000				)				)
           SCREAMING_SNAKE_CASE      						=iter(snake_case				)
           SCREAMING_SNAKE_CASE      						=next(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(snake_case				).logits.cpu().numpy()

           SCREAMING_SNAKE_CASE      						=processor.decode(logits[0]			,output_word_offsets=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(snake_case			,'word'				)				)			,snake_case				)
           self.assertEqual(' '.join(self.get_from_offsets(snake_case			,'word'				)				)			,output.text				)

           # output times
           SCREAMING_SNAKE_CASE      						=torch.tensor(self.get_from_offsets(snake_case			,'start_time'				)				)
           SCREAMING_SNAKE_CASE      						=torch.tensor(self.get_from_offsets(snake_case			,'end_time'				)				)

           # fmt: off
           SCREAMING_SNAKE_CASE      						=torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]				)
           SCREAMING_SNAKE_CASE      						=torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]				)
           # fmt: on

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





import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCAmelCase           =		[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]







def        _lowerCamelCase(	lowercase__	)						->	str:

		'''simple docstring'''







		for file in FILES_TO_FIND:
				if not (transformers_path / file).exists():
						return False
		return True


if __name__ == "__main__":
	lowerCAmelCase           =		argparse.ArgumentParser()
	parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
	lowerCAmelCase           =		parser.parse_args()
	if args.check_lib:
		lowerCAmelCase           =		importlib.import_module('''transformers''')
		lowerCAmelCase           =		Path(transformers_module.__file__).parent
	else:
		lowerCAmelCase           =		Path.cwd() / '''build/lib/transformers'''
	if not test_custom_files_are_present(transformers_path):
		raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')






