


'''simple docstring'''






import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  Any    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  str=99    ,						lowerCamelCase    :  Union[str, Any]=13    ,						lowerCamelCase    :  Any=7    ,						lowerCamelCase    :  int=9    ,						lowerCamelCase    :  str=True    ,						lowerCamelCase    :  str=True    ,						lowerCamelCase    :  Tuple=False    ,						lowerCamelCase    :  Optional[int]=32    ,						lowerCamelCase    :  str=5    ,						lowerCamelCase    :  Union[str, Any]=4    ,						lowerCamelCase    :  int=37    ,						lowerCamelCase    :  Any=8    ,						lowerCamelCase    :  List[str]=0.1    ,						lowerCamelCase    :  str=0.002    ,						lowerCamelCase    :  int=1    ,						lowerCamelCase    :  Any=0    ,						lowerCamelCase    :  str=0    ,						lowerCamelCase    :  Optional[Any]=None    ,						lowerCamelCase    :  List[Any]=None    ,						)   ->  Any:
      lowerCAmelCase_			:      Tuple			=   parent
      lowerCAmelCase_			:      int			=   batch_size
      lowerCAmelCase_			:      Optional[Any]			=   encoder_seq_length
      lowerCAmelCase_			:      Union[str, Any]			=   decoder_seq_length
      # For common tests
      lowerCAmelCase_			:      Optional[Any]			=   self.decoder_seq_length
      lowerCAmelCase_			:      Dict			=   is_training
      lowerCAmelCase_			:      List[str]			=   use_attention_mask
      lowerCAmelCase_			:      List[str]			=   use_labels
      lowerCAmelCase_			:      str			=   vocab_size
      lowerCAmelCase_			:      Optional[int]			=   hidden_size
      lowerCAmelCase_			:      Any			=   num_hidden_layers
      lowerCAmelCase_			:      str			=   num_attention_heads
      lowerCAmelCase_			:      Optional[int]			=   d_ff
      lowerCAmelCase_			:      int			=   relative_attention_num_buckets
      lowerCAmelCase_			:      List[Any]			=   dropout_rate
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_factor
      lowerCAmelCase_			:      str			=   eos_token_id
      lowerCAmelCase_			:      Any			=   pad_token_id
      lowerCAmelCase_			:      List[str]			=   decoder_start_token_id
      lowerCAmelCase_			:      Any			=   None
      lowerCAmelCase_			:      str			=   decoder_layers

    def __lowercase   (   self    :  Optional[Any]		)   ->  Tuple:
      return TaConfig.from_pretrained("""google/umt5-base"""		)

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  Dict    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  Optional[int]=None    ,						lowerCamelCase    :  str=None    ,						lowerCamelCase    :  Any=None    ,						lowerCamelCase    :  Any=None    ,						lowerCamelCase    :  int=None    ,						)   ->  Tuple:
      if attention_mask is None:
        lowerCAmelCase_			:      Dict			=   input_ids.ne(config.pad_token_id		)
      if decoder_attention_mask is None:
        lowerCAmelCase_			:      str			=   decoder_input_ids.ne(config.pad_token_id		)
      if head_mask is None:
        lowerCAmelCase_			:      List[str]			=   torch.ones(config.num_hidden_layers    ,						config.num_attention_heads    ,						device=lowerCamelCase		)
      if decoder_head_mask is None:
        lowerCAmelCase_			:      Union[str, Any]			=   torch.ones(config.num_decoder_layers    ,						config.num_attention_heads    ,						device=lowerCamelCase		)
      if cross_attn_head_mask is None:
        lowerCAmelCase_			:      Optional[Any]			=   torch.ones(
            config.num_decoder_layers    ,						config.num_attention_heads    ,						device=lowerCamelCase		)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }

    def __lowercase   (   self    :  List[Any]		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      Dict			=   ids_tensor([self.batch_size, self.encoder_seq_length]    ,						self.vocab_size		)
      lowerCAmelCase_			:      Any			=   ids_tensor([self.batch_size, self.decoder_seq_length]    ,						self.vocab_size		)

      # we need to clamp the input ids here to avoid having pad token in between
      # this is because for NllbMoe the position_ids are prepared such that
      # all pad tokens have pos id = 2 and rest are between 2..seq_length
      # and the seq_length here is seq_length - num_pad_tokens
      # but when using past, there is no way of knowing if the past input ids had
      # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
      # position_ids being off by num_pad_tokens in past input
      lowerCAmelCase_			:      Optional[int]			=   input_ids.clamp(self.pad_token_id + 1		)
      lowerCAmelCase_			:      int			=   decoder_input_ids.clamp(self.pad_token_id + 1		)

      lowerCAmelCase_			:      Union[str, Any]			=   self.get_config()
      lowerCAmelCase_			:      List[Any]			=   config.num_attention_heads
      lowerCAmelCase_			:      int			=   self.prepare_inputs_dict(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		)
      return config, input_dict

    def __lowercase   (   self    :  Any		)   ->  int:
      lowerCAmelCase_,							lowerCAmelCase_			:      str			=   self.prepare_config_and_inputs()
      return config, inputs_dict

    def __lowercase   (   self    :  Tuple		)   ->  Union[str, Any]:
      return TaConfig(
          vocab_size=1_66    ,						d_model=self.hidden_size    ,						d_ff=self.d_ff    ,						d_kv=self.hidden_size // self.num_attention_heads    ,						num_layers=self.num_hidden_layers    ,						num_decoder_layers=self.decoder_layers    ,						num_heads=self.num_attention_heads    ,						relative_attention_num_buckets=self.relative_attention_num_buckets    ,						dropout_rate=self.dropout_rate    ,						initializer_factor=self.initializer_factor    ,						eos_token_id=self.eos_token_id    ,						bos_token_id=self.pad_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.decoder_start_token_id    ,						)

    def __lowercase   (   self    :  List[Any]		)   ->  Any:
      return TaConfig(
          vocab_size=self.vocab_size    ,						d_model=self.hidden_size    ,						d_ff=self.d_ff    ,						d_kv=self.hidden_size // self.num_attention_heads    ,						num_layers=self.num_hidden_layers    ,						num_decoder_layers=self.decoder_layers    ,						num_heads=self.num_attention_heads    ,						relative_attention_num_buckets=self.relative_attention_num_buckets    ,						dropout_rate=self.dropout_rate    ,						initializer_factor=self.initializer_factor    ,						eos_token_id=self.eos_token_id    ,						bos_token_id=self.pad_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.decoder_start_token_id    ,						)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  Dict    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Any    ,						)   ->  List[str]:
      lowerCAmelCase_			:      str			=   UMTaModel(config=lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_			:      Dict			=   model(
          input_ids=lowerCamelCase    ,						decoder_input_ids=lowerCamelCase    ,						attention_mask=lowerCamelCase    ,						decoder_attention_mask=lowerCamelCase    ,						)
      lowerCAmelCase_			:      Any			=   model(input_ids=lowerCamelCase    ,						decoder_input_ids=lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   result.last_hidden_state
      lowerCAmelCase_			:      Union[str, Any]			=   result.past_key_values
      lowerCAmelCase_			:      List[Any]			=   result.encoder_last_hidden_state

      self.parent.assertEqual(encoder_output.size()    ,						(self.batch_size, self.encoder_seq_length, self.hidden_size)		)
      self.parent.assertEqual(decoder_output.size()    ,						(self.batch_size, self.decoder_seq_length, self.hidden_size)		)
      # There should be `num_layers` key value embeddings stored in decoder_past
      self.parent.assertEqual(len(lowerCamelCase		)    ,						config.num_layers		)
      # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
      self.parent.assertEqual(len(decoder_past[0]		)    ,						4		)

    def __lowercase   (   self    :  List[str]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Optional[int]    ,						lowerCamelCase    :  Tuple    ,						lowerCamelCase    :  Union[str, Any]    ,						lowerCamelCase    :  Optional[int]    ,						lowerCamelCase    :  Dict    ,						)   ->  List[Any]:
      lowerCAmelCase_			:      List[Any]			=   UMTaModel(config=lowerCamelCase		).get_decoder().to(lowerCamelCase		).eval()
      # first forward pass
      lowerCAmelCase_			:      List[str]			=   model(lowerCamelCase    ,						use_cache=lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   model(lowerCamelCase    ,						use_cache=lowerCamelCase		)

      self.parent.assertTrue(len(lowerCamelCase		) == len(lowerCamelCase		)		)
      self.parent.assertTrue(len(lowerCamelCase		) == len(lowerCamelCase		) + 1		)

      lowerCAmelCase_,							lowerCAmelCase_			:      List[str]			=   outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      lowerCAmelCase_			:      Dict			=   ids_tensor((self.batch_size, 1)    ,						config.vocab_size		)

      # append to next input_ids and
      lowerCAmelCase_			:      Union[str, Any]			=   torch.cat([input_ids, next_tokens]    ,						dim=-1		)

      lowerCAmelCase_			:      str			=   model(lowerCamelCase		)["""last_hidden_state"""]
      lowerCAmelCase_			:      List[Any]			=   model(lowerCamelCase    ,						past_key_values=lowerCamelCase		)["""last_hidden_state"""]

      # select random slice
      lowerCAmelCase_			:      Optional[int]			=   ids_tensor((1,)    ,						output_from_past.shape[-1]		).item()
      lowerCAmelCase_			:      Dict			=   output_from_no_past[:, -1, random_slice_idx].detach()
      lowerCAmelCase_			:      Tuple			=   output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(lowerCamelCase    ,						lowerCamelCase    ,						atol=1E-3		)		)





    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  List[Any]    ,						lowerCamelCase    :  Optional[Any]    ,						)   ->  Tuple:
      lowerCAmelCase_			:      Union[str, Any]			=   UMTaModel(config=lowerCamelCase		).to(lowerCamelCase		).half().eval()
      lowerCAmelCase_			:      Optional[Any]			=   model(**lowerCamelCase		)["""last_hidden_state"""]
      self.parent.assertFalse(torch.isnan(lowerCamelCase		).any().item()		)







@require_torch
class    __snake_case       (   _SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    lowercase    =					(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    lowercase    =					(UMTaForConditionalGeneration,) if is_torch_available() else ()
    lowercase    =					(
        {
            'conversational': UMTaForConditionalGeneration,
            'feature-extraction': UMTaModel,
            'summarization': UMTaForConditionalGeneration,
            'text2text-generation': UMTaForConditionalGeneration,
            'translation': UMTaForConditionalGeneration,
            'question-answering': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    lowercase    =					True
    lowercase    =					False
    lowercase    =					False
    lowercase    =					True
    lowercase    =					True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    lowercase    =					[0.8, 0.9]

    def __lowercase   (   self    :  Optional[int]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Dict			=   UMTaModelTester(self		)

    @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""		)
    def __lowercase   (   self    :  int		)   ->  str:
      lowerCAmelCase_			:      str			=   self.model_tester.prepare_config_and_inputs()
      lowerCAmelCase_			:      Any			=   UMTaModel(config_and_inputs[0]		).to(lowerCamelCase		)
      with tempfile.TemporaryDirectory() as tmpdirname:
        torch.onnx.export(
            lowerCamelCase    ,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])    ,						F'{tmpdirname}/t5_test.onnx'    ,						export_params=lowerCamelCase    ,						opset_version=9    ,						input_names=["""input_ids""", """decoder_input_ids"""]    ,						)

    @unittest.skipIf(torch_device == """cpu"""    ,						"""Cant do half precision"""		)
    def __lowercase   (   self    :  Optional[Any]		)   ->  Dict:
      lowerCAmelCase_			:      str			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*lowerCamelCase		)

    def __lowercase   (   self    :  Optional[Any]		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      List[str]			=   ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
      lowerCAmelCase_			:      Optional[int]			=   self.model_tester.prepare_config_and_inputs()
      lowerCAmelCase_			:      Union[str, Any]			=   config_and_inputs[0]
      lowerCAmelCase_			:      Union[str, Any]			=   UMTaForConditionalGeneration(lowerCamelCase		).eval()
      model.to(lowerCamelCase		)

      lowerCAmelCase_			:      Tuple			=   {
          """head_mask""": torch.zeros(config.num_layers    ,						config.num_heads    ,						device=lowerCamelCase		),
          """decoder_head_mask""": torch.zeros(config.num_decoder_layers    ,						config.num_heads    ,						device=lowerCamelCase		),
          """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers    ,						config.num_heads    ,						device=lowerCamelCase		),
      }

      for attn_name, (name, mask) in zip(lowerCamelCase    ,						head_masking.items()		):
        lowerCAmelCase_			:      List[Any]			=   {name: mask}
        # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
        if name == "head_mask":
          lowerCAmelCase_			:      List[Any]			=   torch.ones(
              config.num_decoder_layers    ,						config.num_heads    ,						device=lowerCamelCase		)

        lowerCAmelCase_			:      List[str]			=   model.generate(
            config_and_inputs[1]["""input_ids"""]    ,						num_beams=1    ,						max_length=3    ,						output_attentions=lowerCamelCase    ,						return_dict_in_generate=lowerCamelCase    ,						**lowerCamelCase    ,						)
        # We check the state of decoder_attentions and cross_attentions just from the last step
        lowerCAmelCase_			:      List[Any]			=   out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
        self.assertEqual(sum([w.sum().item() for w in attn_weights]		)    ,						0.0		)





    @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""		)
    def __lowercase   (   self    :  Union[str, Any]		)   ->  int:
      pass







@require_torch
@require_sentencepiece
@require_tokenizers
class    __snake_case       (   unittest.TestCase):







    """simple docstring"""



    @slow
    @unittest.skip(
        """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""		)
    def __lowercase   (   self    :  List[Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      List[str]			=   UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""    ,						return_dict=lowerCamelCase		).to(lowerCamelCase		)
      lowerCAmelCase_			:      Union[str, Any]			=   AutoTokenizer.from_pretrained("""google/umt5-small"""    ,						use_fast=lowerCamelCase    ,						legacy=lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   [
          """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
          """No se como puedo <extra_id_0>.""",
          """This is the reason why we <extra_id_0> them.""",
          """The <extra_id_0> walks in <extra_id_1>, seats""",
          """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
      ]
      lowerCAmelCase_			:      Optional[int]			=   tokenizer(lowerCamelCase    ,						return_tensors="""pt"""    ,						padding=lowerCamelCase		).input_ids
      # fmt: off
      lowerCAmelCase_			:      List[str]			=   torch.tensor(
          [
              [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
              [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
          ]		)
      # fmt: on
      torch.testing.assert_allclose(lowerCamelCase    ,						lowerCamelCase		)

      lowerCAmelCase_			:      List[str]			=   model.generate(input_ids.to(lowerCamelCase		)		)
      lowerCAmelCase_			:      str			=   [
          """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
          """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
          """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
          """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
          """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
      ]
      lowerCAmelCase_			:      Optional[Any]			=   tokenizer.batch_decode(lowerCamelCase		)
      self.assertEqual(lowerCamelCase    ,						lowerCamelCase		)







'''simple docstring'''






import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__A					:  str											=      get_tests_dir("fixtures")






class    __snake_case       (   unittest.TestCase):







    """simple docstring"""



    def __lowercase   (   self    :  int		)   ->  List[str]:
      # A mock response for an HTTP head request to emulate server down
      lowerCAmelCase_			:      str			=   mock.Mock()
      lowerCAmelCase_			:      Optional[Any]			=   5_00
      lowerCAmelCase_			:      List[str]			=   {}
      lowerCAmelCase_			:      str			=   HTTPError
      lowerCAmelCase_			:      Any			=   {}

      # Download this model to make sure it's in the cache.
      lowerCAmelCase_			:      Optional[Any]			=   WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2"""		)
      # Under the mock environment we get a 500 error when trying to reach the model.
      with mock.patch("""requests.Session.request"""    ,						return_value=lowerCamelCase		) as mock_head:
        lowerCAmelCase_			:      Union[str, Any]			=   WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2"""		)
        # This check we did call the fake head request
        mock_head.assert_called()





    def __lowercase   (   self    :  Dict		)   ->  Any:
      # This test is for deprecated behavior and can be removed in v5
      lowerCAmelCase_			:      List[str]			=   WavaVecaFeatureExtractor.from_pretrained(
          """https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"""		)







@is_staging_test
class    __snake_case       (   unittest.TestCase):







    """simple docstring"""



    @classmethod
    def __lowercase   (   cls    :  Tuple		)   ->  str:
      lowerCAmelCase_			:      Dict			=   TOKEN
      HfFolder.save_token(lowerCamelCase		)

    @classmethod
    def __lowercase   (   cls    :  Any		)   ->  Any:
      try:
        delete_repo(token=cls._token    ,						repo_id="""test-feature-extractor"""		)
      except HTTPError:
        pass

      try:
        delete_repo(token=cls._token    ,						repo_id="""valid_org/test-feature-extractor-org"""		)
      except HTTPError:
        pass

      try:
        delete_repo(token=cls._token    ,						repo_id="""test-dynamic-feature-extractor"""		)
      except HTTPError:
        pass

    def __lowercase   (   self    :  int		)   ->  str:
      lowerCAmelCase_			:      Tuple			=   WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase		)
      feature_extractor.push_to_hub("""test-feature-extractor"""    ,						use_auth_token=self._token		)

      lowerCAmelCase_			:      Optional[Any]			=   WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor'		)
      for k, v in feature_extractor.__dict__.items():
        self.assertEqual(lowerCamelCase    ,						getattr(lowerCamelCase    ,						lowerCamelCase		)		)

      # Reset repo
      delete_repo(token=self._token    ,						repo_id="""test-feature-extractor"""		)

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
        feature_extractor.save_pretrained(
            lowerCamelCase    ,						repo_id="""test-feature-extractor"""    ,						push_to_hub=lowerCamelCase    ,						use_auth_token=self._token		)

      lowerCAmelCase_			:      Any			=   WavaVecaFeatureExtractor.from_pretrained(F'{USER}/test-feature-extractor'		)
      for k, v in feature_extractor.__dict__.items():
        self.assertEqual(lowerCamelCase    ,						getattr(lowerCamelCase    ,						lowerCamelCase		)		)

    def __lowercase   (   self    :  Optional[Any]		)   ->  int:
      lowerCAmelCase_			:      Union[str, Any]			=   WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase		)
      feature_extractor.push_to_hub("""valid_org/test-feature-extractor"""    ,						use_auth_token=self._token		)

      lowerCAmelCase_			:      Optional[int]			=   WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor"""		)
      for k, v in feature_extractor.__dict__.items():
        self.assertEqual(lowerCamelCase    ,						getattr(lowerCamelCase    ,						lowerCamelCase		)		)

      # Reset repo
      delete_repo(token=self._token    ,						repo_id="""valid_org/test-feature-extractor"""		)

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
        feature_extractor.save_pretrained(
            lowerCamelCase    ,						repo_id="""valid_org/test-feature-extractor-org"""    ,						push_to_hub=lowerCamelCase    ,						use_auth_token=self._token		)

      lowerCAmelCase_			:      Optional[int]			=   WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor-org"""		)
      for k, v in feature_extractor.__dict__.items():
        self.assertEqual(lowerCamelCase    ,						getattr(lowerCamelCase    ,						lowerCamelCase		)		)





    def __lowercase   (   self    :  Optional[Any]		)   ->  Any:
      CustomFeatureExtractor.register_for_auto_class()
      lowerCAmelCase_			:      Dict			=   CustomFeatureExtractor.from_pretrained(lowerCamelCase		)

      feature_extractor.push_to_hub("""test-dynamic-feature-extractor"""    ,						use_auth_token=self._token		)

      # This has added the proper auto_map field to the config
      self.assertDictEqual(
          feature_extractor.auto_map    ,						{"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor"""}    ,						)

      lowerCAmelCase_			:      Union[str, Any]			=   AutoFeatureExtractor.from_pretrained(
          F'{USER}/test-dynamic-feature-extractor'    ,						trust_remote_code=lowerCamelCase		)
      # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
      self.assertEqual(new_feature_extractor.__class__.__name__    ,						"""CustomFeatureExtractor"""		)



