

"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   _a					 =				'platform'

   import jax
   import jax.numpy as jnp

   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def     _A    (    UpperCamelCase_	:			List[Any],     UpperCamelCase_	:			int,     UpperCamelCase_	:			List[Any]=None,     UpperCamelCase_	:			List[str]=None)      ->		Tuple:
   '''simple docstring'''


   if attention_mask is None:
      __lowercase								=		np.where(input_ids != config.pad_token_id,     1,     0)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }




@require_flax
class    _lowerCAmelCase    :







 """simple docstring"""







 def __init__(     self				:     int,   UpperCAmelCase__				:     int,   UpperCAmelCase__				:     Tuple=1_3,   UpperCAmelCase__				:     Tuple=7,   UpperCAmelCase__				:     Dict=True,   UpperCAmelCase__				:     Optional[int]=False,   UpperCAmelCase__				:     int=9_9,   UpperCAmelCase__				:     Tuple=1_6,   UpperCAmelCase__				:     Optional[int]=2,   UpperCAmelCase__				:     Optional[int]=4,   UpperCAmelCase__				:     Any=4,   UpperCAmelCase__				:     Union[str, Any]="gelu",   UpperCAmelCase__				:     Optional[Any]=0.1,   UpperCAmelCase__				:     Tuple=0.1,   UpperCAmelCase__				:     Dict=2_0,   UpperCAmelCase__				:     List[Any]=2,   UpperCAmelCase__				:     int=1,   UpperCAmelCase__				:     Dict=0,   UpperCAmelCase__				:     Tuple=1_6,   UpperCAmelCase__				:     Union[str, Any]=1_6,   UpperCAmelCase__				:     int=0.02,   ):
    __lowercase								=		parent
    __lowercase								=		batch_size
    __lowercase								=		seq_length
    __lowercase								=		is_training
    __lowercase								=		use_labels
    __lowercase								=		vocab_size
    __lowercase								=		hidden_size
    __lowercase								=		num_hidden_layers
    __lowercase								=		num_attention_heads
    __lowercase								=		intermediate_size
    __lowercase								=		hidden_act
    __lowercase								=		hidden_dropout_prob
    __lowercase								=		attention_probs_dropout_prob
    __lowercase								=		max_position_embeddings
    __lowercase								=		eos_token_id
    __lowercase								=		pad_token_id
    __lowercase								=		bos_token_id
    __lowercase								=		embed_dim
    __lowercase								=		word_embed_proj_dim
    __lowercase								=		initializer_range
    __lowercase								=		False


 def    _lowercase					(     self				:     str      ):
    __lowercase								=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1],   self.vocab_size      ),   3,   self.vocab_size      )
    __lowercase								=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),   dtype=np.intaa      )),   -1      )

    __lowercase								=		OPTConfig(
        vocab_size=self.vocab_size,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   ffn_dim=self.intermediate_size,   dropout=self.hidden_dropout_prob,   attention_dropout=self.attention_probs_dropout_prob,   max_position_embeddings=self.max_position_embeddings,   eos_token_id=self.eos_token_id,   bos_token_id=self.bos_token_id,   pad_token_id=self.pad_token_id,   embed_dim=self.embed_dim,   is_encoder_decoder=lowerCAmelCase__,   word_embed_proj_dim=self.word_embed_proj_dim,   initializer_range=self.initializer_range,   use_cache=lowerCAmelCase__,   )
    __lowercase								=		prepare_opt_inputs_dict(lowerCAmelCase__,   lowerCAmelCase__      )
    return config, inputs_dict


 def    _lowercase					(     self				:     List[str]      ):
    __lowercase								=		self.prepare_config_and_inputs()
    return config, inputs_dict


 def    _lowercase					(     self				:     Any,   UpperCAmelCase__				:     Any,   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     str      ):
    __lowercase								=		2_0
    __lowercase								=		model_class_name(lowerCAmelCase__      )

    __lowercase								=		inputs_dict['''input_ids''']
    __lowercase								=		inputs_dict['''attention_mask''']

    __lowercase								=		model.init_cache(input_ids.shape[0],   lowerCAmelCase__      )
    __lowercase								=		jnp.ones((input_ids.shape[0], max_length),   dtype="i4"      )

    __lowercase								=		jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1      )[None, :],   (input_ids.shape[0], input_ids.shape[-1] - 1),   )
    __lowercase								=		model(
        input_ids[:, :-1],   attention_mask=lowerCAmelCase__,   past_key_values=lowerCAmelCase__,   position_ids=lowerCAmelCase__,   )

    __lowercase								=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],   dtype="i4"      )
    __lowercase								=		model(
        input_ids[:, -1:],   attention_mask=lowerCAmelCase__,   past_key_values=outputs_cache.past_key_values,   position_ids=lowerCAmelCase__,   )

    __lowercase								=		model(lowerCAmelCase__      )

    __lowercase								=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
    self.parent.assertTrue(diff < 1E-3,   msg=F"""Max diff is {diff}"""      )



 def    _lowercase					(     self				:     Union[str, Any],   UpperCAmelCase__				:     Tuple,   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     int      ):
    __lowercase								=		2_0
    __lowercase								=		model_class_name(lowerCAmelCase__      )

    __lowercase								=		(
        inputs_dict['''input_ids'''],
        inputs_dict['''attention_mask'''],
    )

    __lowercase								=		jnp.concatenate(
        [
            attention_mask,
            jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])      ),
        ],   axis=-1,   )

    __lowercase								=		model.init_cache(input_ids.shape[0],   lowerCAmelCase__      )
    __lowercase								=		jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1      )[None, :],   (input_ids.shape[0], input_ids.shape[-1] - 1),   )

    __lowercase								=		model(
        input_ids[:, :-1],   attention_mask=lowerCAmelCase__,   past_key_values=lowerCAmelCase__,   position_ids=lowerCAmelCase__,   )
    __lowercase								=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],   dtype="i4"      )
    __lowercase								=		model(
        input_ids[:, -1:],   past_key_values=outputs_cache.past_key_values,   attention_mask=lowerCAmelCase__,   position_ids=lowerCAmelCase__,   )

    __lowercase								=		model(lowerCAmelCase__,   attention_mask=lowerCAmelCase__      )

    __lowercase								=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
    self.parent.assertTrue(diff < 1E-3,   msg=F"""Max diff is {diff}"""      )







@require_flax
class    _lowerCAmelCase    (      _lowerCAmelCase	,unittest.TestCase	,_lowerCAmelCase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[Any]  						=      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 __UpperCAmelCase      :   Union[str, Any]  						=      () if is_flax_available() else ()


 def    _lowercase					(     self				:     List[str]      ):
    __lowercase								=		FlaxOPTModelTester(self      )


 def    _lowercase					(     self				:     Any      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(lowerCAmelCase__,   lowerCAmelCase__,   lowerCAmelCase__      )


 def    _lowercase					(     self				:     List[Any]      ):
    __lowercase								=		self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__,   lowerCAmelCase__,   lowerCAmelCase__      )



 @slow
 def    _lowercase					(     self				:     Optional[Any]      ):



    for model_class_name in self.all_model_classes:
       __lowercase								=		model_class_name.from_pretrained("facebook/opt-125m"      )
       __lowercase								=		np.ones((1, 1)      ) * model.config.eos_token_id
       __lowercase								=		model(lowerCAmelCase__      )
       self.assertIsNotNone(lowerCAmelCase__      )







@require_sentencepiece
@require_flax
class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 @slow
 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		FlaxOPTModel.from_pretrained("facebook/opt-350m"      )
    __lowercase								=		jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      )
    __lowercase								=		model(input_ids=lowerCAmelCase__      ).last_hidden_state
    __lowercase								=		(1, 1_1, 5_1_2)
    self.assertEqual(output.shape,   lowerCAmelCase__      )
    __lowercase								=		jnp.array(
        [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]      )
    self.assertTrue(jnp.allclose(output[:, :3, :3],   lowerCAmelCase__,   atol=4E-2      )      )







@require_flax
@slow
class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def    _lowercase					(     self				:     str      ):
    super().setUp()
    __lowercase								=		'''facebook/opt-350m'''



 def    _lowercase					(     self				:     List[Any]      ):
    __lowercase								=		FlaxOPTForCausalLM.from_pretrained(self.path_model      )
    __lowercase								=		GPTaTokenizer.from_pretrained(self.path_model      )

    __lowercase								=		[
        '''Today is a beautiful day and I want to''',
        '''In the city of''',
        '''Paris is the capital of France and''',
        '''Computers and mobile phones have taken''',
    ]
    # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
    __lowercase								=		tokenizer(lowerCAmelCase__,   return_tensors="jax",   padding=lowerCAmelCase__,   add_special_tokens=lowerCAmelCase__      )
    __lowercase								=		model(inputs.input_ids,   attention_mask=inputs.attention_mask      )[0].mean(axis=-1      )
    __lowercase								=		jnp.array(
        [
            [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
            [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
            [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
            [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
        ]      )
    self.assertTrue(jnp.allclose(lowerCAmelCase__,   lowerCAmelCase__,   atol=4E-2      )      )

    __lowercase								=		jax.jit(lowerCAmelCase__      )
    __lowercase								=		model(inputs.input_ids,   attention_mask=inputs.attention_mask      )[0].mean(axis=-1      )
    self.assertTrue(jnp.allclose(lowerCAmelCase__,   lowerCAmelCase__,   atol=4E-2      )      )







@require_flax
@slow
class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 @property
 def    _lowercase					(     self				:     str      ):
    return [
        "Today is a beautiful day and I want",
        "In the city of",
        "Paris is the capital of France and",
        "Computers and mobile phones have taken",
    ]


 def    _lowercase					(     self				:     List[Any]      ):
    __lowercase								=		'''facebook/opt-125m'''

    __lowercase								=		[
        '''Today is a beautiful day and I want to''',
        '''In the city of New York, the city''',
        '''Paris is the capital of France and the capital''',
        '''Computers and mobile phones have taken over the''',
    ]

    __lowercase								=		[]

    __lowercase								=		FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__      )
    __lowercase								=		GPTaTokenizer.from_pretrained(lowerCAmelCase__      )

    for prompt in self.prompts:
       __lowercase								=		tokenizer(lowerCAmelCase__,   return_tensors="jax"      ).input_ids

       __lowercase								=		model.generate(lowerCAmelCase__,   max_length=1_0      )
       __lowercase								=		generated_ids[0]

       __lowercase								=		tokenizer.batch_decode(lowerCAmelCase__,   skip_special_tokens=lowerCAmelCase__      )
       predicted_outputs += generated_string

    self.assertListEqual(lowerCAmelCase__,   lowerCAmelCase__      )


 def    _lowercase					(     self				:     List[str]      ):
    __lowercase								=		'''facebook/opt-350m'''

    __lowercase								=		[
        '''Today is a beautiful day and I want to''',
        '''In the city of San Francisco, the city''',
        '''Paris is the capital of France and the capital''',
        '''Computers and mobile phones have taken over the''',
    ]

    __lowercase								=		[]
    __lowercase								=		FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__      )
    __lowercase								=		GPTaTokenizer.from_pretrained(lowerCAmelCase__      )

    for prompt in self.prompts:
       __lowercase								=		tokenizer(lowerCAmelCase__,   return_tensors="jax"      ).input_ids

       __lowercase								=		model.generate(lowerCAmelCase__,   max_length=1_0      )
       __lowercase								=		generated_ids[0]

       __lowercase								=		tokenizer.batch_decode(lowerCAmelCase__,   skip_special_tokens=lowerCAmelCase__      )
       predicted_outputs += generated_string

    self.assertListEqual(lowerCAmelCase__,   lowerCAmelCase__      )


 def    _lowercase					(     self				:     Any      ):
    __lowercase								=		'''facebook/opt-125m'''
    __lowercase								=		[
        '''Today is a beautiful day and I want to thank''',
        '''In the city of Rome Canaver Canaver Canaver Canaver''',
    ]
    __lowercase								=		FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__      )
    __lowercase								=		GPTaTokenizer.from_pretrained(lowerCAmelCase__      )
    __lowercase								=		tokenizer(
        [
            "Today is a beautiful day and I want to",
            "In the city of",
        ],   return_tensors="jax",   padding=lowerCAmelCase__,   )

    __lowercase								=		jax.jit(model.generate      )

    __lowercase								=		jit_generate(inputs["input_ids"],   attention_mask=inputs["attention_mask"]      ).sequences

    __lowercase								=		tokenizer.batch_decode(lowerCAmelCase__,   skip_special_tokens=lowerCAmelCase__      )

    self.assertIsNotNone(lowerCAmelCase__,   lowerCAmelCase__      )



 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		'''facebook/opt-350m'''

    __lowercase								=		GPTaTokenizer.from_pretrained(lowerCAmelCase__      )
    __lowercase								=		FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__      )

    __lowercase								=		'''left'''

    # use different length sentences to test batching
    __lowercase								=		[
        '''Hello, my dog is a little''',
        '''Today, I''',
    ]

    __lowercase								=		tokenizer(lowerCAmelCase__,   return_tensors="jax",   padding=lowerCAmelCase__      )
    __lowercase								=		inputs['''input_ids''']

    __lowercase								=		model.generate(input_ids=lowerCAmelCase__,   attention_mask=inputs["attention_mask"],   trace=lowerCAmelCase__      )

    __lowercase								=		tokenizer(sentences[0],   return_tensors="jax"      ).input_ids
    __lowercase								=		model.generate(input_ids=lowerCAmelCase__      )

    __lowercase								=		inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
    __lowercase								=		tokenizer(sentences[1],   return_tensors="jax"      ).input_ids
    __lowercase								=		model.generate(input_ids=lowerCAmelCase__,   max_length=model.config.max_length - num_paddings      )

    __lowercase								=		tokenizer.batch_decode(outputs[0],   skip_special_tokens=lowerCAmelCase__      )
    __lowercase								=		tokenizer.decode(output_non_padded[0][0],   skip_special_tokens=lowerCAmelCase__      )
    __lowercase								=		tokenizer.decode(output_padded[0][0],   skip_special_tokens=lowerCAmelCase__      )

    __lowercase								=		[
        '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
        '''Today, I was in the middle of a conversation with a friend about the''',
    ]
    self.assertListEqual(lowerCAmelCase__,   lowerCAmelCase__      )
    self.assertListEqual(lowerCAmelCase__,   [non_padded_sentence, padded_sentence]      )











"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class 					_a		( unittest.TestCase							):






				def 							snake_case      (					self						:       int				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''				)
										_UpperCamelCase     :   Tuple      =       dict(zip(lowerCAmelCase__,					range(len(lowerCAmelCase__				)				)				)				)

										_UpperCamelCase     :   Any      =       {
										    '''pad_token''': '''<pad>''',
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										_UpperCamelCase     :   Optional[int]      =       {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 1_6_0_0_0,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

										_UpperCamelCase     :   List[str]      =       tempfile.mkdtemp()
										_UpperCamelCase     :   Any      =       os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']				)
										_UpperCamelCase     :   Optional[int]      =       os.path.join(self.tmpdirname,					lowerCAmelCase__				)
										with open(self.vocab_file,					'''w''',					encoding='''utf-8'''				) as fp:
																fp.write(json.dumps(lowerCAmelCase__				) + '''\n'''				)

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






				def 							snake_case      (					self						:       List[Any],					**lowerCAmelCase__						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       self.add_kwargs_tokens_map.copy()
										kwargs.update(lowerCAmelCase__				)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					**lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()
										_UpperCamelCase     :   str      =       self.get_feature_extractor()

										_UpperCamelCase     :   str      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										processor.save_pretrained(self.tmpdirname				)
										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor.from_pretrained(self.tmpdirname				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       str				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       WavaVecaProcessor(tokenizer=self.get_tokenizer(),					feature_extractor=self.get_feature_extractor()				)
										processor.save_pretrained(self.tmpdirname				)

										_UpperCamelCase     :   Tuple      =       self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''				)
										_UpperCamelCase     :   Any      =       self.get_feature_extractor(do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										_UpperCamelCase     :   int      =       WavaVecaProcessor.from_pretrained(
										    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=lowerCAmelCase__,					padding_value=1.0				)

										self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()				)
										self.assertIsInstance(processor.tokenizer,					lowerCAmelCase__				)

										self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor_add_kwargs.to_json_string()				)
										self.assertIsInstance(processor.feature_extractor,					lowerCAmelCase__				)






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




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       self.get_feature_extractor()
										_UpperCamelCase     :   str      =       self.get_tokenizer()

										_UpperCamelCase     :   Union[str, Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   Any      =       floats_list((3, 1_0_0_0)				)

										_UpperCamelCase     :   int      =       feature_extractor(lowerCAmelCase__,					return_tensors='''np'''				)
										_UpperCamelCase     :   List[Any]      =       processor(lowerCAmelCase__,					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 							snake_case      (					self						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.get_feature_extractor()
										_UpperCamelCase     :   Tuple      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       '''This is a test string'''

										_UpperCamelCase     :   List[Any]      =       processor(text=lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       tokenizer(lowerCAmelCase__				)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key],					encoded_processor[key]				)






				def 							snake_case      (					self						:       Dict				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.get_feature_extractor()
										_UpperCamelCase     :   Dict      =       self.get_tokenizer()

										_UpperCamelCase     :   List[Any]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase     :   List[str]      =       processor.batch_decode(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       tokenizer.batch_decode(lowerCAmelCase__				)

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Union[str, Any]				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       self.get_feature_extractor()
										_UpperCamelCase     :   List[Any]      =       self.get_tokenizer()

										_UpperCamelCase     :   Optional[int]      =       WavaVecaProcessor(tokenizer=lowerCAmelCase__,					feature_extractor=lowerCAmelCase__				)

										self.assertListEqual(
										    processor.model_input_names,					feature_extractor.model_input_names,					msg='''`processor` and `feature_extractor` model input names do not match''',					)



