


"""simple docstring"""



import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


a_			=      random.Random()







def 		__lowercase					(       snake_case_  :  List[str]    ,snake_case_  :  Optional[int]=1.0    ,snake_case_  :  int=None    ,snake_case_  :  Tuple=None						)       ->Tuple:



    '''simple docstring'''







    if rng is None:
        __A   :						int									=					global_rng

    __A   :						Tuple									=					[]
    for batch_idx in range(shape[0]						):
        values.append([]						)
        for _ in range(shape[1]						):
            values[-1].append(rng.random() * scale						)

    return values






class 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase=7 ,						__lowerCamelCase=400 ,						__lowerCamelCase=2000 ,						__lowerCamelCase=1 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=1_6000 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						):



     '''simple docstring'''




     __A   :						Optional[int]									=					parent
     __A   :						int									=					batch_size
     __A   :						List[str]									=					min_seq_length
     __A   :						int									=					max_seq_length
     __A   :						Union[str, Any]									=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     __A   :						Optional[int]									=					feature_size
     __A   :						Optional[int]									=					padding_value
     __A   :						List[Any]									=					sampling_rate
     __A   :						Tuple									=					return_attention_mask
     __A   :						List[Any]									=					do_normalize

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return {
         "feature_size": self.feature_size,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }







 def  UpperCamelCase__(   self ,						__lowerCamelCase=False ,						__lowerCamelCase=False  ):



     '''simple docstring'''




     def _flatten(__lowerCamelCase  ):
         return list(itertools.chain(*__lowerCamelCase  )  )

     if equal_length:
         __A   :						str									=					floats_list((self.batch_size, self.max_seq_length)  )
     else:
         # make sure that inputs increase in size
         __A   :						Union[str, Any]									=					[
             _flatten(floats_list((x, self.feature_size)  )  )
             for x in range(self.min_seq_length ,						self.max_seq_length ,						self.seq_length_diff  )
         ]

     if numpify:
         __A   :						Optional[int]									=					[np.asarray(__lowerCamelCase  ) for x in speech_inputs]

     return speech_inputs






class 		__snake_case (		__UpperCamelCase							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					WavaVecaFeatureExtractor

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						str									=					WavaVecaFeatureExtractionTester(self  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     self.assertTrue(np.all(np.mean(__lowerCamelCase ,						axis=0  ) < 1e-3  )  )
     self.assertTrue(np.all(np.abs(np.var(__lowerCamelCase ,						axis=0  ) - 1  ) < 1e-3  )  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     # create three inputs of length 800, 1000, and 1200
     __A   :						Tuple									=					[floats_list((1, x)  )[0] for x in range(800 ,						1400 ,						200  )]
     __A   :						List[Any]									=					[np.asarray(__lowerCamelCase  ) for speech_input in speech_inputs]

     # Test not batched input
     __A   :						List[str]									=					feat_extract(speech_inputs[0] ,						return_tensors='''np'''  ).input_values
     __A   :						Union[str, Any]									=					feat_extract(np_speech_inputs[0] ,						return_tensors='''np'''  ).input_values
     self.assertTrue(np.allclose(__lowerCamelCase ,						__lowerCamelCase ,						atol=1e-3  )  )

     # Test batched
     __A   :						str									=					feat_extract(__lowerCamelCase ,						return_tensors='''np'''  ).input_values
     __A   :						Any									=					feat_extract(__lowerCamelCase ,						return_tensors='''np'''  ).input_values
     for enc_seq_a, enc_seq_a in zip(__lowerCamelCase ,						__lowerCamelCase  ):
         self.assertTrue(np.allclose(__lowerCamelCase ,						__lowerCamelCase ,						atol=1e-3  )  )

     # Test 2-D numpy arrays are batched.
     __A   :						Any									=					[floats_list((1, x)  )[0] for x in (800, 800, 800)]
     __A   :						Union[str, Any]									=					np.asarray(__lowerCamelCase  )
     __A   :						Union[str, Any]									=					feat_extract(__lowerCamelCase ,						return_tensors='''np'''  ).input_values
     __A   :						List[Any]									=					feat_extract(__lowerCamelCase ,						return_tensors='''np'''  ).input_values
     for enc_seq_a, enc_seq_a in zip(__lowerCamelCase ,						__lowerCamelCase  ):
         self.assertTrue(np.allclose(__lowerCamelCase ,						__lowerCamelCase ,						atol=1e-3  )  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     __A   :						Tuple									=					[floats_list((1, x)  )[0] for x in range(800 ,						1400 ,						200  )]

     __A   :						List[str]									=					["""longest""", """max_length""", """do_not_pad"""]
     __A   :						Optional[Any]									=					[None, 1600, None]
     for max_length, padding in zip(__lowerCamelCase ,						__lowerCamelCase  ):
         __A   :						Any									=					feat_extract(__lowerCamelCase ,						padding=__lowerCamelCase ,						max_length=__lowerCamelCase ,						return_tensors='''np'''  )
         __A   :						Tuple									=					processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:800]  )
         self.assertTrue(input_values[0][800:].sum() < 1e-6  )
         self._check_zero_mean_unit_variance(input_values[1][:1000]  )
         self.assertTrue(input_values[0][1000:].sum() < 1e-6  )
         self._check_zero_mean_unit_variance(input_values[2][:1200]  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Tuple									=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     __A   :						Dict									=					range(800 ,						1400 ,						200  )
     __A   :						Union[str, Any]									=					[floats_list((1, x)  )[0] for x in lengths]

     __A   :						Optional[int]									=					["""longest""", """max_length""", """do_not_pad"""]
     __A   :						Tuple									=					[None, 1600, None]

     for max_length, padding in zip(__lowerCamelCase ,						__lowerCamelCase  ):
         __A   :						int									=					feat_extract(__lowerCamelCase ,						max_length=__lowerCamelCase ,						padding=__lowerCamelCase  )
         __A   :						List[str]									=					processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:800]  )
         self._check_zero_mean_unit_variance(input_values[1][:1000]  )
         self._check_zero_mean_unit_variance(input_values[2][:1200]  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Dict									=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     __A   :						List[Any]									=					[floats_list((1, x)  )[0] for x in range(800 ,						1400 ,						200  )]
     __A   :						List[str]									=					feat_extract(
         __lowerCamelCase ,						truncation=__lowerCamelCase ,						max_length=1000 ,						padding='''max_length''' ,						return_tensors='''np'''  )
     __A   :						Optional[int]									=					processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :800]  )
     self._check_zero_mean_unit_variance(input_values[1]  )
     self._check_zero_mean_unit_variance(input_values[2]  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     __A   :						str									=					[floats_list((1, x)  )[0] for x in range(800 ,						1400 ,						200  )]
     __A   :						List[str]									=					feat_extract(
         __lowerCamelCase ,						truncation=__lowerCamelCase ,						max_length=1000 ,						padding='''longest''' ,						return_tensors='''np'''  )
     __A   :						List[str]									=					processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :800]  )
     self._check_zero_mean_unit_variance(input_values[1, :1000]  )
     self._check_zero_mean_unit_variance(input_values[2]  )

     # make sure that if max_length < longest -> then pad to max_length
     self.assertTrue(input_values.shape == (3, 1000)  )

     __A   :						str									=					[floats_list((1, x)  )[0] for x in range(800 ,						1400 ,						200  )]
     __A   :						Any									=					feat_extract(
         __lowerCamelCase ,						truncation=__lowerCamelCase ,						max_length=2000 ,						padding='''longest''' ,						return_tensors='''np'''  )
     __A   :						Tuple									=					processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :800]  )
     self._check_zero_mean_unit_variance(input_values[1, :1000]  )
     self._check_zero_mean_unit_variance(input_values[2]  )

     # make sure that if max_length > longest -> then pad to longest
     self.assertTrue(input_values.shape == (3, 1200)  )

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     import torch

     __A   :						Optional[int]									=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     __A   :						Optional[Any]									=					np.random.rand(100  ).astype(np.floataa  )
     __A   :						Any									=					np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
         __A   :						Optional[int]									=					feature_extractor.pad([{'''input_values''': inputs}] ,						return_tensors='''np'''  )
         self.assertTrue(np_processed.input_values.dtype == np.floataa  )
         __A   :						str									=					feature_extractor.pad([{'''input_values''': inputs}] ,						return_tensors='''pt'''  )
         self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )







 @slow
 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
         __A   :						Union[str, Any]									=					WavaVecaConfig.from_pretrained(__lowerCamelCase  )
         __A   :						Optional[int]									=					WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase  )

         # only "layer" feature extraction norm should make use of
         # attention_mask
         self.assertEqual(feat_extract.return_attention_mask ,						config.feat_extract_norm == '''layer'''  )











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_			=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TapasForMaskedLM""",
           """TapasForQuestionAnswering""",
           """TapasForSequenceClassification""",
           """TapasModel""",
           """TapasPreTrainedModel""",
           """load_tf_weights_in_tapas""",
       ]
try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFTapasForMaskedLM""",
           """TFTapasForQuestionAnswering""",
           """TFTapasForSequenceClassification""",
           """TFTapasModel""",
           """TFTapasPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
       from .tokenization_tapas import TapasTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tapas import (
                  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TapasForMaskedLM,
                  TapasForQuestionAnswering,
                  TapasForSequenceClassification,
                  TapasModel,
                  TapasPreTrainedModel,
                  load_tf_weights_in_tapas,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_tapas import (
                  TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTapasForMaskedLM,
                  TFTapasForQuestionAnswering,
                  TFTapasForSequenceClassification,
                  TFTapasModel,
                  TFTapasPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




