
'''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


snake_case_			:	Dict 	=       random.Random()



def 			__snake_case  (     _UpperCAmelCase       :							Optional[int],   _UpperCAmelCase       :							Union[str, Any]=1.0,   _UpperCAmelCase       :							List[Any]=None,   _UpperCAmelCase       :							str=None):
   if rng is None:
      UpperCamelCase	  =	global_rng

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

   return values


class     lowercase__						(	unittest.TestCase ):






   '''simple docstring'''







   def __init__(      self			, lowerCamelCase__			, lowerCamelCase__=7			, lowerCamelCase__=4_0_0			, lowerCamelCase__=2_0_0_0			, lowerCamelCase__=1			, lowerCamelCase__=0.0			, lowerCamelCase__=1_6_0_0_0			, lowerCamelCase__=True			, lowerCamelCase__=True			, ):

      '''simple docstring'''







      UpperCamelCase	  =	parent
      UpperCamelCase	  =	batch_size
      UpperCamelCase	  =	min_seq_length
      UpperCamelCase	  =	max_seq_length
      UpperCamelCase	  =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      UpperCamelCase	  =	feature_size
      UpperCamelCase	  =	padding_value
      UpperCamelCase	  =	sampling_rate
      UpperCamelCase	  =	return_attention_mask
      UpperCamelCase	  =	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:
         UpperCamelCase	  =	floats_list((self.batch_size, self.max_seq_length) )
      else:
         # make sure that inputs increase in size
         UpperCamelCase	  =	[
             _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:
         UpperCamelCase	  =	[np.asarray(__lowerCamelCase ) for x in speech_inputs]

      return speech_inputs




class     lowercase__						(	UpperCamelCase__,       unittest.TestCase ):






   '''simple docstring'''







   _snake_case		 =				WavaVecaFeatureExtractor







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	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'''







      UpperCamelCase	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      # create three inputs of length 800, 1000, and 1200
      UpperCamelCase	  =	[floats_list((1, x) )[0] for x in range(8_0_0			, 1_4_0_0			, 2_0_0 )]
      UpperCamelCase	  =	[np.asarray(__lowerCamelCase ) for speech_input in speech_inputs]

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

      # Test batched
      UpperCamelCase	  =	feat_extract(__lowerCamelCase			, return_tensors='''np''' ).input_values
      UpperCamelCase	  =	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.
      UpperCamelCase	  =	[floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
      UpperCamelCase	  =	np.asarray(__lowerCamelCase )
      UpperCamelCase	  =	feat_extract(__lowerCamelCase			, return_tensors='''np''' ).input_values
      UpperCamelCase	  =	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'''







      UpperCamelCase	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      UpperCamelCase	  =	[floats_list((1, x) )[0] for x in range(8_0_0			, 1_4_0_0			, 2_0_0 )]

      UpperCamelCase	  =	['''longest''', '''max_length''', '''do_not_pad''']
      UpperCamelCase	  =	[None, 1_6_0_0, None]
      for max_length, padding in zip(__lowerCamelCase			, __lowerCamelCase ):
         UpperCamelCase	  =	feat_extract(__lowerCamelCase			, padding=__lowerCamelCase			, max_length=__lowerCamelCase			, return_tensors='''np''' )
         UpperCamelCase	  =	processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
         self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6 )
         self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
         self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6 )
         self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      UpperCamelCase	  =	range(8_0_0			, 1_4_0_0			, 2_0_0 )
      UpperCamelCase	  =	[floats_list((1, x) )[0] for x in lengths]

      UpperCamelCase	  =	['''longest''', '''max_length''', '''do_not_pad''']
      UpperCamelCase	  =	[None, 1_6_0_0, None]

      for max_length, padding in zip(__lowerCamelCase			, __lowerCamelCase ):
         UpperCamelCase	  =	feat_extract(__lowerCamelCase			, max_length=__lowerCamelCase			, padding=__lowerCamelCase )
         UpperCamelCase	  =	processed.input_values

         self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
         self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
         self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      UpperCamelCase	  =	[floats_list((1, x) )[0] for x in range(8_0_0			, 1_4_0_0			, 2_0_0 )]
      UpperCamelCase	  =	feat_extract(
          __lowerCamelCase			, truncation=__lowerCamelCase			, max_length=1_0_0_0			, padding='''max_length'''			, return_tensors='''np''' )
      UpperCamelCase	  =	processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
      self._check_zero_mean_unit_variance(input_values[1] )
      self._check_zero_mean_unit_variance(input_values[2] )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      UpperCamelCase	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      UpperCamelCase	  =	[floats_list((1, x) )[0] for x in range(8_0_0			, 1_4_0_0			, 2_0_0 )]
      UpperCamelCase	  =	feat_extract(
          __lowerCamelCase			, truncation=__lowerCamelCase			, max_length=1_0_0_0			, padding='''longest'''			, return_tensors='''np''' )
      UpperCamelCase	  =	processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
      self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
      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, 1_0_0_0) )

      UpperCamelCase	  =	[floats_list((1, x) )[0] for x in range(8_0_0			, 1_4_0_0			, 2_0_0 )]
      UpperCamelCase	  =	feat_extract(
          __lowerCamelCase			, truncation=__lowerCamelCase			, max_length=2_0_0_0			, padding='''longest'''			, return_tensors='''np''' )
      UpperCamelCase	  =	processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
      self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
      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, 1_2_0_0) )







   @require_torch
   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      import torch

      UpperCamelCase	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
      UpperCamelCase	  =	np.random.rand(1_0_0 ).astype(np.floataa )
      UpperCamelCase	  =	np_speech_inputs.tolist()

      for inputs in [py_speech_inputs, np_speech_inputs]:
         UpperCamelCase	  =	feature_extractor.pad([{'''input_values''': inputs}]			, return_tensors='''np''' )
         self.assertTrue(np_processed.input_values.dtype == np.floataa )
         UpperCamelCase	  =	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:
         UpperCamelCase	  =	WavaVecaConfig.from_pretrained(__lowerCamelCase )
         UpperCamelCase	  =	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''' )












from typing import TYPE_CHECKING

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


a__: int							   =						{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: List[Any]							   =						[
           'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
           'M2M100ForConditionalGeneration',
           'M2M100Model',
           'M2M100PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
       from .tokenization_mam_aaa import MaMaaaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mam_aaa import (
                  M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaMaaaForConditionalGeneration,
                  MaMaaaModel,
                  MaMaaaPreTrainedModel,
              )


else:
       import sys

       a__: List[str]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





