

"""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


__lowerCamelCase										=		random.Random()





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase=1.0      ,   __UpperCamelCase=None      ,   __UpperCamelCase=None						) ->      Optional[Any]:
 if rng is None:
  __magic_name__     =      global_rng

 __magic_name__     =      []
 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     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_=7 ,						UpperCamelCase_=400 ,						UpperCamelCase_=2000 ,						UpperCamelCase_=1 ,						UpperCamelCase_=0.0 ,						UpperCamelCase_=1_6000 ,						UpperCamelCase_=True ,						UpperCamelCase_=True ,						):
      __magic_name__     =      parent
      __magic_name__     =      batch_size
      __magic_name__     =      min_seq_length
      __magic_name__     =      max_seq_length
      __magic_name__     =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      __magic_name__     =      feature_size
      __magic_name__     =      padding_value
      __magic_name__     =      sampling_rate
      __magic_name__     =      return_attention_mask
      __magic_name__     =      do_normalize







     def 	lowerCAmelCase__    (      self       ):
      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 	lowerCAmelCase__    (      self ,						UpperCamelCase_=False ,						UpperCamelCase_=False       ):
      def _flatten(UpperCamelCase_       ):
       return list(itertools.chain(*UpperCamelCase_       )       )

      if equal_length:
       __magic_name__     =      floats_list((self.batch_size, self.max_seq_length)       )
      else:
       # make sure that inputs increase in size
       __magic_name__     =      [
           _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:
       __magic_name__     =      [np.asarray(UpperCamelCase_       ) for x in speech_inputs]

      return speech_inputs


class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       WavaVecaFeatureExtractor







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      WavaVecaFeatureExtractionTester(self       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      self.assertTrue(np.all(np.mean(UpperCamelCase_ ,						axis=0       ) < 1E-3       )       )
      self.assertTrue(np.all(np.abs(np.var(UpperCamelCase_ ,						axis=0       ) - 1       ) < 1E-3       )       )







     def 	lowerCAmelCase__    (      self       ):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      # create three inputs of length 800, 1000, and 1200
      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]
      __magic_name__     =      [np.asarray(UpperCamelCase_       ) for speech_input in speech_inputs]

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

      # Test batched
      __magic_name__     =      feat_extract(UpperCamelCase_ ,						return_tensors='''np'''       ).input_values
      __magic_name__     =      feat_extract(UpperCamelCase_ ,						return_tensors='''np'''       ).input_values
      for enc_seq_a, enc_seq_a in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       self.assertTrue(np.allclose(UpperCamelCase_ ,						UpperCamelCase_ ,						atol=1E-3       )       )

      # Test 2-D numpy arrays are batched.
      __magic_name__     =      [floats_list((1, x)       )[0] for x in (800, 800, 800)]
      __magic_name__     =      np.asarray(UpperCamelCase_       )
      __magic_name__     =      feat_extract(UpperCamelCase_ ,						return_tensors='''np'''       ).input_values
      __magic_name__     =      feat_extract(UpperCamelCase_ ,						return_tensors='''np'''       ).input_values
      for enc_seq_a, enc_seq_a in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       self.assertTrue(np.allclose(UpperCamelCase_ ,						UpperCamelCase_ ,						atol=1E-3       )       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]

      __magic_name__     =      ['''longest''', '''max_length''', '''do_not_pad''']
      __magic_name__     =      [None, 1600, None]
      for max_length, padding in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       __magic_name__     =      feat_extract(UpperCamelCase_ ,						padding=UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						return_tensors='''np'''       )
       __magic_name__     =      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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      range(800 ,						1400 ,						200       )
      __magic_name__     =      [floats_list((1, x)       )[0] for x in lengths]

      __magic_name__     =      ['''longest''', '''max_length''', '''do_not_pad''']
      __magic_name__     =      [None, 1600, None]

      for max_length, padding in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       __magic_name__     =      feat_extract(UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding=UpperCamelCase_       )
       __magic_name__     =      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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]
      __magic_name__     =      feat_extract(
          UpperCamelCase_ ,						truncation=UpperCamelCase_ ,						max_length=1000 ,						padding='''max_length''' ,						return_tensors='''np'''       )
      __magic_name__     =      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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]
      __magic_name__     =      feat_extract(
          UpperCamelCase_ ,						truncation=UpperCamelCase_ ,						max_length=1000 ,						padding='''longest''' ,						return_tensors='''np'''       )
      __magic_name__     =      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)       )

      __magic_name__     =      [floats_list((1, x)       )[0] for x in range(800 ,						1400 ,						200       )]
      __magic_name__     =      feat_extract(
          UpperCamelCase_ ,						truncation=UpperCamelCase_ ,						max_length=2000 ,						padding='''longest''' ,						return_tensors='''np'''       )
      __magic_name__     =      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 	lowerCAmelCase__    (      self       ):
      import torch

      __magic_name__     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      __magic_name__     =      np.random.rand(100       ).astype(np.floataa       )
      __magic_name__     =      np_speech_inputs.tolist()

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







     @slow
     @require_torch
     def 	lowerCAmelCase__    (      self       ):
      # this test makes sure that models that are using
      # group norm don't have their feature extractor return the
      # attention_mask
      for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
       __magic_name__     =      WavaVecaConfig.from_pretrained(UpperCamelCase_       )
       __magic_name__     =      WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_       )

       # 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_tokenizers_available, is_torch_available


__lowerCamelCase										=		{
    "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:
 __lowerCamelCase										=		[
     "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

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

