

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


UpperCamelCase__  :      Union[str, Any]			=    random.Random()


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]=1.0 ,   _SCREAMING_SNAKE_CASE					:		Optional[int]=None ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]=None       ):





    """simple docstring"""


    if rng is None:
        SCREAMING_SNAKE_CASE_									=						global_rng

    SCREAMING_SNAKE_CASE_									=						[]
    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       ):

  def __init__(					self						,		_A						,		_A=7						,		_A=400						,		_A=2000						,		_A=1						,		_A=0.0						,		_A=16000						,		_A=True						,		_A=True						,		):
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						batch_size
      SCREAMING_SNAKE_CASE_									=						min_seq_length
      SCREAMING_SNAKE_CASE_									=						max_seq_length
      SCREAMING_SNAKE_CASE_									=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      SCREAMING_SNAKE_CASE_									=						feature_size
      SCREAMING_SNAKE_CASE_									=						padding_value
      SCREAMING_SNAKE_CASE_									=						sampling_rate
      SCREAMING_SNAKE_CASE_									=						return_attention_mask
      SCREAMING_SNAKE_CASE_									=						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						,		_A=False						,		_A=False):
      def _flatten(_A):
          return list(itertools.chain(*_A))

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

      return speech_inputs






class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	str						 =  WavaVecaFeatureExtractor

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

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

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

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

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

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

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

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

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

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

      SCREAMING_SNAKE_CASE_									=						[floats_list((1, x))[0] for x in range(800						,		1400						,		200)]
      SCREAMING_SNAKE_CASE_									=						feat_extract(
          _A						,		truncation=_A						,		max_length=2000						,		padding='longest'						,		return_tensors='np')
      SCREAMING_SNAKE_CASE_									=						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

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

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

          # 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 List

import numpy as np


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{key: len(_SCREAMING_SNAKE_CASE       ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    if len(set(lists_lengths.values()       )       ) > 1:
        raise RuntimeError(
            (
                'Sharding is ambiguous for this dataset: '
                + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                + '\n'.join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()       )
                + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
            )       )
    SCREAMING_SNAKE_CASE_									=						max(lists_lengths.values() ,   default=0       )
    return max(1 ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for group_idx in range(_SCREAMING_SNAKE_CASE       ):
        SCREAMING_SNAKE_CASE_									=						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        SCREAMING_SNAKE_CASE_									=						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        SCREAMING_SNAKE_CASE_									=						range(_SCREAMING_SNAKE_CASE ,   start + num_shards_to_add       )
        shards_indices_per_group.append(_SCREAMING_SNAKE_CASE       )
    return shards_indices_per_group


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						_number_of_shards_in_gen_kwargs(_SCREAMING_SNAKE_CASE       )
    if num_shards == 1:
        return [dict(_SCREAMING_SNAKE_CASE       )]
    else:
        SCREAMING_SNAKE_CASE_									=						_distribute_shards(num_shards=_SCREAMING_SNAKE_CASE ,   max_num_jobs=_SCREAMING_SNAKE_CASE       )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(_SCREAMING_SNAKE_CASE       )       )
        ]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[dict]       ):





    """simple docstring"""


    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key] ,   _SCREAMING_SNAKE_CASE       )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		np.random.Generator ,   _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{len(_SCREAMING_SNAKE_CASE       ) for value in gen_kwargs.values() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    SCREAMING_SNAKE_CASE_									=						{}
    for size in list_sizes:
        SCREAMING_SNAKE_CASE_									=						list(range(_SCREAMING_SNAKE_CASE       )       )
        rng.shuffle(indices_per_size[size]       )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    SCREAMING_SNAKE_CASE_									=						dict(_SCREAMING_SNAKE_CASE       )
    for key, value in shuffled_kwargs.items():
        if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
            SCREAMING_SNAKE_CASE_									=						[value[i] for i in indices_per_size[len(_SCREAMING_SNAKE_CASE       )]]
    return shuffled_kwargs


