

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


lowercase_									=   random.Random()
def      a      (			A__	:   Optional[int]      ,   A__	:   Optional[Any]=1.0      ,   A__	:   Any=None      ,   A__	:   Dict=None     )    ->      List[str]:





      """simple docstring"""



      if rng is None:
            _lowercase	       =global_rng

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

      return values





class  __lowerCAmelCase     (					unittest.TestCase							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase=7							,   lowerCAmelCase=400							,   lowerCAmelCase=2_000							,   lowerCAmelCase=1							,   lowerCAmelCase=0.0							,   lowerCAmelCase=16_000							,   lowerCAmelCase=True							,   lowerCAmelCase=True							,   )  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =parent
        _lowercase	       =batch_size
        _lowercase	       =min_seq_length
        _lowercase	       =max_seq_length
        _lowercase	       =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _lowercase	       =feature_size
        _lowercase	       =padding_value
        _lowercase	       =sampling_rate
        _lowercase	       =return_attention_mask
        _lowercase	       =do_normalize




  def   A__     (				self					)  -> List[str]:
        '''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   A__     (				self							,   lowerCAmelCase=False							,   lowerCAmelCase=False					)  -> int:
        '''simple docstring'''






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

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

        return speech_inputs





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			unittest.TestCase							):
  _a									=  WavaVecaFeatureExtractor




  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        _lowercase	       =WavaVecaFeatureExtractionTester(self					)




  def   A__     (				self							,   lowerCAmelCase					)  -> int:
        '''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   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        # create three inputs of length 800, 1000, and 1200
        _lowercase	       =[floats_list((1, x)					)[0] for x in range(800							,   1_400							,   200					)]
        _lowercase	       =[np.asarray(lowerCAmelCase					) for speech_input in speech_inputs]

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

        # Test batched
        _lowercase	       =feat_extract(lowerCAmelCase							,   return_tensors='np'					).input_values
        _lowercase	       =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.
        _lowercase	       =[floats_list((1, x)					)[0] for x in (800, 800, 800)]
        _lowercase	       =np.asarray(lowerCAmelCase					)
        _lowercase	       =feat_extract(lowerCAmelCase							,   return_tensors='np'					).input_values
        _lowercase	       =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   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        _lowercase	       =[floats_list((1, x)					)[0] for x in range(800							,   1_400							,   200					)]

        _lowercase	       =['longest', 'max_length', 'do_not_pad']
        _lowercase	       =[None, 1_600, None]
        for max_length, padding in zip(lowerCAmelCase							,   lowerCAmelCase					):
              _lowercase	       =feat_extract(lowerCAmelCase							,   padding=lowerCAmelCase							,   max_length=lowerCAmelCase							,   return_tensors='np'					)
              _lowercase	       =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][:1_000]					)
              self.assertTrue(input_values[0][1_000:].sum() < 1e-6					)
              self._check_zero_mean_unit_variance(input_values[2][:1_200]					)




  def   A__     (				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        _lowercase	       =range(800							,   1_400							,   200					)
        _lowercase	       =[floats_list((1, x)					)[0] for x in lengths]

        _lowercase	       =['longest', 'max_length', 'do_not_pad']
        _lowercase	       =[None, 1_600, None]

        for max_length, padding in zip(lowerCAmelCase							,   lowerCAmelCase					):
              _lowercase	       =feat_extract(lowerCAmelCase							,   max_length=lowerCAmelCase							,   padding=lowerCAmelCase					)
              _lowercase	       =processed.input_values

              self._check_zero_mean_unit_variance(input_values[0][:800]					)
              self._check_zero_mean_unit_variance(input_values[1][:1_000]					)
              self._check_zero_mean_unit_variance(input_values[2][:1_200]					)




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        _lowercase	       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        _lowercase	       =[floats_list((1, x)					)[0] for x in range(800							,   1_400							,   200					)]
        _lowercase	       =feat_extract(
            lowerCAmelCase							,   truncation=lowerCAmelCase							,   max_length=1_000							,   padding='max_length'							,   return_tensors='np'					)
        _lowercase	       =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   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
        _lowercase	       =[floats_list((1, x)					)[0] for x in range(800							,   1_400							,   200					)]
        _lowercase	       =feat_extract(
            lowerCAmelCase							,   truncation=lowerCAmelCase							,   max_length=1_000							,   padding='longest'							,   return_tensors='np'					)
        _lowercase	       =processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]					)
        self._check_zero_mean_unit_variance(input_values[1, :1_000]					)
        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_000)					)

        _lowercase	       =[floats_list((1, x)					)[0] for x in range(800							,   1_400							,   200					)]
        _lowercase	       =feat_extract(
            lowerCAmelCase							,   truncation=lowerCAmelCase							,   max_length=2_000							,   padding='longest'							,   return_tensors='np'					)
        _lowercase	       =processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]					)
        self._check_zero_mean_unit_variance(input_values[1, :1_000]					)
        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_200)					)




  @require_torch
  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        import torch

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

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




  @slow
  @require_torch
  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
              _lowercase	       =WavaVecaConfig.from_pretrained(lowerCAmelCase					)
              _lowercase	       =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


lowercase_									=   {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['BloomTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BloomForCausalLM',
      'BloomModel',
      'BloomPreTrainedModel',
      'BloomForSequenceClassification',
      'BloomForTokenClassification',
      'BloomForQuestionAnswering',
  ]

if TYPE_CHECKING:
  from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bloom_fast import BloomTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bloom import (
        BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
        BloomForCausalLM,
        BloomForQuestionAnswering,
        BloomForSequenceClassification,
        BloomForTokenClassification,
        BloomModel,
        BloomPreTrainedModel,
    )

else:
  import sys

  lowercase_									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


