






"""simple docstring"""



import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
    from transformers import SpeechaTextFeatureExtractor

_UpperCamelCase		:  Dict						=		random.Random()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  Union[str, Any]=1.0		,					__snake_case			:  List[str]=None		,					__snake_case			:  List[str]=None    ):





  '''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

@require_torch
@require_torchaudio
class        a     (   unittest.TestCase	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=2_0_0_0							,		_lowerCamelCase=2_4							,		_lowerCamelCase=2_4							,		_lowerCamelCase=0.0							,		_lowerCamelCase=1_6_0_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		):
      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							    =       num_mel_bins
      lowercase							    =       padding_value
      lowercase							    =       sampling_rate
      lowercase							    =       return_attention_mask
      lowercase							    =       do_normalize







    def 			UpperCamelCase_      (			self       ):
      return {
          "feature_size": self.feature_size,
          "num_mel_bins": self.num_mel_bins,
          "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       ):
      def _flatten(_lowerCamelCase       ):
        return list(itertools.chain(*_lowerCamelCase       )       )

      if equal_length:
        lowercase							    =       [floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
      else:
        # make sure that inputs increase in size
        lowercase							    =       [
            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


@require_torch
@require_torchaudio
class        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     int	     =SpeechaTextFeatureExtractor if is_speech_available() else None







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       SpeechaTextFeatureExtractionTester(self       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      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       ):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      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(8_0_0							,		1_4_0_0							,		2_0_0       )]
      lowercase							    =       [np.asarray(_lowerCamelCase       ) for speech_input in speech_inputs]

      # Test feature size
      lowercase							    =       feature_extractor(_lowerCamelCase							,		padding=_lowerCamelCase							,		return_tensors='np'       ).input_features
      self.assertTrue(input_features.ndim == 3       )
      self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size       )

      # Test not batched input
      lowercase							    =       feature_extractor(speech_inputs[0]							,		return_tensors='np'       ).input_features
      lowercase							    =       feature_extractor(np_speech_inputs[0]							,		return_tensors='np'       ).input_features
      self.assertTrue(np.allclose(_lowerCamelCase							,		_lowerCamelCase							,		atol=1e-3       )       )

      # Test batched
      lowercase							    =       feature_extractor(_lowerCamelCase							,		return_tensors='np'       ).input_features
      lowercase							    =       feature_extractor(_lowerCamelCase							,		return_tensors='np'       ).input_features
      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 (8_0_0, 8_0_0, 8_0_0)]
      lowercase							    =       np.asarray(_lowerCamelCase       )
      lowercase							    =       feature_extractor(_lowerCamelCase							,		return_tensors='np'       ).input_features
      lowercase							    =       feature_extractor(_lowerCamelCase							,		return_tensors='np'       ).input_features
      for enc_seq_a, enc_seq_a in zip(_lowerCamelCase							,		_lowerCamelCase       ):
        self.assertTrue(np.allclose(_lowerCamelCase							,		_lowerCamelCase							,		atol=1e-3       )       )







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

      lowercase							    =       ['longest', 'max_length', 'do_not_pad']
      lowercase							    =       [None, 1_6, None]
      for max_length, padding in zip(_lowerCamelCase							,		_lowerCamelCase       ):
        lowercase							    =       feature_extractor(
            _lowerCamelCase							,		padding=_lowerCamelCase							,		max_length=_lowerCamelCase							,		return_attention_mask=_lowerCamelCase       )
        lowercase							    =       inputs.input_features
        lowercase							    =       inputs.attention_mask
        lowercase							    =       [np.sum(_lowerCamelCase       ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]       )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]       )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]       )







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

      lowercase							    =       ['longest', 'max_length', 'do_not_pad']
      lowercase							    =       [None, 1_6, None]
      for max_length, padding in zip(_lowerCamelCase							,		_lowerCamelCase       ):
        lowercase							    =       feature_extractor(
            _lowerCamelCase							,		max_length=_lowerCamelCase							,		padding=_lowerCamelCase							,		return_tensors='np'							,		return_attention_mask=_lowerCamelCase       )
        lowercase							    =       inputs.input_features
        lowercase							    =       inputs.attention_mask
        lowercase							    =       [np.sum(_lowerCamelCase       ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]       )
        self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6       )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]       )
        self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6       )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      lowercase							    =       [floats_list((1, x)       )[0] for x in range(8_0_0							,		1_4_0_0							,		2_0_0       )]
      lowercase							    =       feature_extractor(
          _lowerCamelCase							,		padding='max_length'							,		max_length=4							,		truncation=_lowerCamelCase							,		return_tensors='np'							,		return_attention_mask=_lowerCamelCase							,		)
      lowercase							    =       inputs.input_features
      lowercase							    =       inputs.attention_mask
      lowercase							    =       np.sum(attention_mask == 1							,		axis=1       )

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
      self._check_zero_mean_unit_variance(input_features[1]       )
      self._check_zero_mean_unit_variance(input_features[2]       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      lowercase							    =       [floats_list((1, x)       )[0] for x in range(8_0_0							,		1_4_0_0							,		2_0_0       )]
      lowercase							    =       feature_extractor(
          _lowerCamelCase							,		padding='longest'							,		max_length=4							,		truncation=_lowerCamelCase							,		return_tensors='np'							,		return_attention_mask=_lowerCamelCase							,		)
      lowercase							    =       inputs.input_features
      lowercase							    =       inputs.attention_mask
      lowercase							    =       np.sum(attention_mask == 1							,		axis=1       )

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
      self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]       )
      self._check_zero_mean_unit_variance(input_features[2]       )

      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape							,		(3, 4, 2_4)       )

      lowercase							    =       [floats_list((1, x)       )[0] for x in range(8_0_0							,		1_4_0_0							,		2_0_0       )]
      lowercase							    =       feature_extractor(
          _lowerCamelCase							,		padding='longest'							,		max_length=1_6							,		truncation=_lowerCamelCase							,		return_tensors='np'							,		return_attention_mask=_lowerCamelCase							,		)
      lowercase							    =       inputs.input_features
      lowercase							    =       inputs.attention_mask
      lowercase							    =       np.sum(attention_mask == 1							,		axis=1       )

      self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
      self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]       )
      self._check_zero_mean_unit_variance(input_features[2]       )

      # make sure that if max_length < longest -> then pad to max_length
      self.assertEqual(input_features.shape							,		(3, 6, 2_4)       )







    def 			UpperCamelCase_      (			self       ):
      import torch

      lowercase							    =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      lowercase							    =       np.random.rand(1_0_0							,		3_2       ).astype(np.floataa       )
      lowercase							    =       np_speech_inputs.tolist()

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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      from datasets import load_dataset

      lowercase							    =       load_dataset('hf-internal-testing/librispeech_asr_dummy'							,		'clean'							,		split='validation'       )
      # automatic decoding with librispeech
      lowercase							    =       ds.sort('id'       ).select(range(_lowerCamelCase       )       )[:num_samples]['audio']

      return [x["array"] for x in speech_samples]







    def 			UpperCamelCase_      (			self       ):
      # fmt: off
      lowercase							    =       np.array([
          -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
          -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
          -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
      ]       )
      # fmt: on

      lowercase							    =       self._load_datasamples(1       )
      lowercase							    =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
      lowercase							    =       feature_extractor(_lowerCamelCase							,		return_tensors='pt'       ).input_features
      self.assertEquals(input_features.shape							,		(1, 5_8_4, 2_4)       )
      self.assertTrue(np.allclose(input_features[0, 0, :3_0]							,		_lowerCamelCase							,		atol=1e-4       )       )










"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase		:  Dict						=		{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  str						=		['RobertaTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  Tuple						=		[
        'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RobertaForCausalLM',
        'RobertaForMaskedLM',
        'RobertaForMultipleChoice',
        'RobertaForQuestionAnswering',
        'RobertaForSequenceClassification',
        'RobertaForTokenClassification',
        'RobertaModel',
        'RobertaPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  List[Any]						=		[
        'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRobertaForCausalLM',
        'TFRobertaForMaskedLM',
        'TFRobertaForMultipleChoice',
        'TFRobertaForQuestionAnswering',
        'TFRobertaForSequenceClassification',
        'TFRobertaForTokenClassification',
        'TFRobertaMainLayer',
        'TFRobertaModel',
        'TFRobertaPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  Optional[int]						=		[
        'FlaxRobertaForCausalLM',
        'FlaxRobertaForMaskedLM',
        'FlaxRobertaForMultipleChoice',
        'FlaxRobertaForQuestionAnswering',
        'FlaxRobertaForSequenceClassification',
        'FlaxRobertaForTokenClassification',
        'FlaxRobertaModel',
        'FlaxRobertaPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
    from .tokenization_roberta import RobertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_roberta_fast import RobertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta import (
            ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaForCausalLM,
            RobertaForMaskedLM,
            RobertaForMultipleChoice,
            RobertaForQuestionAnswering,
            RobertaForSequenceClassification,
            RobertaForTokenClassification,
            RobertaModel,
            RobertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta import (
            TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaForCausalLM,
            TFRobertaForMaskedLM,
            TFRobertaForMultipleChoice,
            TFRobertaForQuestionAnswering,
            TFRobertaForSequenceClassification,
            TFRobertaForTokenClassification,
            TFRobertaMainLayer,
            TFRobertaModel,
            TFRobertaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta import (
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaModel,
            FlaxRobertaPreTrainedModel,
        )

else:
    import sys

    _UpperCamelCase		:  Optional[Any]						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


