

"""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 =				random.Random()
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase=1.0							,    __UpperCamelCase=None							,    __UpperCamelCase=None      ):
	'''simple docstring'''





	if rng is None:
		UpperCAmelCase__					:  Tuple					   =					global_rng

	UpperCAmelCase__					:  int					   =					[]
	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      __lowercase			(    unittest.TestCase					):

		def __init__(  self		:			Optional[int]					,A		:			List[Any]					,A		:			int=7					,A		:			List[str]=400					,A		:			int=2_000					,A		:			Optional[int]=24					,A		:			Optional[Any]=24					,A		:			List[str]=0.0					,A		:			List[str]=16_000					,A		:			int=True					,A		:			str=True					,):






			'''simple docstring'''
			UpperCAmelCase__					:  str					   =					parent
			UpperCAmelCase__					:  int					   =					batch_size
			UpperCAmelCase__					:  int					   =					min_seq_length
			UpperCAmelCase__					:  Dict					   =					max_seq_length
			UpperCAmelCase__					:  Tuple					   =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			UpperCAmelCase__					:  Dict					   =					feature_size
			UpperCAmelCase__					:  Any					   =					num_mel_bins
			UpperCAmelCase__					:  Any					   =					padding_value
			UpperCAmelCase__					:  Any					   =					sampling_rate
			UpperCAmelCase__					:  List[str]					   =					return_attention_mask
			UpperCAmelCase__					:  List[str]					   =					do_normalize

		def    __lowercase       (  self		:			Dict    ):






			'''simple docstring'''
			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    __lowercase       (  self		:			Dict					,A		:			Optional[Any]=False					,A		:			Tuple=False    ):






			'''simple docstring'''
			def _flatten(A		:			Any    ):
				return list(itertools.chain(*A    )    )

			if equal_length:
				UpperCAmelCase__					:  Any					   =					[floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
			else:
				# make sure that inputs increase in size
				UpperCAmelCase__					:  Tuple					   =					[
				    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__					:  Dict					   =					[np.asarray(A    ) for x in speech_inputs]
			return speech_inputs







@require_torch
@require_torchaudio
class      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
		snake_case_				       =       SpeechaTextFeatureExtractor if is_speech_available() else None

		def    __lowercase       (  self		:			Dict    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Optional[Any]					   =					SpeechaTextFeatureExtractionTester(self    )

		def    __lowercase       (  self		:			List[str]					,A		:			int    ):






			'''simple docstring'''
			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    __lowercase       (  self		:			Optional[int]    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Dict					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
			# create three inputs of length 800, 1000, and 1200
			UpperCAmelCase__					:  Union[str, Any]					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]
			UpperCAmelCase__					:  Optional[int]					   =					[np.asarray(A    ) for speech_input in speech_inputs]

			# Test feature size
			UpperCAmelCase__					:  Optional[int]					   =					feature_extractor(A					,padding=A					,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
			UpperCAmelCase__					:  List[Any]					   =					feature_extractor(speech_inputs[0]					,return_tensors="""np"""    ).input_features
			UpperCAmelCase__					:  int					   =					feature_extractor(np_speech_inputs[0]					,return_tensors="""np"""    ).input_features
			self.assertTrue(np.allclose(A					,A					,atol=1e-3    )    )

			# Test batched
			UpperCAmelCase__					:  int					   =					feature_extractor(A					,return_tensors="""np"""    ).input_features
			UpperCAmelCase__					:  Optional[int]					   =					feature_extractor(A					,return_tensors="""np"""    ).input_features
			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.
			UpperCAmelCase__					:  Optional[Any]					   =					[floats_list((1, x)    )[0] for x in (800, 800, 800)]
			UpperCAmelCase__					:  Optional[Any]					   =					np.asarray(A    )
			UpperCAmelCase__					:  List[str]					   =					feature_extractor(A					,return_tensors="""np"""    ).input_features
			UpperCAmelCase__					:  List[str]					   =					feature_extractor(A					,return_tensors="""np"""    ).input_features
			for enc_seq_a, enc_seq_a in zip(A					,A    ):
				self.assertTrue(np.allclose(A					,A					,atol=1e-3    )    )

		def    __lowercase       (  self		:			Tuple    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Union[str, Any]					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
			UpperCAmelCase__					:  Dict					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]

			UpperCAmelCase__					:  Dict					   =					["""longest""", """max_length""", """do_not_pad"""]
			UpperCAmelCase__					:  List[Any]					   =					[None, 16, None]
			for max_length, padding in zip(A					,A    ):
				UpperCAmelCase__					:  int					   =					feature_extractor(
				    A					,padding=A					,max_length=A					,return_attention_mask=A    )
				UpperCAmelCase__					:  List[Any]					   =					inputs.input_features
				UpperCAmelCase__					:  Any					   =					inputs.attention_mask
				UpperCAmelCase__					:  int					   =					[np.sum(A    ) 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    __lowercase       (  self		:			Optional[int]    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Union[str, Any]					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
			UpperCAmelCase__					:  int					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]

			UpperCAmelCase__					:  str					   =					["""longest""", """max_length""", """do_not_pad"""]
			UpperCAmelCase__					:  List[str]					   =					[None, 16, None]
			for max_length, padding in zip(A					,A    ):
				UpperCAmelCase__					:  int					   =					feature_extractor(
				    A					,max_length=A					,padding=A					,return_tensors="""np"""					,return_attention_mask=A    )
				UpperCAmelCase__					:  int					   =					inputs.input_features
				UpperCAmelCase__					:  Optional[int]					   =					inputs.attention_mask
				UpperCAmelCase__					:  Union[str, Any]					   =					[np.sum(A    ) 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    __lowercase       (  self		:			str    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Optional[int]					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
			UpperCAmelCase__					:  List[Any]					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]
			UpperCAmelCase__					:  Union[str, Any]					   =					feature_extractor(
			    A					,padding="""max_length"""					,max_length=4					,truncation=A					,return_tensors="""np"""					,return_attention_mask=A					,)
			UpperCAmelCase__					:  Optional[Any]					   =					inputs.input_features
			UpperCAmelCase__					:  Optional[Any]					   =					inputs.attention_mask
			UpperCAmelCase__					:  Any					   =					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    __lowercase       (  self		:			Union[str, Any]    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Optional[int]					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
			UpperCAmelCase__					:  Optional[int]					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]
			UpperCAmelCase__					:  Any					   =					feature_extractor(
			    A					,padding="""longest"""					,max_length=4					,truncation=A					,return_tensors="""np"""					,return_attention_mask=A					,)
			UpperCAmelCase__					:  str					   =					inputs.input_features
			UpperCAmelCase__					:  Union[str, Any]					   =					inputs.attention_mask
			UpperCAmelCase__					:  Tuple					   =					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, 24)    )

			UpperCAmelCase__					:  Optional[Any]					   =					[floats_list((1, x)    )[0] for x in range(800					,1_400					,200    )]
			UpperCAmelCase__					:  Tuple					   =					feature_extractor(
			    A					,padding="""longest"""					,max_length=16					,truncation=A					,return_tensors="""np"""					,return_attention_mask=A					,)
			UpperCAmelCase__					:  List[str]					   =					inputs.input_features
			UpperCAmelCase__					:  Optional[Any]					   =					inputs.attention_mask
			UpperCAmelCase__					:  Optional[int]					   =					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, 24)    )

		def    __lowercase       (  self		:			Tuple    ):






			'''simple docstring'''
			import torch

			UpperCAmelCase__					:  Tuple					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
			UpperCAmelCase__					:  Optional[int]					   =					np.random.rand(100					,32    ).astype(np.floataa    )
			UpperCAmelCase__					:  int					   =					np_speech_inputs.tolist()

			for inputs in [py_speech_inputs, np_speech_inputs]:
				UpperCAmelCase__					:  Union[str, Any]					   =					feature_extractor.pad([{"""input_features""": inputs}]					,return_tensors="""np"""    )
				self.assertTrue(np_processed.input_features.dtype == np.floataa    )
				UpperCAmelCase__					:  List[str]					   =					feature_extractor.pad([{"""input_features""": inputs}]					,return_tensors="""pt"""    )
				self.assertTrue(pt_processed.input_features.dtype == torch.floataa    )

		def    __lowercase       (  self		:			Dict					,A		:			str    ):






			'''simple docstring'''
			from datasets import load_dataset

			UpperCAmelCase__					:  List[str]					   =					load_dataset("""hf-internal-testing/librispeech_asr_dummy"""					,"""clean"""					,split="""validation"""    )
			# automatic decoding with librispeech
			UpperCAmelCase__					:  Optional[Any]					   =					ds.sort("""id"""    ).select(range(A    )    )[:num_samples]["""audio"""]

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




		def    __lowercase       (  self		:			str    ):






			'''simple docstring'''
			UpperCAmelCase__					:  List[Any]					   =					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

			UpperCAmelCase__					:  Tuple					   =					self._load_datasamples(1    )
			UpperCAmelCase__					:  str					   =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
			UpperCAmelCase__					:  int					   =					feature_extractor(A					,return_tensors="""pt"""    ).input_features
			self.assertEquals(input_features.shape					,(1, 584, 24)    )
			self.assertTrue(np.allclose(input_features[0, 0, :30]					,A					,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 =				{
    '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 =				['RobertaTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          '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 =				[
          '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 =				[
          '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 =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





