


'''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

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






def 		_A			(lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Any=1.0			,  lowerCAmelCase__					:Union[str, Any]=None			,  lowerCAmelCase__					:Optional[int]=None						)				->					int:




     '''simple docstring'''



     if rng is None:
          _a      =							global_rng

     _a      =							[]
     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 ,			__magic_name__ ,			__magic_name__=7 ,			__magic_name__=4_00 ,			__magic_name__=20_00 ,			__magic_name__=24 ,			__magic_name__=24 ,			__magic_name__=0.0 ,			__magic_name__=1_60_00 ,			__magic_name__=True ,			__magic_name__=True ,			)   ->							Tuple:
      _a      =							parent
      _a      =							batch_size
      _a      =							min_seq_length
      _a      =							max_seq_length
      _a      =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      _a      =							feature_size
      _a      =							num_mel_bins
      _a      =							padding_value
      _a      =							sampling_rate
      _a      =							return_attention_mask
      _a      =							do_normalize
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      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 ,			__magic_name__=False ,			__magic_name__=False  )   ->							Optional[int]:

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

      if equal_length:
           _a      =							[floats_list((self.max_seq_length, self.feature_size)  ) for _ in range(self.batch_size  )]
      else:
           # make sure that inputs increase in size
           _a      =							[
               floats_list((x, self.feature_size)  )
               for x in range(self.min_seq_length ,			self.max_seq_length ,			self.seq_length_diff  )
           ]
      if numpify:
           _a      =							[np.asarray(__magic_name__  ) for x in speech_inputs]
      return speech_inputs





@require_torch
@require_torchaudio
class 	a       (       __lowercase					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		SpeechaTextFeatureExtractor if is_speech_available() else None
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							SpeechaTextFeatureExtractionTester(self  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      self.assertTrue(np.all(np.mean(__magic_name__ ,			axis=0  ) < 1e-3  )  )
      self.assertTrue(np.all(np.abs(np.var(__magic_name__ ,			axis=0  ) - 1  ) < 1e-3  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
      # create three inputs of length 800, 1000, and 1200
      _a      =							[floats_list((1, x)  )[0] for x in range(8_00 ,			14_00 ,			2_00  )]
      _a      =							[np.asarray(__magic_name__  ) for speech_input in speech_inputs]

      # Test feature size
      _a      =							feature_extractor(__magic_name__ ,			padding=__magic_name__ ,			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
      _a      =							feature_extractor(speech_inputs[0] ,			return_tensors='np'  ).input_features
      _a      =							feature_extractor(np_speech_inputs[0] ,			return_tensors='np'  ).input_features
      self.assertTrue(np.allclose(__magic_name__ ,			__magic_name__ ,			atol=1e-3  )  )

      # Test batched
      _a      =							feature_extractor(__magic_name__ ,			return_tensors='np'  ).input_features
      _a      =							feature_extractor(__magic_name__ ,			return_tensors='np'  ).input_features
      for enc_seq_a, enc_seq_a in zip(__magic_name__ ,			__magic_name__  ):
           self.assertTrue(np.allclose(__magic_name__ ,			__magic_name__ ,			atol=1e-3  )  )

      # Test 2-D numpy arrays are batched.
      _a      =							[floats_list((1, x)  )[0] for x in (8_00, 8_00, 8_00)]
      _a      =							np.asarray(__magic_name__  )
      _a      =							feature_extractor(__magic_name__ ,			return_tensors='np'  ).input_features
      _a      =							feature_extractor(__magic_name__ ,			return_tensors='np'  ).input_features
      for enc_seq_a, enc_seq_a in zip(__magic_name__ ,			__magic_name__  ):
           self.assertTrue(np.allclose(__magic_name__ ,			__magic_name__ ,			atol=1e-3  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
      _a      =							[floats_list((1, x)  )[0] for x in range(8_00 ,			14_00 ,			2_00  )]

      _a      =							["""longest""", """max_length""", """do_not_pad"""]
      _a      =							[None, 16, None]
      for max_length, padding in zip(__magic_name__ ,			__magic_name__  ):
           _a      =							feature_extractor(
               __magic_name__ ,			padding=__magic_name__ ,			max_length=__magic_name__ ,			return_attention_mask=__magic_name__  )
           _a      =							inputs.input_features
           _a      =							inputs.attention_mask
           _a      =							[np.sum(__magic_name__  ) 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  )   ->							Any:
      _a      =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
      _a      =							[floats_list((1, x)  )[0] for x in range(8_00 ,			14_00 ,			2_00  )]

      _a      =							["""longest""", """max_length""", """do_not_pad"""]
      _a      =							[None, 16, None]
      for max_length, padding in zip(__magic_name__ ,			__magic_name__  ):
           _a      =							feature_extractor(
               __magic_name__ ,			max_length=__magic_name__ ,			padding=__magic_name__ ,			return_tensors='np' ,			return_attention_mask=__magic_name__  )
           _a      =							inputs.input_features
           _a      =							inputs.attention_mask
           _a      =							[np.sum(__magic_name__  ) 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  )   ->							Any:
      _a      =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
      _a      =							[floats_list((1, x)  )[0] for x in range(8_00 ,			14_00 ,			2_00  )]
      _a      =							feature_extractor(
          __magic_name__ ,			padding='max_length' ,			max_length=4 ,			truncation=__magic_name__ ,			return_tensors='np' ,			return_attention_mask=__magic_name__ ,			)
      _a      =							inputs.input_features
      _a      =							inputs.attention_mask
      _a      =							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  )   ->							Optional[Any]:
      _a      =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
      _a      =							[floats_list((1, x)  )[0] for x in range(8_00 ,			14_00 ,			2_00  )]
      _a      =							feature_extractor(
          __magic_name__ ,			padding='longest' ,			max_length=4 ,			truncation=__magic_name__ ,			return_tensors='np' ,			return_attention_mask=__magic_name__ ,			)
      _a      =							inputs.input_features
      _a      =							inputs.attention_mask
      _a      =							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)  )

      _a      =							[floats_list((1, x)  )[0] for x in range(8_00 ,			14_00 ,			2_00  )]
      _a      =							feature_extractor(
          __magic_name__ ,			padding='longest' ,			max_length=16 ,			truncation=__magic_name__ ,			return_tensors='np' ,			return_attention_mask=__magic_name__ ,			)
      _a      =							inputs.input_features
      _a      =							inputs.attention_mask
      _a      =							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 							__UpperCAmelCase						(	self  )   ->							Any:
      import torch

      _a      =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
      _a      =							np.random.rand(1_00 ,			32  ).astype(np.floataa  )
      _a      =							np_speech_inputs.tolist()

      for inputs in [py_speech_inputs, np_speech_inputs]:
           _a      =							feature_extractor.pad([{'input_features': inputs}] ,			return_tensors='np'  )
           self.assertTrue(np_processed.input_features.dtype == np.floataa  )
           _a      =							feature_extractor.pad([{'input_features': inputs}] ,			return_tensors='pt'  )
           self.assertTrue(pt_processed.input_features.dtype == torch.floataa  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[int]:
      from datasets import load_dataset

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

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




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							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

      _a      =							self._load_datasamples(1  )
      _a      =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
      _a      =							feature_extractor(__magic_name__ ,			return_tensors='pt'  ).input_features
      self.assertEquals(input_features.shape ,			(1, 5_84, 24)  )
      self.assertTrue(np.allclose(input_features[0, 0, :30] ,			__magic_name__ ,			atol=1e-4  )  )





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      ["GPTNeoXTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

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

