



"""simple docstring"""






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


snake_case   					=				random.Random()




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=1.0  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None	)						->				Optional[Any]:
  if rng is None:
    _snake_case   	=      global_rng

  _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     UpperCAmelCase    (	unittest.TestCase  ):



   def __init__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	int=7							,       __lowerCamelCase    :	List[Any]=4_0_0							,       __lowerCamelCase    :	Optional[int]=2_0_0_0							,       __lowerCamelCase    :	List[Any]=1							,       __lowerCamelCase    :	Optional[Any]=0.0							,       __lowerCamelCase    :	List[Any]=1_6_0_0_0							,       __lowerCamelCase    :	Any=True							,       __lowerCamelCase    :	Optional[int]=True							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      min_seq_length
     _snake_case   	=      max_seq_length
     _snake_case   	=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     _snake_case   	=      feature_size
     _snake_case   	=      padding_value
     _snake_case   	=      sampling_rate
     _snake_case   	=      return_attention_mask
     _snake_case   	=      do_normalize



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """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    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Any=False							,       __lowerCamelCase    :	Optional[int]=False				):



     """simple docstring"""
     def _flatten(__lowerCamelCase    :	Any				):
       return list(itertools.chain(*__lowerCamelCase				)				)

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

     return speech_inputs



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Tuple           =  WavaVecaFeatureExtractor



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      WavaVecaFeatureExtractionTester(self				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Optional[Any]				):



     """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    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     # Tests that all call wrap to encode_plus and batch_encode_plus
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     # create three inputs of length 800, 1000, and 1200
     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]
     _snake_case   	=      [np.asarray(__lowerCamelCase				) for speech_input in speech_inputs]

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

     # Test batched
     _snake_case   	=      feat_extract(__lowerCamelCase							,       return_tensors='''np'''				).input_values
     _snake_case   	=      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.
     _snake_case   	=      [floats_list((1, x)				)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     _snake_case   	=      np.asarray(__lowerCamelCase				)
     _snake_case   	=      feat_extract(__lowerCamelCase							,       return_tensors='''np'''				).input_values
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]

     _snake_case   	=      ['''longest''', '''max_length''', '''do_not_pad''']
     _snake_case   	=      [None, 1_6_0_0, None]
     for max_length, padding in zip(__lowerCamelCase							,       __lowerCamelCase				):
       _snake_case   	=      feat_extract(__lowerCamelCase							,       padding=__lowerCamelCase							,       max_length=__lowerCamelCase							,       return_tensors='''np'''				)
       _snake_case   	=      processed.input_values

       self._check_zero_mean_unit_variance(input_values[0][:8_0_0]				)
       self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6				)
       self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]				)
       self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6				)
       self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     _snake_case   	=      range(8_0_0							,       1_4_0_0							,       2_0_0				)
     _snake_case   	=      [floats_list((1, x)				)[0] for x in lengths]

     _snake_case   	=      ['''longest''', '''max_length''', '''do_not_pad''']
     _snake_case   	=      [None, 1_6_0_0, None]

     for max_length, padding in zip(__lowerCamelCase							,       __lowerCamelCase				):
       _snake_case   	=      feat_extract(__lowerCamelCase							,       max_length=__lowerCamelCase							,       padding=__lowerCamelCase				)
       _snake_case   	=      processed.input_values

       self._check_zero_mean_unit_variance(input_values[0][:8_0_0]				)
       self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]				)
       self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]				)



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]
     _snake_case   	=      feat_extract(
         __lowerCamelCase							,       truncation=__lowerCamelCase							,       max_length=1_0_0_0							,       padding='''max_length'''							,       return_tensors='''np'''				)
     _snake_case   	=      processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :8_0_0]				)
     self._check_zero_mean_unit_variance(input_values[1]				)
     self._check_zero_mean_unit_variance(input_values[2]				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]
     _snake_case   	=      feat_extract(
         __lowerCamelCase							,       truncation=__lowerCamelCase							,       max_length=1_0_0_0							,       padding='''longest'''							,       return_tensors='''np'''				)
     _snake_case   	=      processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :8_0_0]				)
     self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]				)
     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_0_0_0)				)

     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]
     _snake_case   	=      feat_extract(
         __lowerCamelCase							,       truncation=__lowerCamelCase							,       max_length=2_0_0_0							,       padding='''longest'''							,       return_tensors='''np'''				)
     _snake_case   	=      processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :8_0_0]				)
     self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]				)
     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_2_0_0)				)



   @require_torch
   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     import torch

     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
     _snake_case   	=      np.random.rand(1_0_0				).astype(np.floataa				)
     _snake_case   	=      np_speech_inputs.tolist()

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





   @slow
   @require_torch
   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # 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:
       _snake_case   	=      WavaVecaConfig.from_pretrained(__lowerCamelCase				)
       _snake_case   	=      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'''				)










"""simple docstring"""







from typing import TYPE_CHECKING

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


a_							=    {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """OPTForCausalLM""",
	    """OPTModel""",
	    """OPTPreTrainedModel""",
	    """OPTForSequenceClassification""",
	    """OPTForQuestionAnswering""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """FlaxOPTForCausalLM""",
	    """FlaxOPTModel""",
	    """FlaxOPTPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

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





