





"""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


A       	=     random.Random()

def 			__A					(	a_  :Optional[int]      ,				a_  :Optional[Any]=1.0      ,				a_  :Any=None      ,				a_  :Dict=None)						->						List[str]:



				if rng is None:
								__a       :  str					=     global_rng

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

				return values




class  __lowercase	(	unittest.TestCase							):







					'''simple docstring'''




					def __init__(  self		,	_UpperCAmelCase		,	_UpperCAmelCase=7		,	_UpperCAmelCase=400		,	_UpperCAmelCase=2000		,	_UpperCAmelCase=1		,	_UpperCAmelCase=0.0		,	_UpperCAmelCase=16000		,	_UpperCAmelCase=True		,	_UpperCAmelCase=True		,	):
									__a       :  Optional[Any]					=     parent
									__a       :  List[Any]					=     batch_size
									__a       :  int					=     min_seq_length
									__a       :  str					=     max_seq_length
									__a       :  Optional[int]					=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									__a       :  str					=     feature_size
									__a       :  str					=     padding_value
									__a       :  str					=     sampling_rate
									__a       :  List[Any]					=     return_attention_mask
									__a       :  Optional[Any]					=     do_normalize







					def        _lowerCamelCase						(  self   ):
									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        _lowerCamelCase						(  self		,	_UpperCAmelCase=False		,	_UpperCAmelCase=False   ):






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

									if equal_length:
													__a       :  int					=     floats_list((self.batch_size, self.max_seq_length)   )
									else:
													# make sure that inputs increase in size
													__a       :  Optional[Any]					=     [
													    _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:
													__a       :  Dict					=     [np.asarray(_UpperCAmelCase   ) for x in speech_inputs]

									return speech_inputs






class  __lowercase	(	_UpperCamelCase      ,					unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase     =     WavaVecaFeatureExtractor







					def        _lowerCamelCase						(  self   ):
									__a       :  Union[str, Any]					=     WavaVecaFeatureExtractionTester(self   )







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase   ):
									self.assertTrue(np.all(np.mean(_UpperCAmelCase		,	axis=0   ) < 1e-3   )   )
									self.assertTrue(np.all(np.abs(np.var(_UpperCAmelCase		,	axis=0   ) - 1   ) < 1e-3   )   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Tuple					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									# create three inputs of length 800, 1000, and 1200
									__a       :  List[Any]					=     [floats_list((1, x)   )[0] for x in range(800		,	1400		,	200   )]
									__a       :  Union[str, Any]					=     [np.asarray(_UpperCAmelCase   ) for speech_input in speech_inputs]

									# Test not batched input
									__a       :  Dict					=     feat_extract(speech_inputs[0]		,	return_tensors='''np'''   ).input_values
									__a       :  Optional[int]					=     feat_extract(np_speech_inputs[0]		,	return_tensors='''np'''   ).input_values
									self.assertTrue(np.allclose(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1e-3   )   )

									# Test batched
									__a       :  List[Any]					=     feat_extract(_UpperCAmelCase		,	return_tensors='''np'''   ).input_values
									__a       :  str					=     feat_extract(_UpperCAmelCase		,	return_tensors='''np'''   ).input_values
									for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase		,	_UpperCAmelCase   ):
													self.assertTrue(np.allclose(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1e-3   )   )

									# Test 2-D numpy arrays are batched.
									__a       :  Optional[int]					=     [floats_list((1, x)   )[0] for x in (800, 800, 800)]
									__a       :  List[Any]					=     np.asarray(_UpperCAmelCase   )
									__a       :  List[str]					=     feat_extract(_UpperCAmelCase		,	return_tensors='''np'''   ).input_values
									__a       :  str					=     feat_extract(_UpperCAmelCase		,	return_tensors='''np'''   ).input_values
									for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase		,	_UpperCAmelCase   ):
													self.assertTrue(np.allclose(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1e-3   )   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Dict					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__a       :  int					=     [floats_list((1, x)   )[0] for x in range(800		,	1400		,	200   )]

									__a       :  List[Any]					=     ['''longest''', '''max_length''', '''do_not_pad''']
									__a       :  Optional[Any]					=     [None, 1600, None]
									for max_length, padding in zip(_UpperCAmelCase		,	_UpperCAmelCase   ):
													__a       :  Tuple					=     feat_extract(_UpperCAmelCase		,	padding=_UpperCAmelCase		,	max_length=_UpperCAmelCase		,	return_tensors='''np'''   )
													__a       :  Dict					=     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][:1000]   )
													self.assertTrue(input_values[0][1000:].sum() < 1e-6   )
													self._check_zero_mean_unit_variance(input_values[2][:1200]   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Any					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__a       :  Tuple					=     range(800		,	1400		,	200   )
									__a       :  List[str]					=     [floats_list((1, x)   )[0] for x in lengths]

									__a       :  List[str]					=     ['''longest''', '''max_length''', '''do_not_pad''']
									__a       :  Any					=     [None, 1600, None]

									for max_length, padding in zip(_UpperCAmelCase		,	_UpperCAmelCase   ):
													__a       :  Any					=     feat_extract(_UpperCAmelCase		,	max_length=_UpperCAmelCase		,	padding=_UpperCAmelCase   )
													__a       :  str					=     processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:800]   )
													self._check_zero_mean_unit_variance(input_values[1][:1000]   )
													self._check_zero_mean_unit_variance(input_values[2][:1200]   )







					def        _lowerCamelCase						(  self   ):
									__a       :  str					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__a       :  List[str]					=     [floats_list((1, x)   )[0] for x in range(800		,	1400		,	200   )]
									__a       :  str					=     feat_extract(
									    _UpperCAmelCase		,	truncation=_UpperCAmelCase		,	max_length=1000		,	padding='''max_length'''		,	return_tensors='''np'''   )
									__a       :  List[Any]					=     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        _lowerCamelCase						(  self   ):
									__a       :  List[str]					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__a       :  Tuple					=     [floats_list((1, x)   )[0] for x in range(800		,	1400		,	200   )]
									__a       :  Dict					=     feat_extract(
									    _UpperCAmelCase		,	truncation=_UpperCAmelCase		,	max_length=1000		,	padding='''longest'''		,	return_tensors='''np'''   )
									__a       :  Tuple					=     processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]   )
									self._check_zero_mean_unit_variance(input_values[1, :1000]   )
									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, 1000)   )

									__a       :  int					=     [floats_list((1, x)   )[0] for x in range(800		,	1400		,	200   )]
									__a       :  Dict					=     feat_extract(
									    _UpperCAmelCase		,	truncation=_UpperCAmelCase		,	max_length=2000		,	padding='''longest'''		,	return_tensors='''np'''   )
									__a       :  List[str]					=     processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]   )
									self._check_zero_mean_unit_variance(input_values[1, :1000]   )
									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, 1200)   )







					@require_torch
					def        _lowerCamelCase						(  self   ):
									import torch

									__a       :  str					=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
									__a       :  List[Any]					=     np.random.rand(100   ).astype(np.floataa   )
									__a       :  Optional[Any]					=     np_speech_inputs.tolist()

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







					@slow
					@require_torch
					def        _lowerCamelCase						(  self   ):






									for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
													__a       :  Dict					=     WavaVecaConfig.from_pretrained(_UpperCAmelCase   )
													__a       :  Union[str, Any]					=     WavaVecaFeatureExtractor.from_pretrained(_UpperCAmelCase   )

													# 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_torch_available, is_vision_available


lowercase_									=   {
    'configuration_pix2struct': [
        'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Pix2StructConfig',
        'Pix2StructTextConfig',
        'Pix2StructVisionConfig',
    ],
    'processing_pix2struct': ['Pix2StructProcessor'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['Pix2StructImageProcessor']


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'Pix2StructPreTrainedModel',
      'Pix2StructForConditionalGeneration',
      'Pix2StructVisionModel',
      'Pix2StructTextModel',
  ]

if TYPE_CHECKING:
  from .configuration_pixastruct import (
      PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      PixaStructConfig,
      PixaStructTextConfig,
      PixaStructVisionConfig,
  )
  from .processing_pixastruct import PixaStructProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_pixastruct import PixaStructImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_pixastruct import (
        PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
        PixaStructForConditionalGeneration,
        PixaStructPreTrainedModel,
        PixaStructTextModel,
        PixaStructVisionModel,
    )

else:
  import sys

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


