
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


lowercase						:      Tuple									= random.Random()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    int=1.0		,						_lowerCamelCase				:    Any=None		,						_lowerCamelCase				:    Optional[Any]=None)      ->					Tuple:






							'''simple docstring'''





							if rng is None:
														__UpperCamelCase			:     Optional[int]         =      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




class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def __init__(      self	:List[Any]  ,     a	:Union[str, Any]  ,     a	:Tuple=7  ,     a	:Union[str, Any]=4_0_0  ,     a	:Optional[int]=2_0_0_0  ,     a	:Union[str, Any]=1  ,     a	:Tuple=0.0  ,     a	:Union[str, Any]=1_6_0_0_0  ,     a	:Any=True  ,     a	:str=True  ,     )	->				Dict:
								__UpperCamelCase			:     Any         =      parent
								__UpperCamelCase			:     Optional[int]         =      batch_size
								__UpperCamelCase			:     int         =      min_seq_length
								__UpperCamelCase			:     Tuple         =      max_seq_length
								__UpperCamelCase			:     List[str]         =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								__UpperCamelCase			:     List[str]         =      feature_size
								__UpperCamelCase			:     Optional[Any]         =      padding_value
								__UpperCamelCase			:     List[str]         =      sampling_rate
								__UpperCamelCase			:     Optional[int]         =      return_attention_mask
								__UpperCamelCase			:     Dict         =      do_normalize







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Dict:
								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	:Optional[int]  ,     a	:List[str]=False  ,     a	:Union[str, Any]=False		)	->				List[Any]:
								def _flatten(a	:Optional[Any]		):
															return list(itertools.chain(*a		)		)

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

								return speech_inputs




class 				lowerCamelCase__							(       __lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  WavaVecaFeatureExtractor







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Optional[int]:
								__UpperCamelCase			:     str         =      WavaVecaFeatureExtractionTester(self		)







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Union[str, Any]		)	->				str:
								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      _lowerCamelCase       (      self	:Union[str, Any]		)	->				int:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								__UpperCamelCase			:     Any         =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
								# create three inputs of length 800, 1000, and 1200
								__UpperCamelCase			:     int         =      [floats_list((1, x)		)[0] for x in range(8_0_0  ,     1_4_0_0  ,     2_0_0		)]
								__UpperCamelCase			:     Tuple         =      [np.asarray(a		) for speech_input in speech_inputs]

								# Test not batched input
								__UpperCamelCase			:     int         =      feat_extract(speech_inputs[0]  ,     return_tensors="np"		).input_values
								__UpperCamelCase			:     Dict         =      feat_extract(np_speech_inputs[0]  ,     return_tensors="np"		).input_values
								self.assertTrue(np.allclose(a  ,     a  ,     atol=1E-3		)		)

								# Test batched
								__UpperCamelCase			:     Union[str, Any]         =      feat_extract(a  ,     return_tensors="np"		).input_values
								__UpperCamelCase			:     Optional[Any]         =      feat_extract(a  ,     return_tensors="np"		).input_values
								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			:     List[str]         =      [floats_list((1, x)		)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
								__UpperCamelCase			:     Tuple         =      np.asarray(a		)
								__UpperCamelCase			:     List[Any]         =      feat_extract(a  ,     return_tensors="np"		).input_values
								__UpperCamelCase			:     Union[str, Any]         =      feat_extract(a  ,     return_tensors="np"		).input_values
								for enc_seq_a, enc_seq_a in zip(a  ,     a		):
															self.assertTrue(np.allclose(a  ,     a  ,     atol=1E-3		)		)







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				int:
								__UpperCamelCase			:     Tuple         =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
								__UpperCamelCase			:     Tuple         =      [floats_list((1, x)		)[0] for x in range(8_0_0  ,     1_4_0_0  ,     2_0_0		)]

								__UpperCamelCase			:     Dict         =      ["longest", "max_length", "do_not_pad"]
								__UpperCamelCase			:     List[str]         =      [None, 1_6_0_0, None]
								for max_length, padding in zip(a  ,     a		):
															__UpperCamelCase			:     str         =      feat_extract(a  ,     padding=a  ,     max_length=a  ,     return_tensors="np"		)
															__UpperCamelCase			:     Any         =      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      _lowerCamelCase       (      self	:Dict		)	->				Any:
								__UpperCamelCase			:     Optional[Any]         =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
								__UpperCamelCase			:     Any         =      range(8_0_0  ,     1_4_0_0  ,     2_0_0		)
								__UpperCamelCase			:     int         =      [floats_list((1, x)		)[0] for x in lengths]

								__UpperCamelCase			:     Any         =      ["longest", "max_length", "do_not_pad"]
								__UpperCamelCase			:     Union[str, Any]         =      [None, 1_6_0_0, None]

								for max_length, padding in zip(a  ,     a		):
															__UpperCamelCase			:     Union[str, Any]         =      feat_extract(a  ,     max_length=a  ,     padding=a		)
															__UpperCamelCase			:     Union[str, Any]         =      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      _lowerCamelCase       (      self	:List[str]		)	->				Optional[int]:
								__UpperCamelCase			:     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(8_0_0  ,     1_4_0_0  ,     2_0_0		)]
								__UpperCamelCase			:     Dict         =      feat_extract(
								    a  ,     truncation=a  ,     max_length=1_0_0_0  ,     padding="max_length"  ,     return_tensors="np"		)
								__UpperCamelCase			:     Union[str, Any]         =      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      _lowerCamelCase       (      self	:int		)	->				str:
								__UpperCamelCase			:     str         =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
								__UpperCamelCase			:     Tuple         =      [floats_list((1, x)		)[0] for x in range(8_0_0  ,     1_4_0_0  ,     2_0_0		)]
								__UpperCamelCase			:     List[str]         =      feat_extract(
								    a  ,     truncation=a  ,     max_length=1_0_0_0  ,     padding="longest"  ,     return_tensors="np"		)
								__UpperCamelCase			:     List[Any]         =      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)		)

								__UpperCamelCase			:     Optional[Any]         =      [floats_list((1, x)		)[0] for x in range(8_0_0  ,     1_4_0_0  ,     2_0_0		)]
								__UpperCamelCase			:     Any         =      feat_extract(
								    a  ,     truncation=a  ,     max_length=2_0_0_0  ,     padding="longest"  ,     return_tensors="np"		)
								__UpperCamelCase			:     Any         =      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      _lowerCamelCase       (      self	:Optional[int]		)	->				Union[str, Any]:
								import torch

								__UpperCamelCase			:     Union[str, Any]         =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
								__UpperCamelCase			:     Optional[Any]         =      np.random.rand(1_0_0		).astype(np.floataa		)
								__UpperCamelCase			:     Optional[Any]         =      np_speech_inputs.tolist()

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







	@slow
	@require_torch
	def      _lowerCamelCase       (      self	:Any		)	->				Any:
								# 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:
															__UpperCamelCase			:     List[Any]         =      WavaVecaConfig.from_pretrained(a		)
															__UpperCamelCase			:     Tuple         =      WavaVecaFeatureExtractor.from_pretrained(a		)

															# 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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase						:      str									= {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      str									= ['VisionEncoderDecoderModel']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      List[Any]									= ['TFVisionEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      int									= ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)