

import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
						from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image



class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     List[Any]=3   ,						lowerCAmelCase__  :     List[Any]=32   ,						lowerCAmelCase__  :     Any=3   ,						lowerCAmelCase__  :     Optional[Any]=10   ,						lowerCAmelCase__  :     Any=[8, 16, 32, 64]   ,						lowerCAmelCase__  :     Tuple=[1, 1, 2, 1]   ,						lowerCAmelCase__  :     List[str]=True   ,						lowerCAmelCase__  :     Dict=True   ,						lowerCAmelCase__  :     Optional[int]="relu"   ,						lowerCAmelCase__  :     str=3   ,						lowerCAmelCase__  :     List[str]=None   ,						lowerCAmelCase__  :     List[Any]=["stage2", "stage3", "stage4"]   ,						lowerCAmelCase__  :     List[str]=[2, 3, 4]   ,						lowerCAmelCase__  :     int=1   ,						):
									SCREAMING_SNAKE_CASE_: Tuple  		=       parent
									SCREAMING_SNAKE_CASE_: str  		=       batch_size
									SCREAMING_SNAKE_CASE_: List[Any]  		=       image_size
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       num_channels
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       embeddings_size
									SCREAMING_SNAKE_CASE_: Tuple  		=       hidden_sizes
									SCREAMING_SNAKE_CASE_: str  		=       depths
									SCREAMING_SNAKE_CASE_: Dict  		=       is_training
									SCREAMING_SNAKE_CASE_: Tuple  		=       use_labels
									SCREAMING_SNAKE_CASE_: List[str]  		=       hidden_act
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       num_labels
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       scope
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       len(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       out_features
									SCREAMING_SNAKE_CASE_: Any  		=       out_indices
									SCREAMING_SNAKE_CASE_: str  		=       num_groups



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

									SCREAMING_SNAKE_CASE_: int  		=       None
									if self.use_labels:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       ids_tensor([self.batch_size]   ,						self.num_labels)

									SCREAMING_SNAKE_CASE_: str  		=       self.get_config()

									return config, pixel_values, labels



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									return BitConfig(
									    num_channels=self.num_channels   ,						embeddings_size=self.embeddings_size   ,						hidden_sizes=self.hidden_sizes   ,						depths=self.depths   ,						hidden_act=self.hidden_act   ,						num_labels=self.num_labels   ,						out_features=self.out_features   ,						out_indices=self.out_indices   ,						num_groups=self.num_groups   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       BitModel(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(
									    result.last_hidden_state.shape   ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.num_labels
									SCREAMING_SNAKE_CASE_: Tuple  		=       BitForImageClassification(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(lowerCAmelCase__   ,						labels=lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels))



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Dict  		=       BitBackbone(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model(lowerCAmelCase__)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps)   ,						len(config.out_features))
									self.parent.assertListEqual(list(result.feature_maps[0].shape)   ,						[self.batch_size, self.hidden_sizes[1], 4, 4])

									# verify channels
									self.parent.assertEqual(len(model.channels)   ,						len(config.out_features))
									self.parent.assertListEqual(model.channels   ,						config.hidden_sizes[1:])

									# verify backbone works with out_features=None
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       None
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       BitBackbone(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: List[str]  		=       model(lowerCAmelCase__)

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps)   ,						1)
									self.parent.assertListEqual(list(result.feature_maps[0].shape)   ,						[self.batch_size, self.hidden_sizes[-1], 1, 1])

									# verify channels
									self.parent.assertEqual(len(model.channels)   ,						1)
									self.parent.assertListEqual(model.channels   ,						[config.hidden_sizes[-1]])




			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: Any  		=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       config_and_inputs
									SCREAMING_SNAKE_CASE_: str  		=       {"pixel_values": pixel_values}
									return config, inputs_dict







@require_torch
class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Dict										=     (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
			_UpperCAmelCase   :							Optional[Any]										=     (
			    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCAmelCase   :							List[Any]										=     False
			_UpperCAmelCase   :							List[str]										=     False
			_UpperCAmelCase   :							int										=     False
			_UpperCAmelCase   :							Union[str, Any]										=     False
			_UpperCAmelCase   :							str										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       BitModelTester(self)
									SCREAMING_SNAKE_CASE_: Dict  		=       ConfigTester(self   ,						config_class=lowerCAmelCase__   ,						has_text_modality=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									return



			@unittest.skip(reason="Bit does not output attentions")
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									pass



			@unittest.skip(reason="Bit does not use inputs_embeds")
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									pass



			@unittest.skip(reason="Bit does not support input and output embeddings")
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       model_class(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       inspect.signature(model.forward)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															SCREAMING_SNAKE_CASE_: Tuple  		=       [*signature.parameters.keys()]

															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: int  		=       model_class(config=lowerCAmelCase__)
															for name, module in model.named_modules():
																					if isinstance(lowerCAmelCase__   ,						(nn.BatchNormad, nn.GroupNorm)):
																											self.assertTrue(
																											    torch.all(module.weight == 1)   ,						msg=F"Parameter {name} of model {model_class} seems not properly initialized"   ,						)
																											self.assertTrue(
																											    torch.all(module.bias == 0)   ,						msg=F"Parameter {name} of model {model_class} seems not properly initialized"   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									def check_hidden_states_output(lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     List[str]):
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model_class(lowerCAmelCase__)
															model.to(lowerCAmelCase__)
															model.eval()

															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_: str  		=       model(**self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__))

															SCREAMING_SNAKE_CASE_: str  		=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

															SCREAMING_SNAKE_CASE_: List[str]  		=       self.model_tester.num_stages
															self.assertEqual(len(lowerCAmelCase__)   ,						expected_num_stages + 1)

															# Bit's feature maps are of shape (batch_size, num_channels, height, width)
															self.assertListEqual(
															    list(hidden_states[0].shape[-2:])   ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]   ,						)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ["preactivation", "bottleneck"]
									for model_class in self.all_model_classes:
															for layer_type in layers_type:
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       layer_type
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       True
																					check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					SCREAMING_SNAKE_CASE_: Tuple  		=       True

																					check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			@unittest.skip(reason="Bit does not use feedforward chunking")
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       BitModel.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image



@require_torch
@require_vision
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			@cached_property
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									return (
									    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
									)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: List[str]  		=       BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[str]  		=       self.default_image_processor
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

									# forward pass
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: int  		=       model(**lowerCAmelCase__)

									# verify the logits
									SCREAMING_SNAKE_CASE_: int  		=       torch.Size((1, 1000))
									self.assertEqual(outputs.logits.shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[str]  		=       torch.tensor([[-0.6526, -0.5263, -1.4398]]).to(lowerCAmelCase__)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,						lowerCAmelCase__   ,						atol=1E-4))







@require_torch
class   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							int										=     (BitBackbone,) if is_torch_available() else ()
			_UpperCAmelCase   :							List[str]										=     BitConfig

			_UpperCAmelCase   :							str										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       BitModelTester(self)









'''simple docstring'''



import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import WhisperFeatureExtractor

if is_torch_available():
  import torch

UpperCAmelCase :       int			   =     random.Random()







def    a__       (  a__      ,    a__=1.0      ,    a__=None      ,    a__=None						):




     """simple docstring"""


     if rng is None:
          __SCREAMING_SNAKE_CASE     =      global_rng

     __SCREAMING_SNAKE_CASE     =      []
     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 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       str=7					,							__SCREAMING_SNAKE_CASE					:       List[str]=400					,							__SCREAMING_SNAKE_CASE					:       Any=2_000					,							__SCREAMING_SNAKE_CASE					:       List[str]=10					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=160					,							__SCREAMING_SNAKE_CASE					:       List[str]=8					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=0.0					,							__SCREAMING_SNAKE_CASE					:       Dict=4_000					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=False					,							__SCREAMING_SNAKE_CASE					:       List[Any]=True					,							)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      batch_size
      __SCREAMING_SNAKE_CASE     =      min_seq_length
      __SCREAMING_SNAKE_CASE     =      max_seq_length
      __SCREAMING_SNAKE_CASE     =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      __SCREAMING_SNAKE_CASE     =      padding_value
      __SCREAMING_SNAKE_CASE     =      sampling_rate
      __SCREAMING_SNAKE_CASE     =      return_attention_mask
      __SCREAMING_SNAKE_CASE     =      do_normalize
      __SCREAMING_SNAKE_CASE     =      feature_size
      __SCREAMING_SNAKE_CASE     =      chunk_length
      __SCREAMING_SNAKE_CASE     =      hop_length
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Dict:


      """simple docstring"""




      return {
          "feature_size": self.feature_size,
          "hop_length": self.hop_length,
          "chunk_length": self.chunk_length,
          "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					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=False					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=False			)  -> Union[str, Any]:


      """simple docstring"""




      def _flatten(__SCREAMING_SNAKE_CASE					:       Dict			):
           return list(itertools.chain(*__SCREAMING_SNAKE_CASE			)			)

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







@require_torch
@require_torchaudio
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      WhisperFeatureExtractor if is_speech_available() else None
 def       UpperCAmelCase__		(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      WhisperFeatureExtractionTester(self			)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_dict			)

      with tempfile.TemporaryDirectory() as tmpdirname:
           __SCREAMING_SNAKE_CASE     =      feat_extract_first.save_pretrained(__SCREAMING_SNAKE_CASE			)[0]
           check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class.from_pretrained(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      feat_extract_first.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_first.mel_filters
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.mel_filters
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_dict			)

      with tempfile.TemporaryDirectory() as tmpdirname:
           __SCREAMING_SNAKE_CASE     =      os.path.join(__SCREAMING_SNAKE_CASE					,							"""feat_extract.json"""			)
           feat_extract_first.to_json_file(__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class.from_json_file(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      feat_extract_first.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_first.mel_filters
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.mel_filters
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      # create three inputs of length 800, 1000, and 1200
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(800					,							1_400					,							200			)]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs]

      # Test feature size
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							padding="""max_length"""					,							return_tensors="""np"""			).input_features
      self.assertTrue(input_features.ndim == 3			)
      self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames			)
      self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size			)

      # Test not batched input
      __SCREAMING_SNAKE_CASE     =      feature_extractor(speech_inputs[0]					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(np_speech_inputs[0]					,							return_tensors="""np"""			).input_features
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test batched
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test 2-D numpy arrays are batched.
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in (800, 800, 800)]
      __SCREAMING_SNAKE_CASE     =      np.asarray(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test truncation required
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(200					,							(feature_extractor.n_samples + 500)					,							200			)]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs]

      __SCREAMING_SNAKE_CASE     =      [x[: feature_extractor.n_samples] for x in speech_inputs]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs_truncated]

      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[int]:


      """simple docstring"""




      import torch

      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      np.random.rand(100					,							32			).astype(np.floataa			)
      __SCREAMING_SNAKE_CASE     =      np_speech_inputs.tolist()

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


      """simple docstring"""




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

      return [x["array"] for x in speech_samples]
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      torch.tensor(
          [
              0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
              0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
              0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
              -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
          ]			)
      # fmt: on

      __SCREAMING_SNAKE_CASE     =      self._load_datasamples(1			)
      __SCREAMING_SNAKE_CASE     =      WhisperFeatureExtractor()
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			).input_features
      self.assertEqual(input_features.shape					,							(1, 80, 3_000)			)
      self.assertTrue(torch.allclose(input_features[0, 0, :30]					,							__SCREAMING_SNAKE_CASE					,							atol=1E-4			)			)


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      self._load_datasamples(1			)[0]
      __SCREAMING_SNAKE_CASE     =      ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
      __SCREAMING_SNAKE_CASE     =      feat_extract.zero_mean_unit_var_norm([audio]					,							attention_mask=__SCREAMING_SNAKE_CASE			)[0]

      self.assertTrue(np.all(np.mean(__SCREAMING_SNAKE_CASE			) < 1E-3			)			)
      self.assertTrue(np.all(np.abs(np.var(__SCREAMING_SNAKE_CASE			) - 1			) < 1E-3			)			)






