code: 
  
  
  '''simple docstring'''
  
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
  
  from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
  from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
  from diffusers.utils import load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
  
  
  enable_full_determinism()
  
  
  
  
  
  
  class 							__snake_case    (				lowerCAmelCase_					,					unittest.TestCase       ):
  	__lowerCAmelCase						=     UnCLIPPipeline
  	__lowerCAmelCase						=     TEXT_TO_IMAGE_PARAMS - {
  	    """negative_prompt""",
  	    """height""",
  	    """width""",
  	    """negative_prompt_embeds""",
  	    """guidance_scale""",
  	    """prompt_embeds""",
  	    """cross_attention_kwargs""",
  	}
  	__lowerCAmelCase						=     TEXT_TO_IMAGE_BATCH_PARAMS
  	__lowerCAmelCase						=     [
  	    """generator""",
  	    """return_dict""",
  	    """prior_num_inference_steps""",
  	    """decoder_num_inference_steps""",
  	    """super_res_num_inference_steps""",
  	]
  	__lowerCAmelCase						=     False
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Dict:
  		return 32
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		return 32
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		int:
  		return self.time_input_dim
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		List[Any]:
  		return self.time_input_dim * 4
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		List[str]:
  		return 100
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		str:
  		snake_case__  					=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)
  		return tokenizer
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		torch.manual_seed(0		)
  		snake_case__  					=      CLIPTextConfig(
  		    bos_token_id=0			,					eos_token_id=2			,					hidden_size=self.text_embedder_hidden_size			,					projection_dim=self.text_embedder_hidden_size			,					intermediate_size=37			,					layer_norm_eps=1E-0_5			,					num_attention_heads=4			,					num_hidden_layers=5			,					pad_token_id=1			,					vocab_size=1000			,					)
  		return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		str:
  		torch.manual_seed(0		)
  
  		snake_case__  					=      {
  		    'num_attention_heads': 2,
  		    'attention_head_dim': 12,
  		    'embedding_dim': self.text_embedder_hidden_size,
  		    'num_layers': 1,
  		}
  
  		snake_case__  					=      PriorTransformer(**__SCREAMING_SNAKE_CASE		)
  		return model
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		str:
  		torch.manual_seed(0		)
  
  		snake_case__  					=      {
  		    'clip_embeddings_dim': self.text_embedder_hidden_size,
  		    'time_embed_dim': self.time_embed_dim,
  		    'cross_attention_dim': self.cross_attention_dim,
  		}
  
  		snake_case__  					=      UnCLIPTextProjModel(**__SCREAMING_SNAKE_CASE		)
  		return model
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Any:
  		torch.manual_seed(0		)
  
  		snake_case__  					=      {
  		    'sample_size': 32,
  		    # RGB in channels
  		    'in_channels': 3,
  		    # Out channels is double in channels because predicts mean and variance
  		    'out_channels': 6,
  		    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
  		    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
  		    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
  		    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
  		    'layers_per_block': 1,
  		    'cross_attention_dim': self.cross_attention_dim,
  		    'attention_head_dim': 4,
  		    'resnet_time_scale_shift': 'scale_shift',
  		    'class_embed_type': 'identity',
  		}
  
  		snake_case__  					=      UNetaDConditionModel(**__SCREAMING_SNAKE_CASE		)
  		return model
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		List[Any]:
  		return {
  		    "sample_size": 64,
  		    "layers_per_block": 1,
  		    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
  		    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
  		    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
  		    "in_channels": 6,
  		    "out_channels": 3,
  		}
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		List[str]:
  		torch.manual_seed(0		)
  
  		snake_case__  					=      UNetaDModel(**self.dummy_super_res_kwargs		)
  		return model
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		List[Any]:
  		torch.manual_seed(1		)
  
  		snake_case__  					=      UNetaDModel(**self.dummy_super_res_kwargs		)
  		return model
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		snake_case__  					=      self.dummy_prior
  		snake_case__  					=      self.dummy_decoder
  		snake_case__  					=      self.dummy_text_proj
  		snake_case__  					=      self.dummy_text_encoder
  		snake_case__  					=      self.dummy_tokenizer
  		snake_case__  					=      self.dummy_super_res_first
  		snake_case__  					=      self.dummy_super_res_last
  
  		snake_case__  					=      UnCLIPScheduler(
  		    variance_type='fixed_small_log'			,					prediction_type='sample'			,					num_train_timesteps=1000			,					clip_sample_range=5.0			,					)
  
  		snake_case__  					=      UnCLIPScheduler(
  		    variance_type='learned_range'			,					prediction_type='epsilon'			,					num_train_timesteps=1000			,					)
  
  		snake_case__  					=      UnCLIPScheduler(
  		    variance_type='fixed_small_log'			,					prediction_type='epsilon'			,					num_train_timesteps=1000			,					)
  
  		snake_case__  					=      {
  		    'prior': prior,
  		    'decoder': decoder,
  		    'text_proj': text_proj,
  		    'text_encoder': text_encoder,
  		    'tokenizer': tokenizer,
  		    'super_res_first': super_res_first,
  		    'super_res_last': super_res_last,
  		    'prior_scheduler': prior_scheduler,
  		    'decoder_scheduler': decoder_scheduler,
  		    'super_res_scheduler': super_res_scheduler,
  		}
  
  		return components
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_=0		)			->		Optional[Any]:
  
  		if str(__SCREAMING_SNAKE_CASE		).startswith('mps'		):
  			snake_case__  					=      torch.manual_seed(__SCREAMING_SNAKE_CASE		)
  		else:
  			snake_case__  					=      torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(__SCREAMING_SNAKE_CASE		)
  		snake_case__  					=      {
  		    'prompt': 'horse',
  		    'generator': generator,
  		    'prior_num_inference_steps': 2,
  		    'decoder_num_inference_steps': 2,
  		    'super_res_num_inference_steps': 2,
  		    'output_type': 'numpy',
  		}
  		return inputs
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		snake_case__  					=      'cpu'
  
  		snake_case__  					=      self.get_dummy_components()
  
  		snake_case__  					=      self.pipeline_class(**__SCREAMING_SNAKE_CASE		)
  		snake_case__  					=      pipe.to(__SCREAMING_SNAKE_CASE		)
  
  		pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)
  
  		snake_case__  					=      pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)		)
  		snake_case__  					=      output.images
  
  		snake_case__  					=      pipe(
  		    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)			,					return_dict=__SCREAMING_SNAKE_CASE			,					)[0]
  
  		snake_case__  					=      image[0, -3:, -3:, -1]
  		snake_case__  					=      image_from_tuple[0, -3:, -3:, -1]
  
  		assert image.shape == (1, 64, 64, 3)
  
  		snake_case__  					=      np.array(
  		    [
  		        0.9_9_9_7,
  		        0.9_9_8_8,
  		        0.0_0_2_8,
  		        0.9_9_9_7,
  		        0.9_9_8_4,
  		        0.9_9_6_5,
  		        0.0_0_2_9,
  		        0.9_9_8_6,
  		        0.0_0_2_5,
  		    ]		)
  
  		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
  		assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		snake_case__  					=      torch.device('cpu'		)
  
  
  
  
  
  
  		class 							__snake_case    :
  			__lowerCAmelCase						=     1
  
  
  
  
  
  		snake_case__  					=      self.get_dummy_components()
  
  		snake_case__  					=      self.pipeline_class(**__SCREAMING_SNAKE_CASE		)
  		snake_case__  					=      pipe.to(__SCREAMING_SNAKE_CASE		)
  
  		snake_case__  					=      components['prior']
  		snake_case__  					=      components['decoder']
  		snake_case__  					=      components['super_res_first']
  		snake_case__  					=      components['tokenizer']
  		snake_case__  					=      components['text_encoder']
  
  		snake_case__  					=      torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(0		)
  		snake_case__  					=      prior.dtype
  		snake_case__  					=      1
  
  		snake_case__  					=      (batch_size, prior.config.embedding_dim)
  		snake_case__  					=      pipe.prepare_latents(
  		    __SCREAMING_SNAKE_CASE			,					dtype=__SCREAMING_SNAKE_CASE			,					device=__SCREAMING_SNAKE_CASE			,					generator=__SCREAMING_SNAKE_CASE			,					latents=__SCREAMING_SNAKE_CASE			,					scheduler=DummyScheduler()		)
  		snake_case__  					=      (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
  		snake_case__  					=      pipe.prepare_latents(
  		    __SCREAMING_SNAKE_CASE			,					dtype=__SCREAMING_SNAKE_CASE			,					device=__SCREAMING_SNAKE_CASE			,					generator=__SCREAMING_SNAKE_CASE			,					latents=__SCREAMING_SNAKE_CASE			,					scheduler=DummyScheduler()		)
  
  		snake_case__  					=      (
  		    batch_size,
  		    super_res_first.config.in_channels // 2,
  		    super_res_first.config.sample_size,
  		    super_res_first.config.sample_size,
  		)
  		snake_case__  					=      pipe.prepare_latents(
  		    __SCREAMING_SNAKE_CASE			,					dtype=__SCREAMING_SNAKE_CASE			,					device=__SCREAMING_SNAKE_CASE			,					generator=__SCREAMING_SNAKE_CASE			,					latents=__SCREAMING_SNAKE_CASE			,					scheduler=DummyScheduler()		)
  
  		pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)
  
  		snake_case__  					=      'this is a prompt example'
  
  		snake_case__  					=      torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(0		)
  		snake_case__  					=      pipe(
  		    [prompt]			,					generator=__SCREAMING_SNAKE_CASE			,					prior_num_inference_steps=2			,					decoder_num_inference_steps=2			,					super_res_num_inference_steps=2			,					prior_latents=__SCREAMING_SNAKE_CASE			,					decoder_latents=__SCREAMING_SNAKE_CASE			,					super_res_latents=__SCREAMING_SNAKE_CASE			,					output_type='np'			,					)
  		snake_case__  					=      output.images
  
  		snake_case__  					=      tokenizer(
  		    __SCREAMING_SNAKE_CASE			,					padding='max_length'			,					max_length=tokenizer.model_max_length			,					return_tensors='pt'			,					)
  		snake_case__  					=      text_encoder(text_inputs.input_ids		)
  		snake_case__  					=      text_inputs.attention_mask
  
  		snake_case__  					=      torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(0		)
  		snake_case__  					=      pipe(
  		    generator=__SCREAMING_SNAKE_CASE			,					prior_num_inference_steps=2			,					decoder_num_inference_steps=2			,					super_res_num_inference_steps=2			,					prior_latents=__SCREAMING_SNAKE_CASE			,					decoder_latents=__SCREAMING_SNAKE_CASE			,					super_res_latents=__SCREAMING_SNAKE_CASE			,					text_model_output=__SCREAMING_SNAKE_CASE			,					text_attention_mask=__SCREAMING_SNAKE_CASE			,					output_type='np'			,					)[0]
  
  		# make sure passing text embeddings manually is identical
  		assert np.abs(image - image_from_text		).max() < 1E-4
  
  
  
  
  
  	@skip_mps
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      torch_device == 'cpu'
  
  		self._test_attention_slicing_forward_pass(test_max_difference=__SCREAMING_SNAKE_CASE			,					expected_max_diff=0.0_1		)
  
  
  
  
  
  	@skip_mps
  	def      _snake_case      (		self		)			->		int:
  		snake_case__  					=      torch_device == 'cpu'
  		snake_case__  					=      True
  		snake_case__  					=      [
  		    'prior_num_inference_steps',
  		    'decoder_num_inference_steps',
  		    'super_res_num_inference_steps',
  		]
  
  		self._test_inference_batch_single_identical(
  		    test_max_difference=__SCREAMING_SNAKE_CASE			,					relax_max_difference=__SCREAMING_SNAKE_CASE			,					additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE			,					)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      [
  		    'prior_num_inference_steps',
  		    'decoder_num_inference_steps',
  		    'super_res_num_inference_steps',
  		]
  
  		if torch_device == "mps":
  			# TODO: MPS errors with larger batch sizes
  			snake_case__  					=      [2, 3]
  			self._test_inference_batch_consistent(
  			    batch_sizes=__SCREAMING_SNAKE_CASE			,					additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE			,					)
  		else:
  			self._test_inference_batch_consistent(
  			    additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  	@skip_mps
  	def      _snake_case      (		self		)			->		Optional[int]:
  		return super().test_dict_tuple_outputs_equivalent()
  
  
  
  
  
  	@skip_mps
  	def      _snake_case      (		self		)			->		List[Any]:
  		return super().test_save_load_local()
  
  
  
  
  
  	@skip_mps
  	def      _snake_case      (		self		)			->		Any:
  		return super().test_save_load_optional_components()
  
  
  
  
  
  
  @nightly
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		super().tearDown()
  		gc.collect()
  		torch.cuda.empty_cache()
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      load_numpy(
  		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
  		    '/unclip/karlo_v1_alpha_horse_cpu.npy'		)
  
  		snake_case__  					=      UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'		)
  		pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)
  
  		snake_case__  					=      torch.manual_seed(0		)
  		snake_case__  					=      pipeline(
  		    'horse'			,					num_images_per_prompt=1			,					generator=__SCREAMING_SNAKE_CASE			,					output_type='np'			,					)
  
  		snake_case__  					=      output.images[0]
  
  		assert image.shape == (256, 256, 3)
  		assert np.abs(expected_image - image		).max() < 1E-1
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		super().tearDown()
  		gc.collect()
  		torch.cuda.empty_cache()
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      load_numpy(
  		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
  		    '/unclip/karlo_v1_alpha_horse_fp16.npy'		)
  
  		snake_case__  					=      UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'			,					torch_dtype=torch.floataa		)
  		snake_case__  					=      pipeline.to(__SCREAMING_SNAKE_CASE		)
  		pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)
  
  		snake_case__  					=      torch.Generator(device='cpu'		).manual_seed(0		)
  		snake_case__  					=      pipeline(
  		    'horse'			,					generator=__SCREAMING_SNAKE_CASE			,					output_type='np'			,					)
  
  		snake_case__  					=      output.images[0]
  
  		assert image.shape == (256, 256, 3)
  
  		assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,					__SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		torch.cuda.empty_cache()
  		torch.cuda.reset_max_memory_allocated()
  		torch.cuda.reset_peak_memory_stats()
  
  		snake_case__  					=      UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'			,					torch_dtype=torch.floataa		)
  		snake_case__  					=      pipe.to(__SCREAMING_SNAKE_CASE		)
  		pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)
  		pipe.enable_attention_slicing()
  		pipe.enable_sequential_cpu_offload()
  
  		snake_case__  					=      pipe(
  		    'horse'			,					num_images_per_prompt=1			,					prior_num_inference_steps=2			,					decoder_num_inference_steps=2			,					super_res_num_inference_steps=2			,					output_type='np'			,					)
  
  		snake_case__  					=      torch.cuda.max_memory_allocated()
  		# make sure that less than 7 GB is allocated
  		assert mem_bytes < 7 * 10**9
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import shutil
  import tempfile
  import unittest
  
  import pytest
  
  from transformers import WhisperTokenizer, is_speech_available
  from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
  
  from .test_feature_extraction_whisper import floats_list
  
  
  if is_speech_available():
  			from transformers import WhisperFeatureExtractor, WhisperProcessor
  
  
  a__				: Tuple									=    5_0358
  a__				: str									=    5_0362
  
  
  
  
  
  
  @require_torch
  @require_torchaudio
  @require_sentencepiece
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      'openai/whisper-small.en'
  		snake_case__  					=      tempfile.mkdtemp()
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[int]:
  		return WhisperTokenizer.from_pretrained(self.checkpoint			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Any:
  		return WhisperFeatureExtractor.from_pretrained(self.checkpoint			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		shutil.rmtree(self.tmpdirname		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		snake_case__  					=      self.get_tokenizer()
  		snake_case__  					=      self.get_feature_extractor()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		processor.save_pretrained(self.tmpdirname		)
  		snake_case__  					=      WhisperProcessor.from_pretrained(self.tmpdirname		)
  
  		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer.get_vocab()		)
  		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor.to_json_string()		)
  		self.assertIsInstance(processor.feature_extractor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
  		processor.save_pretrained(self.tmpdirname		)
  
  		snake_case__  					=      self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)'		)
  		snake_case__  					=      self.get_feature_extractor(do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		snake_case__  					=      WhisperProcessor.from_pretrained(
  		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab()		)
  		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor_add_kwargs.to_json_string()		)
  		self.assertIsInstance(processor.feature_extractor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		snake_case__  					=      floats_list((3, 1000)		)
  
  		snake_case__  					=      feature_extractor(UpperCamelCase_			,					return_tensors='np'		)
  		snake_case__  					=      processor(UpperCamelCase_			,					return_tensors='np'		)
  
  		for key in input_feat_extract.keys():
  			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		snake_case__  					=      'This is a test string'
  
  		snake_case__  					=      processor(text=UpperCamelCase_		)
  
  		snake_case__  					=      tokenizer(UpperCamelCase_		)
  
  		for key in encoded_tok.keys():
  			self.assertListEqual(encoded_tok[key]			,					encoded_processor[key]		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		snake_case__  					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
  		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)
  
  		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		self.assertListEqual(
  		    processor.model_input_names			,					feature_extractor.model_input_names			,					msg='`processor` and `feature_extractor` model input names do not match'			,					)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  		snake_case__  					=      processor.get_decoder_prompt_ids(task='transcribe'			,					no_timestamps=UpperCamelCase_		)
  
  		self.assertIsInstance(UpperCamelCase_			,					UpperCamelCase_		)
  		for ids in forced_decoder_ids:
  			self.assertIsInstance(UpperCamelCase_			,					(list, tuple)		)
  
  		snake_case__  					=      [TRANSCRIBE, NOTIMESTAMPS]
  		self.assertListEqual([ids[-1] for ids in forced_decoder_ids]			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
  		snake_case__  					=      processor.get_prompt_ids('Mr. Quilter'		)
  		snake_case__  					=      processor.tokenizer.decode(UpperCamelCase_		)
  
  		self.assertListEqual(prompt_ids.tolist()			,					[5_0360, 1770, 13, 2264, 346, 353]		)
  		self.assertEqual(UpperCamelCase_			,					'<|startofprev|> Mr. Quilter'		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
  		snake_case__  					=      processor.get_prompt_ids(''		)
  		snake_case__  					=      processor.tokenizer.decode(UpperCamelCase_		)
  
  		self.assertListEqual(prompt_ids.tolist()			,					[5_0360, 220]		)
  		self.assertEqual(UpperCamelCase_			,					'<|startofprev|> '		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
  
  		def _test_prompt_error_raised_helper(UpperCamelCase_			,					UpperCamelCase_		):
  			with pytest.raises(UpperCamelCase_		) as excinfo:
  				processor.get_prompt_ids(UpperCamelCase_		)
  			snake_case__  					=      F'''Encountered text in the prompt corresponding to disallowed special token: {special_token}.'''
  			self.assertEqual(UpperCamelCase_			,					str(excinfo.value		)		)
  
  		_test_prompt_error_raised_helper('<|startofprev|> test'			,					'<|startofprev|>'		)
  		_test_prompt_error_raised_helper('test <|notimestamps|>'			,					'<|notimestamps|>'		)
  		_test_prompt_error_raised_helper('test <|zh|> test <|transcribe|>'			,					'<|zh|>'		)
  
  
  
  