


'''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










'''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|>'		)



