import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
					import torch





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""char"""
						UpperCamelCase_  =	"""bpe"""
						UpperCamelCase_  =	"""wp"""






__UpperCamelCase	: List[str]    		=  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	["""image_processor""", """char_tokenizer"""]
						UpperCamelCase_  =	"""ViTImageProcessor"""
						UpperCamelCase_  =	"""MgpstrTokenizer"""


						def __init__( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]=None					,	UpperCamelCase__			:       str=None					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							None
												if "feature_extractor" in kwargs:
																		warnings.warn(
																		    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																		    ''' instead.'''					,	__a					,	)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							kwargs.pop('''feature_extractor'''		)

												SCREAMING_SNAKE_CASE							:					str           =							image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																		raise ValueError('''You need to specify an `image_processor`.'''		)
												if tokenizer is None:
																		raise ValueError('''You need to specify a `tokenizer`.'''		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							tokenizer
												SCREAMING_SNAKE_CASE							:					Any           =							AutoTokenizer.from_pretrained('''gpt2'''		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							AutoTokenizer.from_pretrained('''bert-base-uncased'''		)

												super().__init__(__a					,	__a		)


						def __call__( self			:       List[Any]					,	UpperCamelCase__			:       Any=None					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       int=None					,	**UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												if images is None and text is None:
																		raise ValueError('''You need to specify either an `images` or `text` input to process.'''		)

												if images is not None:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.image_processor(__a					,	return_tensors=__a					,	**__a		)
												if text is not None:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.char_tokenizer(__a					,	return_tensors=__a					,	**__a		)

												if text is None:
																		return inputs
												elif images is None:
																		return encodings
												else:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							encodings['input_ids']
																		return inputs


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							sequences
												SCREAMING_SNAKE_CASE							:					int           =							char_preds.size(0		)

												SCREAMING_SNAKE_CASE							:					str           =							self._decode_helper(__a					,	'''char'''		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							self._decode_helper(__a					,	'''bpe'''		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							self._decode_helper(__a					,	'''wp'''		)

												SCREAMING_SNAKE_CASE							:					Any           =							[]
												SCREAMING_SNAKE_CASE							:					List[str]           =							[]
												for i in range(__a		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[char_scores[i], bpe_scores[i], wp_scores[i]]
																		SCREAMING_SNAKE_CASE							:					Any           =							[char_strs[i], bpe_strs[i], wp_strs[i]]
																		SCREAMING_SNAKE_CASE							:					List[str]           =							scores.index(max(__a		)		)
																		final_strs.append(strs[max_score_index]		)
																		final_scores.append(scores[max_score_index]		)

												SCREAMING_SNAKE_CASE							:					str           =							{}
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							final_strs
												SCREAMING_SNAKE_CASE							:					str           =							final_scores
												SCREAMING_SNAKE_CASE							:					Tuple           =							char_strs
												SCREAMING_SNAKE_CASE							:					str           =							bpe_strs
												SCREAMING_SNAKE_CASE							:					Any           =							wp_strs
												return out


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												if format == DecodeType.CHARACTER:
																		SCREAMING_SNAKE_CASE							:					str           =							self.char_decode
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							1
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'[s]'
												elif format == DecodeType.BPE:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							self.bpe_decode
																		SCREAMING_SNAKE_CASE							:					str           =							2
																		SCREAMING_SNAKE_CASE							:					Any           =							'#'
												elif format == DecodeType.WORDPIECE:
																		SCREAMING_SNAKE_CASE							:					Any           =							self.wp_decode
																		SCREAMING_SNAKE_CASE							:					Tuple           =							102
																		SCREAMING_SNAKE_CASE							:					Dict           =							'[SEP]'
												else:
																		raise ValueError(f"""Format {format} is not supported."""		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							[], []
												SCREAMING_SNAKE_CASE							:					List[str]           =							pred_logits.size(0		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pred_logits.size(1		)
												SCREAMING_SNAKE_CASE							:					str           =							pred_logits.topk(1					,	dim=-1					,	largest=__a					,	sorted=__a		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							preds_index.view(-1					,	__a		)[:, 1:]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							decoder(__a		)
												SCREAMING_SNAKE_CASE							:					str           =							torch.nn.functional.softmax(__a					,	dim=2		).max(dim=2		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							preds_max_prob[:, 1:]

												for index in range(__a		):
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							preds_str[index].find(__a		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							preds_str[index][:pred_eos]
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							preds_index[index].cpu().tolist()
																		SCREAMING_SNAKE_CASE							:					str           =							pred_index.index(__a		) if eos_token in pred_index else -1
																		SCREAMING_SNAKE_CASE							:					Any           =							preds_max_prob[index][: pred_eos_index + 1]
																		SCREAMING_SNAKE_CASE							:					Dict           =							pred_max_prob.cumprod(dim=0		)[-1] if pred_max_prob.nelement() != 0 else 0.0
																		dec_strs.append(__a		)
																		conf_scores.append(__a		)

												return dec_strs, conf_scores


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							[seq.replace(''' '''					,	''''''		) for seq in self.char_tokenizer.batch_decode(__a		)]
												return decode_strs


						def  __A ( self			:       int					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												return self.bpe_tokenizer.batch_decode(__a		)



						def  __A ( self			:       Any					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[seq.replace(''' '''					,	''''''		) for seq in self.wp_tokenizer.batch_decode(__a		)]
												return decode_strs






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCamelCase	: str    		=  False





class       lowercase__		(     unittest.TestCase):
						pass






@nightly
@require_torch_gpu
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							generator.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''cyberpunk 2077'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							pipe.dual_guided(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''					,	).images

												SCREAMING_SNAKE_CASE							:					Tuple           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					int           =							np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''A painting of a squirrel eating a burger '''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Any           =							pipe.text_to_image(
												    prompt=UpperCamelCase__					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					List[str]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Any           =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe.image_variation(UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1





