


import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_    		=     logging.get_logger(__name__)

lowercase_    		=     {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}




class        _UpperCamelCase			(   a__						):







							'''simple docstring'''
							_A						=	"mvp"
							_A						=	["past_key_values"]
							_A						=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







							def __init__(		self	:					List[Any]		,					SCREAMING_SNAKE_CASE_	:					Dict=5_0_2_6_7		,					SCREAMING_SNAKE_CASE_	:					List[str]=1_0_2_4		,					SCREAMING_SNAKE_CASE_	:					Optional[Any]=1_2		,					SCREAMING_SNAKE_CASE_	:					Union[str, Any]=4_0_9_6		,					SCREAMING_SNAKE_CASE_	:					Optional[Any]=1_6		,					SCREAMING_SNAKE_CASE_	:					int=1_2		,					SCREAMING_SNAKE_CASE_	:					Dict=4_0_9_6		,					SCREAMING_SNAKE_CASE_	:					int=1_6		,					SCREAMING_SNAKE_CASE_	:					Dict=0.0		,					SCREAMING_SNAKE_CASE_	:					Union[str, Any]=0.0		,					SCREAMING_SNAKE_CASE_	:					List[str]="gelu"		,					SCREAMING_SNAKE_CASE_	:					int=1_0_2_4		,					SCREAMING_SNAKE_CASE_	:					Tuple=0.1		,					SCREAMING_SNAKE_CASE_	:					Any=0.0		,					SCREAMING_SNAKE_CASE_	:					Optional[Any]=0.0		,					SCREAMING_SNAKE_CASE_	:					str=0.02		,					SCREAMING_SNAKE_CASE_	:					str=0.0		,					SCREAMING_SNAKE_CASE_	:					Optional[int]=False		,					SCREAMING_SNAKE_CASE_	:					Dict=True		,					SCREAMING_SNAKE_CASE_	:					int=1		,					SCREAMING_SNAKE_CASE_	:					Optional[Any]=0		,					SCREAMING_SNAKE_CASE_	:					Union[str, Any]=2		,					SCREAMING_SNAKE_CASE_	:					int=True		,					SCREAMING_SNAKE_CASE_	:					int=2		,					SCREAMING_SNAKE_CASE_	:					str=2		,					SCREAMING_SNAKE_CASE_	:					int=False		,					SCREAMING_SNAKE_CASE_	:					Union[str, Any]=1_0_0		,					SCREAMING_SNAKE_CASE_	:					Any=8_0_0		,					**SCREAMING_SNAKE_CASE_	:					List[Any]		,					):
													_a     			=      vocab_size
													_a     			=      max_position_embeddings
													_a     			=      d_model
													_a     			=      encoder_ffn_dim
													_a     			=      encoder_layers
													_a     			=      encoder_attention_heads
													_a     			=      decoder_ffn_dim
													_a     			=      decoder_layers
													_a     			=      decoder_attention_heads
													_a     			=      dropout
													_a     			=      attention_dropout
													_a     			=      activation_dropout
													_a     			=      activation_function
													_a     			=      init_std
													_a     			=      encoder_layerdrop
													_a     			=      decoder_layerdrop
													_a     			=      classifier_dropout
													_a     			=      use_cache
													_a     			=      encoder_layers
													_a     			=      scale_embedding  # scale factor will be sqrt(d_model) if True
													_a     			=      use_prompt
													_a     			=      prompt_length
													_a     			=      prompt_mid_dim

													super().__init__(
													    pad_token_id=lowerCamelCase_		,					bos_token_id=lowerCamelCase_		,					eos_token_id=lowerCamelCase_		,					is_encoder_decoder=lowerCamelCase_		,					decoder_start_token_id=lowerCamelCase_		,					forced_eos_token_id=lowerCamelCase_		,					**lowerCamelCase_		,					)

													if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'		,					lowerCamelCase_		):
																			_a     			=      self.bos_token_id
																			warnings.warn(
																			    f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
																			    'The config can simply be saved and uploaded again to be fixed.'		)






'''simple docstring'''






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       							=				False




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							pass




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





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )

														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=2   ,       output_type='''numpy'''   ,       ).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(lowerCamelCase_  )
																					lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained(lowerCamelCase_   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      generator.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       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     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''cyberpunk 2077'''
														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt=lowerCamelCase_   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''   ,       ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]  )

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

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger '''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.text_to_image(
														    prompt=lowerCamelCase_   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]  )

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

														lowerCAmelCase__			  =      pipe.image_variation(lowerCamelCase_   ,       generator=lowerCamelCase_   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]  )

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