



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


__snake_case     :str			       =logging.get_logger(__name__)

__snake_case     :int			       ={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Union[str, Any]								=     'pegasus'
				A_   :     int								=     ['past_key_values']
				A_   :     Dict								=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


				def __init__(							self      :  Dict						, __UpperCamelCase      :  List[Any]=50_265						, __UpperCamelCase      :  Any=1_024						, __UpperCamelCase      :  List[str]=12						, __UpperCamelCase      :  Optional[Any]=4_096						, __UpperCamelCase      :  Optional[int]=16						, __UpperCamelCase      :  List[Any]=12						, __UpperCamelCase      :  Union[str, Any]=4_096						, __UpperCamelCase      :  int=16						, __UpperCamelCase      :  Optional[Any]=0.0						, __UpperCamelCase      :  Tuple=0.0						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  Tuple=True						, __UpperCamelCase      :  Optional[int]="gelu"						, __UpperCamelCase      :  Optional[Any]=1_024						, __UpperCamelCase      :  str=0.1						, __UpperCamelCase      :  str=0.0						, __UpperCamelCase      :  Dict=0.0						, __UpperCamelCase      :  Tuple=0.0_2						, __UpperCamelCase      :  int=0						, __UpperCamelCase      :  int=False						, __UpperCamelCase      :  Optional[int]=0						, __UpperCamelCase      :  Union[str, Any]=1						, __UpperCamelCase      :  List[str]=1						, **__UpperCamelCase      :  Optional[int]						, )		->     Dict:
											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      						=    use_cache
											A      						=    encoder_layers
											A      						=    scale_embedding  # scale factor will be sqrt(d_model) if True
											super().__init__(
											    pad_token_id=__UpperCamelCase						, eos_token_id=__UpperCamelCase						, is_encoder_decoder=__UpperCamelCase						, decoder_start_token_id=__UpperCamelCase						, forced_eos_token_id=__UpperCamelCase						, **__UpperCamelCase						, )


				@property
				def __UpperCamelCase						(							self      :  int       )		->     int:
											return self.encoder_attention_heads


				@property
				def __UpperCamelCase						(							self      :  Dict       )		->     int:
											return self.d_model




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


__snake_case     :str			       =False



class        lowerCAmelCase__					(  unittest.TestCase       ):
				pass





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


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


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

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

											A      						=    torch.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=2						, output_type='numpy'						, ).images

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

											A      						=    generator.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, 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 __UpperCamelCase						(							self      :  Tuple       )		->     List[str]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    'cyberpunk 2077'
											A      						=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
											A      						=    torch.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt=__UpperCamelCase						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=50						, output_type='numpy'						, ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]       )

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

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

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]       )

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

											A      						=    pipe.image_variation(__UpperCamelCase						, generator=__UpperCamelCase						, output_type='numpy'       ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]       )

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