



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput



@dataclass
class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Optional[torch.FloatTensor]								=     None
				A_   :     torch.FloatTensor								=     None
				A_   :     Optional[Tuple[torch.FloatTensor]]								=     None
				A_   :     Optional[Tuple[torch.FloatTensor]]								=     None





class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  Optional[Any]						, __UpperCamelCase      :  Tuple=1						, __UpperCamelCase      :  Optional[int]=0						, __UpperCamelCase      :  Union[str, Any]=2						, __UpperCamelCase      :  Dict=512						, __UpperCamelCase      :  List[Any]="cls"						, __UpperCamelCase      :  Optional[Any]=False						, __UpperCamelCase      :  List[Any]=True						, **__UpperCamelCase      :  Union[str, Any]						, )		->     str:
											super().__init__(pad_token_id=__UpperCamelCase						, bos_token_id=__UpperCamelCase						, eos_token_id=__UpperCamelCase						, **__UpperCamelCase       )
											A      						=    project_dim
											A      						=    pooler_fn
											A      						=    learn_encoder
											A      						=    use_attention_mask





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Any								=     [r'pooler', r'logit_scale']
				A_   :     int								=     [r'position_ids', r'predictions.decoder.bias']
				A_   :     int								=     'roberta'
				A_   :     int								=     RobertaSeriesConfig


				def __init__(							self      :  Union[str, Any]						, __UpperCamelCase      :  List[str]       )		->     Any:
											super().__init__(__UpperCamelCase       )
											A      						=    XLMRobertaModel(__UpperCamelCase       )
											A      						=    nn.Linear(config.hidden_size						, config.project_dim       )
											A      						=    getattr(__UpperCamelCase						, 'has_pre_transformation'						, __UpperCamelCase       )
											if self.has_pre_transformation:
																		A      						=    nn.Linear(config.hidden_size						, config.project_dim       )
																		A      						=    nn.LayerNorm(config.hidden_size						, eps=config.layer_norm_eps       )
											self.post_init()


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Optional[torch.Tensor] = None						, __UpperCamelCase      :  Optional[torch.Tensor] = None						, __UpperCamelCase      :  Optional[torch.Tensor] = None						, __UpperCamelCase      :  Optional[torch.Tensor] = None						, __UpperCamelCase      :  Optional[torch.Tensor] = None						, __UpperCamelCase      :  Optional[torch.Tensor] = None						, __UpperCamelCase      :  Optional[torch.Tensor] = None						, __UpperCamelCase      :  Optional[torch.Tensor] = None						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  Optional[bool] = None						, )		->     Dict:
											A      						=    return_dict if return_dict is not None else self.config.use_return_dict

											A      						=    self.base_model(
											    input_ids=__UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase						, position_ids=__UpperCamelCase						, head_mask=__UpperCamelCase						, inputs_embeds=__UpperCamelCase						, encoder_hidden_states=__UpperCamelCase						, encoder_attention_mask=__UpperCamelCase						, output_attentions=__UpperCamelCase						, output_hidden_states=True if self.has_pre_transformation else output_hidden_states						, return_dict=__UpperCamelCase						, )

											if self.has_pre_transformation:
																		A      						=    outputs['hidden_states'][-2]
																		A      						=    self.pre_LN(__UpperCamelCase       )
																		A      						=    self.transformation_pre(__UpperCamelCase       )

																		return TransformationModelOutput(
																		    projection_state=__UpperCamelCase						, last_hidden_state=outputs.last_hidden_state						, hidden_states=outputs.hidden_states						, attentions=outputs.attentions						, )
											else:
																		A      						=    self.transformation(outputs.last_hidden_state       )
																		return TransformationModelOutput(
																		    projection_state=__UpperCamelCase						, last_hidden_state=outputs.last_hidden_state						, hidden_states=outputs.hidden_states						, attentions=outputs.attentions						, )




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