

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class        SCREAMING_SNAKE_CASE      (	a__					,							unittest.TestCase ):






					"""simple docstring"""



					lowercase__       				= PaintByExamplePipeline
					lowercase__       				= IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
					lowercase__       				= IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					lowercase__       				= frozenset([] )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







					def  __lowerCAmelCase				( self    :       int	):
						torch.manual_seed(0	)
						lowerCAmelCase__			:		str						       =			UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)     ,layers_per_block=2     ,sample_size=3_2     ,in_channels=9     ,out_channels=4     ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,cross_attention_dim=3_2     ,)
						lowerCAmelCase__			:		Any						       =			PNDMScheduler(skip_prk_steps=__A	)
						torch.manual_seed(0	)
						lowerCAmelCase__			:		Dict						       =			AutoencoderKL(
						    block_out_channels=[3_2, 6_4]     ,in_channels=3     ,out_channels=3     ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,latent_channels=4     ,)
						torch.manual_seed(0	)
						lowerCAmelCase__			:		int						       =			CLIPVisionConfig(
						    hidden_size=3_2     ,projection_dim=3_2     ,intermediate_size=3_7     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,image_size=3_2     ,patch_size=4     ,)
						lowerCAmelCase__			:		List[str]						       =			PaintByExampleImageEncoder(__A     ,proj_size=3_2	)
						lowerCAmelCase__			:		List[Any]						       =			CLIPImageProcessor(crop_size=3_2     ,size=3_2	)

						lowerCAmelCase__			:		Tuple						       =			{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "image_encoder": image_encoder,
						    "safety_checker": None,
						    "feature_extractor": feature_extractor,
						}
						return components







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       Optional[int]	):
						lowerCAmelCase__			:		List[Any]						       =			np.array(image.convert('''RGB'''	)	)
						lowerCAmelCase__			:		Dict						       =			image[None].transpose(0     ,3     ,1     ,2	)
						lowerCAmelCase__			:		Optional[int]						       =			torch.from_numpy(__A	).to(dtype=torch.floataa	) / 127.5 - 1.0
						return image







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       Optional[int]="cpu"     ,lowercase_    :       Optional[Any]=0	):
						lowerCAmelCase__			:		List[str]						       =			floats_tensor((1, 3, 3_2, 3_2)     ,rng=random.Random(__A	)	).to(__A	)
						lowerCAmelCase__			:		Union[str, Any]						       =			image.cpu().permute(0     ,2     ,3     ,1	)[0]
						lowerCAmelCase__			:		Any						       =			Image.fromarray(np.uinta(__A	)	).convert('''RGB'''	).resize((6_4, 6_4)	)
						lowerCAmelCase__			:		int						       =			Image.fromarray(np.uinta(image + 4	)	).convert('''RGB'''	).resize((6_4, 6_4)	)
						lowerCAmelCase__			:		List[str]						       =			Image.fromarray(np.uinta(__A	)	).convert('''RGB'''	).resize((3_2, 3_2)	)

						if str(__A	).startswith('''mps'''	):
							lowerCAmelCase__			:		Optional[Any]						       =			torch.manual_seed(__A	)
						else:
							lowerCAmelCase__			:		Optional[Any]						       =			torch.Generator(device=__A	).manual_seed(__A	)
						lowerCAmelCase__			:		int						       =			{
						    "example_image": example_image,
						    "image": init_image,
						    "mask_image": mask_image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs







					def  __lowerCAmelCase				( self    :       Optional[int]	):
						lowerCAmelCase__			:		Tuple						       =			self.get_dummy_components()

						# make sure here that pndm scheduler skips prk
						lowerCAmelCase__			:		Dict						       =			PaintByExamplePipeline(**__A	)
						lowerCAmelCase__			:		Tuple						       =			pipe.to('''cpu'''	)
						pipe.set_progress_bar_config(disable=__A	)

						lowerCAmelCase__			:		Dict						       =			self.get_dummy_inputs()
						lowerCAmelCase__			:		Dict						       =			pipe(**__A	)
						lowerCAmelCase__			:		Dict						       =			output.images

						lowerCAmelCase__			:		Dict						       =			image[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						lowerCAmelCase__			:		Dict						       =			np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2







					def  __lowerCAmelCase				( self    :       Tuple	):
						lowerCAmelCase__			:		Optional[int]						       =			"cpu"
						lowerCAmelCase__			:		List[Any]						       =			self.get_dummy_inputs()
						inputs.pop('''mask_image'''	)
						lowerCAmelCase__			:		Optional[Any]						       =			self.convert_to_pt(inputs.pop('''image'''	)	)
						lowerCAmelCase__			:		Tuple						       =			image.clamp(0     ,1	) / 2

						# make sure here that pndm scheduler skips prk
						lowerCAmelCase__			:		Tuple						       =			PaintByExamplePipeline(**self.get_dummy_components()	)
						lowerCAmelCase__			:		Optional[Any]						       =			pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)

						lowerCAmelCase__			:		Tuple						       =			pipe(image=__A     ,mask_image=mask_image[:, 0]     ,**__A	)
						lowerCAmelCase__			:		List[str]						       =			output.images

						lowerCAmelCase__			:		Optional[Any]						       =			image.cpu().permute(0     ,2     ,3     ,1	)[0]
						lowerCAmelCase__			:		Any						       =			mask_image.cpu().permute(0     ,2     ,3     ,1	)[0]

						lowerCAmelCase__			:		int						       =			Image.fromarray(np.uinta(__A	)	).convert('''RGB'''	)
						lowerCAmelCase__			:		List[Any]						       =			Image.fromarray(np.uinta(__A	)	).convert('''RGB'''	)

						lowerCAmelCase__			:		Optional[Any]						       =			pipe(**self.get_dummy_inputs()	)
						lowerCAmelCase__			:		Tuple						       =			output.images

						assert out_a.shape == (1, 6_4, 6_4, 3)
						assert np.abs(out_a.flatten() - out_a.flatten()	).max() < 5E-2







					def  __lowerCAmelCase				( self    :       Optional[Any]	):
						super().test_inference_batch_single_identical(expected_max_diff=3E-3	)







@slow
@require_torch_gpu
class        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



					def  __lowerCAmelCase				( self    :       Optional[int]	):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		int						       =			load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/paint_by_example/dog_in_bucket.png'''	)
						lowerCAmelCase__			:		List[str]						       =			load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/paint_by_example/mask.png'''	)
						lowerCAmelCase__			:		List[Any]						       =			load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/paint_by_example/panda.jpg'''	)

						lowerCAmelCase__			:		List[str]						       =			PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''	)
						lowerCAmelCase__			:		List[str]						       =			pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)

						lowerCAmelCase__			:		Dict						       =			torch.manual_seed(3_2_1	)
						lowerCAmelCase__			:		List[str]						       =			pipe(
						    image=__A     ,mask_image=__A     ,example_image=__A     ,generator=__A     ,guidance_scale=5.0     ,num_inference_steps=5_0     ,output_type='''np'''     ,)

						lowerCAmelCase__			:		Optional[Any]						       =			output.images

						lowerCAmelCase__			:		Union[str, Any]						       =			image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						lowerCAmelCase__			:		Union[str, Any]						       =			np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2







"""simple docstring"""





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCamelCase						:   str							   =	logging.get_logger(__name__)

__UpperCamelCase						:   Union[str, Any]							   =	{
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= "gpt2"
					lowercase__       				= ["past_key_values"]
					lowercase__       				= {
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__( self    :       List[str]     ,lowercase_    :       Dict=5_0_2_5_7     ,lowercase_    :       Dict=1_0_2_4     ,lowercase_    :       Any=7_6_8     ,lowercase_    :       str=1_2     ,lowercase_    :       int=1_2     ,lowercase_    :       List[Any]=None     ,lowercase_    :       Dict="gelu_new"     ,lowercase_    :       Any=0.1     ,lowercase_    :       Optional[Any]=0.1     ,lowercase_    :       Tuple=0.1     ,lowercase_    :       Dict=1E-5     ,lowercase_    :       List[Any]=0.02     ,lowercase_    :       Tuple="cls_index"     ,lowercase_    :       Optional[int]=True     ,lowercase_    :       Union[str, Any]=None     ,lowercase_    :       Optional[Any]=True     ,lowercase_    :       List[Any]=0.1     ,lowercase_    :       List[str]=True     ,lowercase_    :       int=True     ,lowercase_    :       Optional[Any]=5_0_2_5_6     ,lowercase_    :       Dict=5_0_2_5_6     ,lowercase_    :       Union[str, Any]=False     ,lowercase_    :       Optional[Any]=False     ,**lowercase_    :       Dict     ,):
						lowerCAmelCase__			:		Tuple						       =			vocab_size
						lowerCAmelCase__			:		List[Any]						       =			n_positions
						lowerCAmelCase__			:		Optional[int]						       =			n_embd
						lowerCAmelCase__			:		Tuple						       =			n_layer
						lowerCAmelCase__			:		Optional[Any]						       =			n_head
						lowerCAmelCase__			:		List[Any]						       =			n_inner
						lowerCAmelCase__			:		Any						       =			activation_function
						lowerCAmelCase__			:		Optional[int]						       =			resid_pdrop
						lowerCAmelCase__			:		str						       =			embd_pdrop
						lowerCAmelCase__			:		Tuple						       =			attn_pdrop
						lowerCAmelCase__			:		List[str]						       =			layer_norm_epsilon
						lowerCAmelCase__			:		Union[str, Any]						       =			initializer_range
						lowerCAmelCase__			:		Optional[int]						       =			summary_type
						lowerCAmelCase__			:		Optional[Any]						       =			summary_use_proj
						lowerCAmelCase__			:		List[str]						       =			summary_activation
						lowerCAmelCase__			:		Optional[int]						       =			summary_first_dropout
						lowerCAmelCase__			:		Dict						       =			summary_proj_to_labels
						lowerCAmelCase__			:		Dict						       =			scale_attn_weights
						lowerCAmelCase__			:		Dict						       =			use_cache
						lowerCAmelCase__			:		List[Any]						       =			scale_attn_by_inverse_layer_idx
						lowerCAmelCase__			:		int						       =			reorder_and_upcast_attn

						lowerCAmelCase__			:		List[str]						       =			bos_token_id
						lowerCAmelCase__			:		List[str]						       =			eos_token_id

						super().__init__(bos_token_id=lowercase_     ,eos_token_id=lowercase_     ,**lowercase_	)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					def __init__( self    :       Optional[int]     ,lowercase_    :       PretrainedConfig     ,lowercase_    :       str = "default"     ,lowercase_    :       List[PatchingSpec] = None     ,lowercase_    :       bool = False     ,):
						super().__init__(lowercase_     ,task=lowercase_     ,patching_specs=lowercase_     ,use_past=lowercase_	)
						if not getattr(self._config     ,'''pad_token_id'''     ,lowercase_	):
							# TODO: how to do that better?
							lowerCAmelCase__			:		int						       =			0







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
						if self.use_past:
							self.fill_with_past_key_values_(lowercase_     ,direction='''inputs'''	)
							lowerCAmelCase__			:		str						       =			{0: '''batch''', 1: '''past_sequence + sequence'''}
						else:
							lowerCAmelCase__			:		List[str]						       =			{0: '''batch''', 1: '''sequence'''}

						return common_inputs







					@property
					def  __lowerCAmelCase				( self    :       Any	):
						return self._config.n_layer







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						return self._config.n_head







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       PreTrainedTokenizer     ,lowercase_    :       int = -1     ,lowercase_    :       int = -1     ,lowercase_    :       bool = False     ,lowercase_    :       Optional[TensorType] = None     ,):
						lowerCAmelCase__			:		Any						       =			super(lowercase_     ,self	).generate_dummy_inputs(
						    lowercase_     ,batch_size=lowercase_     ,seq_length=lowercase_     ,is_pair=lowercase_     ,framework=lowercase_	)

						# We need to order the input in the way they appears in the forward()
						lowerCAmelCase__			:		List[Any]						       =			OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

						# Need to add the past_keys
						if self.use_past:
							if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
							else:
								import torch

								lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			common_inputs['''input_ids'''].shape
								# Not using the same length for past_key_values
								lowerCAmelCase__			:		Dict						       =			seqlen + 2
								lowerCAmelCase__			:		Optional[Any]						       =			(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								lowerCAmelCase__			:		Tuple						       =			[
								    (torch.zeros(lowercase_	), torch.zeros(lowercase_	)) for _ in range(self.num_layers	)
								]

						lowerCAmelCase__			:		Any						       =			common_inputs['''attention_mask''']
						if self.use_past:
							lowerCAmelCase__			:		Optional[Any]						       =			ordered_inputs['''attention_mask'''].dtype
							lowerCAmelCase__			:		Union[str, Any]						       =			torch.cat(
							    [ordered_inputs['''attention_mask'''], torch.ones(lowercase_     ,lowercase_     ,dtype=lowercase_	)]     ,dim=1	)

						return ordered_inputs







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return 1_3




