


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 			a      (      UpperCamelCase_      ,unittest.TestCase   ):
		__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							)->			Union[str, Any]:
						'''simple docstring'''


						torch.manual_seed(0							)
						A__     :							Optional[int]   				=UNetaDConditionModel(
						    block_out_channels=(32, 64)	,     layers_per_block=2	,     sample_size=32	,     in_channels=9	,     out_channels=4	,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,     cross_attention_dim=32	,     )
						A__     :							Any   				=PNDMScheduler(skip_prk_steps=__UpperCamelCase							)
						torch.manual_seed(0							)
						A__     :							int   				=AutoencoderKL(
						    block_out_channels=[32, 64]	,     in_channels=3	,     out_channels=3	,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']	,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']	,     latent_channels=4	,     )
						torch.manual_seed(0							)
						A__     :							Tuple   				=CLIPVisionConfig(
						    hidden_size=32	,     projection_dim=32	,     intermediate_size=37	,     layer_norm_eps=1E-05	,     num_attention_heads=4	,     num_hidden_layers=5	,     image_size=32	,     patch_size=4	,     )
						A__     :							int   				=PaintByExampleImageEncoder(__UpperCamelCase	,     proj_size=32							)
						A__     :							int   				=CLIPImageProcessor(crop_size=32	,     size=32							)

						A__     :							List[str]   				={
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''image_encoder''': image_encoder,
						    '''safety_checker''': None,
						    '''feature_extractor''': feature_extractor,
						}
						return components






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Dict:
						'''simple docstring'''


						A__     :							Tuple   				=np.array(image.convert('''RGB'''							)							)
						A__     :							List[Any]   				=image[None].transpose(0	,     3	,     1	,     2							)
						A__     :							List[Any]   				=torch.from_numpy(__UpperCamelCase							).to(dtype=torch.floataa							) / 127.5 - 1.0
						return image






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase="cpu"	,     __UpperCamelCase=0							)->			Any:
						'''simple docstring'''


						A__     :							Optional[int]   				=floats_tensor((1, 3, 32, 32)	,     rng=random.Random(__UpperCamelCase							)							).to(__UpperCamelCase							)
						A__     :							List[str]   				=image.cpu().permute(0	,     2	,     3	,     1							)[0]
						A__     :							str   				=Image.fromarray(np.uinta(__UpperCamelCase							)							).convert('''RGB'''							).resize((64, 64)							)
						A__     :							Union[str, Any]   				=Image.fromarray(np.uinta(image + 4							)							).convert('''RGB'''							).resize((64, 64)							)
						A__     :							Tuple   				=Image.fromarray(np.uinta(__UpperCamelCase							)							).convert('''RGB'''							).resize((32, 32)							)

						if str(__UpperCamelCase							).startswith('''mps'''							):
										A__     :							Optional[Any]   				=torch.manual_seed(__UpperCamelCase							)
						else:
										A__     :							Optional[int]   				=torch.Generator(device=__UpperCamelCase							).manual_seed(__UpperCamelCase							)
						A__     :							Dict   				={
						    '''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							)->			Dict:
						'''simple docstring'''


						A__     :							List[Any]   				=self.get_dummy_components()

						# make sure here that pndm scheduler skips prk
						A__     :							List[Any]   				=PaintByExamplePipeline(**__UpperCamelCase							)
						A__     :							Dict   				=pipe.to('''cpu'''							)
						pipe.set_progress_bar_config(disable=__UpperCamelCase							)

						A__     :							Optional[int]   				=self.get_dummy_inputs()
						A__     :							Optional[int]   				=pipe(**__UpperCamelCase							)
						A__     :							Dict   				=output.images

						A__     :							List[Any]   				=image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__     :							int   				=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							)->			List[str]:
						'''simple docstring'''


						A__     :							Optional[int]   				='''cpu'''
						A__     :							Any   				=self.get_dummy_inputs()
						inputs.pop('''mask_image'''							)
						A__     :							Dict   				=self.convert_to_pt(inputs.pop('''image'''							)							)
						A__     :							str   				=image.clamp(0	,     1							) / 2

						# make sure here that pndm scheduler skips prk
						A__     :							Any   				=PaintByExamplePipeline(**self.get_dummy_components()							)
						A__     :							Any   				=pipe.to(__UpperCamelCase							)
						pipe.set_progress_bar_config(disable=__UpperCamelCase							)

						A__     :							Union[str, Any]   				=pipe(image=__UpperCamelCase	,     mask_image=mask_image[:, 0]	,     **__UpperCamelCase							)
						A__     :							Dict   				=output.images

						A__     :							Union[str, Any]   				=image.cpu().permute(0	,     2	,     3	,     1							)[0]
						A__     :							Optional[int]   				=mask_image.cpu().permute(0	,     2	,     3	,     1							)[0]

						A__     :							List[Any]   				=Image.fromarray(np.uinta(__UpperCamelCase							)							).convert('''RGB'''							)
						A__     :							str   				=Image.fromarray(np.uinta(__UpperCamelCase							)							).convert('''RGB'''							)

						A__     :							Optional[Any]   				=pipe(**self.get_dummy_inputs()							)
						A__     :							Optional[int]   				=output.images

						assert out_a.shape == (1, 64, 64, 3)
						assert np.abs(out_a.flatten() - out_a.flatten()							).max() < 5E-2






		def       lowerCAmelCase_  (  self							)->			Any:
						'''simple docstring'''


						super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







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






		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
						'''simple docstring'''


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






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


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

						A__     :							Tuple   				=PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''							)
						A__     :							Optional[Any]   				=pipe.to(__UpperCamelCase							)
						pipe.set_progress_bar_config(disable=__UpperCamelCase							)

						A__     :							Any   				=torch.manual_seed(3_21							)
						A__     :							Optional[Any]   				=pipe(
						    image=__UpperCamelCase	,     mask_image=__UpperCamelCase	,     example_image=__UpperCamelCase	,     generator=__UpperCamelCase	,     guidance_scale=5.0	,     num_inference_steps=50	,     output_type='''np'''	,     )

						A__     :							Optional[Any]   				=output.images

						A__     :							Optional[int]   				=image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						A__     :							List[str]   				=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







import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


__lowerCamelCase       :       Optional[Any]				   =			collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							CheckpointState:
				print(f'Reading saved model from {model_file}'	)
				A__     :							Optional[int]   				=torch.load(snake_case_,	map_location=lambda snake_case_,	snake_case_	: default_restore_location(snake_case_,	'''cpu'''	)	)
				return CheckpointState(**snake_case_	)



class 			a      :






		def __init__(  self	,     __UpperCamelCase							)->			Union[str, Any]:
						'''simple docstring'''


						A__     :							Dict   				=src_file






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						raise NotImplementedError






		@staticmethod
		def       lowerCAmelCase_  (  __UpperCamelCase	,     *__UpperCamelCase	,     **__UpperCamelCase							)->			"DPRState":
						'''simple docstring'''


						if comp_type.startswith('''c'''							):
										return DPRContextEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
						if comp_type.startswith('''q'''							):
										return DPRQuestionEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
						if comp_type.startswith('''r'''							):
										return DPRReaderState(*__UpperCamelCase	,     **__UpperCamelCase							)
						else:
										raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''							)







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						A__     :							Optional[int]   				=DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
						print(F'Loading DPR biencoder from {self.src_file}'							)
						A__     :							Optional[int]   				=load_states_from_checkpoint(self.src_file							)
						A__				,  A__     :							List[str]   				=model.ctx_encoder, '''ctx_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						A__     :							Optional[int]   				={'''bert_model.embeddings.position_ids''': model.ctx_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
										if key.startswith(__UpperCamelCase							):
														A__     :							int   				=key[len(__UpperCamelCase							) :]
														if not key.startswith('''encode_proj.'''							):
																		A__     :							Any   				='''bert_model.''' + key
														A__     :							List[str]   				=value
						encoder.load_state_dict(__UpperCamelCase							)
						return model







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			Dict:
						'''simple docstring'''


						A__     :							Union[str, Any]   				=DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
						print(F'Loading DPR biencoder from {self.src_file}'							)
						A__     :							List[str]   				=load_states_from_checkpoint(self.src_file							)
						A__				,  A__     :							Optional[int]   				=model.question_encoder, '''question_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						A__     :							Optional[Any]   				={'''bert_model.embeddings.position_ids''': model.question_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
										if key.startswith(__UpperCamelCase							):
														A__     :							List[Any]   				=key[len(__UpperCamelCase							) :]
														if not key.startswith('''encode_proj.'''							):
																		A__     :							Tuple   				='''bert_model.''' + key
														A__     :							str   				=value
						encoder.load_state_dict(__UpperCamelCase							)
						return model







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			int:
						'''simple docstring'''


						A__     :							Optional[int]   				=DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
						print(F'Loading DPR reader from {self.src_file}'							)
						A__     :							Dict   				=load_states_from_checkpoint(self.src_file							)
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						A__     :							Optional[Any]   				={
						    '''encoder.bert_model.embeddings.position_ids''': model.span_predictor.encoder.bert_model.embeddings.position_ids
						}
						for key, value in saved_state.model_dict.items():
										if key.startswith('''encoder.'''							) and not key.startswith('''encoder.encode_proj'''							):
														A__     :							List[Any]   				='''encoder.bert_model.''' + key[len('''encoder.'''							) :]
										A__     :							Optional[int]   				=value
						model.span_predictor.load_state_dict(__UpperCamelCase							)
						return model







def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Optional[int]:
				A__     :							Optional[int]   				=Path(snake_case_	)
				dest_dir.mkdir(exist_ok=snake_case_	)

				A__     :							Tuple   				=DPRState.from_type(snake_case_,	src_file=snake_case_	)
				A__     :							str   				=dpr_state.load_dpr_model()
				model.save_pretrained(snake_case_	)
				model.from_pretrained(snake_case_	)  # sanity check


if __name__ == "__main__":
						__lowerCamelCase       :       Optional[Any]				   =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						)
						parser.add_argument(
						    "--src",
						    type=str,
						    help=(
						        "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"
						        " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"
						        " 'retriever' checkpoints."
						    ),
						)
						parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.")
						__lowerCamelCase       :       Tuple				   =			parser.parse_args()

						__lowerCamelCase       :       Optional[Any]				   =			Path(args.src)
						__lowerCamelCase       :       Optional[int]				   =			F"converted-{src_file.name}" if args.dest is None else args.dest
						__lowerCamelCase       :       Optional[Any]				   =			Path(dest_dir)
						assert src_file.exists()
						assert (
						    args.type is not None
						), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						convert(args.type, src_file, dest_dir)



