





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
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_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class        _lowerCamelCase      (       snake_case__  ,			snake_case__  ,			snake_case__  ,			unittest.TestCase ):
						UpperCAmelCase_ =			StableDiffusionInstructPixaPixPipeline
						UpperCAmelCase_ =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
						UpperCAmelCase_ =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						UpperCAmelCase_ =			IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase_ =			IMAGE_TO_IMAGE_IMAGE_PARAMS


						def     snake_case_   (self		) ->   Any:
								torch.manual_seed(0		)
								UpperCamelCase   =	UNetaDConditionModel(
								    block_out_channels=(32, 64)   ,							layers_per_block=2   ,							sample_size=32   ,							in_channels=8   ,							out_channels=4   ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,							cross_attention_dim=32   ,							)
								UpperCamelCase   =	PNDMScheduler(skip_prk_steps=lowercase_		)
								torch.manual_seed(0		)
								UpperCamelCase   =	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		)
								UpperCamelCase   =	CLIPTextConfig(
								    bos_token_id=0   ,							eos_token_id=2   ,							hidden_size=32   ,							intermediate_size=37   ,							layer_norm_eps=1e-0_5   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=10_00   ,							)
								UpperCamelCase   =	CLIPTextModel(lowercase_		)
								UpperCamelCase   =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

								UpperCamelCase   =	{
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								return components


						def     snake_case_   (self   ,							__a   ,							__a=0		) ->   Optional[Any]:
								UpperCamelCase   =	floats_tensor((1, 3, 32, 32)   ,							rng=random.Random(lowercase_		)		).to(lowercase_		)
								UpperCamelCase   =	image.cpu().permute(0   ,							2   ,							3   ,							1		)[0]
								UpperCamelCase   =	Image.fromarray(np.uinta(lowercase_		)		).convert("RGB"		)
								if str(lowercase_		).startswith("mps"		):
										UpperCamelCase   =	torch.manual_seed(lowercase_		)
								else:
										UpperCamelCase   =	torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
								UpperCamelCase   =	{
								    "prompt": "A painting of a squirrel eating a burger",
								    "image": image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "guidance_scale": 6.0,
								    "image_guidance_scale": 1,
								    "output_type": "numpy",
								}
								return inputs


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	"cpu"  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline(**lowercase_		)
								UpperCamelCase   =	sd_pipe.to(lowercase_		)
								sd_pipe.set_progress_bar_config(disable=lowercase_		)

								UpperCamelCase   =	self.get_dummy_inputs(lowercase_		)
								UpperCamelCase   =	sd_pipe(**lowercase_		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1]
								assert image.shape == (1, 32, 32, 3)
								UpperCamelCase   =	np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	"cpu"  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline(**lowercase_		)
								UpperCamelCase   =	sd_pipe.to(lowercase_		)
								sd_pipe.set_progress_bar_config(disable=lowercase_		)

								UpperCamelCase   =	self.get_dummy_inputs(lowercase_		)
								UpperCamelCase   =	"french fries"
								UpperCamelCase   =	sd_pipe(**lowercase_   ,							negative_prompt=lowercase_		)
								UpperCamelCase   =	output.images
								UpperCamelCase   =	image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCamelCase   =	np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3


						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	"cpu"  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline(**lowercase_		)
								UpperCamelCase   =	sd_pipe.to(lowercase_		)
								sd_pipe.set_progress_bar_config(disable=lowercase_		)

								UpperCamelCase   =	self.get_dummy_inputs(lowercase_		)
								UpperCamelCase   =	[inputs["prompt"]] * 2

								UpperCamelCase   =	np.array(inputs["image"]		).astype(np.floataa		) / 255.0
								UpperCamelCase   =	torch.from_numpy(lowercase_		).unsqueeze(0		).to(lowercase_		)
								UpperCamelCase   =	image / 2 + 0.5
								UpperCamelCase   =	image.permute(0   ,							3   ,							1   ,							2		)
								UpperCamelCase   =	image.repeat(2   ,							1   ,							1   ,							1		)

								UpperCamelCase   =	sd_pipe(**lowercase_		).images
								UpperCamelCase   =	image[-1, -3:, -3:, -1]

								assert image.shape == (2, 32, 32, 3)
								UpperCamelCase   =	np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	"cpu"  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	EulerAncestralDiscreteScheduler(
								    beta_start=0.00085   ,							beta_end=0.012   ,							beta_schedule="scaled_linear"		)
								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline(**lowercase_		)
								UpperCamelCase   =	sd_pipe.to(lowercase_		)
								sd_pipe.set_progress_bar_config(disable=lowercase_		)

								UpperCamelCase   =	self.get_dummy_inputs(lowercase_		)
								UpperCamelCase   =	sd_pipe(**lowercase_		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1]

								UpperCamelCase   =	[round(lowercase_   ,							4		) for x in image_slice.flatten().tolist()]
								print(",".join([str(lowercase_		) for x in slice]		)		)

								assert image.shape == (1, 32, 32, 3)
								UpperCamelCase   =	np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3


						def     snake_case_   (self		) ->   str:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3		)




						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	self.get_dummy_components()
								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline(**lowercase_		)
								UpperCamelCase   =	VaeImageProcessor(do_resize=lowercase_   ,							do_normalize=lowercase_		)
								UpperCamelCase   =	pipe.to(lowercase_		)
								pipe.set_progress_bar_config(disable=lowercase_		)

								UpperCamelCase   =	pipe(**self.get_dummy_inputs_by_type(lowercase_   ,							input_image_type="pt"		)		)[0]

								UpperCamelCase   =	components["vae"]
								UpperCamelCase   =	self.get_dummy_inputs_by_type(lowercase_   ,							input_image_type="pt"		)

								for image_param in self.image_latents_params:
										if image_param in inputs.keys():
												UpperCamelCase   =	vae.encode(inputs[image_param]		).latent_dist.mode()

								UpperCamelCase   =	pipe(**lowercase_		)[0]

								UpperCamelCase   =	np.abs(out - out_latents_inputs		).max()
								self.assertLess(lowercase_   ,							1e-4   ,							"passing latents as image input generate different result from passing image"		)





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


						def     snake_case_   (self		) ->   List[str]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


						def     snake_case_   (self   ,							__a=0		) ->   Any:
								UpperCamelCase   =	torch.manual_seed(lowercase_		)
								UpperCamelCase   =	load_image(
								    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"		)
								UpperCamelCase   =	{
								    "prompt": "turn him into a cyborg",
								    "image": image,
								    "generator": generator,
								    "num_inference_steps": 3,
								    "guidance_scale": 7.5,
								    "image_guidance_scale": 1.0,
								    "output_type": "numpy",
								}
								return inputs


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    "timbrooks/instruct-pix2pix"   ,							safety_checker=lowercase_		)
								pipe.to(lowercase_		)
								pipe.set_progress_bar_config(disable=lowercase_		)
								pipe.enable_attention_slicing()

								UpperCamelCase   =	self.get_inputs()
								UpperCamelCase   =	pipe(**lowercase_		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCamelCase   =	np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]		)

								assert np.abs(expected_slice - image_slice		).max() < 1e-3


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    "timbrooks/instruct-pix2pix"   ,							safety_checker=lowercase_		)
								UpperCamelCase   =	LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
								pipe.to(lowercase_		)
								pipe.set_progress_bar_config(disable=lowercase_		)
								pipe.enable_attention_slicing()

								UpperCamelCase   =	self.get_inputs()
								UpperCamelCase   =	pipe(**lowercase_		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCamelCase   =	np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]		)

								assert np.abs(expected_slice - image_slice		).max() < 1e-3


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    "timbrooks/instruct-pix2pix"   ,							safety_checker=lowercase_		)
								UpperCamelCase   =	DDIMScheduler.from_config(pipe.scheduler.config		)
								pipe.to(lowercase_		)
								pipe.set_progress_bar_config(disable=lowercase_		)
								pipe.enable_attention_slicing()

								UpperCamelCase   =	self.get_inputs()
								UpperCamelCase   =	pipe(**lowercase_		).images
								UpperCamelCase   =	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCamelCase   =	np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]		)

								assert np.abs(expected_slice - image_slice		).max() < 1e-3


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	0

								def callback_fn(__a   ,							__a   ,							__a		) -> None:
										UpperCamelCase   =	True
										nonlocal number_of_steps
										number_of_steps += 1
										if step == 1:
												UpperCamelCase   =	latents.detach().cpu().numpy()
												assert latents.shape == (1, 4, 64, 64)
												UpperCamelCase   =	latents[0, -3:, -3:, -1]
												UpperCamelCase   =	np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]		)

												assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2
										elif step == 2:
												UpperCamelCase   =	latents.detach().cpu().numpy()
												assert latents.shape == (1, 4, 64, 64)
												UpperCamelCase   =	latents[0, -3:, -3:, -1]
												UpperCamelCase   =	np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]		)

												assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2

								UpperCamelCase   =	False

								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    "timbrooks/instruct-pix2pix"   ,							safety_checker=lowercase_   ,							torch_dtype=torch.floataa		)
								UpperCamelCase   =	pipe.to(lowercase_		)
								pipe.set_progress_bar_config(disable=lowercase_		)
								pipe.enable_attention_slicing()

								UpperCamelCase   =	self.get_inputs()
								pipe(**lowercase_   ,							callback=lowercase_   ,							callback_steps=1		)
								assert callback_fn.has_been_called
								assert number_of_steps == 3


						def     snake_case_   (self		) ->   Dict:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    "timbrooks/instruct-pix2pix"   ,							safety_checker=lowercase_   ,							torch_dtype=torch.floataa		)
								UpperCamelCase   =	pipe.to(lowercase_		)
								pipe.set_progress_bar_config(disable=lowercase_		)
								pipe.enable_attention_slicing(1		)
								pipe.enable_sequential_cpu_offload()

								UpperCamelCase   =	self.get_inputs()
								UpperCamelCase   =	pipe(**lowercase_		)

								UpperCamelCase   =	torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 10**9




						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								UpperCamelCase   =	inputs["image"].resize((5_04, 5_04)		)

								UpperCamelCase   =	"timbrooks/instruct-pix2pix"
								UpperCamelCase   =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    lowercase_   ,							safety_checker=lowercase_   ,							)
								pipe.to(lowercase_		)
								pipe.set_progress_bar_config(disable=lowercase_		)
								pipe.enable_attention_slicing()

								UpperCamelCase   =	pipe(**lowercase_		)
								UpperCamelCase   =	output.images[0]

								UpperCamelCase   =	image[2_55:2_58, 3_83:3_86, -1]

								assert image.shape == (5_04, 5_04, 3)
								UpperCamelCase   =	np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-3











"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[
		    "encoder.version",
		    "decoder.version",
		    "model.encoder.version",
		    "model.decoder.version",
		    "decoder.output_projection.weight",
		    "_float_tensor",
		    "encoder.embed_positions._float_tensor",
		    "decoder.embed_positions._float_tensor",
		]
		for k in ignore_keys:
				state_dict.pop(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	emb.weight.shape
		UpperCamelCase   =	nn.Linear(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	bias=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	emb.weight.data
		return lin_layer


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	mam_aaa["args"] or mam_aaa["cfg"]["model"]
		UpperCamelCase   =	mam_aaa["model"]
		remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	state_dict["encoder.embed_tokens.weight"].shape[0]

		UpperCamelCase   =	MaMaaaConfig(
		    vocab_size=_SCREAMING_SNAKE_CASE  ,	max_position_embeddings=1_024  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="relu"  ,	)

		UpperCamelCase   =	state_dict["decoder.embed_tokens.weight"]
		UpperCamelCase   =	MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )
		model.model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	make_linear_from_emb(model.model.shared    )

		return model


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						lowerCAmelCase__     			=			parser.parse_args()
						lowerCAmelCase__     			=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






