



"""simple docstring"""





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification




def 	UpperCAmelCase     (		A	:						str       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		SwinConfig()
		_UpperCAmelCase							 =		swin_name.split('_'       )

		_UpperCAmelCase							 =		name_split[1]
		_UpperCAmelCase							 =		int(name_split[4]       )
		_UpperCAmelCase							 =		int(name_split[3][-1]       )

		if model_size == "tiny":
				_UpperCAmelCase							 =		96
				_UpperCAmelCase							 =		(2, 2, 6, 2)
				_UpperCAmelCase							 =		(3, 6, 12, 24)
		elif model_size == "small":
				_UpperCAmelCase							 =		96
				_UpperCAmelCase							 =		(2, 2, 18, 2)
				_UpperCAmelCase							 =		(3, 6, 12, 24)
		elif model_size == "base":
				_UpperCAmelCase							 =		128
				_UpperCAmelCase							 =		(2, 2, 18, 2)
				_UpperCAmelCase							 =		(4, 8, 16, 32)
		else:
				_UpperCAmelCase							 =		192
				_UpperCAmelCase							 =		(2, 2, 18, 2)
				_UpperCAmelCase							 =		(6, 12, 24, 48)

		if "in22k" in swin_name:
				_UpperCAmelCase							 =		2_1841
		else:
				_UpperCAmelCase							 =		1000
				_UpperCAmelCase							 =		'huggingface/label-files'
				_UpperCAmelCase							 =		'imagenet-1k-id2label.json'
				_UpperCAmelCase							 =		json.load(open(hf_hub_download(A     ,				A     ,				repo_type='dataset'       )     ,				'r'       )       )
				_UpperCAmelCase							 =		{int(A       ): v for k, v in idalabel.items()}
				_UpperCAmelCase							 =		idalabel
				_UpperCAmelCase							 =		{v: k for k, v in idalabel.items()}

		_UpperCAmelCase							 =		img_size
		_UpperCAmelCase							 =		num_classes
		_UpperCAmelCase							 =		embed_dim
		_UpperCAmelCase							 =		depths
		_UpperCAmelCase							 =		num_heads
		_UpperCAmelCase							 =		window_size

		return config




def 	UpperCAmelCase     (		A	:						List[str]       ):

		'''simple docstring'''


		if "patch_embed.proj" in name:
				_UpperCAmelCase							 =		name.replace('patch_embed.proj'     ,				'embeddings.patch_embeddings.projection'       )
		if "patch_embed.norm" in name:
				_UpperCAmelCase							 =		name.replace('patch_embed.norm'     ,				'embeddings.norm'       )
		if "layers" in name:
				_UpperCAmelCase							 =		'encoder.' + name
		if "attn.proj" in name:
				_UpperCAmelCase							 =		name.replace('attn.proj'     ,				'attention.output.dense'       )
		if "attn" in name:
				_UpperCAmelCase							 =		name.replace('attn'     ,				'attention.self'       )
		if "norm1" in name:
				_UpperCAmelCase							 =		name.replace('norm1'     ,				'layernorm_before'       )
		if "norm2" in name:
				_UpperCAmelCase							 =		name.replace('norm2'     ,				'layernorm_after'       )
		if "mlp.fc1" in name:
				_UpperCAmelCase							 =		name.replace('mlp.fc1'     ,				'intermediate.dense'       )
		if "mlp.fc2" in name:
				_UpperCAmelCase							 =		name.replace('mlp.fc2'     ,				'output.dense'       )

		if name == "norm.weight":
				_UpperCAmelCase							 =		'layernorm.weight'
		if name == "norm.bias":
				_UpperCAmelCase							 =		'layernorm.bias'

		if "head" in name:
				_UpperCAmelCase							 =		name.replace('head'     ,				'classifier'       )
		else:
				_UpperCAmelCase							 =		'swin.' + name

		return name




def 	UpperCAmelCase     (		A	:						Dict     ,				A	:						Tuple       ):

		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				_UpperCAmelCase							 =		orig_state_dict.pop(A       )

				if "mask" in key:
						continue
				elif "qkv" in key:
						_UpperCAmelCase							 =		key.split('.'       )
						_UpperCAmelCase							 =		int(key_split[1]       )
						_UpperCAmelCase							 =		int(key_split[3]       )
						_UpperCAmelCase							 =		model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								_UpperCAmelCase							 =		val[:dim, :]
								_UpperCAmelCase							 =		val[
								    dim : dim * 2, :
								]
								_UpperCAmelCase							 =		val[-dim:, :]
						else:
								_UpperCAmelCase							 =		val[
								    :dim
								]
								_UpperCAmelCase							 =		val[
								    dim : dim * 2
								]
								_UpperCAmelCase							 =		val[
								    -dim:
								]
				else:
						_UpperCAmelCase							 =		val

		return orig_state_dict




def 	UpperCAmelCase     (		A	:						Optional[Any]     ,				A	:						List[str]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		timm.create_model(A     ,				pretrained=A       )
		timm_model.eval()

		_UpperCAmelCase							 =		get_swin_config(A       )
		_UpperCAmelCase							 =		SwinForImageClassification(A       )
		model.eval()

		_UpperCAmelCase							 =		convert_state_dict(timm_model.state_dict()     ,				A       )
		model.load_state_dict(A       )

		_UpperCAmelCase							 =		'http://images.cocodataset.org/val2017/000000039769.jpg'

		_UpperCAmelCase							 =		AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'     ,				'-'       )       )       )
		_UpperCAmelCase							 =		Image.open(requests.get(A     ,				stream=A       ).raw       )
		_UpperCAmelCase							 =		image_processor(images=A     ,				return_tensors='pt'       )

		_UpperCAmelCase							 =		timm_model(inputs['pixel_values']       )
		_UpperCAmelCase							 =		model(**A       ).logits

		assert torch.allclose(A     ,				A     ,				atol=1e-3       )

		print(f'Saving model {swin_name} to {pytorch_dump_folder_path}'       )
		model.save_pretrained(A       )

		print(f'Saving image processor to {pytorch_dump_folder_path}'       )
		image_processor.save_pretrained(A       )


if __name__ == "__main__":
					lowercase  							=					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--swin_name''',
					    default='''swin_tiny_patch4_window7_224''',
					    type=str,
					    help='''Name of the Swin timm model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)

					lowercase  							=					parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					DiTPipeline
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							_UpperCAmelCase      				=					PipelineTesterMixin.required_optional_params - {
							    '''latents''',
							    '''num_images_per_prompt''',
							    '''callback''',
							    '''callback_steps''',
							}
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							str:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		TransformeraDModel(
									    sample_size=16	,       num_layers=2	,       patch_size=4	,       attention_head_dim=8	,       num_attention_heads=2	,       in_channels=4	,       out_channels=8	,       attention_bias=snake_case	,       activation_fn='gelu-approximate'	,       num_embeds_ada_norm=1000	,       norm_type='ada_norm_zero'	,       norm_elementwise_affine=snake_case	,       )
									_UpperCAmelCase							 =		AutoencoderKL()
									_UpperCAmelCase							 =		DDIMScheduler()
									_UpperCAmelCase							 =		{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
									return components






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							Optional[Any]:
									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)
									_UpperCAmelCase							 =		{
									    'class_labels': [1],
									    'generator': generator,
									    'num_inference_steps': 2,
									    'output_type': 'numpy',
									}
									return inputs






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()
									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									pipe.to(snake_case						)
									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case						)
									_UpperCAmelCase							 =		pipe(**snake_case						).images
									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									self.assertEqual(image.shape	,       (1, 16, 16, 3)						)
									_UpperCAmelCase							 =		np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]						)
									_UpperCAmelCase							 =		np.abs(image_slice.flatten() - expected_slice						).max()
									self.assertLessEqual(snake_case	,       1E-3						)






							def        lowerCamelCase_   (  self						)			->							Any:
									self._test_inference_batch_single_identical(relax_max_difference=snake_case	,       expected_max_diff=1E-3						)






							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()	,       reason='XFormers attention is only available with CUDA and `xformers` installed'	,       )
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







@require_torch_gpu
@slow
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		torch.manual_seed(0						)

									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella', 'white shark', 'white wolf']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=40	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'						)
											assert np.abs((expected_image - image).max()						) < 1E-2






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'						)
									_UpperCAmelCase							 =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=25	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    f'/dit/{word}_512.npy'						)

											assert np.abs((expected_image - image).max()						) < 1E-1


