





'''simple docstring'''






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


A :			Optional[int]							   =      {512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}





def    lowercase_   (		lowercase__    ) ->Optional[int]:
						_snake_case: int	       =		F'''pretrained_models/{model_name}'''
						if not os.path.isfile(lowercase__    ):
												os.makedirs('pretrained_models'					,	exist_ok=lowercase__    )
												_snake_case: List[str]	       =		F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
												download_url(lowercase__					,	'pretrained_models'    )
						_snake_case: Union[str, Any]	       =		torch.load(lowercase__					,	map_location=lambda lowercase__					,	lowercase__    : storage    )
						return model





def    lowercase_   (		lowercase__    ) ->Optional[int]:
						_snake_case: List[str]	       =		download_model(pretrained_models[args.image_size]    )

						_snake_case: str	       =		state_dict['x_embedder.proj.weight']
						_snake_case: Tuple	       =		state_dict['x_embedder.proj.bias']
						state_dict.pop('x_embedder.proj.weight'    )
						state_dict.pop('x_embedder.proj.bias'    )

						for depth in range(28    ):
												_snake_case: Union[str, Any]	       =		state_dict[
												    't_embedder.mlp.0.weight'
												]
												_snake_case: Optional[Any]	       =		state_dict[
												    't_embedder.mlp.0.bias'
												]
												_snake_case: Optional[Any]	       =		state_dict[
												    't_embedder.mlp.2.weight'
												]
												_snake_case: Dict	       =		state_dict[
												    't_embedder.mlp.2.bias'
												]
												_snake_case: Optional[Any]	       =		state_dict[
												    'y_embedder.embedding_table.weight'
												]

												_snake_case: int	       =		state_dict[
												    F'''blocks.{depth}.adaLN_modulation.1.weight'''
												]
												_snake_case: Optional[int]	       =		state_dict[
												    F'''blocks.{depth}.adaLN_modulation.1.bias'''
												]

												_snake_case       ,    _snake_case       ,    _snake_case: int	       =		torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight''']					,	3					,	dim=0    )
												_snake_case       ,    _snake_case       ,    _snake_case: str	       =		torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']					,	3					,	dim=0    )

												_snake_case: List[Any]	       =		q
												_snake_case: str	       =		q_bias
												_snake_case: str	       =		k
												_snake_case: List[Any]	       =		k_bias
												_snake_case: Tuple	       =		v
												_snake_case: Union[str, Any]	       =		v_bias

												_snake_case: Optional[Any]	       =		state_dict[
												    F'''blocks.{depth}.attn.proj.weight'''
												]
												_snake_case: Tuple	       =		state_dict[F'''blocks.{depth}.attn.proj.bias''']

												_snake_case: Any	       =		state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
												_snake_case: Tuple	       =		state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
												_snake_case: Tuple	       =		state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
												_snake_case: Any	       =		state_dict[F'''blocks.{depth}.mlp.fc2.bias''']

												state_dict.pop(F'''blocks.{depth}.attn.qkv.weight'''    )
												state_dict.pop(F'''blocks.{depth}.attn.qkv.bias'''    )
												state_dict.pop(F'''blocks.{depth}.attn.proj.weight'''    )
												state_dict.pop(F'''blocks.{depth}.attn.proj.bias'''    )
												state_dict.pop(F'''blocks.{depth}.mlp.fc1.weight'''    )
												state_dict.pop(F'''blocks.{depth}.mlp.fc1.bias'''    )
												state_dict.pop(F'''blocks.{depth}.mlp.fc2.weight'''    )
												state_dict.pop(F'''blocks.{depth}.mlp.fc2.bias'''    )
												state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.weight'''    )
												state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.bias'''    )

						state_dict.pop('t_embedder.mlp.0.weight'    )
						state_dict.pop('t_embedder.mlp.0.bias'    )
						state_dict.pop('t_embedder.mlp.2.weight'    )
						state_dict.pop('t_embedder.mlp.2.bias'    )
						state_dict.pop('y_embedder.embedding_table.weight'    )

						_snake_case: List[Any]	       =		state_dict['final_layer.adaLN_modulation.1.weight']
						_snake_case: str	       =		state_dict['final_layer.adaLN_modulation.1.bias']
						_snake_case: Tuple	       =		state_dict['final_layer.linear.weight']
						_snake_case: Optional[int]	       =		state_dict['final_layer.linear.bias']

						state_dict.pop('final_layer.linear.weight'    )
						state_dict.pop('final_layer.linear.bias'    )
						state_dict.pop('final_layer.adaLN_modulation.1.weight'    )
						state_dict.pop('final_layer.adaLN_modulation.1.bias'    )

						# DiT XL/2
						_snake_case: Any	       =		TransformeraDModel(
						    sample_size=args.image_size // 8					,	num_layers=28					,	attention_head_dim=72					,	in_channels=4					,	out_channels=8					,	patch_size=2					,	attention_bias=lowercase__					,	num_attention_heads=16					,	activation_fn='gelu-approximate'					,	num_embeds_ada_norm=1000					,	norm_type='ada_norm_zero'					,	norm_elementwise_affine=lowercase__					,	)
						transformer.load_state_dict(lowercase__					,	strict=lowercase__    )

						_snake_case: List[Any]	       =		DDIMScheduler(
						    num_train_timesteps=1000					,	beta_schedule='linear'					,	prediction_type='epsilon'					,	clip_sample=lowercase__					,	)

						_snake_case: int	       =		AutoencoderKL.from_pretrained(args.vae_model    )

						_snake_case: Optional[int]	       =		DiTPipeline(transformer=lowercase__					,	vae=lowercase__					,	scheduler=lowercase__    )

						if args.save:
												pipeline.save_pretrained(args.checkpoint_path    )


if __name__ == "__main__":
		A :			str							   =      argparse.ArgumentParser()

		parser.add_argument(
		    '--image_size',
		    default=256,
		    type=int,
		    required=False,
		    help='Image size of pretrained model, either 256 or 512.',
		)
		parser.add_argument(
		    '--vae_model',
		    default='stabilityai/sd-vae-ft-ema',
		    type=str,
		    required=False,
		    help='Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.',
		)
		parser.add_argument(
		    '--save', default=True, type=bool, required=False, help='Whether to save the converted pipeline or not.'
		)
		parser.add_argument(
		    '--checkpoint_path', default=None, type=str, required=True, help='Path to the output pipeline.'
		)

		A :			int							   =      parser.parse_args()
		main(args)












'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class       lowerCamelCase       :


							@staticmethod
							def  SCREAMING_SNAKE_CASE_		(       *__snake_case						:	Optional[Any]							,  **__snake_case						:	str							):





													'''simple docstring'''





													pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class       lowerCamelCase       (    unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					_SCREAMING_SNAKE_CASE				 =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		ImageClassificationPipeline(model=__snake_case							,  image_processor=__snake_case							,  top_k=2							)
											_snake_case: Tuple	       =		[
											    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											    'http://images.cocodataset.org/val2017/000000039769.jpg',
											]
											return image_classifier, examples


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Any							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: Dict	       =		image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'							)

											self.assertEqual(
											    __snake_case							,  [
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											    ]							,  )

											import datasets

											_snake_case: str	       =		datasets.load_dataset('hf-internal-testing/fixtures_image_utils'							,  'image'							,  split='test'							)

											# Accepts URL + PIL.Image + lists
											_snake_case: List[str]	       =		image_classifier(
											    [
											        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        # RGBA
											        dataset[0]['file'],
											        # LA
											        dataset[1]['file'],
											        # L
											        dataset[2]['file'],
											    ]							)
											self.assertEqual(
											    __snake_case							,  [
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											    ]							,  )


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Any	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							)

											_snake_case: List[str]	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: Dict	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					@require_tf
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							,  framework='tf'							)

											_snake_case: Any	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: str	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		PreTrainedTokenizer()

											# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
											_snake_case: Any	       =		pipeline(
											    'image-classification'							,  model='hf-internal-testing/tiny-random-vit'							,  tokenizer=__snake_case							)

											self.assertIs(image_classifier.tokenizer							,  __snake_case							)




					@slow
					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Any	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-conv'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.4_385, 'label': 'tabby, tabby cat'},
											        {'score': 0.321, 'label': 'tiger cat'},
											        {'score': 0.0_502, 'label': 'Egyptian cat'},
											        {'score': 0.0_137, 'label': 'crib, cot'},
											        {'score': 0.007, 'label': 'radiator'},
											    ]							,  )

											_snake_case: Union[str, Any]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-fourier'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.5_658, 'label': 'tabby, tabby cat'},
											        {'score': 0.1_309, 'label': 'tiger cat'},
											        {'score': 0.0_722, 'label': 'Egyptian cat'},
											        {'score': 0.0_707, 'label': 'remote control, remote'},
											        {'score': 0.0_082, 'label': 'computer keyboard, keypad'},
											    ]							,  )

											_snake_case: Optional[int]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-learned'							)
											_snake_case: str	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.3_022, 'label': 'tabby, tabby cat'},
											        {'score': 0.2_362, 'label': 'Egyptian cat'},
											        {'score': 0.1_856, 'label': 'tiger cat'},
											        {'score': 0.0_324, 'label': 'remote control, remote'},
											        {'score': 0.0_096, 'label': 'quilt, comforter, comfort, puff'},
											    ]							,  )





