





'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		if "img_encoder.pos_embed" in name:
				SCREAMING_SNAKE_CASE :				List[str]		=			name.replace("""img_encoder.pos_embed"""			,			"""vision_model.embeddings.position_embeddings"""	)
		if "img_encoder.patch_embed.proj" in name:
				SCREAMING_SNAKE_CASE :				Dict		=			name.replace("""img_encoder.patch_embed.proj"""			,			"""vision_model.embeddings.patch_embeddings.projection"""	)
		if "img_encoder.patch_embed.norm" in name:
				SCREAMING_SNAKE_CASE :				int		=			name.replace("""img_encoder.patch_embed.norm"""			,			"""vision_model.embeddings.layernorm"""	)
		if "img_encoder.layers" in name:
				SCREAMING_SNAKE_CASE :				Any		=			name.replace("""img_encoder.layers"""			,			"""vision_model.encoder.stages"""	)
		if "blocks" in name and "res" not in name:
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			name.replace("""blocks"""			,			"""layers"""	)
		if "attn" in name and "pre_assign" not in name:
				SCREAMING_SNAKE_CASE :				str		=			name.replace("""attn"""			,			"""self_attn"""	)
		if "proj" in name and "self_attn" in name and "text" not in name:
				SCREAMING_SNAKE_CASE :				List[str]		=			name.replace("""proj"""			,			"""out_proj"""	)
		if "pre_assign_attn.attn.proj" in name:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			name.replace("""pre_assign_attn.attn.proj"""			,			"""pre_assign_attn.attn.out_proj"""	)
		if "norm1" in name:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			name.replace("""norm1"""			,			"""layer_norm1"""	)
		if "norm2" in name and "pre_assign" not in name:
				SCREAMING_SNAKE_CASE :				List[Any]		=			name.replace("""norm2"""			,			"""layer_norm2"""	)
		if "img_encoder.norm" in name:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			name.replace("""img_encoder.norm"""			,			"""vision_model.layernorm"""	)
		# text encoder
		if "text_encoder.token_embedding" in name:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			name.replace("""text_encoder.token_embedding"""			,			"""text_model.embeddings.token_embedding"""	)
		if "text_encoder.positional_embedding" in name:
				SCREAMING_SNAKE_CASE :				Tuple		=			name.replace("""text_encoder.positional_embedding"""			,			"""text_model.embeddings.position_embedding.weight"""	)
		if "text_encoder.transformer.resblocks." in name:
				SCREAMING_SNAKE_CASE :				int		=			name.replace("""text_encoder.transformer.resblocks."""			,			"""text_model.encoder.layers."""	)
		if "ln_1" in name:
				SCREAMING_SNAKE_CASE :				List[str]		=			name.replace("""ln_1"""			,			"""layer_norm1"""	)
		if "ln_2" in name:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			name.replace("""ln_2"""			,			"""layer_norm2"""	)
		if "c_fc" in name:
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			name.replace("""c_fc"""			,			"""fc1"""	)
		if "c_proj" in name:
				SCREAMING_SNAKE_CASE :				str		=			name.replace("""c_proj"""			,			"""fc2"""	)
		if "text_encoder" in name:
				SCREAMING_SNAKE_CASE :				str		=			name.replace("""text_encoder"""			,			"""text_model"""	)
		if "ln_final" in name:
				SCREAMING_SNAKE_CASE :				int		=			name.replace("""ln_final"""			,			"""final_layer_norm"""	)
		# projection layers
		if "img_projector.linear_hidden." in name:
				SCREAMING_SNAKE_CASE :				Tuple		=			name.replace("""img_projector.linear_hidden."""			,			"""visual_projection."""	)
		if "img_projector.linear_out." in name:
				SCREAMING_SNAKE_CASE :				List[str]		=			name.replace("""img_projector.linear_out."""			,			"""visual_projection.3."""	)
		if "text_projector.linear_hidden" in name:
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			name.replace("""text_projector.linear_hidden"""			,			"""text_projection"""	)
		if "text_projector.linear_out" in name:
				SCREAMING_SNAKE_CASE :				Dict		=			name.replace("""text_projector.linear_out"""			,			"""text_projection.3"""	)

		return name



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			orig_state_dict.pop(lowerCamelCase_	)

				if "qkv" in key:
						# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			key.split("""."""	)
						SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				List[str]		=			int(key_split[2]	), int(key_split[4]	)
						SCREAMING_SNAKE_CASE :				str		=			config.vision_config.hidden_size
						if "weight" in key:
								SCREAMING_SNAKE_CASE :				Union[str, Any]		=			val[:dim, :]
								SCREAMING_SNAKE_CASE :				Union[str, Any]		=			val[dim : dim * 2, :]
								SCREAMING_SNAKE_CASE :				Dict		=			val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE :				Optional[Any]		=			val[:dim]
								SCREAMING_SNAKE_CASE :				List[Any]		=			val[dim : dim * 2]
								SCREAMING_SNAKE_CASE :				str		=			val[-dim:]
				elif "in_proj" in key:
						# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						SCREAMING_SNAKE_CASE :				Tuple		=			key.split("""."""	)
						SCREAMING_SNAKE_CASE :				str		=			int(key_split[3]	)
						SCREAMING_SNAKE_CASE :				Dict		=			config.text_config.hidden_size
						if "weight" in key:
								SCREAMING_SNAKE_CASE :				str		=			val[:dim, :]
								SCREAMING_SNAKE_CASE :				Union[str, Any]		=			val[
								    dim : dim * 2, :
								]
								SCREAMING_SNAKE_CASE :				int		=			val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE :				Dict		=			val[:dim]
								SCREAMING_SNAKE_CASE :				List[Any]		=			val[dim : dim * 2]
								SCREAMING_SNAKE_CASE :				List[Any]		=			val[-dim:]
				else:
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			rename_key(lowerCamelCase_	)
						# squeeze if necessary
						if (
						    "text_projection.0" in new_name
						    or "text_projection.3" in new_name
						    or "visual_projection.0" in new_name
						    or "visual_projection.3" in new_name
						):
								SCREAMING_SNAKE_CASE :				Any		=			val.squeeze_()
						else:
								SCREAMING_SNAKE_CASE :				Optional[Any]		=			val

		return orig_state_dict



def 		__A		(  ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Dict		=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE :				List[Any]		=			Image.open(requests.get(lowerCamelCase_			,			stream=lowerCamelCase_	).raw	)
		return im



@torch.no_grad()
def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_="groupvit-gcc-yfcc"			,			lowerCamelCase_=False	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Optional[Any]		=			GroupViTConfig()
		SCREAMING_SNAKE_CASE :				Optional[int]		=			GroupViTModel(lowerCamelCase_	).eval()

		SCREAMING_SNAKE_CASE :				str		=			torch.load(lowerCamelCase_			,			map_location="""cpu"""	)["""model"""]
		SCREAMING_SNAKE_CASE :				Tuple		=			convert_state_dict(lowerCamelCase_			,			lowerCamelCase_	)
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Optional[Any]		=			model.load_state_dict(lowerCamelCase_			,			strict=lowerCamelCase_	)
		assert missing_keys == ["text_model.embeddings.position_ids"]
		assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCamelCase_	) == 0)

		# verify result
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""	)
		SCREAMING_SNAKE_CASE :				List[str]		=			prepare_img()
		SCREAMING_SNAKE_CASE :				List[str]		=			processor(text=["""a photo of a cat""", """a photo of a dog"""]			,			images=lowerCamelCase_			,			padding=lowerCamelCase_			,			return_tensors="""pt"""	)

		with torch.no_grad():
				SCREAMING_SNAKE_CASE :				Optional[int]		=			model(**lowerCamelCase_	)

		if model_name == "groupvit-gcc-yfcc":
				SCREAMING_SNAKE_CASE :				Any		=			torch.tensor([[13.3_523, 6.3_629]]	)
		elif model_name == "groupvit-gcc-redcaps":
				SCREAMING_SNAKE_CASE :				int		=			torch.tensor([[16.1_873, 8.6_230]]	)
		else:
				raise ValueError(f'''Model name {model_name} not supported.'''	)
		assert torch.allclose(outputs.logits_per_image			,			lowerCamelCase_			,			atol=1E-3	)

		processor.save_pretrained(lowerCamelCase_	)
		model.save_pretrained(lowerCamelCase_	)
		print("""Successfully saved processor and model to"""			,			lowerCamelCase_	)

		if push_to_hub:
				print("""Pushing to the hub..."""	)
				processor.push_to_hub(lowerCamelCase_			,			organization="""nielsr"""	)
				model.push_to_hub(lowerCamelCase_			,			organization="""nielsr"""	)


if __name__ == "__main__":
							__UpperCAmelCase            =       argparse.ArgumentParser()
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
							)
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
							parser.add_argument(
							    """--model_name""",
							    default="""groupvit-gccy-fcc""",
							    type=str,
							    help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
							)
							parser.add_argument(
							    """--push_to_hub""",
							    action="""store_true""",
							    help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
							)
							__UpperCAmelCase            =       parser.parse_args()

							convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)










'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

					SCREAMING_SNAKE_CASE__				=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



