





"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

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

						return name

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						for key in orig_state_dict.copy().keys():
												UpperCamelCase				:		Tuple				   =			orig_state_dict.pop(SCREAMING_SNAKE_CASE     )

												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
																		UpperCamelCase				:		List[Any]				   =			key.split("""."""     )
																		UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			int(key_split[2]     ), int(key_split[4]     )
																		UpperCamelCase				:		Tuple				   =			config.vision_config.hidden_size
																		if "weight" in key:
																								UpperCamelCase				:		str				   =			val[:dim, :]
																								UpperCamelCase				:		int				   =			val[dim : dim * 2, :]
																								UpperCamelCase				:		Optional[Any]				   =			val[-dim:, :]
																		else:
																								UpperCamelCase				:		Any				   =			val[:dim]
																								UpperCamelCase				:		Optional[Any]				   =			val[dim : dim * 2]
																								UpperCamelCase				:		Union[str, Any]				   =			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
																		UpperCamelCase				:		Optional[int]				   =			key.split("""."""     )
																		UpperCamelCase				:		List[Any]				   =			int(key_split[3]     )
																		UpperCamelCase				:		Union[str, Any]				   =			config.text_config.hidden_size
																		if "weight" in key:
																								UpperCamelCase				:		Dict				   =			val[:dim, :]
																								UpperCamelCase				:		Dict				   =			val[
																								    dim : dim * 2, :
																								]
																								UpperCamelCase				:		int				   =			val[-dim:, :]
																		else:
																								UpperCamelCase				:		str				   =			val[:dim]
																								UpperCamelCase				:		Union[str, Any]				   =			val[dim : dim * 2]
																								UpperCamelCase				:		List[Any]				   =			val[-dim:]
												else:
																		UpperCamelCase				:		Optional[Any]				   =			rename_key(SCREAMING_SNAKE_CASE     )
																		# 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
																		):
																								UpperCamelCase				:		List[Any]				   =			val.squeeze_()
																		else:
																								UpperCamelCase				:		List[str]				   =			val

						return orig_state_dict

def      UpperCamelCase							():
						UpperCamelCase				:		str				   =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase				:		Optional[int]				   =			Image.open(requests.get(SCREAMING_SNAKE_CASE							,     stream=SCREAMING_SNAKE_CASE     ).raw     )
						return im

@torch.no_grad()
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE="groupvit-gcc-yfcc"							,     SCREAMING_SNAKE_CASE=False     ):
						UpperCamelCase				:		str				   =			GroupViTConfig()
						UpperCamelCase				:		Union[str, Any]				   =			GroupViTModel(SCREAMING_SNAKE_CASE     ).eval()

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

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

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

						if model_name == "groupvit-gcc-yfcc":
												UpperCamelCase				:		Optional[int]				   =			torch.tensor([[13.35_23, 6.36_29]]     )
						elif model_name == "groupvit-gcc-redcaps":
												UpperCamelCase				:		List[str]				   =			torch.tensor([[16.18_73, 8.62_30]]     )
						else:
												raise ValueError(f"""Model name {model_name} not supported."""     )
						assert torch.allclose(outputs.logits_per_image							,     SCREAMING_SNAKE_CASE							,     atol=1E-3     )

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

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


if __name__ == "__main__":
		__magic_name__       :					Any						=			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`.""",
		)
		__magic_name__       :					str						=			parser.parse_args()

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










"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__magic_name__       :					Dict						=			logging.getLogger(__name__)






@dataclass
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=0.0					,     metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to SortishSamler or not."""}	)
			__lowerCAmelCase     :				bool										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """whether to use adafactor"""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Attention dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(
			    default="""linear"""					,     metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()	)}'''}					,     )



