
'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





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

						return name





def 			_A		(				lowercase__		,		lowercase__						):
						for key in orig_state_dict.copy().keys():
												lowercase__							=							orig_state_dict.pop(UpperCAmelCase_						)

												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
																		lowercase__							=							key.split("""."""						)
																		lowercase__							=							int(key_split[2]						), int(key_split[4]						)
																		lowercase__							=							config.vision_config.hidden_size
																		if "weight" in key:
																								lowercase__							=							val[:dim, :]
																								lowercase__							=							val[dim : dim * 2, :]
																								lowercase__							=							val[-dim:, :]
																		else:
																								lowercase__							=							val[:dim]
																								lowercase__							=							val[dim : dim * 2]
																								lowercase__							=							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
																		lowercase__							=							key.split("""."""						)
																		lowercase__							=							int(key_split[3]						)
																		lowercase__							=							config.text_config.hidden_size
																		if "weight" in key:
																								lowercase__							=							val[:dim, :]
																								lowercase__							=							val[
																								    dim : dim * 2, :
																								]
																								lowercase__							=							val[-dim:, :]
																		else:
																								lowercase__							=							val[:dim]
																								lowercase__							=							val[dim : dim * 2]
																								lowercase__							=							val[-dim:]
												else:
																		lowercase__							=							rename_key(UpperCAmelCase_						)
																		# 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
																		):
																								lowercase__							=							val.squeeze_()
																		else:
																								lowercase__							=							val

						return orig_state_dict





def 			_A		(				):
						lowercase__							=							'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowercase__							=							Image.open(requests.get(UpperCAmelCase_		,		stream=UpperCAmelCase_						).raw						)
						return im





@torch.no_grad()
def 			_A		(				lowercase__		,		lowercase__		,		lowercase__="groupvit-gcc-yfcc"		,		lowercase__=False						):
						lowercase__							=							GroupViTConfig()
						lowercase__							=							GroupViTModel(UpperCAmelCase_						).eval()

						lowercase__							=							torch.load(UpperCAmelCase_		,		map_location="""cpu"""						)['model']
						lowercase__							=							convert_state_dict(UpperCAmelCase_		,		UpperCAmelCase_						)
						lowercase__							=							model.load_state_dict(UpperCAmelCase_		,		strict=UpperCAmelCase_						)
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCAmelCase_						) == 0)

						# verify result
						lowercase__							=							CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""						)
						lowercase__							=							prepare_img()
						lowercase__							=							processor(text=["""a photo of a cat""", """a photo of a dog"""]		,		images=UpperCAmelCase_		,		padding=UpperCAmelCase_		,		return_tensors="""pt"""						)

						with torch.no_grad():
												lowercase__							=							model(**UpperCAmelCase_						)

						if model_name == "groupvit-gcc-yfcc":
												lowercase__							=							torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]						)
						elif model_name == "groupvit-gcc-redcaps":
												lowercase__							=							torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]						)
						else:
												raise ValueError(f'''Model name {model_name} not supported.'''						)
						assert torch.allclose(outputs.logits_per_image		,		UpperCAmelCase_		,		atol=1e-3						)

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

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


if __name__ == "__main__":
				__A        =     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`.",
				)
				__A        =     parser.parse_args()

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











'''simple docstring'''
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_					:      List[Any]    			=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'VanForImageClassification',
					    'VanModel',
					    'VanPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      List[Any]    			=    _LazyModule(__name__, globals()['__file__'], _import_structure)


