
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def 	A_ (							snake_case							:   List[Any] )      ->    str:



						'''simple docstring'''

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

						return name




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   Tuple )      ->    Any:



						'''simple docstring'''

						for key in orig_state_dict.copy().keys():
												__UpperCamelCase       =			orig_state_dict.pop(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       =			key.split('''.''' )
																		__UpperCamelCase  ,							__UpperCamelCase       =			int(key_split[2] ), int(key_split[4] )
																		__UpperCamelCase       =			config.vision_config.hidden_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:]
												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       =			key.split('''.''' )
																		__UpperCamelCase       =			int(key_split[3] )
																		__UpperCamelCase       =			config.text_config.hidden_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       =			rename_key(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       =			val.squeeze_()
																		else:
																								__UpperCamelCase       =			val

						return orig_state_dict




def 	A_ (							)      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__UpperCamelCase       =			Image.open(requests.get(snake_case ,      stream=snake_case ).raw )
						return im




@torch.no_grad()
def 	A_ (							snake_case							:   Union[str, Any] ,      snake_case							:   str ,      snake_case							:   str="groupvit-gcc-yfcc" ,      snake_case							:   str=False )      ->    Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       =			GroupViTConfig()
						__UpperCamelCase       =			GroupViTModel(snake_case ).eval()

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

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

						with torch.no_grad():
												__UpperCamelCase       =			model(**snake_case )

						if model_name == "groupvit-gcc-yfcc":
												__UpperCamelCase       =			torch.tensor([[13.3523, 6.3629]] )
						elif model_name == "groupvit-gcc-redcaps":
												__UpperCamelCase       =			torch.tensor([[16.1873, 8.6230]] )
						else:
												raise ValueError(f"Model name {model_name} not supported." )
						assert torch.allclose(outputs.logits_per_image ,      snake_case ,      atol=1e-3 )

						processor.save_pretrained(snake_case )
						model.save_pretrained(snake_case )
						print('''Successfully saved processor and model to''' ,      snake_case )

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


if __name__ == "__main__":
				lowercase__							:							int									=			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`.",
				)
				lowercase__							:							List[str]									=			parser.parse_args()

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






# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


lowercase__							:							List[str]									=			"3"

print("Python version:", sys.version)

print("OS platform:", platform.platform())
print("OS architecture:", platform.machine())

try:
				import torch

				print("Torch version:", torch.__version__)
				print("Cuda available:", torch.cuda.is_available())
				print("Cuda version:", torch.version.cuda)
				print("CuDNN version:", torch.backends.cudnn.version())
				print("Number of GPUs available:", torch.cuda.device_count())
except ImportError:
				print("Torch version:", None)

try:
				import transformers

				print("transformers version:", transformers.__version__)
except ImportError:
				print("transformers version:", None)




