

'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




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

 return name




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 for key in orig_state_dict.copy().keys():
  UpperCAmelCase	:     Tuple						 =			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
   UpperCAmelCase	:     Dict						 =			key.split('.'     )
   UpperCAmelCase ,			UpperCAmelCase	:     Tuple						 =			int(key_split[2]     ), int(key_split[4]     )
   UpperCAmelCase	:     Tuple						 =			config.vision_config.hidden_size
   if "weight" in key:
    UpperCAmelCase	:     Union[str, Any]						 =			val[:dim, :]
    UpperCAmelCase	:     str						 =			val[dim : dim * 2, :]
    UpperCAmelCase	:     List[str]						 =			val[-dim:, :]
   else:
    UpperCAmelCase	:     Optional[int]						 =			val[:dim]
    UpperCAmelCase	:     Optional[int]						 =			val[dim : dim * 2]
    UpperCAmelCase	:     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
   UpperCAmelCase	:     Dict						 =			key.split('.'     )
   UpperCAmelCase	:     Dict						 =			int(key_split[3]     )
   UpperCAmelCase	:     Dict						 =			config.text_config.hidden_size
   if "weight" in key:
    UpperCAmelCase	:     Optional[int]						 =			val[:dim, :]
    UpperCAmelCase	:     List[Any]						 =			val[
        dim : dim * 2, :
    ]
    UpperCAmelCase	:     str						 =			val[-dim:, :]
   else:
    UpperCAmelCase	:     Union[str, Any]						 =			val[:dim]
    UpperCAmelCase	:     str						 =			val[dim : dim * 2]
    UpperCAmelCase	:     List[Any]						 =			val[-dim:]
  else:
   UpperCAmelCase	:     Dict						 =			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
   ):
    UpperCAmelCase	:     List[Any]						 =			val.squeeze_()
   else:
    UpperCAmelCase	:     List[str]						 =			val

 return orig_state_dict




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




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

 UpperCAmelCase	:     Any						 =			torch.load(UpperCAmelCase_  ,	map_location='cpu'     )['model']
 UpperCAmelCase	:     Dict						 =			convert_state_dict(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			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
 UpperCAmelCase	:     Tuple						 =			CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'     )
 UpperCAmelCase	:     Tuple						 =			prepare_img()
 UpperCAmelCase	:     List[Any]						 =			processor(text=['a photo of a cat', 'a photo of a dog']  ,	images=UpperCAmelCase_  ,	padding=UpperCAmelCase_  ,	return_tensors='pt'     )

 with torch.no_grad():
  UpperCAmelCase	:     Tuple						 =			model(**UpperCAmelCase_     )

 if model_name == "groupvit-gcc-yfcc":
  UpperCAmelCase	:     str						 =			torch.tensor([[13.3523, 6.3629]]     )
 elif model_name == "groupvit-gcc-redcaps":
  UpperCAmelCase	:     Optional[int]						 =			torch.tensor([[16.1873, 8.6230]]     )
 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__":
 lowercase__         =      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__         =      parser.parse_args()

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







'''simple docstring'''

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
      UpperCAmelCase	:     Any						 =			inspect.getfile(accelerate.test_utils	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_script.py']	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(
          mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_distributed_data_loop.py']	)
      UpperCAmelCase	:     List[Any]						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_ops.py']	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Optional[int]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     List[Any]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     int						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(f"""Command: {cmd}"""	)
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     Optional[int]						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)




     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[Any]:
      print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1					,      cuda_visible_devices='0,1'	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


if __name__ == "__main__":
 lowercase__         =      Accelerator()
 lowercase__         =      (accelerator.state.process_index + 2, 10)
 lowercase__         =      torch.randint(0, 10, shape).to(accelerator.device)

 lowercase__         =      ""

 lowercase__         =      accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 lowercase__         =      accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 lowercase__         =      accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)




