
"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def    lowerCAmelCase_   (		snake_case_		:			Union[str, Any]			)   ->Any:



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

			return name






def    lowerCAmelCase_   (		snake_case_		:			Optional[int]							,    snake_case_		:			Any			)   ->Optional[Any]:



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

			return orig_state_dict






def    lowerCAmelCase_   (		)   ->List[Any]:
			lowerCamelCase__	:     Tuple              ='http://images.cocodataset.org/val2017/000000039769.jpg'
			lowerCamelCase__	:     Optional[Any]              =Image.open(requests.get(snake_case_							,    stream=snake_case_			).raw			)
			return im






@torch.no_grad()
def    lowerCAmelCase_   (		snake_case_		:			str							,    snake_case_		:			Optional[Any]							,    snake_case_		:			str="groupvit-gcc-yfcc"							,    snake_case_		:			Optional[int]=False			)   ->Dict:
			lowerCamelCase__	:     Optional[int]              =GroupViTConfig()
			lowerCamelCase__	:     Optional[Any]              =GroupViTModel(snake_case_			).eval()

			lowerCamelCase__	:     List[Any]              =torch.load(snake_case_							,    map_location='cpu'			)['model']
			lowerCamelCase__	:     Tuple              =convert_state_dict(snake_case_							,    snake_case_			)
			lowerCamelCase__       ,				lowerCamelCase__	:     List[Any]              =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
			lowerCamelCase__	:     List[Any]              =CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'			)
			lowerCamelCase__	:     Any              =prepare_img()
			lowerCamelCase__	:     Tuple              =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():
						lowerCamelCase__	:     List[str]              =model(**snake_case_			)

			if model_name == "groupvit-gcc-yfcc":
						lowerCamelCase__	:     Optional[int]              =torch.tensor([[13.3523, 6.3629]]			)
			elif model_name == "groupvit-gcc-redcaps":
						lowerCamelCase__	:     List[Any]              =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__":
							lowerCAmelCase								=						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`.""",
							)
							lowerCAmelCase								=						parser.parse_args()

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





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase				:		Union[str, Any]     =   "src/diffusers"

# Matches is_xxx_available()
lowerCamelCase				:		Dict     =   re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
lowerCamelCase				:		Union[str, Any]     =   re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


lowerCamelCase				:		Any     =   "\n{0} = None\n"

lowerCamelCase				:		List[str]     =   "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


lowerCamelCase				:		str     =   "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    _re_backend.findall(lowercase     )
      if len(lowercase     ) == 0:
            return None

      return "_and_".join(lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      with open(os.path.join(lowercase	,							'__init__.py'     )	,							'r'	,							encoding='utf-8'	,							newline='\n'     ) as f:
            lowerCamelCase_		     =    f.readlines()

      # Get to the point we do the actual imports for type checking
      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    {}
      # Go through the end of the file
      while line_index < len(lowercase     ):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            lowerCamelCase_		     =    find_backend(lines[line_index]     )
            if backend is not None:
                  while not lines[line_index].startswith('else:'     ):
                        line_index += 1
                  line_index += 1
                  lowerCamelCase_		     =    []
                  # Until we unindent, add backend objects to the list
                  while line_index < len(lowercase     ) and len(lines[line_index]     ) > 1:
                        lowerCamelCase_		     =    lines[line_index]
                        lowerCamelCase_		     =    _re_single_line_import.search(lowercase     )
                        if single_line_import_search is not None:
                              objects.extend(single_line_import_search.groups()[0].split(', '     )     )
                        elif line.startswith(' ' * 8     ):
                              objects.append(line[8:-2]     )
                        line_index += 1

                  if len(lowercase     ) > 0:
                        lowerCamelCase_		     =    objects
            else:
                  line_index += 1

      return backend_specific_objects

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       str     ):


      '''simple docstring'''



      if name.isupper():
            return DUMMY_CONSTANT.format(lowercase     )
      elif name.islower():
            return DUMMY_FUNCTION.format(lowercase	,							lowercase     )
      else:
            return DUMMY_CLASS.format(lowercase	,							lowercase     )

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]=None     ):


      '''simple docstring'''



      if backend_specific_objects is None:
            lowerCamelCase_		     =    read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      lowerCamelCase_		     =    {}

      for backend, objects in backend_specific_objects.items():
            lowerCamelCase_		     =    '[' + ', '.join(f"""\"{b}\"""" for b in backend.split('_and_'     )     ) + ']'
            lowerCamelCase_		     =    '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
            dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
            dummy_file += "\n".join([create_dummy_object(lowercase	,							lowercase     ) for o in objects]     )
            lowerCamelCase_		     =    dummy_file

      return dummy_files

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]=False     ):


      '''simple docstring'''



      lowerCamelCase_		     =    create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      lowerCamelCase_		     =    {'torch': 'pt'}

      # Locate actual dummy modules and read their content.
      lowerCamelCase_		     =    os.path.join(lowercase	,							'utils'     )
      lowerCamelCase_		     =    {
          backend: os.path.join(lowercase	,							f"""dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py"""     )
          for backend in dummy_files.keys()
      }

      lowerCamelCase_		     =    {}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(lowercase     ):
                  with open(lowercase	,							'r'	,							encoding='utf-8'	,							newline='\n'     ) as f:
                        lowerCamelCase_		     =    f.read()
            else:
                  lowerCamelCase_		     =    ''

      for backend in dummy_files.keys():
            if dummy_files[backend] != actual_dummies[backend]:
                  if overwrite:
                        print(
                            f"""Updating diffusers.utils.dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py as the main """
                            '__init__ has new objects.'     )
                        with open(dummy_file_paths[backend]	,							'w'	,							encoding='utf-8'	,							newline='\n'     ) as f:
                              f.write(dummy_files[backend]     )
                  else:
                        raise ValueError(
                            'The main __init__ has objects that are not present in '
                            f"""diffusers.utils.dummy_{short_names.get(lowercase	,							lowercase     )}_objects.py. Run `make fix-copies` """
                            'to fix this.'     )


if __name__ == "__main__":
 lowerCamelCase				:		List[Any]     =   argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 lowerCamelCase				:		Tuple     =   parser.parse_args()

 check_dummies(args.fix_and_overwrite)



