





"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

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

 return name

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    Any			):
 for key in orig_state_dict.copy().keys():
  lowerCAmelCase    				= orig_state_dict.pop(_lowerCAmelCase			)

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

 return orig_state_dict

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= """http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowerCAmelCase    				= Image.open(requests.get(_lowerCAmelCase  ,     stream=_lowerCAmelCase			).raw			)
 return im

@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    Dict  ,     _UpperCAmelCase					:    Union[str, Any]="groupvit-gcc-yfcc"  ,     _UpperCAmelCase					:    List[str]=False			):
 lowerCAmelCase    				= GroupViTConfig()
 lowerCAmelCase    				= GroupViTModel(_lowerCAmelCase			).eval()

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

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

 with torch.no_grad():
  lowerCAmelCase    				= model(**_lowerCAmelCase			)

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

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

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


if __name__ == "__main__":
       __UpperCamelCase     :						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`.''',
       )
       __UpperCamelCase     :						Union[str, Any]						       =  parser.parse_args()

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










'''simple docstring'''





import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	List[str]		):
          if isinstance(snake_case_					,	snake_case_		):
                # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
                # modified it, it will not be accepted here again, since `auto` values would have been overridden
                snake_case__    :       Optional[Any]       		= deepcopy(snake_case_		)
          elif os.path.exists(snake_case_		):
                with io.open(snake_case_					,	"""r"""					,	encoding="""utf-8"""		) as f:
                      snake_case__    :       Union[str, Any]       		= json.load(snake_case_		)
          else:
                try:
                      snake_case__    :       List[Any]       		= baseaa.urlsafe_baadecode(snake_case_		).decode("""utf-8"""		)
                      snake_case__    :       Optional[int]       		= json.loads(snake_case_		)
                except (UnicodeDecodeError, AttributeError, ValueError):
                      raise ValueError(
                          f"Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"		)

          snake_case__    :       str       		= config

          self.set_stage_and_offload()




    def lowerCamelCase			(       self   :	int		):
          # zero stage - this is done as early as possible, before model is created, to allow
          # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
          # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
          snake_case__    :       Union[str, Any]       		= self.get_value("""zero_optimization.stage"""					,	-1		)

          # offload
          snake_case__    :       Optional[Any]       		= False
          if self.is_zeroa() or self.is_zeroa():
                snake_case__    :       int       		= set(["""cpu""", """nvme"""]		)
                snake_case__    :       Any       		= set(
                    [
                        self.get_value("""zero_optimization.offload_optimizer.device"""		),
                        self.get_value("""zero_optimization.offload_param.device"""		),
                    ]		)
                if len(offload_devices & offload_devices_valid		) > 0:
                      snake_case__    :       List[Any]       		= True




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	Optional[Any]		):
          snake_case__    :       Optional[Any]       		= self.config

          # find the config node of interest if it exists
          snake_case__    :       List[Any]       		= ds_key_long.split("""."""		)
          snake_case__    :       Tuple       		= nodes.pop()
          for node in nodes:
                snake_case__    :       Optional[Any]       		= config.get(snake_case_		)
                if config is None:
                      return None, ds_key

          return config, ds_key




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	int					,	snake_case_   :	List[str]=None		):
          snake_case__		,							snake_case__    :       Dict       		= self.find_config_node(snake_case_		)
          if config is None:
                return default
          return config.get(snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Optional[int]					,	snake_case_   :	int=False		):
          snake_case__    :       Dict       		= self.config

          # find the config node of interest if it exists
          snake_case__    :       str       		= ds_key_long.split("""."""		)
          for node in nodes:
                snake_case__    :       Union[str, Any]       		= config
                snake_case__    :       List[Any]       		= config.get(snake_case_		)
                if config is None:
                      if must_exist:
                            raise ValueError(f"Can't find {ds_key_long} entry in the config: {self.config}"		)
                      else:
                            return

        # if found remove it
          if parent_config is not None:
                parent_config.pop(snake_case_		)




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	Optional[Any]		):
          snake_case__    :       Dict       		= self.get_value(snake_case_		)
          return False if value is None else bool(snake_case_		)




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	Union[str, Any]		):
          snake_case__    :       Optional[int]       		= self.get_value(snake_case_		)
          return False if value is None else not bool(snake_case_		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          return self._stage == 2




    def lowerCamelCase			(       self   :	Dict		):
          return self._stage == 3




    def lowerCamelCase			(       self   :	List[Any]		):
          return self._offload







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Any					,	snake_case_   :	List[Any]		):
          snake_case__    :       Tuple       		= engine




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Optional[Any]					,	**snake_case_   :	int		):
          # runs backpropagation and handles mixed precision
          self.engine.backward(snake_case_					,	**snake_case_		)

          # Deepspeed's `engine.step` performs the following operations:
          # - gradient accumulation check
          # - gradient clipping
          # - optimizer step
          # - zero grad
          # - checking overflow
          # - lr_scheduler step (only if engine.lr_scheduler is not None)
          self.engine.step()
          # and this plugin overrides the above calls with no-ops when Accelerate runs under
          # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
          # training loop that works transparently under many training regimes.







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	Any		):
          super().__init__(snake_case_					,	device_placement=snake_case_					,	scaler=snake_case_		)
          snake_case__    :       Optional[int]       		= hasattr(self.optimizer					,	"""overflow"""		)




    def lowerCamelCase			(       self   :	int					,	snake_case_   :	Optional[Any]=None		):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




    def lowerCamelCase			(       self   :	List[Any]		):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




    @property
    def lowerCamelCase			(       self   :	List[str]		):
          if self.__has_overflow__:
                return self.optimizer.overflow
          return False







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	Optional[Any]					,	snake_case_   :	Tuple		):
          super().__init__(snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Optional[int]		):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Optional[int]					,	snake_case_   :	Dict					,	snake_case_   :	int=0.001					,	snake_case_   :	Optional[Any]=0					,	**snake_case_   :	List[Any]		):
          snake_case__    :       int       		= params
          snake_case__    :       Optional[Any]       		= lr
          snake_case__    :       Any       		= weight_decay
          snake_case__    :       Optional[Any]       		= kwargs







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Dict					,	snake_case_   :	str					,	snake_case_   :	Optional[int]=None					,	snake_case_   :	Any=0					,	**snake_case_   :	List[str]		):
          snake_case__    :       List[Any]       		= optimizer
          snake_case__    :       List[Any]       		= total_num_steps
          snake_case__    :       List[Any]       		= warmup_num_steps
          snake_case__    :       Optional[Any]       		= kwargs
