



'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





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

      return name





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

      return orig_state_dict





def 		__snake_case(							)						->					Dict:
      snake_case__    :       List[str]       		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__    :       Dict       		= Image.open(requests.get(_lowerCAmelCase	,  stream=_lowerCAmelCase						).raw						)
      return im





@torch.no_grad()
def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase="groupvit-gcc-yfcc"	,  _lowerCAmelCase=False						)						->					Optional[int]:
      snake_case__    :       Union[str, Any]       		= GroupViTConfig()
      snake_case__    :       int       		= GroupViTModel(_lowerCAmelCase						).eval()

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

      with torch.no_grad():
            snake_case__    :       Union[str, Any]       		= model(**_lowerCAmelCase						)

      if model_name == "groupvit-gcc-yfcc":
            snake_case__    :       Dict       		= torch.tensor([[13.3523, 6.3629]]						)
      elif model_name == "groupvit-gcc-redcaps":
            snake_case__    :       Tuple       		= 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__":
       __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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







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







    lowercase   =						"layoutlmv3"




    def __init__(       self   :	Any					,	snake_case_   :	Dict=50_265					,	snake_case_   :	Union[str, Any]=768					,	snake_case_   :	Union[str, Any]=12					,	snake_case_   :	Optional[int]=12					,	snake_case_   :	Union[str, Any]=3_072					,	snake_case_   :	List[str]="gelu"					,	snake_case_   :	List[Any]=0.1					,	snake_case_   :	Union[str, Any]=0.1					,	snake_case_   :	Optional[int]=512					,	snake_case_   :	List[str]=2					,	snake_case_   :	List[str]=0.02					,	snake_case_   :	List[Any]=1E-5					,	snake_case_   :	Any=1					,	snake_case_   :	Any=0					,	snake_case_   :	int=2					,	snake_case_   :	Optional[Any]=1_024					,	snake_case_   :	int=128					,	snake_case_   :	Union[str, Any]=128					,	snake_case_   :	int=True					,	snake_case_   :	Tuple=32					,	snake_case_   :	str=128					,	snake_case_   :	Optional[int]=64					,	snake_case_   :	Optional[Any]=256					,	snake_case_   :	List[str]=True					,	snake_case_   :	str=True					,	snake_case_   :	Dict=True					,	snake_case_   :	int=224					,	snake_case_   :	Any=3					,	snake_case_   :	int=16					,	snake_case_   :	Tuple=None					,	**snake_case_   :	Optional[Any]					,	):
          super().__init__(
              vocab_size=snake_case_					,	hidden_size=snake_case_					,	num_hidden_layers=snake_case_					,	num_attention_heads=snake_case_					,	intermediate_size=snake_case_					,	hidden_act=snake_case_					,	hidden_dropout_prob=snake_case_					,	attention_probs_dropout_prob=snake_case_					,	max_position_embeddings=snake_case_					,	type_vocab_size=snake_case_					,	initializer_range=snake_case_					,	layer_norm_eps=snake_case_					,	pad_token_id=snake_case_					,	bos_token_id=snake_case_					,	eos_token_id=snake_case_					,	**snake_case_					,	)
          snake_case__    :       List[Any]       		= max_ad_position_embeddings
          snake_case__    :       Optional[int]       		= coordinate_size
          snake_case__    :       int       		= shape_size
          snake_case__    :       Optional[int]       		= has_relative_attention_bias
          snake_case__    :       Dict       		= rel_pos_bins
          snake_case__    :       List[Any]       		= max_rel_pos
          snake_case__    :       str       		= has_spatial_attention_bias
          snake_case__    :       Optional[Any]       		= rel_ad_pos_bins
          snake_case__    :       Optional[int]       		= max_rel_ad_pos
          snake_case__    :       Union[str, Any]       		= text_embed
          snake_case__    :       List[Any]       		= visual_embed
          snake_case__    :       Union[str, Any]       		= input_size
          snake_case__    :       int       		= num_channels
          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Dict       		= classifier_dropout







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







    lowercase   =						version.parse("1.12"      )




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          # The order of inputs is different for question answering and sequence classification
          if self.task in ["question-answering", "sequence-classification"]:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                    ]		)
          else:
                return OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                        ("""bbox""", {0: """batch""", 1: """sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
                        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
                    ]		)




    @property
    def lowerCamelCase			(       self   :	str		):
          return 1E-5




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          return 12




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	"ProcessorMixin"					,	snake_case_   :	int = -1					,	snake_case_   :	int = -1					,	snake_case_   :	bool = False					,	snake_case_   :	Optional["TensorType"] = None					,	snake_case_   :	int = 3					,	snake_case_   :	int = 40					,	snake_case_   :	int = 40					,	):
          setattr(processor.image_processor					,	"""apply_ocr"""					,	snake_case_		)

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          snake_case__    :       Optional[Any]       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_batch					,	num_token_to_add=0		)
          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          snake_case__    :       List[Any]       		= processor.tokenizer.num_special_tokens_to_add(snake_case_		)
          snake_case__    :       Tuple       		= compute_effective_axis_dimension(
              snake_case_					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=snake_case_		)
          # Generate dummy inputs according to compute batch and sequence
          snake_case__    :       int       		= [[""" """.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          snake_case__    :       int       		= [[[48, 84, 73, 128]]] * batch_size

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
          snake_case__    :       Optional[int]       		= self._generate_dummy_images(snake_case_					,	snake_case_					,	snake_case_					,	snake_case_		)

          snake_case__    :       Optional[int]       		= dict(
              processor(
                  snake_case_					,	text=snake_case_					,	boxes=snake_case_					,	return_tensors=snake_case_					,	)		)

          return inputs
