





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """pix2struct_text_model"""
      UpperCAmelCase__							=   ["""past_key_values"""]
      UpperCAmelCase__							=   {
          """hidden_size""": """hidden_size""",
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }


      def __init__(		self   ,				a__=5_0244   ,				a__=768   ,				a__=64   ,				a__=2048   ,				a__=12   ,				a__=12   ,				a__=32   ,				a__=128   ,				a__=0.1   ,				a__=1e-6   ,				a__=1.0   ,				a__="gelu_new"   ,				a__=0   ,				a__=False   ,				a__=0   ,				a__=1   ,				a__=False   ,				a__=True   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		vocab_size
             _lowerCamelCase				:  List[str]						=		hidden_size
             _lowerCamelCase				:  str						=		d_kv
             _lowerCamelCase				:  Optional[int]						=		d_ff
             _lowerCamelCase				:  int						=		num_layers
             _lowerCamelCase				:  Dict						=		num_heads
             _lowerCamelCase				:  Any						=		relative_attention_num_buckets
             _lowerCamelCase				:  Union[str, Any]						=		relative_attention_max_distance
             _lowerCamelCase				:  Optional[Any]						=		dropout_rate
             _lowerCamelCase				:  int						=		layer_norm_epsilon
             _lowerCamelCase				:  Union[str, Any]						=		initializer_factor
             _lowerCamelCase				:  Union[str, Any]						=		use_cache

             _lowerCamelCase				:  List[str]						=		eos_token_id
             _lowerCamelCase				:  str						=		decoder_start_token_id

             # for backwards compatibility
             _lowerCamelCase				:  Dict						=		dense_act_fn

             super().__init__(
                 pad_token_id=a__   ,				eos_token_id=a__   ,				decoder_start_token_id=a__   ,				tie_word_embeddings=a__   ,				is_decoder=a__   ,				**a__   ,				)



      @classmethod
      def 					__snake_case      (		cls   ,				a__   ,				**a__):



             """simple docstring"""





             cls._set_token_in_kwargs(a__)

             _lowerCamelCase,	_lowerCamelCase				:  Union[str, Any]						=		cls.get_config_dict(a__   ,				**a__)

             # get the text config dict if we are loading from Pix2StructConfig
             if config_dict.get('''model_type''') == "pix2struct":
                    _lowerCamelCase				:  Optional[int]						=		config_dict['''text_config''']

             if "model_type" in config_dict and hasattr(cls   ,				'''model_type''') and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

             return cls.from_dict(a__   ,				**a__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """pix2struct_vision_model"""


      def __init__(		self   ,				a__=768   ,				a__=768   ,				a__=2048   ,				a__=64   ,				a__=12   ,				a__=12   ,				a__="gelu_new"   ,				a__=1e-6   ,				a__=0.0   ,				a__=0.0   ,				a__=1e-10   ,				a__=1.0   ,				a__=4096   ,				a__=32   ,				a__=128   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(**a__)

             _lowerCamelCase				:  List[str]						=		hidden_size
             _lowerCamelCase				:  Union[str, Any]						=		patch_embed_hidden_size
             _lowerCamelCase				:  List[Any]						=		d_ff
             _lowerCamelCase				:  List[str]						=		dropout_rate
             _lowerCamelCase				:  Any						=		num_hidden_layers
             _lowerCamelCase				:  Optional[int]						=		num_attention_heads
             _lowerCamelCase				:  Any						=		initializer_range
             _lowerCamelCase				:  Tuple						=		initializer_factor
             _lowerCamelCase				:  Optional[Any]						=		attention_dropout
             _lowerCamelCase				:  Dict						=		layer_norm_eps
             _lowerCamelCase				:  Union[str, Any]						=		dense_act_fn
             _lowerCamelCase				:  Dict						=		seq_len
             _lowerCamelCase				:  Optional[Any]						=		relative_attention_num_buckets
             _lowerCamelCase				:  str						=		relative_attention_max_distance
             _lowerCamelCase				:  Any						=		d_kv



      @classmethod
      def 					__snake_case      (		cls   ,				a__   ,				**a__):



             """simple docstring"""





             cls._set_token_in_kwargs(a__)

             _lowerCamelCase,	_lowerCamelCase				:  int						=		cls.get_config_dict(a__   ,				**a__)

             # get the vision config dict if we are loading from Pix2StructConfig
             if config_dict.get('''model_type''') == "pix2struct":
                    _lowerCamelCase				:  List[str]						=		config_dict['''vision_config''']

             if "model_type" in config_dict and hasattr(cls   ,				'''model_type''') and config_dict["model_type"] != cls.model_type:
                    logger.warning(
                        F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
                        F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

             return cls.from_dict(a__   ,				**a__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """pix2struct"""
      UpperCAmelCase__							=   True


      def __init__(		self   ,				a__=None   ,				a__=None   ,				a__=1.0   ,				a__=0.02   ,				a__=False   ,				a__=False   ,				a__=True   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(tie_word_embeddings=a__   ,				is_encoder_decoder=a__   ,				**a__)

             if text_config is None:
                    _lowerCamelCase				:  Optional[int]						=		{}
                    logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''')

             if vision_config is None:
                    _lowerCamelCase				:  Optional[int]						=		{}
                    logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''')

             _lowerCamelCase				:  List[Any]						=		PixaStructTextConfig(**a__)
             _lowerCamelCase				:  str						=		PixaStructVisionConfig(**a__)

             _lowerCamelCase				:  Union[str, Any]						=		self.text_config.decoder_start_token_id
             _lowerCamelCase				:  Any						=		self.text_config.pad_token_id
             _lowerCamelCase				:  Any						=		self.text_config.eos_token_id

             _lowerCamelCase				:  Optional[int]						=		initializer_factor
             _lowerCamelCase				:  Optional[Any]						=		initializer_range

             _lowerCamelCase				:  str						=		self.initializer_range
             _lowerCamelCase				:  Optional[Any]						=		self.initializer_range

             _lowerCamelCase				:  List[str]						=		is_vqa


      @classmethod
      def 					__snake_case      (		cls   ,				a__   ,				a__   ,				**a__):



             """simple docstring"""





             return cls(text_config=text_config.to_dict()   ,				vision_config=vision_config.to_dict()   ,				**a__)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		copy.deepcopy(self.__dict__)
             _lowerCamelCase				:  Any						=		self.text_config.to_dict()
             _lowerCamelCase				:  Any						=		self.vision_config.to_dict()
             _lowerCamelCase				:  Optional[Any]						=		self.__class__.model_type
             return output








import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




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

       return name




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       for key in orig_state_dict.copy().keys():
              _lowerCamelCase				:  Optional[int]						=		orig_state_dict.pop(lowercase_   )

              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				:  Tuple						=		key.split('''.'''   )
                     _lowerCamelCase,	_lowerCamelCase				:  Dict						=		int(key_split[2]   ), int(key_split[4]   )
                     _lowerCamelCase				:  Optional[int]						=		config.vision_config.hidden_size
                     if "weight" in key:
                            _lowerCamelCase				:  List[Any]						=		val[:dim, :]
                            _lowerCamelCase				:  List[Any]						=		val[dim : dim * 2, :]
                            _lowerCamelCase				:  Optional[Any]						=		val[-dim:, :]
                     else:
                            _lowerCamelCase				:  Union[str, Any]						=		val[:dim]
                            _lowerCamelCase				:  Optional[Any]						=		val[dim : dim * 2]
                            _lowerCamelCase				:  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
                     _lowerCamelCase				:  Optional[int]						=		key.split('''.'''   )
                     _lowerCamelCase				:  Optional[Any]						=		int(key_split[3]   )
                     _lowerCamelCase				:  Union[str, Any]						=		config.text_config.hidden_size
                     if "weight" in key:
                            _lowerCamelCase				:  Any						=		val[:dim, :]
                            _lowerCamelCase				:  Optional[Any]						=		val[
                                dim : dim * 2, :
                            ]
                            _lowerCamelCase				:  Dict						=		val[-dim:, :]
                     else:
                            _lowerCamelCase				:  List[Any]						=		val[:dim]
                            _lowerCamelCase				:  Tuple						=		val[dim : dim * 2]
                            _lowerCamelCase				:  str						=		val[-dim:]
              else:
                     _lowerCamelCase				:  Optional[Any]						=		rename_key(lowercase_   )
                     # 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				:  Tuple						=		val

       return orig_state_dict




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  Union[str, Any]						=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase				:  Any						=		Image.open(requests.get(lowercase_			,     stream=lowercase_   ).raw   )
       return im




@torch.no_grad()
def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_="groupvit-gcc-yfcc"			,     lowercase_=False   ):
       _lowerCamelCase				:  Optional[Any]						=		GroupViTConfig()
       _lowerCamelCase				:  Any						=		GroupViTModel(lowercase_   ).eval()

       _lowerCamelCase				:  Optional[Any]						=		torch.load(lowercase_			,     map_location='''cpu'''   )['''model''']
       _lowerCamelCase				:  List[str]						=		convert_state_dict(lowercase_			,     lowercase_   )
       _lowerCamelCase,	_lowerCamelCase				:  List[str]						=		model.load_state_dict(lowercase_			,     strict=lowercase_   )
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase_   ) == 0)

       # verify result
       _lowerCamelCase				:  Optional[Any]						=		CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''   )
       _lowerCamelCase				:  Dict						=		prepare_img()
       _lowerCamelCase				:  str						=		processor(text=['''a photo of a cat''', '''a photo of a dog''']			,     images=lowercase_			,     padding=lowercase_			,     return_tensors='''pt'''   )

       with torch.no_grad():
              _lowerCamelCase				:  Optional[int]						=		model(**lowercase_   )

       if model_name == "groupvit-gcc-yfcc":
              _lowerCamelCase				:  Dict						=		torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]   )
       elif model_name == "groupvit-gcc-redcaps":
              _lowerCamelCase				:  Tuple						=		torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]   )
       else:
              raise ValueError(F"""Model name {model_name} not supported."""   )
       assert torch.allclose(outputs.logits_per_image			,     lowercase_			,     atol=1e-3   )

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

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

