


import copy
import os
from typing import Union

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


__UpperCamelCase				:  Any      					=							logging.get_logger(__name__)

__UpperCamelCase				:  List[Any]      					=							{
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     List[str]			= 'blip_text_model'





    def __init__(	self				:   Optional[int]       ,   __snake_case				:   Any=30_524       ,   __snake_case				:   str=768       ,   __snake_case				:   Dict=768       ,   __snake_case				:   Dict=3_072       ,   __snake_case				:   List[Any]=768       ,   __snake_case				:   Optional[Any]=12       ,   __snake_case				:   int=8       ,   __snake_case				:   Tuple=512       ,   __snake_case				:   Optional[int]="gelu"       ,   __snake_case				:   List[str]=1E-12       ,   __snake_case				:   Union[str, Any]=0.0       ,   __snake_case				:   Dict=0.0       ,   __snake_case				:   List[str]=0.02       ,   __snake_case				:   Optional[Any]=30_522       ,   __snake_case				:   List[str]=2       ,   __snake_case				:   str=0       ,   __snake_case				:   Union[str, Any]=102       ,   __snake_case				:   Any=True       ,   __snake_case				:   Tuple=True       ,   **__snake_case				:   List[str]       ,   ):





       '''simple docstring'''
       super().__init__(
           pad_token_id=__snake_case       ,   bos_token_id=__snake_case       ,   eos_token_id=__snake_case       ,   sep_token_id=__snake_case       ,   **__snake_case       ,   )

       UpperCAmelCase_							:      Tuple						=   vocab_size
       UpperCAmelCase_							:      Any						=   hidden_size
       UpperCAmelCase_							:      Optional[Any]						=   encoder_hidden_size
       UpperCAmelCase_							:      Optional[Any]						=   intermediate_size
       UpperCAmelCase_							:      int						=   projection_dim
       UpperCAmelCase_							:      Union[str, Any]						=   hidden_dropout_prob
       UpperCAmelCase_							:      List[Any]						=   num_hidden_layers
       UpperCAmelCase_							:      Dict						=   num_attention_heads
       UpperCAmelCase_							:      Optional[int]						=   max_position_embeddings
       UpperCAmelCase_							:      Dict						=   layer_norm_eps
       UpperCAmelCase_							:      Optional[int]						=   hidden_act
       UpperCAmelCase_							:      List[Any]						=   initializer_range
       UpperCAmelCase_							:      Tuple						=   attention_probs_dropout_prob
       UpperCAmelCase_							:      Any						=   is_decoder
       UpperCAmelCase_							:      int						=   use_cache





    @classmethod
    def 					_lowerCamelCase (	cls				:   Any       ,   __snake_case				:   Union[str, os.PathLike]       ,   **__snake_case				:   str	):





       '''simple docstring'''
       cls._set_token_in_kwargs(__snake_case	)

       UpperCAmelCase_							:      List[Any]						=   cls.get_config_dict(__snake_case       ,   **__snake_case	)

       # get the text config dict if we are loading from BlipConfig
       if config_dict.get('''model_type'''	) == "blip":
          UpperCAmelCase_							:      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(__snake_case       ,   **__snake_case	)





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     List[Any]			= 'blip_vision_model'





    def __init__(	self				:   Union[str, Any]       ,   __snake_case				:   int=768       ,   __snake_case				:   str=3_072       ,   __snake_case				:   Dict=512       ,   __snake_case				:   Optional[Any]=12       ,   __snake_case				:   Any=12       ,   __snake_case				:   Union[str, Any]=384       ,   __snake_case				:   Tuple=16       ,   __snake_case				:   Union[str, Any]="gelu"       ,   __snake_case				:   Tuple=1E-5       ,   __snake_case				:   List[Any]=0.0       ,   __snake_case				:   Dict=1E-10       ,   **__snake_case				:   Any       ,   ):





       '''simple docstring'''
       super().__init__(**__snake_case	)

       UpperCAmelCase_							:      Any						=   hidden_size
       UpperCAmelCase_							:      int						=   intermediate_size
       UpperCAmelCase_							:      List[Any]						=   projection_dim
       UpperCAmelCase_							:      str						=   num_hidden_layers
       UpperCAmelCase_							:      List[Any]						=   num_attention_heads
       UpperCAmelCase_							:      str						=   patch_size
       UpperCAmelCase_							:      str						=   image_size
       UpperCAmelCase_							:      Dict						=   initializer_range
       UpperCAmelCase_							:      Any						=   attention_dropout
       UpperCAmelCase_							:      Optional[Any]						=   layer_norm_eps
       UpperCAmelCase_							:      Union[str, Any]						=   hidden_act





    @classmethod
    def 					_lowerCamelCase (	cls				:   int       ,   __snake_case				:   Union[str, os.PathLike]       ,   **__snake_case				:   Tuple	):





       '''simple docstring'''
       cls._set_token_in_kwargs(__snake_case	)

       UpperCAmelCase_							:      Tuple						=   cls.get_config_dict(__snake_case       ,   **__snake_case	)

       # get the vision config dict if we are loading from BlipConfig
       if config_dict.get('''model_type'''	) == "blip":
          UpperCAmelCase_							:      Tuple						=   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(__snake_case       ,   **__snake_case	)





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Tuple			= 'blip'
    A_  :     List[Any]			= True





    def __init__(	self				:   List[str]       ,   __snake_case				:   Optional[Any]=None       ,   __snake_case				:   Optional[Any]=None       ,   __snake_case				:   Union[str, Any]=512       ,   __snake_case				:   Optional[int]=2.6_592       ,   __snake_case				:   Tuple=256       ,   **__snake_case				:   Optional[int]       ,   ):





       '''simple docstring'''
       super().__init__(**__snake_case	)

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

       if vision_config is None:
          UpperCAmelCase_							:      List[Any]						=   {}
          logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''	)

       UpperCAmelCase_							:      Tuple						=   BlipTextConfig(**__snake_case	)
       UpperCAmelCase_							:      Tuple						=   BlipVisionConfig(**__snake_case	)

       UpperCAmelCase_							:      Any						=   self.vision_config.hidden_size

       UpperCAmelCase_							:      str						=   projection_dim
       UpperCAmelCase_							:      str						=   logit_scale_init_value
       UpperCAmelCase_							:      Optional[int]						=   1.0
       UpperCAmelCase_							:      Dict						=   0.02
       UpperCAmelCase_							:      List[str]						=   image_text_hidden_size





    @classmethod
    def 					_lowerCamelCase (	cls				:   Optional[Any]       ,   __snake_case				:   BlipTextConfig       ,   __snake_case				:   BlipVisionConfig       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       return cls(text_config=text_config.to_dict()       ,   vision_config=vision_config.to_dict()       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[Any]						=   copy.deepcopy(self.__dict__	)
       UpperCAmelCase_							:      List[str]						=   self.text_config.to_dict()
       UpperCAmelCase_							:      Tuple						=   self.vision_config.to_dict()
       UpperCAmelCase_							:      List[str]						=   self.__class__.model_type
       return output



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

   print(F'''Save configuration file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )

   print(F'''Save vocab file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)