






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 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  Dict:
   # Load checkpoint
   __UpperCamelCase	:			Union[str, Any]					=					torch.load(__lowerCAmelCase					,							map_location="""cpu"""							)

   __UpperCamelCase	:			Optional[Any]					=					chkpt["""model"""]

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

   __UpperCamelCase	:			str					=					chkpt["""params"""]
   __UpperCamelCase	:			Optional[int]					=					{n: v for n, v in config.items() if not isinstance(__lowerCAmelCase					,							(torch.FloatTensor, numpy.ndarray)							)}

   __UpperCamelCase	:			Any					=					chkpt["""dico_word2id"""]
   __UpperCamelCase	:			List[Any]					=					{s + """</w>""" if s.find("""@@"""							) == -1 and i > 13 else s.replace("""@@"""					,							""""""							): i for s, i in vocab.items()}

   # Save pytorch-model
   __UpperCamelCase	:			Any					=					pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
   __UpperCamelCase	:			Any					=					pytorch_dump_folder_path + """/""" + CONFIG_NAME
   __UpperCamelCase	:			str					=					pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

   print(f'Save PyTorch model to {pytorch_weights_dump_path}'							)
   torch.save(__lowerCAmelCase					,							__lowerCAmelCase							)

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

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


if __name__ == "__main__":
       UpperCamelCase    							=      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    							=      parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)











from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Tuple						=					['''image_processor''', '''tokenizer''']
  lowercase_						:			str						=					'''BlipImageProcessor'''
  lowercase_						:			str						=					('''BertTokenizer''', '''BertTokenizerFast''')

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					False
     super().__init__(lowerCamelCase__   ,				lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					self.image_processor

  def __call__(    self						:	Optional[Any]   ,				lowerCamelCase__						:	ImageInput = None   ,				lowerCamelCase__						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Union[bool, str, PaddingStrategy] = False   ,				lowerCamelCase__						:	Union[bool, str, TruncationStrategy] = None   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	int = 0   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	Optional[bool] = None   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Optional[Union[str, TensorType]] = None   ,				**lowerCamelCase__						:	int   ,				):
     """simple docstring"""

     if images is None and text is None:
        raise ValueError("""You have to specify either images or text."""			)

     # Get only text
     if images is None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer
        __UpperCamelCase	:			Dict					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
        return text_encoding

     # add pixel_values
     __UpperCamelCase	:			str					=					self.image_processor(lowerCamelCase__   ,				return_tensors=lowerCamelCase__			)

     if text is not None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
     else:
        __UpperCamelCase	:			Optional[Any]					=					None

     if text_encoding is not None:
        encoding_image_processor.update(lowerCamelCase__			)

     return encoding_image_processor

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	Any   ,				**lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

     return self.tokenizer.batch_decode(*lowerCamelCase__   ,				**lowerCamelCase__			)

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	int   ,				**lowerCamelCase__						:	Tuple			):
     """simple docstring"""

     return self.tokenizer.decode(*lowerCamelCase__   ,				**lowerCamelCase__			)

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.tokenizer.model_input_names
     __UpperCamelCase	:			str					=					self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



