


"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	List[str]							  =  ['''image_processor''', '''tokenizer''']
   __UpperCAmelCase			:	str							  =  '''LayoutLMv2ImageProcessor'''
   __UpperCAmelCase			:	Dict							  =  ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."						,     UpperCamelCase__						,     )
           snake_case   :				Dict				 =   kwargs.pop("feature_extractor"						)

       snake_case   :				int				 =   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."						)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."						)

       super().__init__(UpperCamelCase__						,     UpperCamelCase__						)







   def __call__(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = 0						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		BatchEncoding:

       '''simple docstring'''




       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes "
               "if you initialized the image processor with apply_ocr set to True."						)

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."						)

       # first, apply the image processor
       snake_case   :				Any				 =   self.image_processor(images=UpperCamelCase__						,     return_tensors=UpperCamelCase__						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               snake_case   :				Optional[int]				 =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
           snake_case   :				Optional[Any]				 =   features["words"]

       snake_case   :				Dict				 =   self.tokenizer(
           text=text if text is not None else features["words"]						,     text_pair=text_pair if text_pair is not None else None						,     boxes=boxes if boxes is not None else features["boxes"]						,     word_labels=UpperCamelCase__						,     add_special_tokens=UpperCamelCase__						,     padding=UpperCamelCase__						,     truncation=UpperCamelCase__						,     max_length=UpperCamelCase__						,     stride=UpperCamelCase__						,     pad_to_multiple_of=UpperCamelCase__						,     return_token_type_ids=UpperCamelCase__						,     return_attention_mask=UpperCamelCase__						,     return_overflowing_tokens=UpperCamelCase__						,     return_special_tokens_mask=UpperCamelCase__						,     return_offsets_mapping=UpperCamelCase__						,     return_length=UpperCamelCase__						,     verbose=UpperCamelCase__						,     return_tensors=UpperCamelCase__						,     **UpperCamelCase__						,     )

       # add pixel values
       snake_case   :				Dict				 =   features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           snake_case   :				Any				 =   self.get_overflowing_images(UpperCamelCase__						,     encoded_inputs["overflow_to_sample_mapping"]						)
       snake_case   :				str				 =   images

       return encoded_inputs







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

       if len(UpperCamelCase__						) != len(UpperCamelCase__						):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F' {len(UpperCamelCase__						)} and {len(UpperCamelCase__						)}'						)

       return images_with_overflow







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




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







   @property
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       return ["input_ids", "bbox", "attention_mask", "image"]







   @property
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,     UpperCamelCase__						,     )
       return self.image_processor_class







   @property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,     UpperCamelCase__						,     )
       return self.image_processor









"""simple docstring"""



import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__snake_case             =   data_utils.TransfoXLTokenizer
__snake_case             =   data_utils.TransfoXLCorpus
__snake_case             =   data_utils
__snake_case             =   data_utils







def   __lowerCAmelCase						(						lowercase  :  Optional[int]						,							lowercase  :  int						,							lowercase  :  List[Any]						,							lowercase  :  Union[str, Any]			)     ->						List[Any]:


    """simple docstring"""





    if transfo_xl_dataset_file:
        # Convert a pre-processed corpus (see original TensorFlow repo)
        with open(lowercase						,							"rb"			) as fp:
            snake_case   :				int				 =   pickle.load(lowercase						,							encoding="latin1"			)
        # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
        snake_case   :				int				 =   pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
        print(F'Save vocabulary to {pytorch_vocab_dump_path}'			)
        snake_case   :				str				 =   corpus.vocab.__dict__
        torch.save(lowercase						,							lowercase			)

        snake_case   :				str				 =   corpus.__dict__
        corpus_dict_no_vocab.pop("vocab"						,							lowercase			)
        snake_case   :				Dict				 =   pytorch_dump_folder_path + "/" + CORPUS_NAME
        print(F'Save dataset to {pytorch_dataset_dump_path}'			)
        torch.save(lowercase						,							lowercase			)

    if tf_checkpoint_path:
        # Convert a pre-trained TensorFlow model
        snake_case   :				Union[str, Any]				 =   os.path.abspath(lowercase			)
        snake_case   :				str				 =   os.path.abspath(lowercase			)

        print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'			)
        # Initialise PyTorch model
        if transfo_xl_config_file == "":
            snake_case   :				int				 =   TransfoXLConfig()
        else:
            snake_case   :				Optional[int]				 =   TransfoXLConfig.from_json_file(lowercase			)
        print(F'Building PyTorch model from configuration: {config}'			)
        snake_case   :				str				 =   TransfoXLLMHeadModel(lowercase			)

        snake_case   :				str				 =   load_tf_weights_in_transfo_xl(lowercase						,							lowercase						,							lowercase			)
        # Save pytorch-model
        snake_case   :				Union[str, Any]				 =   os.path.join(lowercase						,							lowercase			)
        snake_case   :				Optional[Any]				 =   os.path.join(lowercase						,							lowercase			)
        print(F'Save PyTorch model to {os.path.abspath(lowercase			)}'			)
        torch.save(model.state_dict()						,							lowercase			)
        print(F'Save configuration file to {os.path.abspath(lowercase			)}'			)
        with open(lowercase						,							"w"						,							encoding="utf-8"			) as f:
            f.write(config.to_json_string()			)


if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
  )
  parser.add_argument(
      """--tf_checkpoint_path""",
      default="""""",
      type=str,
      help="""An optional path to a TensorFlow checkpoint path to be converted.""",
  )
  parser.add_argument(
      """--transfo_xl_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--transfo_xl_dataset_file""",
      default="""""",
      type=str,
      help="""An optional dataset file to be converted in a vocabulary.""",
  )
  __snake_case             =   parser.parse_args()
  convert_transfo_xl_checkpoint_to_pytorch(
      args.tf_checkpoint_path,
      args.transfo_xl_config_file,
      args.pytorch_dump_folder_path,
      args.transfo_xl_dataset_file,
  )





