




from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__magic_name__  :  List[str]					=       logging.get_logger(__name__)





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Dict				= ["pixel_values"]
     def __init__(						self	:		List[str]       ,						__lowerCamelCase	:		bool = True       ,						__lowerCamelCase	:		Union[int, float] = 1 / 2_55       ,						__lowerCamelCase	:		bool = True       ,						__lowerCamelCase	:		int = 8       ,						**__lowerCamelCase	:		Tuple       ,						):



       """simple docstring"""
       super().__init__(**__lowerCamelCase						)

       lowerCAmelCase__										=      do_rescale
       lowerCAmelCase__										=      rescale_factor
       lowerCAmelCase__										=      do_pad
       lowerCAmelCase__										=      pad_size
     def        A__      (						self	:		Any       ,						__lowerCamelCase	:		np.ndarray       ,						__lowerCamelCase	:		float       ,						__lowerCamelCase	:		Optional[Union[str, ChannelDimension]] = None       ,						**__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       return rescale(__lowerCamelCase       ,						scale=__lowerCamelCase       ,						data_format=__lowerCamelCase       ,						**__lowerCamelCase						)
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		np.ndarray       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Optional[Union[str, ChannelDimension]] = None						):



       """simple docstring"""
       lowerCAmelCase__				,      lowerCAmelCase__										=      get_image_size(__lowerCamelCase						)
       lowerCAmelCase__										=      (old_height // size + 1) * size - old_height
       lowerCAmelCase__										=      (old_width // size + 1) * size - old_width

       return pad(__lowerCamelCase       ,						((0, pad_height), (0, pad_width))       ,						mode='''symmetric'''       ,						data_format=__lowerCamelCase						)



     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		ImageInput       ,						__lowerCamelCase	:		Optional[bool] = None       ,						__lowerCamelCase	:		Optional[float] = None       ,						__lowerCamelCase	:		Optional[bool] = None       ,						__lowerCamelCase	:		Optional[int] = None       ,						__lowerCamelCase	:		Optional[Union[str, TensorType]] = None       ,						__lowerCamelCase	:		Union[str, ChannelDimension] = ChannelDimension.FIRST       ,						**__lowerCamelCase	:		Optional[Any]       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      do_rescale if do_rescale is not None else self.do_rescale
       lowerCAmelCase__										=      rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCAmelCase__										=      do_pad if do_pad is not None else self.do_pad
       lowerCAmelCase__										=      pad_size if pad_size is not None else self.pad_size

       lowerCAmelCase__										=      make_list_of_images(__lowerCamelCase						)

       if not valid_images(__lowerCamelCase						):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

       if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

       # All transformations expect numpy arrays.
       lowerCAmelCase__										=      [to_numpy_array(__lowerCamelCase						) for image in images]

       if do_rescale:
         lowerCAmelCase__										=      [self.rescale(image=__lowerCamelCase       ,						scale=__lowerCamelCase						) for image in images]

       if do_pad:
         lowerCAmelCase__										=      [self.pad(__lowerCamelCase       ,						size=__lowerCamelCase						) for image in images]

       lowerCAmelCase__										=      [to_channel_dimension_format(__lowerCamelCase       ,						__lowerCamelCase						) for image in images]

       lowerCAmelCase__										=      {'''pixel_values''': images}
       return BatchFeature(data=__lowerCamelCase       ,						tensor_type=__lowerCamelCase						)







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

    lowerCAmelCase__										=      corpus.__dict__
    corpus_dict_no_vocab.pop('''vocab'''   , __lowerCAmelCase				)
    lowerCAmelCase__										=      pytorch_dump_folder_path + '''/''' + CORPUS_NAME
    print(F"""Save dataset to {pytorch_dataset_dump_path}"""				)
    torch.save(__lowerCAmelCase   , __lowerCAmelCase				)

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)

    print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""				)
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      lowerCAmelCase__										=      TransfoXLConfig()
    else:
      lowerCAmelCase__										=      TransfoXLConfig.from_json_file(__lowerCAmelCase				)
    print(F"""Building PyTorch model from configuration: {config}"""				)
    lowerCAmelCase__										=      TransfoXLLMHeadModel(__lowerCAmelCase				)

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


if __name__ == "__main__":
      __magic_name__  :  int					=       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.""",
      )
      __magic_name__  :  Optional[Any]					=       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,
      )

