


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__						:   Dict				=    logging.get_logger(__name__)

A__						:   List[str]				=    {
    """google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
    # See all ViT models at https://huggingface.co/models?filter=vit
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Optional[Any]					=     'vit'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=30_72     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-12     ,	SCREAMING_SNAKE_CASE_=2_24     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=16     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Tuple:
         super().__init__(**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      List[str]														=						hidden_size
         __lowerCamelCase  :      Any														=						num_hidden_layers
         __lowerCamelCase  :      Union[str, Any]														=						num_attention_heads
         __lowerCamelCase  :      Optional[int]														=						intermediate_size
         __lowerCamelCase  :      Union[str, Any]														=						hidden_act
         __lowerCamelCase  :      Dict														=						hidden_dropout_prob
         __lowerCamelCase  :      int														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Any														=						initializer_range
         __lowerCamelCase  :      str														=						layer_norm_eps
         __lowerCamelCase  :      List[str]														=						image_size
         __lowerCamelCase  :      int														=						patch_size
         __lowerCamelCase  :      List[Any]														=						num_channels
         __lowerCamelCase  :      List[Any]														=						qkv_bias
         __lowerCamelCase  :      Union[str, Any]														=						encoder_stride







class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Optional[Any]					=     version.parse('1.11'    )







    @property
    def  lowercase_							(				self      )							->	Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]      )







    @property
    def  lowercase_							(				self      )							->	float:
         return 1E-4







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
UpperCAmelCase_           =				data_utils.TransfoXLTokenizer
UpperCAmelCase_           =				data_utils.TransfoXLCorpus
UpperCAmelCase_           =				data_utils
UpperCAmelCase_           =				data_utils




def    lowerCAmelCase_  (		lowercase:				Tuple	,       lowercase:				Union[str, Any]	,       lowercase:				Any	,       lowercase:				List[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:
   _UpperCamelCase:  Optional[int]    						=			pickle.load(lowercase	,       encoding='''latin1'''		)
  # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
  _UpperCamelCase:  Tuple    						=			pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
  print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""		)
  _UpperCamelCase:  List[str]    						=			corpus.vocab.__dict__
  torch.save(lowercase	,       lowercase		)

  _UpperCamelCase:  str    						=			corpus.__dict__
  corpus_dict_no_vocab.pop('''vocab'''	,       lowercase		)
  _UpperCamelCase:  str    						=			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
  _UpperCamelCase:  Optional[Any]    						=			os.path.abspath(lowercase		)
  _UpperCamelCase:  List[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 == "":
   _UpperCamelCase:  Tuple    						=			TransfoXLConfig()
  else:
   _UpperCamelCase:  Union[str, Any]    						=			TransfoXLConfig.from_json_file(lowercase		)
  print(F"""Building PyTorch model from configuration: {config}"""		)
  _UpperCamelCase:  Optional[int]    						=			TransfoXLLMHeadModel(lowercase		)

  _UpperCamelCase:  int    						=			load_tf_weights_in_transfo_xl(lowercase	,       lowercase	,       lowercase		)
  # Save pytorch-model
  _UpperCamelCase:  List[str]    						=			os.path.join(lowercase	,       lowercase		)
  _UpperCamelCase:  Union[str, 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__":
  UpperCAmelCase_           =				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.''',
  )
  UpperCAmelCase_           =				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,
  )