






'''simple docstring'''





from typing import Mapping

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


_UpperCamelCase		:		str            = logging.get_logger(__name__)

_UpperCamelCase		:		Optional[Any]            = {
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}


class       snake_case__	( UpperCamelCase):
					a_								= "t5"
					a_								= ["past_key_values"]
					a_								= {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




					def __init__(   self   :						List[Any]							,      _A   :						Any=3_21_28							,      _A   :						List[str]=5_12							,      _A   :						Dict=64							,      _A   :						List[str]=20_48							,      _A   :						Optional[int]=6							,      _A   :						Union[str, Any]=None							,      _A   :						Union[str, Any]=8							,      _A   :						Tuple=32							,      _A   :						List[Any]=1_28							,      _A   :						Tuple=0.1							,      _A   :						Any=1e-6							,      _A   :						Tuple=1.0							,      _A   :						Dict="relu"							,      _A   :						List[Any]=True							,      _A   :						Dict=True							,      _A   :						Optional[Any]=0							,      _A   :						Union[str, Any]=1							,      **_A   :						str							,      )  ->				Dict:
								UpperCAmelCase_	:    Optional[Any]	  =    vocab_size
								UpperCAmelCase_	:    List[str]	  =    d_model
								UpperCAmelCase_	:    Dict	  =    d_kv
								UpperCAmelCase_	:    List[str]	  =    d_ff
								UpperCAmelCase_	:    Any	  =    num_layers
								UpperCAmelCase_	:    Dict	  =    (
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								UpperCAmelCase_	:    Tuple	  =    num_heads
								UpperCAmelCase_	:    str	  =    relative_attention_num_buckets
								UpperCAmelCase_	:    List[Any]	  =    relative_attention_max_distance
								UpperCAmelCase_	:    Tuple	  =    dropout_rate
								UpperCAmelCase_	:    List[str]	  =    layer_norm_epsilon
								UpperCAmelCase_	:    Union[str, Any]	  =    initializer_factor
								UpperCAmelCase_	:    Dict	  =    feed_forward_proj
								UpperCAmelCase_	:    Any	  =    use_cache

								UpperCAmelCase_	:    int	  =    self.feed_forward_proj.split('''-'''	)
								UpperCAmelCase_	:    Any	  =    act_info[-1]
								UpperCAmelCase_	:    Any	  =    act_info[0] == '''gated'''

								if len(_A	) > 1 and act_info[0] != "gated" or len(_A	) > 2:
											raise ValueError(
											    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
											    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
											    '''\'gated-gelu\' or \'relu\''''	)

								# for backwards compatibility
								if feed_forward_proj == "gated-gelu":
											UpperCAmelCase_	:    Dict	  =    '''gelu_new'''

								super().__init__(
								    pad_token_id=_A							,      eos_token_id=_A							,      is_encoder_decoder=_A							,      **_A							,      )






class       snake_case__	( UpperCamelCase):




					@property
					def 						A	(   self   :						Tuple	)  ->				Mapping[str, Mapping[int, str]]:
								UpperCAmelCase_	:    Union[str, Any]	  =    {
								    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
								    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
								}
								if self.use_past:
											UpperCAmelCase_	:    Tuple	  =    '''past_encoder_sequence + sequence'''
											UpperCAmelCase_	:    Any	  =    {0: '''batch'''}
											UpperCAmelCase_	:    Union[str, Any]	  =    {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
											UpperCAmelCase_	:    Optional[int]	  =    {0: '''batch''', 1: '''decoder_sequence'''}
											UpperCAmelCase_	:    Any	  =    {0: '''batch''', 1: '''decoder_sequence'''}

								if self.use_past:
											self.fill_with_past_key_values_(_A							,      direction='''inputs'''	)

								return common_inputs





					@property
					def 						A	(   self   :						List[Any]	)  ->				int:
								return 13





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
lowercase_									=   data_utils.TransfoXLTokenizer
lowercase_									=   data_utils.TransfoXLCorpus
lowercase_									=   data_utils
lowercase_									=   data_utils
def      a      (			A__	:   int      ,   A__	:   Dict      ,   A__	:   Union[str, Any]      ,   A__	:   Union[str, Any]     )    ->      List[str]:





      """simple docstring"""



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

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

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

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

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


if __name__ == "__main__":
  lowercase_									=   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.',
  )
  lowercase_									=   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,
  )


