
"""simple docstring"""

from typing import Mapping

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


a         =      logging.get_logger(__name__)

a         =      {
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class     lowercase_	(					_UpperCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[Any]				  =     'umt5'
 UpperCAmelCase   :					Union[str, Any]				  =     ['past_key_values']

 def __init__(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Dict=250_112   ,							_UpperCAmelCase	:	Any=512   ,							_UpperCAmelCase	:	Optional[int]=64   ,							_UpperCAmelCase	:	Dict=1_024   ,							_UpperCAmelCase	:	int=8   ,							_UpperCAmelCase	:	Union[str, Any]=None   ,							_UpperCAmelCase	:	str=6   ,							_UpperCAmelCase	:	List[Any]=32   ,							_UpperCAmelCase	:	Optional[int]=128   ,							_UpperCAmelCase	:	int=0.1   ,							_UpperCAmelCase	:	int=1E-6   ,							_UpperCAmelCase	:	str=1.0   ,							_UpperCAmelCase	:	Optional[int]="gated-gelu"   ,							_UpperCAmelCase	:	Dict=True   ,							_UpperCAmelCase	:	int=True   ,							_UpperCAmelCase	:	Tuple="T5Tokenizer"   ,							_UpperCAmelCase	:	List[str]=True   ,							_UpperCAmelCase	:	Union[str, Any]=0   ,							_UpperCAmelCase	:	Optional[Any]=1   ,							_UpperCAmelCase	:	List[str]=0   ,							**_UpperCAmelCase	:	str   ,							):
       super().__init__(
           is_encoder_decoder=SCREAMING_SNAKE_CASE_   ,							tokenizer_class=SCREAMING_SNAKE_CASE_   ,							tie_word_embeddings=SCREAMING_SNAKE_CASE_   ,							pad_token_id=SCREAMING_SNAKE_CASE_   ,							eos_token_id=SCREAMING_SNAKE_CASE_   ,							decoder_start_token_id=SCREAMING_SNAKE_CASE_   ,							**SCREAMING_SNAKE_CASE_   ,							)
       _A        =       vocab_size
       _A        =       d_model
       _A        =       d_kv
       _A        =       d_ff
       _A        =       num_layers
       _A        =       (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       _A        =       num_heads
       _A        =       relative_attention_num_buckets
       _A        =       relative_attention_max_distance
       _A        =       dropout_rate
       _A        =       layer_norm_epsilon
       _A        =       initializer_factor
       _A        =       feed_forward_proj
       _A        =       use_cache

       _A        =       self.feed_forward_proj.split('-'		)
       _A        =       act_info[-1]
       _A        =       act_info[0] == """gated"""

       if len(SCREAMING_SNAKE_CASE_		) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_		) > 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\''		)

       if feed_forward_proj == "gated-gelu":
             _A        =       """gelu_new"""

 @property
 def       lowerCAmelCase_							(			self	:	Tuple		):
       return self.d_model

 @property
 def       lowerCAmelCase_							(			self	:	str		):
       return self.num_heads



 @property
 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return self.num_layers





class     lowercase_	(					_UpperCAmelCase							):



 '''simple docstring'''

 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       {
           """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
           """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
       }
       if self.use_past:
             _A        =       """past_encoder_sequence + sequence"""
             _A        =       {0: """batch"""}
             _A        =       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
       else:
             _A        =       {0: """batch""", 1: """decoder_sequence"""}
             _A        =       {0: """batch""", 1: """decoder_sequence"""}

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

       return common_inputs

 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def       lowerCAmelCase_							(			self	:	List[str]		):
       return 13



 @property
 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return 5E-4







"""simple docstring"""

import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


a         =      {
    '''cola''': 2,
    '''mnli''': 3,
    '''mrpc''': 2,
    '''sst-2''': 2,
    '''sts-b''': 1,
    '''qqp''': 2,
    '''qnli''': 2,
    '''rte''': 2,
    '''wnli''': 2,
}


logging.set_verbosity_info()






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: Tuple=None							)   -> List[str]:






      '''simple docstring'''


      _A        =       XLNetConfig.from_json_file(_snake_case							)

      _A        =       finetuning_task.lower() if finetuning_task is not None else ''
      if finetuning_task in GLUE_TASKS_NUM_LABELS:
            print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''							)
            _A        =       finetuning_task
            _A        =       GLUE_TASKS_NUM_LABELS[finetuning_task]
            _A        =       XLNetForSequenceClassification(_snake_case							)
      elif "squad" in finetuning_task:
            _A        =       finetuning_task
            _A        =       XLNetForQuestionAnswering(_snake_case							)
      else:
            _A        =       XLNetLMHeadModel(_snake_case							)

      # Load weights from tf checkpoint
      load_tf_weights_in_xlnet(_snake_case    ,  _snake_case    ,  _snake_case							)

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


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
       )
       parser.add_argument(
           '''--xlnet_config_file''',
           default=None,
           type=str,
           required=True,
           help=(
               '''The config json file corresponding to the pre-trained XLNet model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       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(
           '''--finetuning_task''',
           default=None,
           type=str,
           help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
       )
       a         =      parser.parse_args()
       print(args)

       convert_xlnet_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
       )

