
import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()






def 							A						(    snake_case				:Any    , snake_case				:Union[str, Any]    , snake_case				:Optional[int]    , snake_case				:Optional[Any]		)				->     Any:
 # Initialise PyTorch model
 __UpperCamelCase      =	FunnelConfig.from_json_file(lowerCAmelCase_		)
 print(f'Building PyTorch model from configuration: {config}'		)
 __UpperCamelCase      =	FunnelBaseModel(lowerCAmelCase_		) if base_model else FunnelModel(lowerCAmelCase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_funnel(lowerCAmelCase_    , lowerCAmelCase_    , lowerCAmelCase_		)

 # Save pytorch-model
 print(f'Save PyTorch model to {pytorch_dump_path}'		)
 torch.save(model.state_dict()    , lowerCAmelCase_		)


if __name__ == "__main__":
      UpperCamelCase     :    Dict       =			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(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
      )
      UpperCamelCase     :    str       =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
      )





"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase     :    Union[str, Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    int       =			{
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"distilbert"
     lowercase						       =						{
         "hidden_size": "dim",
         "num_attention_heads": "n_heads",
         "num_hidden_layers": "n_layers",
     }





     def __init__(     self     ,					__UpperCAmelCase=3_0522     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=6     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=4 * 768     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.2     ,					__UpperCAmelCase=0     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	vocab_size
      __UpperCamelCase      =	max_position_embeddings
      __UpperCamelCase      =	sinusoidal_pos_embds
      __UpperCamelCase      =	n_layers
      __UpperCamelCase      =	n_heads
      __UpperCamelCase      =	dim
      __UpperCamelCase      =	hidden_dim
      __UpperCamelCase      =	dropout
      __UpperCamelCase      =	attention_dropout
      __UpperCamelCase      =	activation
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	qa_dropout
      __UpperCamelCase      =	seq_classif_dropout
      super().__init__(**__UpperCAmelCase     ,					pad_token_id=__UpperCAmelCase				)


class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      if self.task == "multiple-choice":
       __UpperCamelCase      =	{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       __UpperCamelCase      =	{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]				)



