


from collections import OrderedDict
from typing import Mapping

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


__snake_case    :    int         =     logging.get_logger(__name__)

__snake_case    :    Union[str, Any]         =     {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}


class     __SCREAMING_SNAKE_CASE					(   UpperCamelCase_):
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					"""xmod"""






						def __init__(	self     ,						_UpperCamelCase=3_05_22     ,						_UpperCamelCase=7_68     ,						_UpperCamelCase=12     ,						_UpperCamelCase=12     ,						_UpperCamelCase=30_72     ,						_UpperCamelCase="gelu"     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=5_12     ,						_UpperCamelCase=2     ,						_UpperCamelCase=0.02     ,						_UpperCamelCase=1E-12     ,						_UpperCamelCase=1     ,						_UpperCamelCase=0     ,						_UpperCamelCase=2     ,						_UpperCamelCase="absolute"     ,						_UpperCamelCase=True     ,						_UpperCamelCase=None     ,						_UpperCamelCase=False     ,						_UpperCamelCase=2     ,						_UpperCamelCase=False     ,						_UpperCamelCase=True     ,						_UpperCamelCase=True     ,						_UpperCamelCase=("en_XX",)     ,						_UpperCamelCase=None     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__(pad_token_id=_a     ,						bos_token_id=_a     ,						eos_token_id=_a     ,						**_a   )

												lowerCAmelCase__    						=						vocab_size
												lowerCAmelCase__    						=						hidden_size
												lowerCAmelCase__    						=						num_hidden_layers
												lowerCAmelCase__    						=						num_attention_heads
												lowerCAmelCase__    						=						hidden_act
												lowerCAmelCase__    						=						intermediate_size
												lowerCAmelCase__    						=						hidden_dropout_prob
												lowerCAmelCase__    						=						attention_probs_dropout_prob
												lowerCAmelCase__    						=						max_position_embeddings
												lowerCAmelCase__    						=						type_vocab_size
												lowerCAmelCase__    						=						initializer_range
												lowerCAmelCase__    						=						layer_norm_eps
												lowerCAmelCase__    						=						position_embedding_type
												lowerCAmelCase__    						=						use_cache
												lowerCAmelCase__    						=						classifier_dropout
												lowerCAmelCase__    						=						pre_norm
												lowerCAmelCase__    						=						adapter_reduction_factor
												lowerCAmelCase__    						=						adapter_layer_norm
												lowerCAmelCase__    						=						adapter_reuse_layer_norm
												lowerCAmelCase__    						=						ln_before_adapter
												lowerCAmelCase__    						=						list(_a   )
												lowerCAmelCase__    						=						default_language




class     __SCREAMING_SNAKE_CASE					(   UpperCamelCase_):






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""


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













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[Any]		)  ->   Any:

						"""simple docstring"""



						lowerCAmelCase__    						=						BertConfig.from_json_file(UpperCamelCase_		)
						print(F"Building PyTorch model from configuration: {config}"		)
						lowerCAmelCase__    						=						BertForPreTraining(UpperCamelCase_		)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						# Save pytorch-model
						print(F"Save PyTorch model to {pytorch_dump_path}"		)
						torch.save(model.state_dict()    ,       UpperCamelCase_		)


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     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(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					__snake_case    :    List[str]         =     parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





