


'''simple docstring'''
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   (    __a				,      __a				,      __a				,      __a     )						->			List[str]:



					'''simple docstring'''






					UpperCamelCase__     :Dict							      =				FunnelConfig.from_json_file(__a     )
					print(f'''Building PyTorch model from configuration: {config}'''     )
					UpperCamelCase__     :List[Any]							      =				FunnelBaseModel(__a     ) if base_model else FunnelModel(__a     )

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

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


if __name__ == "__main__":
		__snake_case							   =  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.'''
		)
		__snake_case							   =  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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'transfo-xl'
		_a									=       ['mems']
		_a									=       {
		    'n_token': 'vocab_size',
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}




		def __init__(	self					,     UpperCamelCase_=267735					,     UpperCamelCase_=[20000, 40000, 200000]					,     UpperCamelCase_=1024					,     UpperCamelCase_=1024					,     UpperCamelCase_=16					,     UpperCamelCase_=64					,     UpperCamelCase_=4096					,     UpperCamelCase_=4					,     UpperCamelCase_=False					,     UpperCamelCase_=18					,     UpperCamelCase_=1600					,     UpperCamelCase_=1000					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=0					,     UpperCamelCase_=-1					,     UpperCamelCase_=True					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.0					,     UpperCamelCase_=True					,     UpperCamelCase_="normal"					,     UpperCamelCase_=0.01					,     UpperCamelCase_=0.01					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=0					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				vocab_size
							UpperCamelCase__     :Optional[int]							      =				[]
							self.cutoffs.extend(UpperCamelCase_							)
							if proj_share_all_but_first:
												UpperCamelCase__     :Any							      =				[False] + [True] * len(self.cutoffs							)
							else:
												UpperCamelCase__     :Dict							      =				[False] + [False] * len(self.cutoffs							)
							UpperCamelCase__     :Union[str, Any]							      =				d_model
							UpperCamelCase__     :List[Any]							      =				d_embed
							UpperCamelCase__     :Any							      =				d_head
							UpperCamelCase__     :Tuple							      =				d_inner
							UpperCamelCase__     :int							      =				div_val
							UpperCamelCase__     :int							      =				pre_lnorm
							UpperCamelCase__     :Any							      =				n_layer
							UpperCamelCase__     :int							      =				n_head
							UpperCamelCase__     :Tuple							      =				mem_len
							UpperCamelCase__     :List[str]							      =				same_length
							UpperCamelCase__     :Any							      =				attn_type
							UpperCamelCase__     :List[str]							      =				clamp_len
							UpperCamelCase__     :Union[str, Any]							      =				sample_softmax
							UpperCamelCase__     :List[Any]							      =				adaptive
							UpperCamelCase__     :List[Any]							      =				dropout
							UpperCamelCase__     :Optional[Any]							      =				dropatt
							UpperCamelCase__     :str							      =				untie_r
							UpperCamelCase__     :List[Any]							      =				init
							UpperCamelCase__     :Optional[Any]							      =				init_range
							UpperCamelCase__     :int							      =				proj_init_std
							UpperCamelCase__     :List[str]							      =				init_std
							UpperCamelCase__     :Optional[int]							      =				layer_norm_epsilon
							super().__init__(eos_token_id=UpperCamelCase_					,     **UpperCamelCase_							)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''							)
							return -1




		@max_position_embeddings.setter
		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError(
							    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''							)