
"""simple docstring"""


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Union[str, Any]      =    '''sew-d'''


						def __init__( self     :      List[str]  ,A_     :      Dict=32  ,A_     :      Optional[int]=768  ,A_     :      Optional[Any]=12  ,A_     :      Optional[int]=12  ,A_     :      Tuple=3072  ,A_     :      Any=2  ,A_     :      List[Any]=512  ,A_     :      Tuple=256  ,A_     :      Optional[int]=True  ,A_     :      List[str]=True  ,A_     :      List[Any]=("p2c", "c2p")  ,A_     :      Dict="layer_norm"  ,A_     :      List[str]="gelu_python"  ,A_     :      int=0.1  ,A_     :      Tuple=0.1  ,A_     :      Dict=0.1  ,A_     :      List[str]=0.0  ,A_     :      List[str]=0.1  ,A_     :      Optional[Any]=0.02  ,A_     :      Optional[int]=1e-7  ,A_     :      Optional[Any]=1e-5  ,A_     :      Optional[int]="group"  ,A_     :      Union[str, Any]="gelu"  ,A_     :      Tuple=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,A_     :      Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,A_     :      int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,A_     :      Union[str, Any]=False  ,A_     :      str=128  ,A_     :      Tuple=16  ,A_     :      List[Any]=True  ,A_     :      Tuple=0.05  ,A_     :      List[Any]=10  ,A_     :      List[str]=2  ,A_     :      str=0.0  ,A_     :      Union[str, Any]=10  ,A_     :      Tuple=0  ,A_     :      Tuple="mean"  ,A_     :      Union[str, Any]=False  ,A_     :      Dict=False  ,A_     :      str=256  ,A_     :      int=0  ,A_     :      Dict=1  ,A_     :      List[str]=2  ,**A_     :      Tuple  ,)     ->   Any:
							super().__init__(**A_  ,pad_token_id=A_  ,bos_token_id=A_  ,eos_token_id=A_      )
							A						   =   hidden_size
							A						   =   feat_extract_norm
							A						   =   feat_extract_activation
							A						   =   list(A_      )
							A						   =   list(A_      )
							A						   =   list(A_      )
							A						   =   conv_bias
							A						   =   num_conv_pos_embeddings
							A						   =   num_conv_pos_embedding_groups
							A						   =   len(self.conv_dim      )
							A						   =   num_hidden_layers
							A						   =   intermediate_size
							A						   =   squeeze_factor
							A						   =   max_position_embeddings
							A						   =   position_buckets
							A						   =   share_att_key
							A						   =   relative_attention
							A						   =   norm_rel_ebd
							A						   =   list(A_      )
							A						   =   hidden_act
							A						   =   num_attention_heads
							A						   =   hidden_dropout
							A						   =   attention_dropout
							A						   =   activation_dropout
							A						   =   feat_proj_dropout
							A						   =   final_dropout
							A						   =   layer_norm_eps
							A						   =   feature_layer_norm_eps
							A						   =   initializer_range
							A						   =   vocab_size

							if (
							    (len(self.conv_stride      ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim      ) != self.num_feat_extract_layers)
							):
								raise ValueError(
								    'Configuration for convolutional layers is incorrect.'
								    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
								    F'but is `len(config.conv_dim) = {len(self.conv_dim      )}`, `len(config.conv_stride)'
								    F'= {len(self.conv_stride      )}`, `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'      )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							A						   =   apply_spec_augment
							A						   =   mask_time_prob
							A						   =   mask_time_length
							A						   =   mask_time_min_masks
							A						   =   mask_feature_prob
							A						   =   mask_feature_length
							A						   =   mask_feature_min_masks

							# ctc loss
							A						   =   ctc_loss_reduction
							A						   =   ctc_zero_infinity

							# sequence classification
							A						   =   use_weighted_layer_sum
							A						   =   classifier_proj_size




						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[int]:
							return functools.reduce(operator.mul  ,self.conv_stride  ,1      )

"""simple docstring"""


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

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

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

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

		A						   =   load_tf_weights_in_transfo_xl(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__":
		_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,
		)