





"""simple docstring"""







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


a      =							logging.get_logger(__name__)





def 							lowercase					(snake_case__   : str=None      ,       snake_case__   : List[Any]=None )     ->  Optional[Any]:


				'''simple docstring'''






				return field(default_factory=lambda: default      ,       metadata=snake_case__ )






@dataclass
class       SCREAMING_SNAKE_CASE__					:
						_a    						=						list_field(
						    default=[]						,				metadata={
						        'help': (
						            'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
						            ' of all available models'
						        )
						    }						,				)

						_a    						=						list_field(
						    default=[8]						,				metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}					)

						_a    						=						list_field(
						    default=[8, 32, 1_28, 5_12]						,				metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}						,				)

						_a    						=						field(
						    default=_a						,				metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}						,				)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}						,				)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Use FP16 to accelerate inference.'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Benchmark training of model'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Verbose memory tracing'}					)
						_a    						=						field(
						    default=_a						,				metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}						,				)
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
						    }						,				)
						_a    						=						field(default=_a						,				metadata={'help': 'Trace memory line by line'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Save result to a CSV file'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Save all print statements in a log file'}					)
						_a    						=						field(default=_a						,				metadata={'help': 'Whether to print environment information'}					)
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': (
						            'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
						            ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
						            ' for debugging / testing and on TPU.'
						        )
						    }						,				)
						_a    						=						field(
						    default=f"""inference_time_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving time results to csv.'}						,				)
						_a    						=						field(
						    default=f"""inference_memory_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving memory results to csv.'}						,				)
						_a    						=						field(
						    default=f"""train_time_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving time results to csv for training.'}						,				)
						_a    						=						field(
						    default=f"""train_memory_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving memory results to csv for training.'}						,				)
						_a    						=						field(
						    default=f"""env_info_{round(time()					)}.csv"""						,				metadata={'help': 'CSV filename used if saving environment information.'}						,				)
						_a    						=						field(
						    default=f"""log_{round(time()					)}.csv"""						,				metadata={'help': 'Log filename used if print statements are saved in log.'}						,				)
						_a    						=						field(default=3						,				metadata={'help': 'Times an experiment will be run.'}					)
						_a    						=						field(
						    default=_a						,				metadata={
						        'help': (
						            'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
						            ' model weights.'
						        )
						    }						,				)


						def   __lowercase (							self				:			Any					):
										warnings.warn(
										    f'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
										    """ are deprecated in general and it is advised to use external Benchmarking libraries """
										    """ to benchmark Transformer models."""						,						lowerCAmelCase						,						)


						def   __lowercase (							self				:			List[str]					):
										return json.dumps(dataclasses.asdict(self					)						,						indent=2					)


						@property
						def   __lowercase (							self				:			int					):
										if len(self.models					) <= 0:
														raise ValueError(
														    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
														    """ bert-base-cased` or `args.models = ['bert-base-cased']."""					)
										return self.models




						@property
						def   __lowercase (							self				:			Optional[Any]					):
										if not self.multi_process:
														return False
										elif self.is_tpu:
														logger.info("""Multiprocessing is currently not possible on TPU."""					)
														return False
										else:
														return True











"""simple docstring"""







import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Union[str, Any] )     ->  List[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )

				lowerCAmelCase  =				chkpt["""model"""]

				# We have the base model one level deeper than the original XLM repository
				lowerCAmelCase  =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												lowerCAmelCase  =				v
								else:
												lowerCAmelCase  =				v

				lowerCAmelCase  =				chkpt["""params"""]
				lowerCAmelCase  =				{n: v for n, v in config.items() if not isinstance(snake_case__      ,       (torch.FloatTensor, numpy.ndarray) )}

				lowerCAmelCase  =				chkpt["""dico_word2id"""]
				lowerCAmelCase  =				{s + """</w>""" if s.find("""@@""" ) == -1 and i > 13 else s.replace("""@@"""      ,       """""" ): i for s, i in vocab.items()}

				# Save pytorch-model
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + CONFIG_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

				print(f'''Save PyTorch model to {pytorch_weights_dump_path}''' )
				torch.save(snake_case__      ,       snake_case__ )

				print(f'''Save configuration file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )

				print(f'''Save vocab file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		a      =							parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





