





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					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				# Load checkpoint
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      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(_UpperCAmelCase			,      (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(_UpperCAmelCase			,      _UpperCAmelCase				)

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

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


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=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.'''
							)
							UpperCAmelCase__						:			int									=parser.parse_args()
							convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__						:			List[Any]									={'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=['''YolosFeatureExtractor''']
							UpperCAmelCase__						:			List[Any]									=['''YolosImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Optional[Any]									=[
							    '''YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''YolosForObjectDetection''',
							    '''YolosModel''',
							    '''YolosPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_yolos import YolosFeatureExtractor
														from .image_processing_yolos import YolosImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yolos import (
														    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YolosForObjectDetection,
														    YolosModel,
														    YolosPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[Any]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





