

'''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       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					torch.load(UpperCamelCase__     ,							map_location='cpu'   )

							snake_case_           =					chkpt['model']

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

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

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

							# Save pytorch-model
							snake_case_           =					pytorch_dump_folder_path + '/' + WEIGHTS_NAME
							snake_case_           =					pytorch_dump_folder_path + '/' + CONFIG_NAME
							snake_case_           =					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	:     Optional[int]		       = 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	:     str		       = 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_flax_available, is_torch_available


_UpperCAmelCase	:     Any		       = {
    """configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Any		       = [
					    """LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LongT5EncoderModel""",
					    """LongT5ForConditionalGeneration""",
					    """LongT5Model""",
					    """LongT5PreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Optional[int]		       = [
					    """FlaxLongT5ForConditionalGeneration""",
					    """FlaxLongT5Model""",
					    """FlaxLongT5PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_longta import (
										    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LongTaEncoderModel,
										    LongTaForConditionalGeneration,
										    LongTaModel,
										    LongTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_longta import (
										    FlaxLongTaForConditionalGeneration,
										    FlaxLongTaModel,
										    FlaxLongTaPreTrainedModel,
										)


else:
					import sys

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




