

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  _UpperCAmelCase							(		a__   ,     a__):

			'''simple docstring'''



			a_		:		Optional[Any]      				=      torch.load(_a   ,     map_location="""cpu""")

			a_		:		Union[str, Any]      				=      chkpt['''model''']

			# We have the base model one level deeper than the original XLM repository
			a_		:		Dict      				=      {}
			for k, v in state_dict.items():
						if "pred_layer" in k:
									a_		:		Union[str, Any]      				=      v
						else:
									a_		:		Dict      				=      v

			a_		:		Union[str, Any]      				=      chkpt['''params''']
			a_		:		int      				=      {n: v for n, v in config.items() if not isinstance(_a   ,     (torch.FloatTensor, numpy.ndarray))}

			a_		:		int      				=      chkpt['''dico_word2id''']
			a_		:		str      				=      {s + '''</w>''' if s.find("""@@""") == -1 and i > 1_3 else s.replace("""@@"""   ,     """"""): i for s, i in vocab.items()}

			# Save pytorch-model
			a_		:		Union[str, Any]      				=      pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			a_		:		Union[str, Any]      				=      pytorch_dump_folder_path + '''/''' + CONFIG_NAME
			a_		:		Any      				=      pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

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

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

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


if __name__ == "__main__":
	__snake_case							:	Optional[Any]											=       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."""
	)
	__snake_case							:	List[str]											=       parser.parse_args()
	convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""

from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





