"""simple docstring"""







import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
	import onnxruntime as ort


_a					:					Optional[int]=			logging.get_logger(__name__)

_a					:					List[Any]=			{
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}

class 		UpperCamelCase      :



				def __init__(self    :				Optional[int]     ,  _A    :				str=None     ,  **_A    :				Union[str, Any])   ->     List[str]:
					logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.')
					__snake_case      :							int						=			model
					__snake_case      :							str						=			kwargs.get('model_save_dir'     ,  _A)
					__snake_case      :							Union[str, Any]						=			kwargs.get('latest_model_name'     ,  _A)



				def __call__(self    :				Optional[int]     ,  **_A    :				Union[str, Any])   ->     List[Any]:
					__snake_case      :							Dict						=			{k: np.array(_A) for k, v in kwargs.items()}
					return self.model.run(_A     ,  _A)



				@staticmethod
				def  _lowercase					(_A    :				Union[str, Path]     ,  _A    :				str=None     ,  _A    :				Optional[int]=None)   ->     Optional[Any]:
					if provider is None:
						logger.info('No onnxruntime provider specified, using CPUExecutionProvider')
						__snake_case      :							Optional[Any]						=			'CPUExecutionProvider'

					return ort.InferenceSession(_A     ,  providers=[provider]     ,  sess_options=_A)



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				Union[str, Path]     ,  _A    :				Optional[str] = None     ,  **_A    :				List[str])   ->     int:
					__snake_case      :							Dict						=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

					__snake_case      :							Any						=			self.model_save_dir.joinpath(self.latest_model_name)
					__snake_case      :							Union[str, Any]						=			Path(_A).joinpath(_A)
					try:
						shutil.copyfile(_A     ,  _A)
					except shutil.SameFileError:
						pass

					# copy external weights (for models >2GB)
					__snake_case      :							Optional[Any]						=			self.model_save_dir.joinpath(_A)
					if src_path.exists():
						__snake_case      :							Tuple						=			Path(_A).joinpath(_A)
						try:
							shutil.copyfile(_A     ,  _A)
						except shutil.SameFileError:
							pass



				def  _lowercase					(self    :				int     ,  _A    :				Union[str, os.PathLike]     ,  **_A    :				Any     ,  )   ->     List[Any]:
					if os.path.isfile(_A):
						logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
						return

					os.makedirs(_A     ,  exist_ok=_A)

					# saving model weights/files
					self._save_pretrained(_A     ,  **_A)



				@classmethod
				def  _lowercase					(cls    :				List[str]     ,  _A    :				Union[str, Path]     ,  _A    :				Optional[Union[bool, str, None]] = None     ,  _A    :				Optional[Union[str, None]] = None     ,  _A    :				bool = False     ,  _A    :				Optional[str] = None     ,  _A    :				Optional[str] = None     ,  _A    :				Optional[str] = None     ,  _A    :				Optional["ort.SessionOptions"] = None     ,  **_A    :				Union[str, Any]     ,  )   ->     int:
					__snake_case      :							Any						=			file_name if file_name is not None else ONNX_WEIGHTS_NAME
					# load model from local directory
					if os.path.isdir(_A):
						__snake_case      :							int						=			OnnxRuntimeModel.load_model(
						    os.path.join(_A     ,  _A)     ,  provider=_A     ,  sess_options=_A)
						__snake_case      :							Tuple						=			Path(_A)
					# load model from hub
					else:
						# download model
						__snake_case      :							int						=			hf_hub_download(
						    repo_id=_A     ,  filename=_A     ,  use_auth_token=_A     ,  revision=_A     ,  cache_dir=_A     ,  force_download=_A     ,  )
						__snake_case      :							Any						=			Path(_A).parent
						__snake_case      :							List[str]						=			Path(_A).name
						__snake_case      :							int						=			OnnxRuntimeModel.load_model(_A     ,  provider=_A     ,  sess_options=_A)
					return cls(model=_A     ,  **_A)






				@classmethod
				def  _lowercase					(cls    :				str     ,  _A    :				Union[str, Path]     ,  _A    :				bool = True     ,  _A    :				Optional[str] = None     ,  _A    :				Optional[str] = None     ,  **_A    :				List[str]     ,  )   ->     str:
					__snake_case      :							List[Any]						=			None
					if len(str(_A).split('@')) == 2:
						__snake_case		,    __snake_case      :							int						=			model_id.split('@')

					return cls._from_pretrained(
					    model_id=_A     ,  revision=_A     ,  cache_dir=_A     ,  force_download=_A     ,  use_auth_token=_A     ,  **_A     ,  )






"""simple docstring"""







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Namespace  )							->      Union[str, Any]:

	'''simple docstring'''





	return ConvertCommand(
	    args.model_type      ,							args.tf_checkpoint      ,							args.pytorch_dump_output      ,							args.config      ,							args.finetuning_task_name  )


_a					:					str=			"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"

class 		UpperCamelCase      (   lowercase						):



				@staticmethod
				def  _lowercase					(_A    :				ArgumentParser)   ->     Tuple:
					__snake_case      :							Optional[Any]						=			parser.add_parser(
					    'convert'     ,  help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'     ,  )
					train_parser.add_argument('--model_type'     ,  type=_A     ,  required=_A     ,  help='Model\'s type.')
					train_parser.add_argument(
					    '--tf_checkpoint'     ,  type=_A     ,  required=_A     ,  help='TensorFlow checkpoint path or folder.')
					train_parser.add_argument(
					    '--pytorch_dump_output'     ,  type=_A     ,  required=_A     ,  help='Path to the PyTorch saved model output.')
					train_parser.add_argument('--config'     ,  type=_A     ,  default=''     ,  help='Configuration file path or folder.')
					train_parser.add_argument(
					    '--finetuning_task_name'     ,  type=_A     ,  default=_A     ,  help='Optional fine-tuning task name if the TF model was a finetuned model.'     ,  )
					train_parser.set_defaults(func=_A)



				def __init__(self    :				List[str]     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  _A    :				str     ,  *_A    :				Any     ,  )   ->     Optional[Any]:
					__snake_case      :							List[Any]						=			logging.get_logger('transformers-cli/converting')

					self._logger.info(f"Loading model {model_type}")
					__snake_case      :							List[str]						=			model_type
					__snake_case      :							int						=			tf_checkpoint
					__snake_case      :							Optional[int]						=			pytorch_dump_output
					__snake_case      :							Optional[Any]						=			config
					__snake_case      :							Optional[Any]						=			finetuning_task_name






				def  _lowercase					(self    :				List[str])   ->     str:
					if self._model_type == "albert":
						try:
							from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "bert":
						try:
							from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "funnel":
						try:
							from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
							    convert_tf_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "t5":
						try:
							from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
						except ImportError:
							raise ImportError(_A)

						convert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "gpt":
						from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
						    convert_openai_checkpoint_to_pytorch,
						)

						convert_openai_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "transfo_xl":
						try:
							from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
							    convert_transfo_xl_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						if "ckpt" in self._tf_checkpoint.lower():
							__snake_case      :							Union[str, Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						else:
							__snake_case      :							Optional[Any]						=			self._tf_checkpoint
							__snake_case      :							List[Any]						=			''
						convert_transfo_xl_checkpoint_to_pytorch(
						    _A     ,  self._config     ,  self._pytorch_dump_output     ,  _A)
					elif self._model_type == "gpt2":
						try:
							from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
							    convert_gpta_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					elif self._model_type == "xlnet":
						try:
							from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
							    convert_xlnet_checkpoint_to_pytorch,
							)
						except ImportError:
							raise ImportError(_A)

						convert_xlnet_checkpoint_to_pytorch(
						    self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output     ,  self._finetuning_task_name)
					elif self._model_type == "xlm":
						from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
						    convert_xlm_checkpoint_to_pytorch,
						)

						convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "lxmert":
						from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
						    convert_lxmert_checkpoint_to_pytorch,
						)

						convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._pytorch_dump_output)
					elif self._model_type == "rembert":
						from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
						    convert_rembert_tf_checkpoint_to_pytorch,
						)

						convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint     ,  self._config     ,  self._pytorch_dump_output)
					else:
						raise ValueError(
						    '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]')





