
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 							_lowercase							(     UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


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


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




class 						lowercase__  (						_UpperCAmelCase  ):
	@staticmethod
	def      A_				(			UpperCAmelCase_							:   ArgumentParser							):
						SCREAMING_SNAKE_CASE__													= 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=UpperCAmelCase_       ,   required=UpperCAmelCase_       ,   help='Model\'s type.'							)
						train_parser.add_argument(
						    '--tf_checkpoint'       ,   type=UpperCAmelCase_       ,   required=UpperCAmelCase_       ,   help='TensorFlow checkpoint path or folder.'							)
						train_parser.add_argument(
						    '--pytorch_dump_output'       ,   type=UpperCAmelCase_       ,   required=UpperCAmelCase_       ,   help='Path to the PyTorch saved model output.'							)
						train_parser.add_argument('--config'       ,   type=UpperCAmelCase_       ,   default=''       ,   help='Configuration file path or folder.'							)
						train_parser.add_argument(
						    '--finetuning_task_name'       ,   type=UpperCAmelCase_       ,   default=UpperCAmelCase_       ,   help='Optional fine-tuning task name if the TF model was a finetuned model.'       ,   )
						train_parser.set_defaults(func=UpperCAmelCase_							)
	def __init__(			self							:   str       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   str       ,   *UpperCAmelCase_							:   Optional[int]       ,   ):
						SCREAMING_SNAKE_CASE__													= logging.get_logger('transformers-cli/converting'							)

						self._logger.info(F'Loading model {model_type}'							)
						SCREAMING_SNAKE_CASE__													= model_type
						SCREAMING_SNAKE_CASE__													= tf_checkpoint
						SCREAMING_SNAKE_CASE__													= pytorch_dump_output
						SCREAMING_SNAKE_CASE__													= config
						SCREAMING_SNAKE_CASE__													= finetuning_task_name





	def      A_				(			self							:   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(UpperCAmelCase_							)

											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(UpperCAmelCase_							)

											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(UpperCAmelCase_							)

											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(UpperCAmelCase_							)

											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(UpperCAmelCase_							)

											if "ckpt" in self._tf_checkpoint.lower():
																SCREAMING_SNAKE_CASE__													= self._tf_checkpoint
																SCREAMING_SNAKE_CASE__													= ''
											else:
																SCREAMING_SNAKE_CASE__													= self._tf_checkpoint
																SCREAMING_SNAKE_CASE__													= ''
											convert_transfo_xl_checkpoint_to_pytorch(
											    UpperCAmelCase_       ,   self._config       ,   self._pytorch_dump_output       ,   UpperCAmelCase_							)
						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(UpperCAmelCase_							)

											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(UpperCAmelCase_							)

											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]'							)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   int				       ="""xmod"""
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[int]=30522       ,   UpperCAmelCase_							:   Union[str, Any]=768       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=3072       ,   UpperCAmelCase_							:   Dict="gelu"       ,   UpperCAmelCase_							:   str=0.1       ,   UpperCAmelCase_							:   Dict=0.1       ,   UpperCAmelCase_							:   Optional[Any]=512       ,   UpperCAmelCase_							:   Any=2       ,   UpperCAmelCase_							:   Optional[Any]=0.02       ,   UpperCAmelCase_							:   Optional[Any]=1e-1_2       ,   UpperCAmelCase_							:   Any=1       ,   UpperCAmelCase_							:   Dict=0       ,   UpperCAmelCase_							:   Optional[int]=2       ,   UpperCAmelCase_							:   Tuple="absolute"       ,   UpperCAmelCase_							:   Union[str, Any]=True       ,   UpperCAmelCase_							:   Dict=None       ,   UpperCAmelCase_							:   Union[str, Any]=False       ,   UpperCAmelCase_							:   int=2       ,   UpperCAmelCase_							:   int=False       ,   UpperCAmelCase_							:   Optional[int]=True       ,   UpperCAmelCase_							:   List[Any]=True       ,   UpperCAmelCase_							:   Union[str, Any]=("en_XX",)       ,   UpperCAmelCase_							:   int=None       ,   **UpperCAmelCase_							:   str       ,   ):
						super().__init__(pad_token_id=UpperCAmelCase_       ,   bos_token_id=UpperCAmelCase_       ,   eos_token_id=UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= vocab_size
						SCREAMING_SNAKE_CASE__													= hidden_size
						SCREAMING_SNAKE_CASE__													= num_hidden_layers
						SCREAMING_SNAKE_CASE__													= num_attention_heads
						SCREAMING_SNAKE_CASE__													= hidden_act
						SCREAMING_SNAKE_CASE__													= intermediate_size
						SCREAMING_SNAKE_CASE__													= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__													= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__													= max_position_embeddings
						SCREAMING_SNAKE_CASE__													= type_vocab_size
						SCREAMING_SNAKE_CASE__													= initializer_range
						SCREAMING_SNAKE_CASE__													= layer_norm_eps
						SCREAMING_SNAKE_CASE__													= position_embedding_type
						SCREAMING_SNAKE_CASE__													= use_cache
						SCREAMING_SNAKE_CASE__													= classifier_dropout
						SCREAMING_SNAKE_CASE__													= pre_norm
						SCREAMING_SNAKE_CASE__													= adapter_reduction_factor
						SCREAMING_SNAKE_CASE__													= adapter_layer_norm
						SCREAMING_SNAKE_CASE__													= adapter_reuse_layer_norm
						SCREAMING_SNAKE_CASE__													= ln_before_adapter
						SCREAMING_SNAKE_CASE__													= list(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= default_language





class 						lowercase__  (						_UpperCAmelCase  ):
	@property
	def      A_				(			self							:   List[Any]							):
						if self.task == "multiple-choice":
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)





