
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		"deta"
						UpperCamelCase_							: Union[str, Any]	=		{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}

						def __init__(			self       ,							a=None       ,							a=9_00       ,							a=20_48       ,							a=6       ,							a=20_48       ,							a=8       ,							a=6       ,							a=10_24       ,							a=8       ,							a=0.0       ,							a=True       ,							a="relu"       ,							a=2_56       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=1.0       ,							a=True       ,							a=False       ,							a="sine"       ,							a=5       ,							a=4       ,							a=4       ,							a=True       ,							a=3_00       ,							a=True       ,							a=True       ,							a=1       ,							a=5       ,							a=2       ,							a=1       ,							a=1       ,							a=5       ,							a=2       ,							a=0.1       ,							a=0.25       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

								if backbone_config is None:
										logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
										_UpperCamelCase						    =	CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]				)
								else:
										if isinstance(a       ,							a				):
												_UpperCamelCase						    =	backbone_config.pop("""model_type"""				)
												_UpperCamelCase						    =	CONFIG_MAPPING[backbone_model_type]
												_UpperCamelCase						    =	config_class.from_dict(a				)

								_UpperCamelCase						    =	backbone_config
								_UpperCamelCase						    =	num_queries
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	init_xavier_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	auxiliary_loss
								_UpperCamelCase						    =	position_embedding_type
								# deformable attributes
								_UpperCamelCase						    =	num_feature_levels
								_UpperCamelCase						    =	encoder_n_points
								_UpperCamelCase						    =	decoder_n_points
								_UpperCamelCase						    =	two_stage
								_UpperCamelCase						    =	two_stage_num_proposals
								_UpperCamelCase						    =	with_box_refine
								_UpperCamelCase						    =	assign_first_stage
								if two_stage is True and with_box_refine is False:
										raise ValueError("""If two_stage is True, with_box_refine must be True."""				)
								# Hungarian matcher
								_UpperCamelCase						    =	class_cost
								_UpperCamelCase						    =	bbox_cost
								_UpperCamelCase						    =	giou_cost
								# Loss coefficients
								_UpperCamelCase						    =	mask_loss_coefficient
								_UpperCamelCase						    =	dice_loss_coefficient
								_UpperCamelCase						    =	bbox_loss_coefficient
								_UpperCamelCase						    =	giou_loss_coefficient
								_UpperCamelCase						    =	eos_coefficient
								_UpperCamelCase						    =	focal_alpha
								super().__init__(is_encoder_decoder=a       ,							**a				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.encoder_attention_heads

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.d_model







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.backbone_config.to_dict()
								_UpperCamelCase						    =	self.__class__.model_type
								return output





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






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


lowerCamelCase__	=							"\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        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	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       ,							a       ,							a       ,							a       ,							a       ,							a       ,							*a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	logging.get_logger("""transformers-cli/converting"""				)

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







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								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():
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										else:
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										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]"""				)



