



"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

# TODO Update this
SCREAMING_SNAKE_CASE_				= {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"esm"






							def __init__(   self			,     a_=None			,     a_=None			,     a_=None			,     a_=7_6_8			,     a_=1_2			,     a_=1_2			,     a_=3_0_7_2			,     a_=0.1			,     a_=0.1			,     a_=1_0_2_6			,     a_=0.02			,     a_=1e-12			,     a_="absolute"			,     a_=True			,     a_=None			,     a_=False			,     a_=False			,     a_=None			,     a_=None			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     mask_token_id=a_			,     **a_      )

									a_     :      Dict 						=     vocab_size
									a_     :      Optional[Any] 						=     hidden_size
									a_     :      List[str] 						=     num_hidden_layers
									a_     :      str 						=     num_attention_heads
									a_     :      Any 						=     intermediate_size
									a_     :      Any 						=     hidden_dropout_prob
									a_     :      Union[str, Any] 						=     attention_probs_dropout_prob
									a_     :      int 						=     max_position_embeddings
									a_     :      Union[str, Any] 						=     initializer_range
									a_     :      Dict 						=     layer_norm_eps
									a_     :      Tuple 						=     position_embedding_type
									a_     :      int 						=     use_cache
									a_     :      Any 						=     emb_layer_norm_before
									a_     :      Union[str, Any] 						=     token_dropout
									a_     :      str 						=     is_folding_model
									if is_folding_model:
											if esmfold_config is None:
													logger.info("No esmfold_config supplied for folding model, using default values."      )
													a_     :      Union[str, Any] 						=     EsmFoldConfig()
											elif isinstance(a_			,     a_      ):
													a_     :      str 						=     EsmFoldConfig(**a_      )
											a_     :      str 						=     esmfold_config
											if vocab_list is None:
													logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"      )
													a_     :      Tuple 						=     get_default_vocab_list()
											else:
													a_     :      Optional[Any] 						=     vocab_list
									else:
											a_     :      List[Any] 						=     None
											a_     :      Optional[Any] 						=     None
									if self.esmfold_config is not None and getattr(self.esmfold_config			,     "use_esm_attn_map"			,     a_      ):
											raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"      )






							def snake_case_  (   self      ):
									a_     :      str 						=     super().to_dict()
									if isinstance(self.esmfold_config			,     a_      ):
											a_     :      List[Any] 						=     self.esmfold_config.to_dict()
									return output




@dataclass
class 			snake_case_						:
							__lowerCAmelCase    					=					None
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					0

							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False

							__lowerCAmelCase    					=					1_2_8
							__lowerCAmelCase    					=					None






							def snake_case_  (   self      ):
									if self.trunk is None:
											a_     :      List[str] 						=     TrunkConfig()
									elif isinstance(self.trunk			,     a_      ):
											a_     :      List[str] 						=     TrunkConfig(**self.trunk      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     asdict(self      )
									a_     :      Optional[Any] 						=     self.trunk.to_dict()
									return output




@dataclass
class 			snake_case_						:
							__lowerCAmelCase    					=					4_8
							__lowerCAmelCase    					=					1_0_2_4
							__lowerCAmelCase    					=					1_2_8
							__lowerCAmelCase    					=					3_2
							__lowerCAmelCase    					=					3_2
							__lowerCAmelCase    					=					3_2
							__lowerCAmelCase    					=					0
							__lowerCAmelCase    					=					0
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					4
							__lowerCAmelCase    					=					1_2_8
							__lowerCAmelCase    					=					None






							def snake_case_  (   self      ):
									if self.structure_module is None:
											a_     :      Optional[Any] 						=     StructureModuleConfig()
									elif isinstance(self.structure_module			,     a_      ):
											a_     :      List[str] 						=     StructureModuleConfig(**self.structure_module      )

									if self.max_recycles <= 0:
											raise ValueError(F"""`max_recycles` should be positive, got {self.max_recycles}."""      )
									if self.sequence_state_dim % self.sequence_state_dim != 0:
											raise ValueError(
											    "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
											    F""" {self.sequence_state_dim} and {self.sequence_state_dim}."""      )
									if self.pairwise_state_dim % self.pairwise_state_dim != 0:
											raise ValueError(
											    "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
											    F""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""      )

									a_     :      str 						=     self.sequence_state_dim // self.sequence_head_width
									a_     :      Dict 						=     self.pairwise_state_dim // self.pairwise_head_width

									if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
											raise ValueError(
											    "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
											    F""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""      )
									if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
											raise ValueError(
											    "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
											    F""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""      )
									if self.pairwise_state_dim % 2 != 0:
											raise ValueError(F"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""      )

									if self.dropout >= 0.4:
											raise ValueError(F"""`dropout` should not be greater than 0.4, got {self.dropout}."""      )






							def snake_case_  (   self      ):
									a_     :      str 						=     asdict(self      )
									a_     :      List[str] 						=     self.structure_module.to_dict()
									return output




@dataclass
class 			snake_case_						:
							__lowerCAmelCase    					=					3_8_4
							__lowerCAmelCase    					=					1_2_8
							__lowerCAmelCase    					=					1_6
							__lowerCAmelCase    					=					1_2_8
							__lowerCAmelCase    					=					1_2
							__lowerCAmelCase    					=					4
							__lowerCAmelCase    					=					8
							__lowerCAmelCase    					=					0.1
							__lowerCAmelCase    					=					8
							__lowerCAmelCase    					=					1
							__lowerCAmelCase    					=					2
							__lowerCAmelCase    					=					7
							__lowerCAmelCase    					=					1_0
							__lowerCAmelCase    					=					1e-8
							__lowerCAmelCase    					=					1e5






							def snake_case_  (   self      ):
									return asdict(self      )



def        lowerCAmelCase_						(							)			->      Tuple:
		return (
		    "<cls>",
		    "<pad>",
		    "<eos>",
		    "<unk>",
		    "L",
		    "A",
		    "G",
		    "V",
		    "S",
		    "E",
		    "R",
		    "T",
		    "I",
		    "D",
		    "P",
		    "K",
		    "Q",
		    "N",
		    "F",
		    "Y",
		    "M",
		    "H",
		    "W",
		    "C",
		    "X",
		    "B",
		    "U",
		    "Z",
		    "O",
		    ".",
		    "-",
		    "<null_1>",
		    "<mask>",
		)




"""simple docstring"""
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


SCREAMING_SNAKE_CASE_				= {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      int:
		# Initialise PyTorch model
		a_     :      Optional[int] 						=     XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE__	)

		a_     :      List[Any] 						=     finetuning_task.lower() if finetuning_task is not None else ""
		if finetuning_task in GLUE_TASKS_NUM_LABELS:
				print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
				a_     :      Tuple 						=     finetuning_task
				a_     :      Dict 						=     GLUE_TASKS_NUM_LABELS[finetuning_task]
				a_     :      Tuple 						=     XLNetForSequenceClassification(SCREAMING_SNAKE_CASE__	)
		elif "squad" in finetuning_task:
				a_     :      Tuple 						=     finetuning_task
				a_     :      List[Any] 						=     XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE__	)
		else:
				a_     :      Any 						=     XLNetLMHeadModel(SCREAMING_SNAKE_CASE__	)

		# Load weights from tf checkpoint
		load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		# Save pytorch-model
		a_     :      Tuple 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		a_     :      str 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		print(F"""Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		torch.save(model.state_dict(), SCREAMING_SNAKE_CASE__	)
		print(F"""Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		with open(SCREAMING_SNAKE_CASE__, "w", encoding="utf-8"	) as f:
				f.write(config.to_json_string()	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--xlnet_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained XLNet model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
					)
					parser.add_argument(
					    """--finetuning_task""",
					    default=None,
					    type=str,
					    help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
					)
					SCREAMING_SNAKE_CASE_				= parser.parse_args()
					print(args)

					convert_xlnet_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
					)