
"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   'camembert'





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

												__snake_case     			=       vocab_size
												__snake_case     			=       hidden_size
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       num_attention_heads
												__snake_case     			=       hidden_act
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_dropout_prob
												__snake_case     			=       attention_probs_dropout_prob
												__snake_case     			=       max_position_embeddings
												__snake_case     			=       type_vocab_size
												__snake_case     			=       initializer_range
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       position_embedding_type
												__snake_case     			=       use_cache
												__snake_case     			=       classifier_dropout







class 							__magic_name__     (		lowercase__   ):





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												if self.task == "multiple-choice":
																			__snake_case     			=       {0: "batch", 1: "choice", 2: "sequence"}
												else:
																			__snake_case     			=       {0: "batch", 1: "sequence"}
												return OrderedDict(
												    [
												        ("input_ids", dynamic_axis),
												        ("attention_mask", dynamic_axis),
												    ]    )


"""simple docstring"""







import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE )
							# set absolute/relative position embeddings parameter
							__snake_case     			=       reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WTQ":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       True
														# hparam_utils.py hparams
														__snake_case     			=       0.664_694
														__snake_case     			=       0.207_951
														__snake_case     			=       0.121_194
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       False
														__snake_case     			=       0.0_352_513

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       False
														# hparam_utils.py hparams
														__snake_case     			=       36.4_519
														__snake_case     			=       0.903_421
														__snake_case     			=       222.088
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       0.763_141

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "TABFACT":
														__snake_case     			=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE )
							elif task == "MLM":
														__snake_case     			=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE )
							elif task == "INTERMEDIATE_PRETRAINING":
														__snake_case     			=       TapasModel(config=SCREAMING_SNAKE_CASE )
							else:
														raise ValueError(F'''Task {task} not supported.''' )

							print(F'''Building PyTorch model from configuration: {config}''' )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							# Save pytorch-model (weights and configuration)
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							model.save_pretrained(SCREAMING_SNAKE_CASE )

							# Save tokenizer files
							print(F'''Save tokenizer files to {pytorch_dump_path}''' )
							__snake_case     			=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"							,    model_max_length=5_12 )
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE )

							print("Used relative position embeddings:"							,    model.config.reset_position_index_per_cell )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
		)
		parser.add_argument(
		    """--reset_position_index_per_cell""",
		    default=False,
		    action="""store_true""",
		    help="""Whether to use relative position embeddings or not. Defaults to True.""",
		)
		parser.add_argument(
		    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
		)
		parser.add_argument(
		    """--tapas_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained TAPAS model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		_SCREAMING_SNAKE_CASE	       =      parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)
