


"""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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							# Initialise PyTorch model.
							# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
							# TapasConfig to False.

							# initialize configuration from json file
							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_          =						0.66_4694
														snake_case_          =						0.20_7951
														snake_case_          =						0.12_1194
														snake_case_          =						True
														snake_case_          =						True
														snake_case_          =						False
														snake_case_          =						0.035_2513

														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_          =						36.4519
														snake_case_          =						0.90_3421
														snake_case_          =						222.088
														snake_case_          =						True
														snake_case_          =						True
														snake_case_          =						True
														snake_case_          =						0.76_3141

														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=512			)
							tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      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							:      Optional[Any]						=      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,
				)







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      int						=      {
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[str]							 =      """canine"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=768						,    UpperCAmelCase_							: Union[str, Any]=12						,    UpperCAmelCase_							: Optional[Any]=12						,    UpperCAmelCase_							: Optional[Any]=3_072						,    UpperCAmelCase_							: List[Any]="gelu"						,    UpperCAmelCase_							: int=0.1						,    UpperCAmelCase_							: Any=0.1						,    UpperCAmelCase_							: List[str]=16_384						,    UpperCAmelCase_							: Tuple=16						,    UpperCAmelCase_							: int=0.02						,    UpperCAmelCase_							: Tuple=1E-12						,    UpperCAmelCase_							: str=0						,    UpperCAmelCase_							: int=0XE000						,    UpperCAmelCase_							: Optional[int]=0XE001						,    UpperCAmelCase_							: Dict=4						,    UpperCAmelCase_							: List[Any]=4						,    UpperCAmelCase_							: List[Any]=8						,    UpperCAmelCase_							: Dict=16_384						,    UpperCAmelCase_							: Optional[int]=128						,    **UpperCAmelCase_							: Any						,    )   ->int:



														"""simple docstring"""



														super().__init__(pad_token_id=UpperCAmelCase_						,    bos_token_id=UpperCAmelCase_						,    eos_token_id=UpperCAmelCase_						,    **UpperCAmelCase_    )

														snake_case_          =						max_position_embeddings
														snake_case_          =						hidden_size
														snake_case_          =						num_hidden_layers
														snake_case_          =						num_attention_heads
														snake_case_          =						intermediate_size
														snake_case_          =						hidden_act
														snake_case_          =						hidden_dropout_prob
														snake_case_          =						attention_probs_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						type_vocab_size
														snake_case_          =						layer_norm_eps

														# Character config:
														snake_case_          =						downsampling_rate
														snake_case_          =						upsampling_kernel_size
														snake_case_          =						num_hash_functions
														snake_case_          =						num_hash_buckets
														snake_case_          =						local_transformer_stride



