
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      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						Any       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			TapasConfig.from_json_file(__lowerCAmelCase       )
			# set absolute/relative position embeddings parameter
			lowerCAmelCase_      			=			reset_position_index_per_cell

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

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

						lowerCAmelCase_      			=			TapasForQuestionAnswering(config=__lowerCAmelCase       )
			elif task == "TABFACT":
						lowerCAmelCase_      			=			TapasForSequenceClassification(config=__lowerCAmelCase       )
			elif task == "MLM":
						lowerCAmelCase_      			=			TapasForMaskedLM(config=__lowerCAmelCase       )
			elif task == "INTERMEDIATE_PRETRAINING":
						lowerCAmelCase_      			=			TapasModel(config=__lowerCAmelCase       )
			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(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase       )

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

			# Save tokenizer files
			print(F"""Save tokenizer files to {pytorch_dump_path}"""       )
			lowerCAmelCase_      			=			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"					,							model_max_length=512       )
			tokenizer.save_pretrained(__lowerCAmelCase       )

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


if __name__ == "__main__":
			_A     =  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."
			)
			_A     =  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,
			)





def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


			if upper_limit < 0:
						raise ValueError("Limit for the Catalan sequence must be ≥ 0"       )

			lowerCAmelCase_      			=			[0] * (upper_limit + 1)

			# Base case: C(0) = C(1) = 1
			lowerCAmelCase_      			=			1
			if upper_limit > 0:
						lowerCAmelCase_      			=			1

			# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
			for i in range(2					,							upper_limit + 1       ):
						for j in range(__lowerCAmelCase       ):
									catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

			return catalan_list


if __name__ == "__main__":
			print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
			print("\n*** Enter -1 at any time to quit ***")
			print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
			try:
						while True:
									_A     =  int(input().strip())
									if N < 0:
												print("\n********* Goodbye!! ************")
												break
									else:
												print(f"""The Catalan numbers from 0 through {N} are:""")
												print(catalan_numbers(N))
												print("Try another upper limit for the sequence: ", end="")
			except (NameError, ValueError):
						print("\n********* Invalid input, goodbye! ************\n")

			import doctest

			doctest.testmod()



