
"""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        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a    ):
		# 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
		SCREAMING_SNAKE_CASE_						 = TapasConfig.from_json_file(__a    )
		# set absolute/relative position embeddings parameter
		SCREAMING_SNAKE_CASE_						 = reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				SCREAMING_SNAKE_CASE_						 = TapasForQuestionAnswering(config=__a    )
		elif task == "WTQ":
				# run_task_main.py hparams
				SCREAMING_SNAKE_CASE_						 = 4
				SCREAMING_SNAKE_CASE_						 = True
				# hparam_utils.py hparams
				SCREAMING_SNAKE_CASE_						 = 0.6_6_4_6_9_4
				SCREAMING_SNAKE_CASE_						 = 0.2_0_7_9_5_1
				SCREAMING_SNAKE_CASE_						 = 0.1_2_1_1_9_4
				SCREAMING_SNAKE_CASE_						 = True
				SCREAMING_SNAKE_CASE_						 = True
				SCREAMING_SNAKE_CASE_						 = False
				SCREAMING_SNAKE_CASE_						 = 0.0_3_5_2_5_1_3

				SCREAMING_SNAKE_CASE_						 = TapasForQuestionAnswering(config=__a    )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				SCREAMING_SNAKE_CASE_						 = 4
				SCREAMING_SNAKE_CASE_						 = False
				# hparam_utils.py hparams
				SCREAMING_SNAKE_CASE_						 = 3_6.4_5_1_9
				SCREAMING_SNAKE_CASE_						 = 0.9_0_3_4_2_1
				SCREAMING_SNAKE_CASE_						 = 2_2_2.0_8_8
				SCREAMING_SNAKE_CASE_						 = True
				SCREAMING_SNAKE_CASE_						 = True
				SCREAMING_SNAKE_CASE_						 = True
				SCREAMING_SNAKE_CASE_						 = 0.7_6_3_1_4_1

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

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

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

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


if __name__ == "__main__":
						lowerCAmelCase__							=				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.'
						)
						lowerCAmelCase__							=				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"""




import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      CTRLTokenizer
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									SCREAMING_SNAKE_CASE_						 = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
									SCREAMING_SNAKE_CASE_						 = {'''unk_token''': '''<unk>'''}

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''merges_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )
									with open(self.merges_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_    )    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map    )
									return CTRLTokenizer.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									return input_text, output_text







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = CTRLTokenizer(self.vocab_file       ,       self.merges_file       ,       **self.special_tokens_map    )
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokens + [tokenizer.unk_token]

									SCREAMING_SNAKE_CASE_						 = [0, 1, 2, 4, 5, 1, 0, 3, 6]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )