






import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCamelCase       :       Any        =				logging.getLogger(__name__)

@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	str
					_A  :	List[str]
					_A  :	Optional[List[str]]







@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	List[int]
					_A  :	List[int]
					_A  :	Optional[List[int]]     =					None
					_A  :	Optional[List[int]]     =					None







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Union[str, Any]     =					'''train'''
					_A  :	List[Any]     =					'''dev'''
					_A  :	Optional[int]     =					'''test'''







class 						lowerCAmelCase   :

					'''simple docstring'''

					@staticmethod
					def 					lowerCAmelCase (     __a :		str						,   __a :		Union[Split, str]							)	->     List[InputExample]:


												"""simple docstring"""

												raise NotImplementedError



					@staticmethod
					def 					lowerCAmelCase (     __a :		str							)	->     List[str]:


												"""simple docstring"""

												raise NotImplementedError



					@staticmethod
					def 					lowerCAmelCase (     __a :		List[InputExample]						,   __a :		List[str]						,   __a :		int						,   __a :		PreTrainedTokenizer						,   __a :		str=False						,   __a :		Optional[int]="[CLS]"						,   __a :		Any=1						,   __a :		List[str]="[SEP]"						,   __a :		Any=False						,   __a :		List[str]=False						,   __a :		List[Any]=0						,   __a :		int=0						,   __a :		Union[str, Any]=-100						,   __a :		str=0						,   __a :		Optional[Any]=True						,   )	->     List[InputFeatures]:


												"""simple docstring"""

												__lowercase					:							int			=   {label: i for i, label in enumerate(__a							)}

												__lowercase					:							Optional[int]			=   []
												for ex_index, example in enumerate(__a							):
																			if ex_index % 10000 == 0:
																										logger.info("""Writing example %d of %d"""						,   __a						,   len(__a							)							)

																			__lowercase					:							Optional[Any]			=   []
																			__lowercase					:							Any			=   []
																			for word, label in zip(example.words						,   example.labels							):
																										__lowercase					:							Any			=   tokenizer.tokenize(__a							)

																										# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																										if len(__a							) > 0:
																																	tokens.extend(__a							)
																																	# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																																	label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__a							) - 1)							)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																			__lowercase					:							List[Any]			=   tokenizer.num_special_tokens_to_add()
																			if len(__a							) > max_seq_length - special_tokens_count:
																										__lowercase					:							Tuple			=   tokens[: (max_seq_length - special_tokens_count)]
																										__lowercase					:							List[str]			=   label_ids[: (max_seq_length - special_tokens_count)]

																			# The convention in BERT is:
																			# (a) For sequence pairs:
																			#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
																			#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
																			# (b) For single sequences:
																			#  tokens:   [CLS] the dog is hairy . [SEP]
																			#  type_ids:   0   0   0   0  0     0   0
																			#
																			# Where "type_ids" are used to indicate whether this is the first
																			# sequence or the second sequence. The embedding vectors for `type=0` and
																			# `type=1` were learned during pre-training and are added to the wordpiece
																			# embedding vector (and position vector). This is not *strictly* necessary
																			# since the [SEP] token unambiguously separates the sequences, but it makes
																			# it easier for the model to learn the concept of sequences.
																			#
																			# For classification tasks, the first vector (corresponding to [CLS]) is
																			# used as the "sentence vector". Note that this only makes sense because
																			# the entire model is fine-tuned.
																			tokens += [sep_token]
																			label_ids += [pad_token_label_id]
																			if sep_token_extra:
																										# roberta uses an extra separator b/w pairs of sentences
																										tokens += [sep_token]
																										label_ids += [pad_token_label_id]
																			__lowercase					:							str			=   [sequence_a_segment_id] * len(__a							)

																			if cls_token_at_end:
																										tokens += [cls_token]
																										label_ids += [pad_token_label_id]
																										segment_ids += [cls_token_segment_id]
																			else:
																										__lowercase					:							int			=   [cls_token] + tokens
																										__lowercase					:							List[Any]			=   [pad_token_label_id] + label_ids
																										__lowercase					:							Union[str, Any]			=   [cls_token_segment_id] + segment_ids

																			__lowercase					:							Tuple			=   tokenizer.convert_tokens_to_ids(__a							)

																			# The mask has 1 for real tokens and 0 for padding tokens. Only real
																			# tokens are attended to.
																			__lowercase					:							Dict			=   [1 if mask_padding_with_zero else 0] * len(__a							)

																			# Zero-pad up to the sequence length.
																			__lowercase					:							List[Any]			=   max_seq_length - len(__a							)
																			if pad_on_left:
																										__lowercase					:							List[Any]			=   ([pad_token] * padding_length) + input_ids
																										__lowercase					:							Optional[int]			=   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																										__lowercase					:							Optional[int]			=   ([pad_token_segment_id] * padding_length) + segment_ids
																										__lowercase					:							Optional[Any]			=   ([pad_token_label_id] * padding_length) + label_ids
																			else:
																										input_ids += [pad_token] * padding_length
																										input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																										segment_ids += [pad_token_segment_id] * padding_length
																										label_ids += [pad_token_label_id] * padding_length

																			assert len(__a							) == max_seq_length
																			assert len(__a							) == max_seq_length
																			assert len(__a							) == max_seq_length
																			assert len(__a							) == max_seq_length

																			if ex_index < 5:
																										logger.info("""*** Example ***"""							)
																										logger.info("""guid: %s"""						,   example.guid							)
																										logger.info("""tokens: %s"""						,   """ """.join([str(__a							) for x in tokens]							)							)
																										logger.info("""input_ids: %s"""						,   """ """.join([str(__a							) for x in input_ids]							)							)
																										logger.info("""input_mask: %s"""						,   """ """.join([str(__a							) for x in input_mask]							)							)
																										logger.info("""segment_ids: %s"""						,   """ """.join([str(__a							) for x in segment_ids]							)							)
																										logger.info("""label_ids: %s"""						,   """ """.join([str(__a							) for x in label_ids]							)							)

																			if "token_type_ids" not in tokenizer.model_input_names:
																										__lowercase					:							Union[str, Any]			=   None

																			features.append(
																			    InputFeatures(
																			        input_ids=__a						,   attention_mask=__a						,   token_type_ids=__a						,   label_ids=__a							)							)
												return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset

	class 						lowerCAmelCase   (       __a	):

						'''simple docstring'''

						_A  :	List[InputFeatures]
						_A  :	int     =					nn.CrossEntropyLoss().ignore_index



						def __init__(     self :		int						,   __a :		TokenClassificationTask						,   __a :		str						,   __a :		PreTrainedTokenizer						,   __a :		List[str]						,   __a :		str						,   __a :		Optional[int] = None						,   __a :		Dict=False						,   __a :		Split = Split.train						,   )	->     Any:


													"""simple docstring"""

													__lowercase					:							Dict			=   os.path.join(
													    __a						,   """cached_{}_{}_{}""".format(mode.value						,   tokenizer.__class__.__name__						,   str(__a							)							)						,   )

													# Make sure only the first process in distributed training processes the dataset,
													# and the others will use the cache.
													__lowercase					:							int			=   cached_features_file + """.lock"""
													with FileLock(__a							):
																				if os.path.exists(__a							) and not overwrite_cache:
																											logger.info(F"Loading features from cached file {cached_features_file}"							)
																											__lowercase					:							List[str]			=   torch.load(__a							)
																				else:
																											logger.info(F"Creating features from dataset file at {data_dir}"							)
																											__lowercase					:							Tuple			=   token_classification_task.read_examples_from_file(__a						,   __a							)
																											# TODO clean up all this to leverage built-in features of tokenizers
																											__lowercase					:							str			=   token_classification_task.convert_examples_to_features(
																											    __a						,   __a						,   __a						,   __a						,   cls_token_at_end=bool(model_type in ["""xlnet"""]							)						,   cls_token=tokenizer.cls_token						,   cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0						,   sep_token=tokenizer.sep_token						,   sep_token_extra=__a						,   pad_on_left=bool(tokenizer.padding_side == """left"""							)						,   pad_token=tokenizer.pad_token_id						,   pad_token_segment_id=tokenizer.pad_token_type_id						,   pad_token_label_id=self.pad_token_label_id						,   )
																											logger.info(F"Saving features into cached file {cached_features_file}"							)
																											torch.save(self.features						,   __a							)



						def __len__(     self :		Dict							)	->     Tuple:


													"""simple docstring"""

													return len(self.features							)



						def __getitem__(     self :		List[str]						,   __a :		int							)	->     InputFeatures:


													"""simple docstring"""

													return self.features[i]


if is_tf_available():
	import tensorflow as tf

	class 						lowerCAmelCase   :

						'''simple docstring'''

						_A  :	List[InputFeatures]
						_A  :	int     =					-100



						def __init__(     self :		str						,   __a :		TokenClassificationTask						,   __a :		str						,   __a :		PreTrainedTokenizer						,   __a :		List[str]						,   __a :		str						,   __a :		Optional[int] = None						,   __a :		List[Any]=False						,   __a :		Split = Split.train						,   )	->     Optional[Any]:


													"""simple docstring"""

													__lowercase					:							List[Any]			=   token_classification_task.read_examples_from_file(__a						,   __a							)
													# TODO clean up all this to leverage built-in features of tokenizers
													__lowercase					:							Tuple			=   token_classification_task.convert_examples_to_features(
													    __a						,   __a						,   __a						,   __a						,   cls_token_at_end=bool(model_type in ["""xlnet"""]							)						,   cls_token=tokenizer.cls_token						,   cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0						,   sep_token=tokenizer.sep_token						,   sep_token_extra=__a						,   pad_on_left=bool(tokenizer.padding_side == """left"""							)						,   pad_token=tokenizer.pad_token_id						,   pad_token_segment_id=tokenizer.pad_token_type_id						,   pad_token_label_id=self.pad_token_label_id						,   )

													def gen():
																				for ex in self.features:
																											if ex.token_type_ids is None:
																																		yield (
																																		    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																																		    ex.label_ids,
																																		)
																											else:
																																		yield (
																																		    {
																																		        "input_ids": ex.input_ids,
																																		        "attention_mask": ex.attention_mask,
																																		        "token_type_ids": ex.token_type_ids,
																																		    },
																																		    ex.label_ids,
																																		)

													if "token_type_ids" not in tokenizer.model_input_names:
																				__lowercase					:							Optional[Any]			=   tf.data.Dataset.from_generator(
																				    __a						,   ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)						,   (
																				        {"""input_ids""": tf.TensorShape([None]							), """attention_mask""": tf.TensorShape([None]							)},
																				        tf.TensorShape([None]							),
																				    )						,   )
													else:
																				__lowercase					:							List[Any]			=   tf.data.Dataset.from_generator(
																				    __a						,   ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)						,   (
																				        {
																				            """input_ids""": tf.TensorShape([None]							),
																				            """attention_mask""": tf.TensorShape([None]							),
																				            """token_type_ids""": tf.TensorShape([None]							),
																				        },
																				        tf.TensorShape([None]							),
																				    )						,   )



						def 					lowerCAmelCase (     self :		str							)	->     Dict:


													"""simple docstring"""

													__lowercase					:							Optional[Any]			=   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features							)							)							)

													return self.dataset



						def __len__(     self :		List[Any]							)	->     int:


													"""simple docstring"""

													return len(self.features							)



						def __getitem__(     self :		Tuple						,   __a :		Union[str, Any]							)	->     InputFeatures:


													"""simple docstring"""

													return self.features[i]







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 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  int					):
							# 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
							__lowercase					:							Optional[int]			=   TapasConfig.from_json_file(lowerCAmelCase_					)
							# set absolute/relative position embeddings parameter
							__lowercase					:							Optional[Any]			=   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowercase					:							Union[str, Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowercase					:							List[Any]			=   4
														__lowercase					:							Union[str, Any]			=   True
														# hparam_utils.py hparams
														__lowercase					:							Any			=   0.664_694
														__lowercase					:							Tuple			=   0.207_951
														__lowercase					:							Dict			=   0.121_194
														__lowercase					:							List[str]			=   True
														__lowercase					:							str			=   True
														__lowercase					:							Dict			=   False
														__lowercase					:							Tuple			=   0.0_352_513

														__lowercase					:							List[Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowercase					:							Optional[int]			=   4
														__lowercase					:							int			=   False
														# hparam_utils.py hparams
														__lowercase					:							Tuple			=   36.4_519
														__lowercase					:							str			=   0.903_421
														__lowercase					:							List[Any]			=   222.088
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Tuple			=   True
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Optional[Any]			=   0.763_141

														__lowercase					:							str			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "TABFACT":
														__lowercase					:							List[Any]			=   TapasForSequenceClassification(config=lowerCAmelCase_					)
							elif task == "MLM":
														__lowercase					:							Optional[int]			=   TapasForMaskedLM(config=lowerCAmelCase_					)
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowercase					:							Dict			=   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}"					)
							__lowercase					:							Any			=   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__":
	lowerCamelCase       :       List[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.'''
	)
	lowerCamelCase       :       int        =				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,
	)