

'''simple docstring'''



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


__snake_case			:     Optional[int]										=      logging.getLogger(__name__)



@dataclass
class lowerCamelCase			:



			'''simple docstring'''






			__snake_case        =	42
			__snake_case        =	42
			__snake_case        =	42



@dataclass
class lowerCamelCase			:



			'''simple docstring'''






			__snake_case        =	42
			__snake_case        =	42
			__snake_case        =	None
			__snake_case        =	None



class lowerCamelCase			(  A_						):



			'''simple docstring'''






			__snake_case        =	'train'
			__snake_case        =	'dev'
			__snake_case        =	'test'



class lowerCamelCase			:



			'''simple docstring'''






			@staticmethod
			def lowercase__						(				lowerCAmelCase_	:						Optional[Any]		,      lowerCAmelCase_	:						Union[Split, str]			)      ->		str:





									'''simple docstring'''
									raise NotImplementedError


			@staticmethod
			def lowercase__						(				lowerCAmelCase_	:						str			)      ->		Dict:





									'''simple docstring'''
									raise NotImplementedError



			@staticmethod
			def lowercase__						(				lowerCAmelCase_	:						List[InputExample]		,      lowerCAmelCase_	:						List[str]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						PreTrainedTokenizer		,      lowerCAmelCase_	:						str=False		,      lowerCAmelCase_	:						List[Any]="[CLS]"		,      lowerCAmelCase_	:						Optional[int]=1		,      lowerCAmelCase_	:						List[str]="[SEP]"		,      lowerCAmelCase_	:						int=False		,      lowerCAmelCase_	:						List[Any]=False		,      lowerCAmelCase_	:						List[Any]=0		,      lowerCAmelCase_	:						Dict=0		,      lowerCAmelCase_	:						List[str]=-1_00		,      lowerCAmelCase_	:						Optional[Any]=0		,      lowerCAmelCase_	:						Any=True		,      )      ->		Dict:





									'''simple docstring'''
									A__					:       List[str]     ={label: i for i, label in enumerate(_lowerCamelCase			)}

									A__					:       Union[str, Any]     =[]
									for ex_index, example in enumerate(_lowerCamelCase			):
															if ex_index % 1_00_00 == 0:
																					logger.info("""Writing example %d of %d"""		,      _lowerCamelCase		,      len(_lowerCamelCase			)			)

															A__					:       Union[str, Any]     =[]
															A__					:       Optional[int]     =[]
															for word, label in zip(example.words		,      example.labels			):
																					A__					:       int     =tokenizer.tokenize(_lowerCamelCase			)

																					# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																					if len(_lowerCamelCase			) > 0:
																											tokens.extend(_lowerCamelCase			)
																											# 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(_lowerCamelCase			) - 1)			)

																					 # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
															A__					:       Optional[int]     =tokenizer.num_special_tokens_to_add()
															if len(_lowerCamelCase			) > max_seq_length - special_tokens_count:
																					A__					:       List[Any]     =tokens[: (max_seq_length - special_tokens_count)]
																					A__					:       Dict     =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]
															A__					:       Optional[int]     =[sequence_a_segment_id] * len(_lowerCamelCase			)

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

															A__					:       Optional[Any]     =tokenizer.convert_tokens_to_ids(_lowerCamelCase			)

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

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

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

															if "token_type_ids" not in tokenizer.model_input_names:
																					A__					:       int     =None

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


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



		class lowerCamelCase			(  A_						):



					'''simple docstring'''






					__snake_case        =	42
					__snake_case        =	nn.CrossEntropyLoss().ignore_index


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





											'''simple docstring'''
											# Load data features from cache or dataset file
											A__					:       List[str]     =os.path.join(
											    _lowerCamelCase		,      """cached_{}_{}_{}""".format(mode.value		,      tokenizer.__class__.__name__		,      str(_lowerCamelCase			)			)		,      )

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											A__					:       Optional[Any]     =cached_features_file + """.lock"""
											with FileLock(_lowerCamelCase			):
																	if os.path.exists(_lowerCamelCase			) and not overwrite_cache:
																							logger.info(f"Loading features from cached file {cached_features_file}"			)
																							A__					:       Union[str, Any]     =torch.load(_lowerCamelCase			)
																	else:
																							logger.info(f"Creating features from dataset file at {data_dir}"			)
																							A__					:       Any     =token_classification_task.read_examples_from_file(_lowerCamelCase		,      _lowerCamelCase			)
																							# TODO clean up all this to leverage built-in features of tokenizers
																							A__					:       Dict     =token_classification_task.convert_examples_to_features(
																							    _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      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=_lowerCamelCase		,      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		,      _lowerCamelCase			)


					def __len__(				self	:						Any			)      ->		Union[str, Any]:





											'''simple docstring'''
											return len(self.features			)



					def __getitem__(				self	:						List[Any]		,      lowerCAmelCase_	:						List[Any]			)      ->		Union[str, Any]:





											'''simple docstring'''
											return self.features[i]


if is_tf_available():
		import tensorflow as tf



		class lowerCamelCase			:



					'''simple docstring'''






					__snake_case        =	42
					__snake_case        =	-100


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





											'''simple docstring'''
											A__					:       Tuple     =token_classification_task.read_examples_from_file(_lowerCamelCase		,      _lowerCamelCase			)
											# TODO clean up all this to leverage built-in features of tokenizers
											A__					:       str     =token_classification_task.convert_examples_to_features(
											    _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      _lowerCamelCase		,      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=_lowerCamelCase		,      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:
																	A__					:       Union[str, Any]     =tf.data.Dataset.from_generator(
																	    _lowerCamelCase		,      ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)		,      (
																	        {"""input_ids""": tf.TensorShape([None]			), """attention_mask""": tf.TensorShape([None]			)},
																	        tf.TensorShape([None]			),
																	    )		,      )
											else:
																	A__					:       int     =tf.data.Dataset.from_generator(
																	    _lowerCamelCase		,      ({"""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 lowercase__						(				self	:						Optional[int]			)      ->		int:





											'''simple docstring'''
											A__					:       Any     =self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features			)			)			)

											return self.dataset


					def __len__(				self	:						Any			)      ->		Optional[Any]:





											'''simple docstring'''
											return len(self.features			)



					def __getitem__(				self	:						List[Any]		,      lowerCAmelCase_	:						Tuple			)      ->		Any:





											'''simple docstring'''
											return self.features[i]




'''simple docstring'''



from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def 						__lowerCamelCase	(       )	->							List[Any]:






						"""simple docstring"""





						A__					:       List[Any]     =ArgumentParser("""Transformers CLI tool""",		usage="""transformers-cli <command> [<args>]"""	)
						A__					:       List[Any]     =parser.add_subparsers(help="""transformers-cli command helpers"""	)

						# Register commands
						ConvertCommand.register_subcommand(__snake_case	)
						DownloadCommand.register_subcommand(__snake_case	)
						EnvironmentCommand.register_subcommand(__snake_case	)
						RunCommand.register_subcommand(__snake_case	)
						ServeCommand.register_subcommand(__snake_case	)
						UserCommands.register_subcommand(__snake_case	)
						AddNewModelCommand.register_subcommand(__snake_case	)
						AddNewModelLikeCommand.register_subcommand(__snake_case	)
						LfsCommands.register_subcommand(__snake_case	)
						PTtoTFCommand.register_subcommand(__snake_case	)

						# Let's go
						A__					:       List[str]     =parser.parse_args()

						if not hasattr(__snake_case,		"""func"""	):
												parser.print_help()
												exit(1	)

						# Run
						A__					:       Optional[Any]     =args.func(__snake_case	)
						service.run()


if __name__ == "__main__":
		main()

