
"""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


_lowercase							   =				logging.getLogger(__name__)







@dataclass
class 				lowerCAmelCase_				:



						'''simple docstring'''


						_lowerCamelCase:    str
						_lowerCamelCase:    List[str]
						_lowerCamelCase:    Optional[List[str]]







@dataclass
class 				lowerCAmelCase_				:



						'''simple docstring'''


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







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[int]      =    '''train'''
						_lowerCamelCase:    Tuple      =    '''dev'''
						_lowerCamelCase:    Optional[Any]      =    '''test'''







class 				lowerCAmelCase_				:



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      List[str]  ,A_     :      Union[Split, str]      )     ->   List[InputExample]:
							raise NotImplementedError


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      str      )     ->   List[str]:
							raise NotImplementedError




						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      List[InputExample]  ,A_     :      List[str]  ,A_     :      int  ,A_     :      PreTrainedTokenizer  ,A_     :      int=False  ,A_     :      Optional[int]="[CLS]"  ,A_     :      Union[str, Any]=1  ,A_     :      Union[str, Any]="[SEP]"  ,A_     :      int=False  ,A_     :      List[Any]=False  ,A_     :      Any=0  ,A_     :      List[str]=0  ,A_     :      int=-100  ,A_     :      List[str]=0  ,A_     :      Tuple=True  ,)     ->   List[InputFeatures]:
							A						   =   {label: i for i, label in enumerate(A_      )}

							A						   =   []
							for ex_index, example in enumerate(A_      ):
								if ex_index % 1_0000 == 0:
									logger.info('Writing example %d of %d'  ,A_  ,len(A_      )      )

								A						   =   []
								A						   =   []
								for word, label in zip(example.words  ,example.labels      ):
									A						   =   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.
								A						   =   tokenizer.num_special_tokens_to_add()
								if len(A_      ) > max_seq_length - special_tokens_count:
									A						   =   tokens[: (max_seq_length - special_tokens_count)]
									A						   =   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						   =   [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:
									A						   =   [cls_token] + tokens
									A						   =   [pad_token_label_id] + label_ids
									A						   =   [cls_token_segment_id] + segment_ids

								A						   =   tokenizer.convert_tokens_to_ids(A_      )

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

								# Zero-pad up to the sequence length.
								A						   =   max_seq_length - len(A_      )
								if pad_on_left:
									A						   =   ([pad_token] * padding_length) + input_ids
									A						   =   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
									A						   =   ([pad_token_segment_id] * padding_length) + segment_ids
									A						   =   ([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:
									A						   =   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_				(      _lowercase    ):



								'''simple docstring'''


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


								def __init__( self     :      Tuple  ,A_     :      TokenClassificationTask  ,A_     :      str  ,A_     :      PreTrainedTokenizer  ,A_     :      List[str]  ,A_     :      str  ,A_     :      Optional[int] = None  ,A_     :      Optional[int]=False  ,A_     :      Split = Split.train  ,)     ->   Dict:
									# Load data features from cache or dataset file
									A						   =   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.
									A						   =   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}'      )
											A						   =   torch.load(A_      )
										else:
											logger.info(F'Creating features from dataset file at {data_dir}'      )
											A						   =   token_classification_task.read_examples_from_file(A_  ,A_      )
											# TODO clean up all this to leverage built-in features of tokenizers
											A						   =   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     :      Optional[int]      )     ->   List[str]:
									return len(self.features      )




								def __getitem__( self     :      List[Any]  ,A_     :      Optional[int]      )     ->   InputFeatures:
									return self.features[i]


if is_tf_available():
		import tensorflow as tf







		class 				lowerCAmelCase_				:



								'''simple docstring'''


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


								def __init__( self     :      Any  ,A_     :      TokenClassificationTask  ,A_     :      str  ,A_     :      PreTrainedTokenizer  ,A_     :      List[str]  ,A_     :      str  ,A_     :      Optional[int] = None  ,A_     :      str=False  ,A_     :      Split = Split.train  ,)     ->   Tuple:
									A						   =   token_classification_task.read_examples_from_file(A_  ,A_      )
									# TODO clean up all this to leverage built-in features of tokenizers
									A						   =   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:
										A						   =   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:
										A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Any:
									A						   =   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

									return self.dataset


								def __len__( self     :      Optional[Any]      )     ->   str:
									return len(self.features      )




								def __getitem__( self     :      Union[str, Any]  ,A_     :      Optional[Any]      )     ->   InputFeatures:
									return self.features[i]

"""simple docstring"""


# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_lowercase							   =				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
_lowercase							   =				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

_lowercase							   =				'''|'''.join(sys.argv[1:])
_lowercase							   =				re.compile(rF"""^({joined_dirs}).*?\.py$""")

_lowercase							   =				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')