




'''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


_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			logging.getLogger(__name__)




@dataclass
class 			_snake_case		:
			lowerCAmelCase_					:				str
			lowerCAmelCase_					:				List[str]
			lowerCAmelCase_					:				Optional[List[str]]




@dataclass
class 			_snake_case		:
			lowerCAmelCase_					:				List[int]
			lowerCAmelCase_					:				List[int]
			lowerCAmelCase_					:				Optional[List[int]]							=   None
			lowerCAmelCase_					:				Optional[List[int]]							=   None




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				str							=   "train"
			lowerCAmelCase_					:				Optional[int]							=   "dev"
			lowerCAmelCase_					:				Union[str, Any]							=   "test"




class 			_snake_case		:




			@staticmethod
			def        lowerCAmelCase__    ( a__    ,							a__							)    ->    List[InputExample]:

										'''simple docstring'''




										raise NotImplementedError




			@staticmethod
			def        lowerCAmelCase__    ( a__							)    ->    List[str]:

										'''simple docstring'''




										raise NotImplementedError




			@staticmethod
			def        lowerCAmelCase__    ( a__    ,							a__    ,							a__    ,							a__    ,							a__=False    ,							a__="[CLS]"    ,							a__=1    ,							a__="[SEP]"    ,							a__=False    ,							a__=False    ,							a__=0    ,							a__=0    ,							a__=-100    ,							a__=0    ,							a__=True    ,							)    ->    List[InputFeatures]:

										'''simple docstring'''




										snake_case_      =				{label: i for i, label in enumerate(a__							)}

										snake_case_      =				[]
										for ex_index, example in enumerate(a__							):
																	if ex_index % 10_000 == 0:
																								logger.info("Writing example %d of %d"    ,							a__    ,							len(a__							)							)

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

																	snake_case_      =				tokenizer.convert_tokens_to_ids(a__							)

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

																	# Zero-pad up to the sequence length.
																	snake_case_      =				max_seq_length - len(a__							)
																	if pad_on_left:
																								snake_case_      =				([pad_token] * padding_length) + input_ids
																								snake_case_      =				([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																								snake_case_      =				([pad_token_segment_id] * padding_length) + segment_ids
																								snake_case_      =				([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:
																								snake_case_      =				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 			_snake_case		(     lowercase_							):
					lowerCAmelCase_					:				List[InputFeatures]
					lowerCAmelCase_					:				int							=   nn.CrossEntropyLoss().ignore_index




					def __init__( self    ,							a__    ,							a__    ,							a__    ,							a__    ,							a__    ,							a__ = None    ,							a__=False    ,							a__ = Split.train    ,							)    ->    str:

												'''simple docstring'''




												snake_case_      =				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.
												snake_case_      =				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}'							)
																										snake_case_      =				torch.load(a__							)
																			else:
																										logger.info(F'Creating features from dataset file at {data_dir}'							)
																										snake_case_      =				token_classification_task.read_examples_from_file(a__    ,							a__							)
																										# TODO clean up all this to leverage built-in features of tokenizers
																										snake_case_      =				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							)    ->    List[Any]:

												'''simple docstring'''




												return len(self.features							)




					def __getitem__( self    ,							a__							)    ->    InputFeatures:

												'''simple docstring'''




												return self.features[i]


if is_tf_available():
		import tensorflow as tf




		class 			_snake_case		:
					lowerCAmelCase_					:				List[InputFeatures]
					lowerCAmelCase_					:				int							=   -100




					def __init__( self    ,							a__    ,							a__    ,							a__    ,							a__    ,							a__    ,							a__ = None    ,							a__=False    ,							a__ = Split.train    ,							)    ->    Optional[int]:

												'''simple docstring'''




												snake_case_      =				token_classification_task.read_examples_from_file(a__    ,							a__							)
												# TODO clean up all this to leverage built-in features of tokenizers
												snake_case_      =				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:
																			snake_case_      =				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:
																			snake_case_      =				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:

												'''simple docstring'''




												snake_case_      =				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features							)							)							)

												return self.dataset




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

												'''simple docstring'''




												return len(self.features							)




					def __getitem__( self    ,							a__							)    ->    InputFeatures:

												'''simple docstring'''




												return self.features[i]










'''simple docstring'''





import argparse
from collections import defaultdict

import yaml


_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			"docs/source/en/_toctree.yml"


def 					UpperCamelCase_(     snake_case							:	Optional[Any]					):

							'''simple docstring'''






							snake_case_      =				defaultdict(snake_case					)
							for doc in model_doc:
														counts[doc["local"]] += 1
							snake_case_      =				[key for key, value in counts.items() if value > 1]

							snake_case_      =				[]
							for duplicate_key in duplicates:
														snake_case_      =				list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}					)
														if len(snake_case					) > 1:
																					raise ValueError(
																					    f'{duplicate_key} is present several times in the documentation table of content at '
																					    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
																					    "others."					)
														# Only add this once
														new_doc.append({"local": duplicate_key, "title": titles[0]}					)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]					)

							# Sort
							return sorted(snake_case							,    key=lambda snake_case					: s["title"].lower()					)


def 					UpperCamelCase_(     snake_case							:	Optional[int]=False					):

							'''simple docstring'''






							with open(snake_case							,    encoding="utf-8"					) as f:
														snake_case_      =				yaml.safe_load(f.read()					)

							# Get to the API doc
							snake_case_      =				0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							snake_case_      =				content[api_idx]["sections"]

							# Then to the model doc
							snake_case_      =				0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							snake_case_      =				api_doc[model_idx]["sections"]

							snake_case_      =				[(idx, section) for idx, section in enumerate(snake_case					) if "sections" in section]
							snake_case_      =				False
							for idx, modality_doc in modalities_docs:
														snake_case_      =				modality_doc["sections"]
														snake_case_      =				clean_model_doc_toc(snake_case					)

														if old_modality_doc != new_modality_doc:
																					snake_case_      =				True
																					if overwrite:
																												snake_case_      =				new_modality_doc

							if diff:
														if overwrite:
																					snake_case_      =				model_doc
																					snake_case_      =				api_doc
																					with open(snake_case							,    "w"							,    encoding="utf-8"					) as f:
																												f.write(yaml.dump(snake_case							,    allow_unicode=snake_case					)					)
														else:
																					raise ValueError(
																					    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."					)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[str]						=			argparse.ArgumentParser()
		parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
		_SCREAMING_SNAKE_CASE							:       Any						=			parser.parse_args()

		check_model_doc(args.fix_and_overwrite)




