





'''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


lowerCAmelCase       		=      logging.getLogger(__name__)
@dataclass
class 		lowerCamelCase  :
	snake_case_      =   42
	snake_case_      =   42
	snake_case_      =   42






@dataclass
class 		lowerCamelCase  :
	snake_case_      =   42
	snake_case_      =   42
	snake_case_      =   None
	snake_case_      =   None






class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "train"
	snake_case_      =   "dev"
	snake_case_      =   "test"






class 		lowerCamelCase  :



	@staticmethod
	def   _lowerCamelCase    (   a_		,      a_	):
			raise NotImplementedError



	@staticmethod
	def   _lowerCamelCase    (   a_	):
			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		,      ):
			lowerCAmelCase     :    int						=   {label: i for i, label in enumerate(a_	)}

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

					lowerCAmelCase     :    str						=   []
					lowerCAmelCase     :    str						=   []
					for word, label in zip(example.words		,      example.labels	):
							lowerCAmelCase     :    List[str]						=   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.
					lowerCAmelCase     :    Union[str, Any]						=   tokenizer.num_special_tokens_to_add()
					if len(a_	) > max_seq_length - special_tokens_count:
							lowerCAmelCase     :    List[Any]						=   tokens[: (max_seq_length - special_tokens_count)]
							lowerCAmelCase     :    Tuple						=   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]
					lowerCAmelCase     :    Optional[Any]						=   [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:
							lowerCAmelCase     :    Optional[Any]						=   [cls_token] + tokens
							lowerCAmelCase     :    Tuple						=   [pad_token_label_id] + label_ids
							lowerCAmelCase     :    Optional[int]						=   [cls_token_segment_id] + segment_ids

					lowerCAmelCase     :    Optional[Any]						=   tokenizer.convert_tokens_to_ids(a_	)

					# The mask has 1 for real tokens and 0 for padding tokens. Only real
					# tokens are attended to.
					lowerCAmelCase     :    Optional[int]						=   [1 if mask_padding_with_zero else 0] * len(a_	)

					# Zero-pad up to the sequence length.
					lowerCAmelCase     :    Any						=   max_seq_length - len(a_	)
					if pad_on_left:
							lowerCAmelCase     :    str						=   ([pad_token] * padding_length) + input_ids
							lowerCAmelCase     :    Any						=   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
							lowerCAmelCase     :    Dict						=   ([pad_token_segment_id] * padding_length) + segment_ids
							lowerCAmelCase     :    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:
							lowerCAmelCase     :    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       ):
						snake_case_      =   42
						snake_case_      =   nn.CrossEntropyLoss().ignore_index



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





						def __getitem__(   self		,      a_	):
								return self.features[i]


if is_tf_available():
					import tensorflow as tf
					class 		lowerCamelCase  :
						snake_case_      =   42
						snake_case_      =   -100



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

								return self.dataset



						def __len__(   self	):
								return len(self.features	)





						def __getitem__(   self		,      a_	):
								return self.features[i]










'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowerCAmelCase       		=      logging.get_logger(__name__)
class 		lowerCamelCase  (      _A       ):



	def __init__(   self		,      *a_		,      **a_	):
			warnings.warn(
			    "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
			    " Please use ChineseCLIPImageProcessor instead."		,      a_		,      )
			super().__init__(*a_		,      **a_	)



