
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				:   int     =				logging.getLogger(__name__)


@dataclass
class 			lowerCAmelCase :
		a							:					str
		a							:					List[str]
		a							:					Optional[List[str]]


@dataclass
class 			lowerCAmelCase :
		a							:					List[int]
		a							:					List[int]
		a							:					Optional[List[int]]       =					None
		a							:					Optional[List[int]]       =					None


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					List[Any]       =					"""train"""
		a							:					Optional[Any]       =					"""dev"""
		a							:					int       =					"""test"""


class 			lowerCAmelCase :




		@staticmethod
		def     lowercase       (				UpperCamelCase					,						UpperCamelCase      ):
								raise NotImplementedError




		@staticmethod
		def     lowercase       (				UpperCamelCase      ):
								raise NotImplementedError




		@staticmethod
		def     lowercase       (				UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=False					,						UpperCamelCase="[CLS]"					,						UpperCamelCase=1					,						UpperCamelCase="[SEP]"					,						UpperCamelCase=False					,						UpperCamelCase=False					,						UpperCamelCase=0					,						UpperCamelCase=0					,						UpperCamelCase=-100					,						UpperCamelCase=0					,						UpperCamelCase=True					,						):
								_SCREAMING_SNAKE_CASE				= {label: i for i, label in enumerate(UpperCamelCase      )}

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

														_SCREAMING_SNAKE_CASE				= []
														_SCREAMING_SNAKE_CASE				= []
														for word, label in zip(example.words					,						example.labels      ):
																				_SCREAMING_SNAKE_CASE				= tokenizer.tokenize(UpperCamelCase      )

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
														_SCREAMING_SNAKE_CASE				= tokenizer.num_special_tokens_to_add()
														if len(UpperCamelCase      ) > max_seq_length - special_tokens_count:
																				_SCREAMING_SNAKE_CASE				= tokens[: (max_seq_length - special_tokens_count)]
																				_SCREAMING_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]
														_SCREAMING_SNAKE_CASE				= [sequence_a_segment_id] * len(UpperCamelCase      )

														if cls_token_at_end:
																				tokens += [cls_token]
																				label_ids += [pad_token_label_id]
																				segment_ids += [cls_token_segment_id]
														else:
																				_SCREAMING_SNAKE_CASE				= [cls_token] + tokens
																				_SCREAMING_SNAKE_CASE				= [pad_token_label_id] + label_ids
																				_SCREAMING_SNAKE_CASE				= [cls_token_segment_id] + segment_ids

														_SCREAMING_SNAKE_CASE				= tokenizer.convert_tokens_to_ids(UpperCamelCase      )

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

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

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

														if "token_type_ids" not in tokenizer.model_input_names:
																				_SCREAMING_SNAKE_CASE				= None

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


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


						class 			lowerCAmelCase (				__UpperCAmelCase			):
								a							:					List[InputFeatures]
								a							:					int       =					nn.CrossEntropyLoss().ignore_index




								def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						UpperCamelCase=False					,						UpperCamelCase = Split.train					,						):
														# Load data features from cache or dataset file
														_SCREAMING_SNAKE_CASE				= os.path.join(
														    UpperCamelCase					,						"cached_{}_{}_{}".format(mode.value					,						tokenizer.__class__.__name__					,						str(UpperCamelCase      )      )					,						)

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




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




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


if is_tf_available():
						import tensorflow as tf


						class 			lowerCAmelCase :
								a							:					List[InputFeatures]
								a							:					int       =					-100




								def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						UpperCamelCase=False					,						UpperCamelCase = Split.train					,						):
														_SCREAMING_SNAKE_CASE				= token_classification_task.read_examples_from_file(UpperCamelCase					,						UpperCamelCase      )
														# TODO clean up all this to leverage built-in features of tokenizers
														_SCREAMING_SNAKE_CASE				= token_classification_task.convert_examples_to_features(
														    UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						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=UpperCamelCase					,						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:
																				_SCREAMING_SNAKE_CASE				= tf.data.Dataset.from_generator(
																				    UpperCamelCase					,						({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)					,						(
																				        {"input_ids": tf.TensorShape([None]      ), "attention_mask": tf.TensorShape([None]      )},
																				        tf.TensorShape([None]      ),
																				    )					,						)
														else:
																				_SCREAMING_SNAKE_CASE				= tf.data.Dataset.from_generator(
																				    UpperCamelCase					,						({"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      ):
														_SCREAMING_SNAKE_CASE				= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

														return self.dataset




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




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

'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


_snake_case				:   List[Any]     =				logging.get_logger(__name__)


class 			lowerCAmelCase (				__UpperCAmelCase			):




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