from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  ['image_processor', 'tokenizer']
  lowercase_								=  'BlipImageProcessor'
  lowercase_								=  'AutoTokenizer'




  def __init__(   self  :						Optional[int]  ,			a_  :						Union[str, Any]  ,			a_  :						List[Any]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		False
       super().__init__(a_  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.image_processor




  def __call__(   self  :						List[Any]  ,			a_  :						ImageInput = None  ,			a_  :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,			a_  :						bool = True  ,			a_  :						Union[bool, str, PaddingStrategy] = False  ,			a_  :						Union[bool, str, TruncationStrategy] = None  ,			a_  :						Optional[int] = None  ,			a_  :						int = 0  ,			a_  :						Optional[int] = None  ,			a_  :						Optional[bool] = None  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			a_  :						bool = True  ,			a_  :						Optional[Union[str, TensorType]] = None  ,			**a_  :						int  ,			)->							BatchEncoding:




       """simple docstring"""
       if images is None and text is None:
            raise ValueError('You have to specify either images or text.'					)

       # Get only text
       if images is None:
            SCREAMING_SNAKE_CASE__					:					int      =		self.tokenizer
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.tokenizer(
                text=a_  ,			add_special_tokens=a_  ,			padding=a_  ,			truncation=a_  ,			max_length=a_  ,			stride=a_  ,			pad_to_multiple_of=a_  ,			return_attention_mask=a_  ,			return_overflowing_tokens=a_  ,			return_special_tokens_mask=a_  ,			return_offsets_mapping=a_  ,			return_token_type_ids=a_  ,			return_length=a_  ,			verbose=a_  ,			return_tensors=a_  ,			**a_  ,			)
            return text_encoding

       # add pixel_values
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.image_processor(a_  ,			return_tensors=a_					)

       if text is not None:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.tokenizer(
                text=a_  ,			add_special_tokens=a_  ,			padding=a_  ,			truncation=a_  ,			max_length=a_  ,			stride=a_  ,			pad_to_multiple_of=a_  ,			return_attention_mask=a_  ,			return_overflowing_tokens=a_  ,			return_special_tokens_mask=a_  ,			return_offsets_mapping=a_  ,			return_token_type_ids=a_  ,			return_length=a_  ,			verbose=a_  ,			return_tensors=a_  ,			**a_  ,			)
       else:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		None

       if text_encoding is not None:
            encoding_image_processor.update(a_					)

       return encoding_image_processor




  def 						__lowercase(   self  :						Any  ,			*a_  :						Tuple  ,			**a_  :						Tuple					)->							List[str]:




       """simple docstring"""
       return self.tokenizer.batch_decode(*a_  ,			**a_					)




  def 						__lowercase(   self  :						List[Any]  ,			*a_  :						Any  ,			**a_  :						Dict					)->							int:




       """simple docstring"""
       return self.tokenizer.decode(*a_  ,			**a_					)




  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def 						__lowercase(   self  :						Tuple					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)




'''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						:							int    							=						logging.getLogger(__name__)






@dataclass
class     A :
							a_										=   42
							a_										=   42
							a_										=   42






@dataclass
class     A :
							a_										=   42
							a_										=   42
							a_										=   None
							a_										=   None






class     A ( UpperCAmelCase ):
							a_										=   '''train'''
							a_										=   '''dev'''
							a_										=   '''test'''






class     A :





							@staticmethod
							def    snake_case__    (   __a					:				List[Any]  ,		__a					:				Union[Split, str]						)				->						List[InputExample]:
								raise NotImplementedError





							@staticmethod
							def    snake_case__    (   __a					:				str						)				->						List[str]:
								raise NotImplementedError






							@staticmethod
							def    snake_case__    (   __a					:				List[InputExample]  ,		__a					:				List[str]  ,		__a					:				int  ,		__a					:				PreTrainedTokenizer  ,		__a					:				Dict=False  ,		__a					:				int="[CLS]"  ,		__a					:				Dict=1  ,		__a					:				Tuple="[SEP]"  ,		__a					:				Any=False  ,		__a					:				Union[str, Any]=False  ,		__a					:				Any=0  ,		__a					:				Optional[int]=0  ,		__a					:				Tuple=-1_0_0  ,		__a					:				Optional[Any]=0  ,		__a					:				int=True  ,		)				->						List[InputFeatures]:
								__UpperCAmelCase      					=      {label: i for i, label in enumerate(__a						)}

								__UpperCAmelCase      					=      []
								for ex_index, example in enumerate(__a						):
									if ex_index % 1_0_0_0_0 == 0:
										logger.info('''Writing example %d of %d'''  ,		__a  ,		len(__a						)						)

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

									__UpperCAmelCase      					=      tokenizer.convert_tokens_to_ids(__a						)

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

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





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






												def __getitem__(   self					:				int  ,		__a					:				int						)				->						InputFeatures:
													return self.features[i]


if is_tf_available():
					import tensorflow as tf






					class     A :
												a_										=   42
												a_										=   -1_0_0





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

													return self.dataset





												def __len__(   self					:				int						)				->						str:
													return len(self.features						)






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