"""simple docstring"""





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class  _snake_case     (					a__		):
						snake_case__			  =			["image_processor", "tokenizer"]
						snake_case__			  =			"BlipImageProcessor"
						snake_case__			  =			"AutoTokenizer"






						def __init__(       self      :			Any							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Tuple    ):
													super().__init__(UpperCAmelCase							,					UpperCAmelCase    )

													# add QFormer tokenizer
													__lowerCamelCase					:		str		   =     qformer_tokenizer






						def __call__(       self      :			Optional[int]							,					UpperCAmelCase      :			ImageInput = None							,					UpperCAmelCase      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Union[bool, str, PaddingStrategy] = False							,					UpperCAmelCase      :			Union[bool, str, TruncationStrategy] = None							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			Optional[bool] = None							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[Union[str, TensorType]] = None							,					**UpperCAmelCase      :			str							,					):
													if images is None and text is None:
																				raise ValueError("You have to specify at least images or text."    )

													__lowerCamelCase					:		Any		   =     BatchFeature()

													if text is not None:
																				__lowerCamelCase					:		Optional[Any]		   =     self.tokenizer(
																				    text=UpperCAmelCase							,					add_special_tokens=UpperCAmelCase							,					padding=UpperCAmelCase							,					truncation=UpperCAmelCase							,					max_length=UpperCAmelCase							,					stride=UpperCAmelCase							,					pad_to_multiple_of=UpperCAmelCase							,					return_attention_mask=UpperCAmelCase							,					return_overflowing_tokens=UpperCAmelCase							,					return_special_tokens_mask=UpperCAmelCase							,					return_offsets_mapping=UpperCAmelCase							,					return_token_type_ids=UpperCAmelCase							,					return_length=UpperCAmelCase							,					verbose=UpperCAmelCase							,					return_tensors=UpperCAmelCase							,					**UpperCAmelCase							,					)
																				encoding.update(UpperCAmelCase    )
																				__lowerCamelCase					:		List[str]		   =     self.qformer_tokenizer(
																				    text=UpperCAmelCase							,					add_special_tokens=UpperCAmelCase							,					padding=UpperCAmelCase							,					truncation=UpperCAmelCase							,					max_length=UpperCAmelCase							,					stride=UpperCAmelCase							,					pad_to_multiple_of=UpperCAmelCase							,					return_attention_mask=UpperCAmelCase							,					return_overflowing_tokens=UpperCAmelCase							,					return_special_tokens_mask=UpperCAmelCase							,					return_offsets_mapping=UpperCAmelCase							,					return_token_type_ids=UpperCAmelCase							,					return_length=UpperCAmelCase							,					verbose=UpperCAmelCase							,					return_tensors=UpperCAmelCase							,					**UpperCAmelCase							,					)
																				__lowerCamelCase					:		Any		   =     qformer_text_encoding.pop("input_ids"    )
																				__lowerCamelCase					:		Dict		   =     qformer_text_encoding.pop("attention_mask"    )

													if images is not None:
																				__lowerCamelCase					:		int		   =     self.image_processor(UpperCAmelCase							,					return_tensors=UpperCAmelCase    )
																				encoding.update(UpperCAmelCase    )

													return encoding






						def       lowerCamelCase__     (       self      :			str							,					*UpperCAmelCase      :			Optional[int]							,					**UpperCAmelCase      :			str    ):
													return self.tokenizer.batch_decode(*UpperCAmelCase							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					*UpperCAmelCase      :			List[str]							,					**UpperCAmelCase      :			Tuple    ):
													return self.tokenizer.decode(*UpperCAmelCase							,					**UpperCAmelCase    )






						@property
						# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Dict		   =     self.tokenizer.model_input_names
													__lowerCamelCase					:		Optional[Any]		   =     self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )






						def       lowerCamelCase__     (       self      :			List[Any]							,					UpperCAmelCase      :			Tuple							,					**UpperCAmelCase      :			Optional[Any]    ):
													if os.path.isfile(UpperCAmelCase    ):
																				raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""    )
													os.makedirs(UpperCAmelCase							,					exist_ok=UpperCAmelCase    )
													__lowerCamelCase					:		List[str]		   =     os.path.join(UpperCAmelCase							,					"qformer_tokenizer"    )
													self.qformer_tokenizer.save_pretrained(UpperCAmelCase    )
													return super().save_pretrained(UpperCAmelCase							,					**UpperCAmelCase    )







						@classmethod
						def       lowerCamelCase__     (       cls      :			Any							,					UpperCAmelCase      :			Any							,					**UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		Dict		   =     AutoTokenizer.from_pretrained(UpperCAmelCase							,					subfolder="qformer_tokenizer"    )
													__lowerCamelCase					:		Optional[Any]		   =     cls._get_arguments_from_pretrained(UpperCAmelCase							,					**UpperCAmelCase    )
													args.append(UpperCAmelCase    )
													return cls(*UpperCAmelCase    )
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A  =				{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''BertForMaskedLM''',
				    '''BertForMultipleChoice''',
				    '''BertForNextSentencePrediction''',
				    '''BertForPreTraining''',
				    '''BertForQuestionAnswering''',
				    '''BertForSequenceClassification''',
				    '''BertForTokenClassification''',
				    '''BertLayer''',
				    '''BertLMHeadModel''',
				    '''BertModel''',
				    '''BertPreTrainedModel''',
				    '''load_tf_weights_in_bert''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFBertEmbeddings''',
				    '''TFBertForMaskedLM''',
				    '''TFBertForMultipleChoice''',
				    '''TFBertForNextSentencePrediction''',
				    '''TFBertForPreTraining''',
				    '''TFBertForQuestionAnswering''',
				    '''TFBertForSequenceClassification''',
				    '''TFBertForTokenClassification''',
				    '''TFBertLMHeadModel''',
				    '''TFBertMainLayer''',
				    '''TFBertModel''',
				    '''TFBertPreTrainedModel''',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''TFBertTokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''FlaxBertForCausalLM''',
				    '''FlaxBertForMaskedLM''',
				    '''FlaxBertForMultipleChoice''',
				    '''FlaxBertForNextSentencePrediction''',
				    '''FlaxBertForPreTraining''',
				    '''FlaxBertForQuestionAnswering''',
				    '''FlaxBertForSequenceClassification''',
				    '''FlaxBertForTokenClassification''',
				    '''FlaxBertModel''',
				    '''FlaxBertPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)