


'''simple docstring'''







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	['image_processor', 'tokenizer']
    __UpperCAmelCase   			=	'ViltImageProcessor'
    __UpperCAmelCase   			=	('BertTokenizer', 'BertTokenizerFast')


    def __init__(     self  ,					a=None  ,					a=None  ,					**a    ):
          UpperCamelCase__										=				None
          if "feature_extractor" in kwargs:
                warnings.warn(
                    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                    " instead."  ,					a  ,					)
                UpperCamelCase__										=				kwargs.pop("feature_extractor"    )

          UpperCamelCase__										=				image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
                raise ValueError("You need to specify an `image_processor`."    )
          if tokenizer is None:
                raise ValueError("You need to specify a `tokenizer`."    )

          super().__init__(a  ,					a    )
          UpperCamelCase__										=				self.image_processor


    def __call__(     self  ,					a  ,					a = None  ,					a = True  ,					a = False  ,					a = None  ,					a = None  ,					a = 0  ,					a = None  ,					a = None  ,					a = None  ,					a = False  ,					a = False  ,					a = False  ,					a = False  ,					a = True  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				self.tokenizer(
              text=a  ,					add_special_tokens=a  ,					padding=a  ,					truncation=a  ,					max_length=a  ,					stride=a  ,					pad_to_multiple_of=a  ,					return_token_type_ids=a  ,					return_attention_mask=a  ,					return_overflowing_tokens=a  ,					return_special_tokens_mask=a  ,					return_offsets_mapping=a  ,					return_length=a  ,					verbose=a  ,					return_tensors=a  ,					**a  ,					)
          # add pixel_values + pixel_mask
          UpperCamelCase__										=				self.image_processor(a  ,					return_tensors=a    )
          encoding.update(a    )

          return encoding


    def    __a							(     self  ,					*a  ,					**a    ):
          return self.tokenizer.batch_decode(*a  ,					**a    )


    def    __a							(     self  ,					*a  ,					**a    ):
          return self.tokenizer.decode(*a  ,					**a    )


    @property
    def    __a							(     self    ):
          UpperCamelCase__										=				self.tokenizer.model_input_names
          UpperCamelCase__										=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )


    @property
    def    __a							(     self    ):
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,					a  ,					)
          return self.image_processor_class


    @property
    def    __a							(     self    ):
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,					a  ,					)
          return self.image_processor







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

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


