



"""simple docstring"""
import warnings
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 							lowerCamelCase_(   __SCREAMING_SNAKE_CASE    ):





					'''simple docstring'''



					lowercase__   :       Dict										= ['image_processor', 'tokenizer']
					lowercase__   :       List[str]										= 'FlavaImageProcessor'
					lowercase__   :       Dict										= ('BertTokenizer', 'BertTokenizerFast')

					def __init__(    self				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				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				,       )
													_lowerCamelCase        =				kwargs.pop('''feature_extractor'''   )

									_lowerCamelCase        =				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   )
									_lowerCamelCase        =				self.image_processor

					def __call__(    self				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = True				,       lowerCamelCase__ = False				,       lowerCamelCase__ = False				,       lowerCamelCase__ = None				,       lowerCamelCase__ = 0				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = False				,       lowerCamelCase__ = False				,       lowerCamelCase__ = False				,       lowerCamelCase__ = False				,       lowerCamelCase__ = True				,       lowerCamelCase__ = None				,       **lowerCamelCase__				,       ):




									if text is None and images is None:
													raise ValueError('''You have to specify either text or images. Both cannot be none.'''   )

									if text is not None:
													_lowerCamelCase        =				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				,       )
									if images is not None:
													_lowerCamelCase        =				self.image_processor(
													    _a				,       return_image_mask=_a				,       return_codebook_pixels=_a				,       return_tensors=_a				,       **_a				,       )

									if text is not None and images is not None:
													encoding.update(_a   )
													return encoding
									elif text is not None:
													return encoding
									else:
													return BatchEncoding(data=dict(**_a   )				,       tensor_type=_a   )

					def 			snake_case__      (    self				,       *lowerCamelCase__				,       **lowerCamelCase__   ):
									return self.tokenizer.batch_decode(*_a				,       **_a   )

					def 			snake_case__      (    self				,       *lowerCamelCase__				,       **lowerCamelCase__   ):
									return self.tokenizer.decode(*_a				,       **_a   )

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

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





"""simple docstring"""
import warnings

from ..trainer import Trainer
from ..utils import logging


__SCREAMING_SNAKE_CASE  :						Dict    							=      logging.get_logger(__name__)


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									warnings.warn(
									    '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
									    '''instead.'''				,       lowerCamelCase__				,       )
									super().__init__(args=lowerCamelCase__				,       **lowerCamelCase__   )
