






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 							_UpperCamelCase			(a_			):
      snake_case_  =							["""image_processor""", """tokenizer"""]
      snake_case_  =							"""BlipImageProcessor"""
      snake_case_  =							"""AutoTokenizer"""


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	int:
           __lowerCAmelCase								=					False
           super().__init__(__UpperCamelCase					,						__UpperCamelCase	)
           __lowerCAmelCase								=					self.image_processor


      def __call__(	self					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = True					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = 0					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = False					,						__UpperCamelCase = False					,						__UpperCamelCase = False					,						__UpperCamelCase = False					,						__UpperCamelCase = False					,						__UpperCamelCase = True					,						__UpperCamelCase = None					,						**__UpperCamelCase					,						)->	BatchEncoding:
           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:
                __lowerCAmelCase								=					self.tokenizer
                __lowerCAmelCase								=					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					,						)
                return text_encoding

           # add pixel_values
           __lowerCAmelCase								=					self.image_processor(__UpperCamelCase					,						return_tensors=__UpperCamelCase	)

           if text is not None:
                __lowerCAmelCase								=					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					,						)
           else:
                __lowerCAmelCase								=					None

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

           return encoding_image_processor


      def  __UpperCAmelCase		(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	Any:
           return self.tokenizer.batch_decode(*__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						*__UpperCamelCase					,						**__UpperCamelCase	)->	int:
           return self.tokenizer.decode(*__UpperCamelCase					,						**__UpperCamelCase	)


      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.tokenizer.model_input_names
           __lowerCAmelCase								=					self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)














from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)

lowerCamelCase			:					Dict         =	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""biogpt"""


      def __init__(	self					,						__UpperCamelCase=4_2_3_8_4					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_4					,						__UpperCamelCase=1_6					,						__UpperCamelCase=4_0_9_6					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Optional[Any]:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					scale_embedding
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					layerdrop
           __lowerCAmelCase								=					activation_dropout
           super().__init__(pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase	)






