


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_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		["image_processor", "tokenizer"]
   __A :      List[str]     		=		"BlipImageProcessor"
   __A :      str     		=		"AutoTokenizer"






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				False
     super().__init__(__A						,				__A )
     lowerCamelCase :					int    =				self.image_processor






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






     """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:
       lowerCamelCase :					Optional[Any]    =				self.tokenizer
       lowerCamelCase :					Tuple    =				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
     lowerCamelCase :					List[str]    =				self.image_processor(__A						,				return_tensors=__A )

     if text is not None:
       lowerCamelCase :					Optional[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:
       lowerCamelCase :					List[Any]    =				None

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

     return encoding_image_processor






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,				**__A )






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,				**__A )






   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.tokenizer.model_input_names
     lowerCamelCase :					List[str]    =				self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



