import torch

from transformers import AutoModel






class 					__SCREAMING_SNAKE_CASE						(	torch.nn.Module	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_="sayef/fsner-bert-base-uncased"						):




       '''simple docstring'''

       super(__snake_case				,		self						).__init__()

       UpperCAmelCase_							:       List[str]       =				AutoModel.from_pretrained(__snake_case				,		return_dict=__snake_case						)
       UpperCAmelCase_							:       Optional[Any]       =				torch.nn.CosineSimilarity(3				,		1E-08						)
       UpperCAmelCase_							:       Tuple       =				torch.nn.Softmax(dim=1						)


  def _UpperCamelCase					(			self				,		**snake_case_						):




       '''simple docstring'''

       return self.bert(**__snake_case						).last_hidden_state


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       return token_embeddings.sum(2				,		keepdim=__snake_case						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_=1						):




       '''simple docstring'''

       return self.softmax(T * self.cos(__snake_case				,		__snake_case						)						)






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				W_supports['sizes'].tolist()
       UpperCAmelCase_							:       Any       =				W_supports['start_token_id'].item()
       UpperCAmelCase_							:       int       =				W_supports['end_token_id'].item()

       del W_supports["sizes"]
       del W_supports["start_token_id"]
       del W_supports["end_token_id"]

       UpperCAmelCase_							:       Optional[Any]       =				self.BERT(**__snake_case						)
       UpperCAmelCase_							:       Tuple       =				self.BERT(**__snake_case						)

       UpperCAmelCase_							:       Dict       =				None
       UpperCAmelCase_							:       Optional[Any]       =				None

       UpperCAmelCase_							:       Union[str, Any]       =				W_supports['input_ids'] == start_token_id
       UpperCAmelCase_							:       str       =				W_supports['input_ids'] == end_token_id

       for i, size in enumerate(__snake_case						):
            if i == 0:
                 UpperCAmelCase_							:       Optional[int]       =				0
            else:
                 UpperCAmelCase_							:       Tuple       =				support_sizes[i - 1]

            UpperCAmelCase_							:       Tuple       =				S[s : s + size][start_token_masks[s : s + size]]
            UpperCAmelCase_							:       int       =				S[s : s + size][end_token_masks[s : s + size]]

            UpperCAmelCase_							:       Any       =				torch.matmul(q[i]				,		s_start.T						).sum(1						).softmax(0						)
            UpperCAmelCase_							:       List[str]       =				torch.matmul(q[i]				,		s_end.T						).sum(1						).softmax(0						)

            if p_starts is not None:
                 UpperCAmelCase_							:       List[Any]       =				torch.vstack((p_starts, p_start)						)
                 UpperCAmelCase_							:       List[Any]       =				torch.vstack((p_ends, p_end)						)
            else:
                 UpperCAmelCase_							:       List[Any]       =				p_start
                 UpperCAmelCase_							:       Optional[int]       =				p_end

       return p_starts, p_ends







'''simple docstring'''


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		['''image_processor''', '''tokenizer''']
  lowerCamelCase_		:Optional[Any]						   =		'''ViTImageProcessor'''
  lowerCamelCase_		:int						   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				None
       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'				,		snake_case_				,		)
            UpperCAmelCase_							:       int       =				kwargs.pop('feature_extractor'						)

       UpperCAmelCase_							:       str       =				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__(snake_case_				,		snake_case_						)


  def __call__(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       if text is None and visual_prompt is None and images is None:
            raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
            raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
            UpperCAmelCase_							:       Optional[int]       =				self.tokenizer(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None:
            UpperCAmelCase_							:       Optional[Any]       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if images is not None:
            UpperCAmelCase_							:       int       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None and images is not None:
            UpperCAmelCase_							:       Tuple       =				{
                'pixel_values': image_features.pixel_values,
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       elif text is not None and images is not None:
            UpperCAmelCase_							:       Optional[int]       =				image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       elif visual_prompt is not None:
            UpperCAmelCase_							:       Dict       =				{
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       else:
            return BatchEncoding(data=dict(**snake_case_						)				,		tensor_type=snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

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


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,		snake_case_				,		)
       return self.image_processor_class






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,		snake_case_				,		)
       return self.image_processor






