



'''simple docstring'''





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate






@dataclass(frozen=SCREAMING_SNAKE_CASE_     )
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=field(default="""summarization"""   ,   metadata={"""include_in_asdict_even_if_is_default""": True}     )
  a_												=Features({"""text""": Value("""string"""     )}     )
  a_												=Features({"""summary""": Value("""string"""     )}     )
  a_												="text"
  a_												="summary"




  @property
  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Dict[str, str]:
    return {self.text_column: "text", self.summary_column: "summary"}











'''simple docstring'''





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=["""image_processor""", """tokenizer"""]
  a_												="""OwlViTImageProcessor"""
  a_												=("""CLIPTokenizer""", """CLIPTokenizerFast""")




  def __init__( self      :	Any			,	_a      :	str=None			,	_a      :	Optional[Any]=None			,	**_a      :	int   )					->					List[str]:
    __lowerCamelCase       :       List[Any]           = 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       :       Tuple           = kwargs.pop('feature_extractor'   )

    __lowerCamelCase       :       int           = 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   )




  def __call__( self      :	Tuple			,	_a      :	Tuple=None			,	_a      :	int=None			,	_a      :	List[Any]=None			,	_a      :	List[Any]="max_length"			,	_a      :	Union[str, Any]="np"			,	**_a      :	Union[str, Any]   )					->					List[str]:

    if text is None and query_images is None and images is None:
      raise ValueError(
          'You have to specify at least one text or query image or image. All three cannot be none.'   )

    if text is not None:
      if isinstance(_a			,	_a   ) or (isinstance(_a			,	_a   ) and not isinstance(text[0]			,	_a   )):
        __lowerCamelCase       :       Any           = [self.tokenizer(_a			,	padding=_a			,	return_tensors=_a			,	**_a   )]

      elif isinstance(_a			,	_a   ) and isinstance(text[0]			,	_a   ):
        __lowerCamelCase       :       List[Any]           = []

        # Maximum number of queries across batch
        __lowerCamelCase       :       str           = max([len(_a   ) for t in text]   )

        # Pad all batch samples to max number of text queries
        for t in text:
          if len(_a   ) != max_num_queries:
            __lowerCamelCase       :       List[Any]           = t + [' '] * (max_num_queries - len(_a   ))

          __lowerCamelCase       :       Dict           = self.tokenizer(_a			,	padding=_a			,	return_tensors=_a			,	**_a   )
          encodings.append(_a   )
      else:
        raise TypeError('Input text should be a string, a list of strings or a nested list of strings'   )

      if return_tensors == "np":
        __lowerCamelCase       :       Optional[Any]           = np.concatenate([encoding['input_ids'] for encoding in encodings]			,	axis=0   )
        __lowerCamelCase       :       str           = np.concatenate([encoding['attention_mask'] for encoding in encodings]			,	axis=0   )

      elif return_tensors == "jax" and is_flax_available():
        import jax.numpy as jnp

        __lowerCamelCase       :       int           = jnp.concatenate([encoding['input_ids'] for encoding in encodings]			,	axis=0   )
        __lowerCamelCase       :       List[str]           = jnp.concatenate([encoding['attention_mask'] for encoding in encodings]			,	axis=0   )

      elif return_tensors == "pt" and is_torch_available():
        import torch

        __lowerCamelCase       :       str           = torch.cat([encoding['input_ids'] for encoding in encodings]			,	dim=0   )
        __lowerCamelCase       :       Optional[Any]           = torch.cat([encoding['attention_mask'] for encoding in encodings]			,	dim=0   )

      elif return_tensors == "tf" and is_tf_available():
        import tensorflow as tf

        __lowerCamelCase       :       List[Any]           = tf.stack([encoding['input_ids'] for encoding in encodings]			,	axis=0   )
        __lowerCamelCase       :       int           = tf.stack([encoding['attention_mask'] for encoding in encodings]			,	axis=0   )

      else:
        raise ValueError('Target return tensor type could not be returned'   )

      __lowerCamelCase       :       Any           = BatchEncoding()
      __lowerCamelCase       :       Dict           = input_ids
      __lowerCamelCase       :       str           = attention_mask

    if query_images is not None:
      __lowerCamelCase       :       Optional[int]           = BatchEncoding()
      __lowerCamelCase       :       List[Any]           = self.image_processor(
          _a			,	return_tensors=_a			,	**_a   ).pixel_values
      __lowerCamelCase       :       str           = query_pixel_values

    if images is not None:
      __lowerCamelCase       :       Union[str, Any]           = self.image_processor(_a			,	return_tensors=_a			,	**_a   )

    if text is not None and images is not None:
      __lowerCamelCase       :       Tuple           = image_features.pixel_values
      return encoding
    elif query_images is not None and images is not None:
      __lowerCamelCase       :       Tuple           = image_features.pixel_values
      return encoding
    elif text is not None or query_images is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**_a   )			,	tensor_type=_a   )




  def 						_lowercase     ( self      :	Optional[Any]			,	*_a      :	List[str]			,	**_a      :	Dict   )					->					int:
    return self.image_processor.post_process(*_a			,	**_a   )




  def 						_lowercase     ( self      :	str			,	*_a      :	str			,	**_a      :	List[str]   )					->					int:
    return self.image_processor.post_process_object_detection(*_a			,	**_a   )




  def 						_lowercase     ( self      :	int			,	*_a      :	List[Any]			,	**_a      :	Optional[int]   )					->					str:
    return self.image_processor.post_process_image_guided_detection(*_a			,	**_a   )




  def 						_lowercase     ( self      :	Tuple			,	*_a      :	Optional[Any]			,	**_a      :	List[Any]   )					->					Tuple:
    return self.tokenizer.batch_decode(*_a			,	**_a   )




  def 						_lowercase     ( self      :	str			,	*_a      :	Optional[Any]			,	**_a      :	str   )					->					Union[str, Any]:
    return self.tokenizer.decode(*_a			,	**_a   )




  @property
  def 						_lowercase     ( self      :	Any   )					->					Any:
    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 						_lowercase     ( self      :	Union[str, Any]   )					->					Any:
    warnings.warn(
        '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,	_a			,	)
    return self.image_processor






