'''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       UpperCAmelCase  (						A__ ):





       '''simple docstring'''



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

       def __init__(						self     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       **__lowerCAmelCase			)  ->  List[str]:
        lowercase__    :   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.'''     ,       __snake_case     ,       )
         lowercase__    :   Any						= kwargs.pop('''feature_extractor'''			)

        lowercase__    :   Optional[Any]						= 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     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase="max_length"     ,       __lowerCAmelCase="np"     ,       **__lowerCAmelCase			)  ->  Any:

        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(__snake_case     ,       __snake_case			) or (isinstance(__snake_case     ,       __snake_case			) and not isinstance(text[0]     ,       __snake_case			)):
          lowercase__    :   Dict						= [self.tokenizer(__snake_case     ,       padding=__snake_case     ,       return_tensors=__snake_case     ,       **__snake_case			)]

         elif isinstance(__snake_case     ,       __snake_case			) and isinstance(text[0]     ,       __snake_case			):
          lowercase__    :   Optional[Any]						= []

          # Maximum number of queries across batch
          lowercase__    :   List[Any]						= max([len(__snake_case			) for t in text]			)

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

           lowercase__    :   Union[str, Any]						= self.tokenizer(__snake_case     ,       padding=__snake_case     ,       return_tensors=__snake_case     ,       **__snake_case			)
           encodings.append(__snake_case			)
         else:
          raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''			)

         if return_tensors == "np":
          lowercase__    :   List[Any]						= np.concatenate([encoding['''input_ids'''] for encoding in encodings]     ,       axis=0			)
          lowercase__    :   Tuple						= np.concatenate([encoding['''attention_mask'''] for encoding in encodings]     ,       axis=0			)

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

          lowercase__    :   Dict						= jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]     ,       axis=0			)
          lowercase__    :   List[Any]						= jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]     ,       axis=0			)

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

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

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

          lowercase__    :   List[str]						= tf.stack([encoding['''input_ids'''] for encoding in encodings]     ,       axis=0			)
          lowercase__    :   Tuple						= tf.stack([encoding['''attention_mask'''] for encoding in encodings]     ,       axis=0			)

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

         lowercase__    :   List[Any]						= BatchEncoding()
         lowercase__    :   Any						= input_ids
         lowercase__    :   Tuple						= attention_mask

        if query_images is not None:
         lowercase__    :   List[Any]						= BatchEncoding()
         lowercase__    :   Optional[Any]						= self.image_processor(
             __snake_case     ,       return_tensors=__snake_case     ,       **__snake_case			).pixel_values
         lowercase__    :   Tuple						= query_pixel_values

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

        if text is not None and images is not None:
         lowercase__    :   Any						= image_features.pixel_values
         return encoding
        elif query_images is not None and images is not None:
         lowercase__    :   Dict						= image_features.pixel_values
         return encoding
        elif text is not None or query_images is not None:
         return encoding
        else:
         return BatchEncoding(data=dict(**__snake_case			)     ,       tensor_type=__snake_case			)

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  str:
        return self.image_processor.post_process(*__snake_case     ,       **__snake_case			)

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  List[str]:
        return self.image_processor.post_process_object_detection(*__snake_case     ,       **__snake_case			)

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Optional[int]:
        return self.image_processor.post_process_image_guided_detection(*__snake_case     ,       **__snake_case			)

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Optional[Any]:
        return self.tokenizer.batch_decode(*__snake_case     ,       **__snake_case			)

       def _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Any:
        return self.tokenizer.decode(*__snake_case     ,       **__snake_case			)

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


'''simple docstring'''

import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,					[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,					['''default''', 0, 100 * 2**20, 900 * 2**20]					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config		,					'''IN_MEMORY_MAX_SIZE'''		,					UpperCAmelCase					)
 lowercase__    :   List[Any]						= datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  lowercase__    :   str						= dataset_size < in_memory_max_size
 else:
  lowercase__    :   Optional[int]						= False
 lowercase__    :   Optional[Any]						= is_small_dataset(UpperCAmelCase					)
 assert result == expected

