


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_						(			lowerCamelCase__	):

 '''simple docstring'''

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



 def __init__(     self		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				**_UpperCAmelCase ):
       snake_case_	=				None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''		,				_UpperCAmelCase		,				)
             snake_case_	=				kwargs.pop('''feature_extractor''' )

       snake_case_	=				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__(_UpperCAmelCase		,				_UpperCAmelCase )



 def __call__(     self		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase="max_length"		,				_UpperCAmelCase="np"		,				**_UpperCAmelCase ):

       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(_UpperCAmelCase		,				_UpperCAmelCase ) or (isinstance(_UpperCAmelCase		,				_UpperCAmelCase ) and not isinstance(text[0]		,				_UpperCAmelCase )):
                   snake_case_	=				[self.tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				return_tensors=_UpperCAmelCase		,				**_UpperCAmelCase )]

             elif isinstance(_UpperCAmelCase		,				_UpperCAmelCase ) and isinstance(text[0]		,				_UpperCAmelCase ):
                   snake_case_	=				[]

                   # Maximum number of queries across batch
                   snake_case_	=				max([len(_UpperCAmelCase ) for t in text] )

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

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

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

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

                   snake_case_	=				jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]		,				axis=0 )
                   snake_case_	=				jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]		,				axis=0 )

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

                   snake_case_	=				torch.cat([encoding['''input_ids'''] for encoding in encodings]		,				dim=0 )
                   snake_case_	=				torch.cat([encoding['''attention_mask'''] for encoding in encodings]		,				dim=0 )

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

                   snake_case_	=				tf.stack([encoding['''input_ids'''] for encoding in encodings]		,				axis=0 )
                   snake_case_	=				tf.stack([encoding['''attention_mask'''] for encoding in encodings]		,				axis=0 )

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

             snake_case_	=				BatchEncoding()
             snake_case_	=				input_ids
             snake_case_	=				attention_mask

       if query_images is not None:
             snake_case_	=				BatchEncoding()
             snake_case_	=				self.image_processor(
                 _UpperCAmelCase		,				return_tensors=_UpperCAmelCase		,				**_UpperCAmelCase ).pixel_values
             snake_case_	=				query_pixel_values

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

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



 def 						UpperCamelCase__					(     self		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       return self.image_processor.post_process(*_UpperCAmelCase		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       return self.image_processor.post_process_object_detection(*_UpperCAmelCase		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       return self.image_processor.post_process_image_guided_detection(*_UpperCAmelCase		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       return self.tokenizer.batch_decode(*_UpperCAmelCase		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       return self.tokenizer.decode(*_UpperCAmelCase		,				**_UpperCAmelCase )



 @property
 def 						UpperCamelCase__					(     self ):
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,				_UpperCAmelCase		,				)
       return self.image_processor_class





 @property
 def 						UpperCamelCase__					(     self ):
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,				_UpperCAmelCase		,				)
       return self.image_processor



import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''a''', '''b''', '''c''']

       # Defaults to last layer if both are None
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[2] )

       # Out indices set to match out features
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(['''a''', '''c''']		,				_UpperCAmelCase		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features set to match out indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[0, 2]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[0, 2] )

       # Out features selected from negative indices
       snake_case_	,   snake_case_	=				get_aligned_output_features_output_indices(_UpperCAmelCase		,				[-3, -1]		,				_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				['''a''', '''c'''] )
       self.assertEqual(_UpperCAmelCase		,				[-3, -1] )



 def 						UpperCamelCase__					(     self ):
       # Stage names must be set
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				_UpperCAmelCase )

       # Out features must be a list
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(('''a''', '''b''')		,				(0, 1)		,				['''a''', '''b'''] )

       # Out features must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 1)		,				['''a'''] )

       # Out indices must be a list or tuple
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				0		,				['''a''', '''b'''] )

       # Out indices must be a subset of stage names
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(_UpperCAmelCase		,				(0, 1)		,				['''a'''] )

       # Out features and out indices must be the same length
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0,)		,				['''a''', '''b''', '''c'''] )

       # Out features should match out indices
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''a''', '''b''']		,				(0, 2)		,				['''a''', '''b''', '''c'''] )

       # Out features and out indices should be in order
       with self.assertRaises(_UpperCAmelCase ):
             verify_out_features_out_indices(['''b''', '''a''']		,				(0, 1)		,				['''a''', '''b'''] )

       # Check passes with valid inputs
       verify_out_features_out_indices(['''a''', '''b''', '''d''']		,				(0, 1, -1)		,				['''a''', '''b''', '''c''', '''d'''] )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				BackboneMixin()

       snake_case_	=				['''a''', '''b''', '''c''']
       snake_case_	=				['''a''', '''c''']
       snake_case_	=				[0, 2]

       # Check that the output features and indices are set correctly
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[0, 2] )

       # Check out features and indices are updated correctly
       snake_case_	=				['''a''', '''b''']
       self.assertEqual(backbone.out_features		,				['''a''', '''b'''] )
       self.assertEqual(backbone.out_indices		,				[0, 1] )

       snake_case_	=				[-3, -1]
       self.assertEqual(backbone.out_features		,				['''a''', '''c'''] )
       self.assertEqual(backbone.out_indices		,				[-3, -1] )