


'''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__					):
	A_				=    ['''image_processor''', '''tokenizer''']
	A_				=    '''OwlViTImageProcessor'''
	A_				=    ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




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

						'''simple docstring'''





						__a			:       List[str]					    =  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						,				)
											__a			:       Tuple					    =  kwargs.pop('feature_extractor'							)

						__a			:       Optional[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						,				__a=None						,				__a=None						,				__a=None						,				__a="max_length"						,				__a="np"						,				**__a							):

						'''simple docstring'''





						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							)):
																__a			:       Tuple					    =  [self.tokenizer(__a						,				padding=__a						,				return_tensors=__a						,				**__a							)]

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

																# Maximum number of queries across batch
																__a			:       Union[str, Any]					    =  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:
																										__a			:       Union[str, Any]					    =  t + [" "] * (max_num_queries - len(__a							))

																					__a			:       Optional[Any]					    =  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":
																__a			:       int					    =  np.concatenate([encoding['input_ids'] for encoding in encodings]						,				axis=0							)
																__a			:       Optional[Any]					    =  np.concatenate([encoding['attention_mask'] for encoding in encodings]						,				axis=0							)

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

																__a			:       Optional[int]					    =  jnp.concatenate([encoding['input_ids'] for encoding in encodings]						,				axis=0							)
																__a			:       Union[str, Any]					    =  jnp.concatenate([encoding['attention_mask'] for encoding in encodings]						,				axis=0							)

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

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

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

																__a			:       str					    =  tf.stack([encoding['input_ids'] for encoding in encodings]						,				axis=0							)
																__a			:       Optional[Any]					    =  tf.stack([encoding['attention_mask'] for encoding in encodings]						,				axis=0							)

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

											__a			:       Optional[int]					    =  BatchEncoding()
											__a			:       List[str]					    =  input_ids
											__a			:       List[str]					    =  attention_mask

						if query_images is not None:
											__a			:       Optional[int]					    =  BatchEncoding()
											__a			:       Dict					    =  self.image_processor(
											    __a						,				return_tensors=__a						,				**__a							).pixel_values
											__a			:       Dict					    =  query_pixel_values

						if images is not None:
											__a			:       str					    =  self.image_processor(__a						,				return_tensors=__a						,				**__a							)

						if text is not None and images is not None:
											__a			:       Union[str, Any]					    =  image_features.pixel_values
											return encoding
						elif query_images is not None and images is not None:
											__a			:       List[Any]					    =  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       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





						return self.image_processor.post_process(*__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





						return self.image_processor.post_process_object_detection(*__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





						return self.image_processor.post_process_image_guided_detection(*__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





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




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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











lowerCamelCase	:							List[Any]				=[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Optional[int]       				=  {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
     UpperCamelCase__			:   Any       				=  0
     UpperCamelCase__			:   Any       				=  0
     while place < len(__lowerCAmelCase			):
          if (place + 1 < len(__lowerCAmelCase			)) and (vals[roman[place]] < vals[roman[place + 1]]):
               total += vals[roman[place + 1]] - vals[roman[place]]
               place += 2
          else:
               total += vals[roman[place]]
               place += 1
     return total







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Optional[int]       				=  []
     for arabic, roman in ROMAN:
          ((UpperCamelCase__)				,     (UpperCamelCase__))			:   Optional[int]       				=  divmod(__lowerCAmelCase					,    __lowerCAmelCase			)
          result.append(roman * factor			)
          if number == 0:
               break
     return "".join(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()