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   lowercase      (       a       ):
							lowercase__  :    Dict									=							["""image_processor""", """tokenizer"""]
							lowercase__  :    Union[str, Any]									=							"""OwlViTImageProcessor"""
							lowercase__  :    Optional[int]									=							("""CLIPTokenizer""", """CLIPTokenizerFast""")




							def __init__(  self				:   int		,							_UpperCamelCase				:   Union[str, Any]=None		,							_UpperCamelCase				:   Optional[int]=None		,							**_UpperCamelCase				:   int )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_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		,							)
																	SCREAMING_SNAKE_CASE           =    kwargs.pop("feature_extractor" )

												SCREAMING_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				:   Optional[Any]		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   List[str]=None		,							_UpperCamelCase				:   List[str]=None		,							_UpperCamelCase				:   Tuple="max_length"		,							_UpperCamelCase				:   Optional[int]="np"		,							**_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''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(_UpperCamelCase		,							_UpperCamelCase ) or (isinstance(_UpperCamelCase		,							_UpperCamelCase ) and not isinstance(text[0]		,							_UpperCamelCase )):
																						SCREAMING_SNAKE_CASE           =    [self.tokenizer(_UpperCamelCase		,							padding=_UpperCamelCase		,							return_tensors=_UpperCamelCase		,							**_UpperCamelCase )]

																	elif isinstance(_UpperCamelCase		,							_UpperCamelCase ) and isinstance(text[0]		,							_UpperCamelCase ):
																						SCREAMING_SNAKE_CASE           =    []

																						# Maximum number of queries across batch
																						SCREAMING_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:
																																SCREAMING_SNAKE_CASE           =    t + [" "] * (max_num_queries - len(_UpperCamelCase ))

																											SCREAMING_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":
																						SCREAMING_SNAKE_CASE           =    np.concatenate([encoding["input_ids"] for encoding in encodings]		,							axis=0 )
																						SCREAMING_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

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

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

																						SCREAMING_SNAKE_CASE           =    torch.cat([encoding["input_ids"] for encoding in encodings]		,							dim=0 )
																						SCREAMING_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

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

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

																	SCREAMING_SNAKE_CASE           =    BatchEncoding()
																	SCREAMING_SNAKE_CASE           =    input_ids
																	SCREAMING_SNAKE_CASE           =    attention_mask

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

												if images is not None:
																	SCREAMING_SNAKE_CASE           =    self.image_processor(_UpperCamelCase		,							return_tensors=_UpperCamelCase		,							**_UpperCamelCase )

												if text is not None and images is not None:
																	SCREAMING_SNAKE_CASE           =    image_features.pixel_values
																	return encoding
												elif query_images is not None and images is not None:
																	SCREAMING_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 		__snake_case(  self				:   Tuple		,							*_UpperCamelCase				:   Optional[int]		,							**_UpperCamelCase				:   List[str] )   ->			Union[str, Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Any		,							*_UpperCamelCase				:   List[str]		,							**_UpperCamelCase				:   List[str] )   ->			Tuple:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   int		,							*_UpperCamelCase				:   Tuple		,							**_UpperCamelCase				:   int )   ->			Union[str, Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Tuple		,							*_UpperCamelCase				:   Optional[int]		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Optional[Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   str		,							*_UpperCamelCase				:   Any		,							**_UpperCamelCase				:   Union[str, Any] )   ->			Dict:



												'''simple docstring'''

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




							@property
							def 		__snake_case(  self				:   Union[str, Any] )   ->			Any:



												'''simple docstring'''

												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 		__snake_case(  self				:   int )   ->			Any:



												'''simple docstring'''

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str = " "					):
					SCREAMING_SNAKE_CASE           =    []

					SCREAMING_SNAKE_CASE           =    0
					for index, char in enumerate(UpperCAmelCase__					):
										if char == separator:
															split_words.append(string[last_index:index]					)
															SCREAMING_SNAKE_CASE           =    index + 1
										elif index + 1 == len(UpperCAmelCase__					):
															split_words.append(string[last_index : index + 1]					)
					return split_words


if __name__ == "__main__":
							from doctest import testmod

							testmod()




