

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 				a  (	UpperCAmelCase				):
						_lowercase    						=			["image_processor", "tokenizer"]
						_lowercase    						=			"OwlViTImageProcessor"
						_lowercase    						=			("CLIPTokenizer", "CLIPTokenizerFast")


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







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    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_						,					)
														_UpperCAmelCase       :		Optional[Any] 		=    kwargs.pop("feature_extractor"	)

										_UpperCAmelCase       :		List[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__(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_	)):
																		_UpperCAmelCase       :		Tuple 		=    [self.tokenizer(A_						,					padding=A_						,					return_tensors=A_						,					**A_	)]

														elif isinstance(A_						,					A_	) and isinstance(text[0]						,					A_	):
																		_UpperCAmelCase       :		Optional[int] 		=    []

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

																						_UpperCAmelCase       :		int 		=    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":
																		_UpperCAmelCase       :		int 		=    np.concatenate([encoding["input_ids"] for encoding in encodings]						,					axis=0	)
																		_UpperCAmelCase       :		List[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

																		_UpperCAmelCase       :		str 		=    jnp.concatenate([encoding["input_ids"] for encoding in encodings]						,					axis=0	)
																		_UpperCAmelCase       :		Union[str, Any] 		=    jnp.concatenate([encoding["attention_mask"] for encoding in encodings]						,					axis=0	)

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

																		_UpperCAmelCase       :		Any 		=    torch.cat([encoding["input_ids"] for encoding in encodings]						,					dim=0	)
																		_UpperCAmelCase       :		Dict 		=    torch.cat([encoding["attention_mask"] for encoding in encodings]						,					dim=0	)

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

																		_UpperCAmelCase       :		Union[str, Any] 		=    tf.stack([encoding["input_ids"] for encoding in encodings]						,					axis=0	)
																		_UpperCAmelCase       :		str 		=    tf.stack([encoding["attention_mask"] for encoding in encodings]						,					axis=0	)

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

														_UpperCAmelCase       :		str 		=    BatchEncoding()
														_UpperCAmelCase       :		int 		=    input_ids
														_UpperCAmelCase       :		List[Any] 		=    attention_mask

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

										if images is not None:
														_UpperCAmelCase       :		Optional[int] 		=    self.image_processor(A_						,					return_tensors=A_						,					**A_	)

										if text is not None and images is not None:
														_UpperCAmelCase       :		Optional[Any] 		=    image_features.pixel_values
														return encoding
										elif query_images is not None and images is not None:
														_UpperCAmelCase       :		int 		=    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





import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use PerceiverImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


