





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_  (  snake_case__							):
					_a      :						List[str]						    =					['image_processor', 'tokenizer']
					_a      :						Optional[int]						    =					'OwlViTImageProcessor'
					_a      :						Optional[int]						    =					('CLIPTokenizer', 'CLIPTokenizerFast')



					def __init__(		self     :     Any       ,      lowerCamelCase     :     Optional[Any]=None       ,      lowerCamelCase     :     Any=None       ,      **lowerCamelCase     :     Tuple						):
							lowerCamelCase_	:     Dict 						=      None
							if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.'       ,      lowerCamelCase       ,      )
									lowerCamelCase_	:     Union[str, Any] 						=      kwargs.pop('feature_extractor'						)

							lowerCamelCase_	:     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__(lowerCamelCase       ,      lowerCamelCase						)



					def __call__(		self     :     Optional[int]       ,      lowerCamelCase     :     Any=None       ,      lowerCamelCase     :     Optional[Any]=None       ,      lowerCamelCase     :     List[Any]=None       ,      lowerCamelCase     :     str="max_length"       ,      lowerCamelCase     :     Optional[int]="np"       ,      **lowerCamelCase     :     List[str]						):

							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(lowerCamelCase       ,      lowerCamelCase						) or (isinstance(lowerCamelCase       ,      lowerCamelCase						) and not isinstance(text[0]       ,      lowerCamelCase						)):
											lowerCamelCase_	:     List[str] 						=      [self.tokenizer(lowerCamelCase       ,      padding=lowerCamelCase       ,      return_tensors=lowerCamelCase       ,      **lowerCamelCase						)]

									elif isinstance(lowerCamelCase       ,      lowerCamelCase						) and isinstance(text[0]       ,      lowerCamelCase						):
											lowerCamelCase_	:     List[str] 						=      []

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

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

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

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

											lowerCamelCase_	:     List[str] 						=      jnp.concatenate([encoding['input_ids'] for encoding in encodings]       ,      axis=0						)
											lowerCamelCase_	:     int 						=      jnp.concatenate([encoding['attention_mask'] for encoding in encodings]       ,      axis=0						)

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

											lowerCamelCase_	:     Optional[int] 						=      torch.cat([encoding['input_ids'] for encoding in encodings]       ,      dim=0						)
											lowerCamelCase_	:     Optional[int] 						=      torch.cat([encoding['attention_mask'] for encoding in encodings]       ,      dim=0						)

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

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

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

									lowerCamelCase_	:     Optional[int] 						=      BatchEncoding()
									lowerCamelCase_	:     Union[str, Any] 						=      input_ids
									lowerCamelCase_	:     Tuple 						=      attention_mask

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

							if images is not None:
									lowerCamelCase_	:     Optional[Any] 						=      self.image_processor(lowerCamelCase       ,      return_tensors=lowerCamelCase       ,      **lowerCamelCase						)

							if text is not None and images is not None:
									lowerCamelCase_	:     List[Any] 						=      image_features.pixel_values
									return encoding
							elif query_images is not None and images is not None:
									lowerCamelCase_	:     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(**lowerCamelCase						)       ,      tensor_type=lowerCamelCase						)



					def   __a     (		self     :     Tuple       ,      *lowerCamelCase     :     Dict       ,      **lowerCamelCase     :     Any						):
							return self.image_processor.post_process(*lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     List[Any]       ,      *lowerCamelCase     :     List[str]       ,      **lowerCamelCase     :     Optional[Any]						):
							return self.image_processor.post_process_object_detection(*lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     int       ,      *lowerCamelCase     :     int       ,      **lowerCamelCase     :     Union[str, Any]						):
							return self.image_processor.post_process_image_guided_detection(*lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     str       ,      *lowerCamelCase     :     Union[str, Any]       ,      **lowerCamelCase     :     Optional[int]						):
							return self.tokenizer.batch_decode(*lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     Union[str, Any]       ,      *lowerCamelCase     :     int       ,      **lowerCamelCase     :     List[Any]						):
							return self.tokenizer.decode(*lowerCamelCase       ,      **lowerCamelCase						)



					@property
					def   __a     (		self     :     Optional[Any]						):
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,      lowerCamelCase       ,      )
							return self.image_processor_class





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







import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_lowercase		:  int			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  snake_case__							):



					def __init__(		self     :     Tuple       ,      *lowerCamelCase     :     List[Any]       ,      **lowerCamelCase     :     Union[str, Any]						):
							warnings.warn(
							    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
							    ' use YolosImageProcessor instead.'       ,      lowerCamelCase       ,      )
							super().__init__(*lowerCamelCase       ,      **lowerCamelCase						)
