

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							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					int			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Any			     =     '''OwlViTImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




				def __init__(   self ,  snake_case=None ,  snake_case=None ,  **snake_case			):
											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.' ,  snake_case ,  )
																		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__(snake_case ,  snake_case			)




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

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

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

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

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

																																snake_case_     							=       self.tokenizer(snake_case ,  padding=snake_case ,  return_tensors=snake_case ,  **snake_case			)
																																encodings.append(snake_case			)
																		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(
																		    snake_case ,  return_tensors=snake_case ,  **snake_case			).pixel_values
																		snake_case_     							=       query_pixel_values

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

											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(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.image_processor.post_process(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.image_processor.post_process_object_detection(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.image_processor.post_process_image_guided_detection(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




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




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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




