
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 			_lowerCAmelCase      (			__a ):
				_lowercase    	=['''image_processor''', '''tokenizer''']
				_lowercase    	='''OwlViTImageProcessor'''
				_lowercase    	=('''CLIPTokenizer''', '''CLIPTokenizerFast''')




				def __init__(      self       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			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       ,       )
										lowerCAmelCase_      			=			kwargs.pop("feature_extractor"  )

							lowerCAmelCase_      			=			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       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase="max_length"       ,       _UpperCamelCase="np"       ,       **_UpperCamelCase  )       ->   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(_UpperCamelCase       ,       _UpperCamelCase  ) or (isinstance(_UpperCamelCase       ,       _UpperCamelCase  ) and not isinstance(text[0]       ,       _UpperCamelCase  )):
													lowerCAmelCase_      			=			[self.tokenizer(_UpperCamelCase       ,       padding=_UpperCamelCase       ,       return_tensors=_UpperCamelCase       ,       **_UpperCamelCase  )]

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

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

																lowerCAmelCase_      			=			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":
													lowerCAmelCase_      			=			np.concatenate([encoding["input_ids"] for encoding in encodings]       ,       axis=0  )
													lowerCAmelCase_      			=			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_      			=			jnp.concatenate([encoding["input_ids"] for encoding in encodings]       ,       axis=0  )
													lowerCAmelCase_      			=			jnp.concatenate([encoding["attention_mask"] for encoding in encodings]       ,       axis=0  )

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

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

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

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

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

										lowerCAmelCase_      			=			BatchEncoding()
										lowerCAmelCase_      			=			input_ids
										lowerCAmelCase_      			=			attention_mask

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

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

							if text is not None and images is not None:
										lowerCAmelCase_      			=			image_features.pixel_values
										return encoding
							elif query_images is not None and images is not None:
										lowerCAmelCase_      			=			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   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Tuple:
							return self.image_processor.post_process(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[str]:
							return self.image_processor.post_process_object_detection(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Dict:
							return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Tuple:
							return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				@property
				def   __a		(      self  )       ->   List[Any]:
							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   __a		(      self  )       ->   Optional[int]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,       _UpperCamelCase       ,       )
							return self.image_processor





def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[int]]					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						set       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			len(__lowerCAmelCase       ), len(grid[0]       )
			if (
			    min(__lowerCAmelCase					,							__lowerCAmelCase       ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)       )

			lowerCAmelCase_      			=			0
			count += depth_first_search(__lowerCAmelCase					,							row + 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							row - 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col + 1					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col - 1					,							__lowerCAmelCase       )

			visit.remove((row, col)       )
			return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()



