
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_ (__SCREAMING_SNAKE_CASE							):
	"""simple docstring"""

	lowerCamelCase     :       str            =			['image_processor', 'tokenizer']
	lowerCamelCase     :       Dict            =			'OwlViTImageProcessor'
	lowerCamelCase     :       Tuple            =			('CLIPTokenizer', 'CLIPTokenizerFast')







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







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

								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(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE			) or (isinstance(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE			) and not isinstance(text[0]							,   _SCREAMING_SNAKE_CASE			)):
																						_lowerCAmelCase :Optional[Any]      	=						[self.tokenizer(_SCREAMING_SNAKE_CASE							,   padding=_SCREAMING_SNAKE_CASE							,   return_tensors=_SCREAMING_SNAKE_CASE							,   **_SCREAMING_SNAKE_CASE			)]

															elif isinstance(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE			) and isinstance(text[0]							,   _SCREAMING_SNAKE_CASE			):
																						_lowerCAmelCase :Union[str, Any]      	=						[]

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

																						# Pad all batch samples to max number of text queries
																						for t in text:
																													if len(_SCREAMING_SNAKE_CASE			) != max_num_queries:
																																				_lowerCAmelCase :Optional[Any]      	=						t + [" "] * (max_num_queries - len(_SCREAMING_SNAKE_CASE			))

																													_lowerCAmelCase :Optional[int]      	=						self.tokenizer(_SCREAMING_SNAKE_CASE							,   padding=_SCREAMING_SNAKE_CASE							,   return_tensors=_SCREAMING_SNAKE_CASE							,   **_SCREAMING_SNAKE_CASE			)
																													encodings.append(_SCREAMING_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":
																						_lowerCAmelCase :Dict      	=						np.concatenate([encoding['input_ids'] for encoding in encodings]							,   axis=0			)
																						_lowerCAmelCase :Dict      	=						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 :Optional[Any]      	=						jnp.concatenate([encoding['input_ids'] for encoding in encodings]							,   axis=0			)
																						_lowerCAmelCase :List[str]      	=						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 :List[str]      	=						tf.stack([encoding['input_ids'] for encoding in encodings]							,   axis=0			)
																						_lowerCAmelCase :Optional[Any]      	=						tf.stack([encoding['attention_mask'] for encoding in encodings]							,   axis=0			)

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

															_lowerCAmelCase :int      	=						BatchEncoding()
															_lowerCAmelCase :Tuple      	=						input_ids
															_lowerCAmelCase :Dict      	=						attention_mask

								if query_images is not None:
															_lowerCAmelCase :List[str]      	=						BatchEncoding()
															_lowerCAmelCase :Optional[Any]      	=						self.image_processor(
															    _SCREAMING_SNAKE_CASE							,   return_tensors=_SCREAMING_SNAKE_CASE							,   **_SCREAMING_SNAKE_CASE			).pixel_values
															_lowerCAmelCase :str      	=						query_pixel_values

								if images is not None:
															_lowerCAmelCase :str      	=						self.image_processor(_SCREAMING_SNAKE_CASE							,   return_tensors=_SCREAMING_SNAKE_CASE							,   **_SCREAMING_SNAKE_CASE			)

								if text is not None and images is not None:
															_lowerCAmelCase :int      	=						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(**_SCREAMING_SNAKE_CASE			)							,   tensor_type=_SCREAMING_SNAKE_CASE			)







	def       SCREAMING_SNAKE_CASE__     (     self:     int							,   *_UpperCAmelCase:     List[str]							,   **_UpperCAmelCase:     List[Any]			):
								return self.image_processor.post_process(*_SCREAMING_SNAKE_CASE							,   **_SCREAMING_SNAKE_CASE			)







	def       SCREAMING_SNAKE_CASE__     (     self:     List[Any]							,   *_UpperCAmelCase:     Dict							,   **_UpperCAmelCase:     int			):
								return self.image_processor.post_process_object_detection(*_SCREAMING_SNAKE_CASE							,   **_SCREAMING_SNAKE_CASE			)







	def       SCREAMING_SNAKE_CASE__     (     self:     List[Any]							,   *_UpperCAmelCase:     Tuple							,   **_UpperCAmelCase:     Optional[int]			):
								return self.image_processor.post_process_image_guided_detection(*_SCREAMING_SNAKE_CASE							,   **_SCREAMING_SNAKE_CASE			)







	def       SCREAMING_SNAKE_CASE__     (     self:     Union[str, Any]							,   *_UpperCAmelCase:     Union[str, Any]							,   **_UpperCAmelCase:     Dict			):
								return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE							,   **_SCREAMING_SNAKE_CASE			)







	def       SCREAMING_SNAKE_CASE__     (     self:     List[Any]							,   *_UpperCAmelCase:     Any							,   **_UpperCAmelCase:     Union[str, Any]			):
								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE							,   **_SCREAMING_SNAKE_CASE			)







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







	@property
	def       SCREAMING_SNAKE_CASE__     (     self:     Optional[Any]			):
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,   _SCREAMING_SNAKE_CASE							,   )
								return self.image_processor




"""simple docstring"""






import argparse
from collections import defaultdict


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    str				=			F"""{file}_{class_name}_{test_name}"""
			done_test[_id] += 1

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    List[str]				=			f.readlines()

			__lowerCAmelCase :    Tuple				=			F"""class {class_name}("""
			__lowerCAmelCase :    Tuple				=			F"""{4 * ' '}def {test_name}("""
			__lowerCAmelCase :    List[str]				=			F"""{8 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    List[Any]				=			F"""{16 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    Any				=			False
			__lowerCAmelCase :    List[Any]				=			False
			__lowerCAmelCase :    Optional[Any]				=			False
			__lowerCAmelCase :    str				=			False
			__lowerCAmelCase :    Optional[int]				=			0
			__lowerCAmelCase :    Dict				=			0

			__lowerCAmelCase :    int				=			[]
			for line in lines:
						if line.startswith(__snake_case      ):
									__lowerCAmelCase :    Any				=			True
						elif in_class and line.startswith(__snake_case      ):
									__lowerCAmelCase :    str				=			True
						elif in_class and in_func and (line.startswith(__snake_case      ) or line.startswith(__snake_case      )):
									__lowerCAmelCase :    Optional[Any]				=			len(line.split(correct_line.split()[0]      )[0]      )
									count += 1

									if count == done_test[_id]:
												__lowerCAmelCase :    Optional[int]				=			True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowerCAmelCase :    Dict				=			True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
									__lowerCAmelCase :    Union[str, Any]				=			False
						else:
									new_lines.append(__snake_case      )

			with open(__snake_case   ,"w"      ) as f:
						for line in new_lines:
									f.write(__snake_case      )


def      _lowercase							(    __snake_case   ,__snake_case=None      )		->  int:
			if fail is not None:
						with open(__snake_case   ,"r"      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			{l.strip() for l in f.readlines()}
			else:
						__lowerCAmelCase :    Optional[Any]				=			None

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			f.readlines()

			__lowerCAmelCase :    Optional[Any]				=			defaultdict(__snake_case      )
			for line in correct_lines:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			line.split(";"      )
						if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
									overwrite_file(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )


if __name__ == "__main__":
	__snake_case				:  Union[str, Any] =       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	__snake_case				:  int =       parser.parse_args()

	main(args.correct_filename, args.fail_filename)