



from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
	import torch





class  __lowerCAmelCase			(					lowerCamelCase__	):
				__lowerCamelCase			=    ['''image_processor''', '''tokenizer''']
				__lowerCamelCase			=    '''OneFormerImageProcessor'''
				__lowerCamelCase			=    ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




				def __init__(							self						,    _snake_case=None						,    _snake_case=None						,    _snake_case = 77						,    _snake_case = 77						,    **_snake_case		):



									"""simple docstring"""





									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`."""		)

									_lowerCAmelCase      =   max_seq_length
									_lowerCAmelCase      =   task_seq_length

									super().__init__(_snake_case						,    _snake_case		)




				def 							snake_case       (							self						,    _snake_case=None						,    _snake_case=77		):



									"""simple docstring"""





									if text_list is None:
														raise ValueError("""tokens cannot be None."""		)

									_lowerCAmelCase      =   self.tokenizer(_snake_case						,    padding="""max_length"""						,    max_length=_snake_case						,    truncation=_snake_case		)

									_lowerCAmelCase     ,     _lowerCAmelCase      =   tokens["""attention_mask"""], tokens["""input_ids"""]

									_lowerCAmelCase      =   []
									for attn_mask, input_id in zip(_snake_case						,    _snake_case		):
														_lowerCAmelCase      =   torch.tensor(_snake_case		) * torch.tensor(_snake_case		)
														token_inputs.append(token.unsqueeze(0		)		)

									_lowerCAmelCase      =   torch.cat(_snake_case						,    dim=0		)
									return token_inputs




				def __call__(							self						,    _snake_case=None						,    _snake_case=None						,    _snake_case=None						,    **_snake_case		):



									"""simple docstring"""





									if task_inputs is None:
														raise ValueError("""You have to specify the task_input. Found None."""		)
									elif images is None:
														raise ValueError("""You have to specify the image. Found None."""		)

									if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
														raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

									_lowerCAmelCase      =   self.image_processor(_snake_case						,    _snake_case						,    _snake_case						,    **_snake_case		)

									if isinstance(_snake_case						,    _snake_case		):
														_lowerCAmelCase      =   [task_inputs]

									if isinstance(_snake_case						,    _snake_case		) and all(isinstance(_snake_case						,    _snake_case		) for task_input in task_inputs		):
														_lowerCAmelCase      =   []
														for task in task_inputs:
																			_lowerCAmelCase      =   F'the task is {task}'
																			task_token_inputs.append(_snake_case		)
														_lowerCAmelCase      =   self._preprocess_text(_snake_case						,    max_length=self.task_seq_length		)
									else:
														raise TypeError("""Task Inputs should be a string or a list of strings."""		)

									if hasattr(_snake_case						,    """text_inputs"""		):
														_lowerCAmelCase      =   encoded_inputs.text_inputs

														_lowerCAmelCase      =   []
														for texts in texts_list:
																			_lowerCAmelCase      =   self._preprocess_text(_snake_case						,    max_length=self.max_seq_length		)
																			text_inputs.append(text_input_list.unsqueeze(0		)		)

														_lowerCAmelCase      =   torch.cat(_snake_case						,    dim=0		)

									return encoded_inputs




				def 							snake_case       (							self						,    _snake_case=None						,    _snake_case=None						,    _snake_case=None						,    **_snake_case		):



									"""simple docstring"""





									if task_inputs is None:
														raise ValueError("""You have to specify the task_input. Found None."""		)
									elif images is None:
														raise ValueError("""You have to specify the image. Found None."""		)

									if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
														raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

									_lowerCAmelCase      =   self.image_processor.encode_inputs(_snake_case						,    _snake_case						,    _snake_case						,    **_snake_case		)

									if isinstance(_snake_case						,    _snake_case		):
														_lowerCAmelCase      =   [task_inputs]

									if isinstance(_snake_case						,    _snake_case		) and all(isinstance(_snake_case						,    _snake_case		) for task_input in task_inputs		):
														_lowerCAmelCase      =   []
														for task in task_inputs:
																			_lowerCAmelCase      =   F'the task is {task}'
																			task_token_inputs.append(_snake_case		)
														_lowerCAmelCase      =   self._preprocess_text(_snake_case						,    max_length=self.task_seq_length		)
									else:
														raise TypeError("""Task Inputs should be a string or a list of strings."""		)

									if hasattr(_snake_case						,    """text_inputs"""		):
														_lowerCAmelCase      =   encoded_inputs.text_inputs

														_lowerCAmelCase      =   []
														for texts in texts_list:
																			_lowerCAmelCase      =   self._preprocess_text(_snake_case						,    max_length=self.max_seq_length		)
																			text_inputs.append(text_input_list.unsqueeze(0		)		)

														_lowerCAmelCase      =   torch.cat(_snake_case						,    dim=0		)

									return encoded_inputs




				def 							snake_case       (							self						,    *_snake_case						,    **_snake_case		):



									"""simple docstring"""





									return self.image_processor.post_process_semantic_segmentation(*_snake_case						,    **_snake_case		)




				def 							snake_case       (							self						,    *_snake_case						,    **_snake_case		):



									"""simple docstring"""





									return self.image_processor.post_process_instance_segmentation(*_snake_case						,    **_snake_case		)




				def 							snake_case       (							self						,    *_snake_case						,    **_snake_case		):



									"""simple docstring"""





									return self.image_processor.post_process_panoptic_segmentation(*_snake_case						,    **_snake_case		)








# Lint as: python3

import hashlib
from typing import Union


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					if isinstance(snake_case     ,						snake_case      ):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(snake_case     ,						snake_case      ):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										_lowerCAmelCase      =   hash_data.replace("""\\"""     ,						"""/"""      )
					elif isinstance(snake_case     ,						snake_case      ):
										_lowerCAmelCase      =   str(snake_case      )
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(snake_case      )

					return hash_data.encode("""utf-8"""      )





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   """\nFAILURE TO GENERATE DATASET: Invalid key type detected"""
									_lowerCAmelCase      =   F'\nFound Key {hash_data} of type {type(_snake_case		)}'
									_lowerCAmelCase      =   """\nKeys should be either str, int or bytes type"""
									super().__init__(F'{self.prefix}{self.err_msg}{self.suffix}'		)





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def __init__(							self						,    _snake_case						,    _snake_case						,    _snake_case=""		):



									"""simple docstring"""





									_lowerCAmelCase      =   key
									_lowerCAmelCase      =   duplicate_key_indices
									_lowerCAmelCase      =   fix_msg
									_lowerCAmelCase      =   """Found multiple examples generated with the same key"""
									if len(_snake_case		) <= 20:
														_lowerCAmelCase      =   F'\nThe examples at index {", ".join(_snake_case		)} have the key {key}'
									else:
														_lowerCAmelCase      =   F'\nThe examples at index {", ".join(duplicate_key_indices[:20]		)}... ({len(_snake_case		) - 20} more) have the key {key}'
									_lowerCAmelCase      =   """\n""" + fix_msg if fix_msg else """"""
									super().__init__(F'{self.prefix}{self.err_msg}{self.suffix}'		)





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   hashlib.mda(_as_bytes(_snake_case		)		)




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   self._split_mda.copy()
									_lowerCAmelCase      =   _as_bytes(_snake_case		)
									mda.update(_snake_case		)
									# Convert to integer with hexadecimal conversion
									return int(mda.hexdigest()						,    16		)



