





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_  :  Dict     =   ['image_processor', 'tokenizer']
			lowerCamelCase_  :  int     =   'OneFormerImageProcessor'
			lowerCamelCase_  :  str     =   ('CLIPTokenizer', 'CLIPTokenizerFast')






			def __init__(self      ,							__magic_name__=None      ,							__magic_name__=None      ,							__magic_name__ = 77      ,							__magic_name__ = 77      ,							**__magic_name__					)  ->     Union[str, Any]:






							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`.'''					)

							snake_case_		:					Union[str, Any]			    =						max_seq_length
							snake_case_		:					Any			    =						task_seq_length

							super().__init__(__magic_name__      ,							__magic_name__					)






			def     lowerCamelCase						(self      ,							__magic_name__=None      ,							__magic_name__=77					)  ->     Any:






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

							snake_case_		:					Any			    =						self.tokenizer(__magic_name__      ,							padding='''max_length'''      ,							max_length=__magic_name__      ,							truncation=__magic_name__					)

							snake_case_    ,							snake_case_		:					int			    =						tokens['''attention_mask'''], tokens['''input_ids''']

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

							snake_case_		:					Union[str, Any]			    =						torch.cat(__magic_name__      ,							dim=0					)
							return token_inputs






			def __call__(self      ,							__magic_name__=None      ,							__magic_name__=None      ,							__magic_name__=None      ,							**__magic_name__					)  ->     Dict:






							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.'''					)

							snake_case_		:					Any			    =						self.image_processor(__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							**__magic_name__					)

							if isinstance(__magic_name__      ,							__magic_name__					):
											snake_case_		:					List[Any]			    =						[task_inputs]

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

							if hasattr(__magic_name__      ,							'''text_inputs'''					):
											snake_case_		:					Tuple			    =						encoded_inputs.text_inputs

											snake_case_		:					Optional[int]			    =						[]
											for texts in texts_list:
															snake_case_		:					Optional[int]			    =						self._preprocess_text(__magic_name__      ,							max_length=self.max_seq_length					)
															text_inputs.append(text_input_list.unsqueeze(0					)					)

											snake_case_		:					List[str]			    =						torch.cat(__magic_name__      ,							dim=0					)

							return encoded_inputs






			def     lowerCamelCase						(self      ,							__magic_name__=None      ,							__magic_name__=None      ,							__magic_name__=None      ,							**__magic_name__					)  ->     List[Any]:






							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.'''					)

							snake_case_		:					Dict			    =						self.image_processor.encode_inputs(__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							**__magic_name__					)

							if isinstance(__magic_name__      ,							__magic_name__					):
											snake_case_		:					Optional[Any]			    =						[task_inputs]

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

							if hasattr(__magic_name__      ,							'''text_inputs'''					):
											snake_case_		:					Tuple			    =						encoded_inputs.text_inputs

											snake_case_		:					Dict			    =						[]
											for texts in texts_list:
															snake_case_		:					str			    =						self._preprocess_text(__magic_name__      ,							max_length=self.max_seq_length					)
															text_inputs.append(text_input_list.unsqueeze(0					)					)

											snake_case_		:					Union[str, Any]			    =						torch.cat(__magic_name__      ,							dim=0					)

							return encoded_inputs






			def     lowerCamelCase						(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     Dict:
							return self.image_processor.post_process_semantic_segmentation(*__magic_name__      ,							**__magic_name__					)






			def     lowerCamelCase						(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     List[Any]:
							return self.image_processor.post_process_instance_segmentation(*__magic_name__      ,							**__magic_name__					)






			def     lowerCamelCase						(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     Optional[int]:
							return self.image_processor.post_process_panoptic_segmentation(*__magic_name__      ,							**__magic_name__					)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase_												=       {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50Tokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa import MBartaaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
	import sys

	lowerCAmelCase_												=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
