
from typing import List

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


if is_torch_available():
			import torch




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Tuple					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Dict					     =      "OneFormerImageProcessor"
	_UpperCAmelCase :	Tuple					     =      ("CLIPTokenizer", "CLIPTokenizerFast")



	def __init__(						self			:					List[str]    ,						lowercase			:					Tuple=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					int = 77    ,						lowercase			:					int = 77    ,						**lowercase			:					int			):


				'''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`.'			)

				_snake_case						=    max_seq_length
				_snake_case						=    task_seq_length

				super().__init__(lowercase    ,						lowercase			)



	def 				A							(						self			:					Optional[int]    ,						lowercase			:					str=None    ,						lowercase			:					List[str]=77			):


				'''simple docstring'''


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

				_snake_case						=    self.tokenizer(lowercase    ,						padding='max_length'    ,						max_length=lowercase    ,						truncation=lowercase			)

				_snake_case			,      _snake_case						=    tokens['attention_mask'], tokens['input_ids']

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

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



	def __call__(						self			:					Dict    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[Any]=None    ,						**lowercase			:					int			):


				'''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.'			)

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

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

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

				if hasattr(lowercase    ,						'text_inputs'			):
							_snake_case						=    encoded_inputs.text_inputs

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

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

				return encoded_inputs



	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					Any=None    ,						lowercase			:					List[str]=None    ,						**lowercase			:					Union[str, Any]			):


				'''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.'			)

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

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

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

				if hasattr(lowercase    ,						'text_inputs'			):
							_snake_case						=    encoded_inputs.text_inputs

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

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

				return encoded_inputs



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Optional[Any]    ,						**lowercase			:					Dict			):


				'''simple docstring'''


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



	def 				A							(						self			:					Tuple    ,						*lowercase			:					str    ,						**lowercase			:					List[Any]			):


				'''simple docstring'''


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





	def 				A							(						self			:					Optional[int]    ,						*lowercase			:					Any    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


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


from typing import TYPE_CHECKING

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


_lowerCamelCase :       Optional[int]     					=    {}

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

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       Optional[int]     					=    ['''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 :       int     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)