

"""simple docstring"""




import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types





class 			__UpperCamelCase (   unittest.TestCase     ,		_A						):


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					load_tool("translation")
							self.tool.setup()
							A			=					load_tool("translation"     ,     remote=__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							A			=					self.tool("Hey, what's up?"     ,     src_lang="English"     ,     tgt_lang="French")
							self.assertEqual(__SCREAMING_SNAKE_CASE     ,     "- Hé, comment ça va?")


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							A			=					self.remote_tool("Hey, what's up?"     ,     src_lang="English"     ,     tgt_lang="French")
							self.assertEqual(__SCREAMING_SNAKE_CASE     ,     "- Hé, comment ça va?")


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					self.tool(text="Hey, what's up?"     ,     src_lang="English"     ,     tgt_lang="French")
							self.assertEqual(__SCREAMING_SNAKE_CASE     ,     "- Hé, comment ça va?")


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			=					self.remote_tool(text="Hey, what's up?"     ,     src_lang="English"     ,     tgt_lang="French")
							self.assertEqual(__SCREAMING_SNAKE_CASE     ,     "- Hé, comment ça va?")


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					["Hey, what's up?", "English", "Spanish"]
							A			=					self.tool(*__SCREAMING_SNAKE_CASE)

							# There is a single output
							if len(self.tool.outputs) == 1:
												A			=					[outputs]

							self.assertListEqual(output_types(__SCREAMING_SNAKE_CASE)     ,     self.tool.outputs)


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			=					["Hey, what's up?", "English", "Spanish"]
							A			=					self.tool(*__SCREAMING_SNAKE_CASE)

							if not isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					[outputs]

							self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,     len(self.tool.outputs))

							for output, output_type in zip(__SCREAMING_SNAKE_CASE     ,     self.tool.outputs):
												A			=					AGENT_TYPE_MAPPING[output_type]
												self.assertTrue(isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE))


		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							A			=					["Hey, what's up?", "English", "Spanish"]

							A			=					[]

							for _input, input_type in zip(__SCREAMING_SNAKE_CASE     ,     self.tool.inputs):
												if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																	_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type])
												else:
																	_inputs.append(AGENT_TYPE_MAPPING[input_type](_input))

        # Should not raise an error
							A			=					self.tool(*__SCREAMING_SNAKE_CASE)

							if not isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					[outputs]

							self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,     len(self.tool.outputs))






from typing import List

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


if is_torch_available():
 import torch




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict 			=       ['image_processor', 'tokenizer']
 _lowerCamelCase  :		Tuple 			=       'OneFormerImageProcessor'
 _lowerCamelCase  :		Dict 			=       ('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__(	self				:						str				,	lowerCamelCase				:						Any=None				,	lowerCamelCase				:						List[Any]=None				,	lowerCamelCase				:						int = 7_7				,	lowerCamelCase				:						int = 7_7				,	**lowerCamelCase				:						Union[str, Any]     )       ->							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`.'     )

        _UpperCamelCase   :				int       =     max_seq_length
        _UpperCamelCase   :				Optional[Any]       =     task_seq_length

        super().__init__(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						Optional[int]=7_7     )       ->							Tuple:
        if text_list is None:
               raise ValueError('tokens cannot be None.'     )

        _UpperCamelCase   :				Dict       =     self.tokenizer(lowerCamelCase				,	padding='max_length'				,	max_length=lowerCamelCase				,	truncation=lowerCamelCase     )

        _UpperCamelCase  ,					_UpperCamelCase   :				List[str]       =     tokens['attention_mask'], tokens['input_ids']

        _UpperCamelCase   :				Optional[int]       =     []
        for attn_mask, input_id in zip(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     torch.tensor(lowerCamelCase     ) * torch.tensor(lowerCamelCase     )
               token_inputs.append(token.unsqueeze(0     )     )

        _UpperCamelCase   :				Dict       =     torch.cat(lowerCamelCase				,	dim=0     )
        return token_inputs

 def __call__(	self				:						Union[str, Any]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						Any=None				,	**lowerCamelCase				:						List[Any]     )       ->							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.'     )

        _UpperCamelCase   :				Optional[Any]       =     self.image_processor(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

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

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				str       =     encoded_inputs.text_inputs

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

               _UpperCamelCase   :				Optional[int]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						int=None				,	**lowerCamelCase				:						List[str]     )       ->							Tuple:

        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.'     )

        _UpperCamelCase   :				Any       =     self.image_processor.encode_inputs(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

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

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				Optional[int]       =     encoded_inputs.text_inputs

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

               _UpperCamelCase   :				Optional[Any]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						Tuple				,	**lowerCamelCase				:						Any     )       ->							str:
        return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Union[str, Any]				,	*lowerCamelCase				:						Optional[int]				,	**lowerCamelCase				:						Any     )       ->							Any:
        return self.image_processor.post_process_instance_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						List[str]				,	**lowerCamelCase				:						int     )       ->							List[Any]:
        return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )



