


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 						snake_case			(       lowerCAmelCase__	):
			'''simple docstring'''

			UpperCAmelCase       :       Union[str, Any]       = ["""image_processor""", """tokenizer"""]
			UpperCAmelCase       :       Any       = """AutoImageProcessor"""
			UpperCAmelCase       :       str       = """AutoTokenizer"""

			def __init__(	self     :     Tuple							,							lowerCAmelCase_     :     Dict=None							,							lowerCAmelCase_     :     Any=None							,							**lowerCAmelCase_     :     Tuple					)					->					Optional[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       None
								if "feature_extractor" in kwargs:
													warnings.warn(
													    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
													    ''' instead.'''							,							lowerCAmelCase_							,							)
													SCREAMING_SNAKE_CASE_	      =       kwargs.pop('''feature_extractor'''					)

								SCREAMING_SNAKE_CASE_	      =       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__(lowerCAmelCase_							,							lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       self.image_processor
								SCREAMING_SNAKE_CASE_	      =       False

			def __call__(	self     :     Union[str, Any]							,							*lowerCAmelCase_     :     int							,							**lowerCAmelCase_     :     Union[str, Any]					)					->					str:




								"""simple docstring"""






								if self._in_target_context_manager:
													return self.current_processor(*lowerCAmelCase_							,							**lowerCAmelCase_					)

								SCREAMING_SNAKE_CASE_	      =       kwargs.pop('''images'''							,							lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       kwargs.pop('''text'''							,							lowerCAmelCase_					)
								if len(lowerCAmelCase_					) > 0:
													SCREAMING_SNAKE_CASE_	      =       args[0]
													SCREAMING_SNAKE_CASE_	      =       args[1:]

								if images is None and text is None:
													raise ValueError('''You need to specify either an `images` or `text` input to process.'''					)

								if images is not None:
													SCREAMING_SNAKE_CASE_	      =       self.image_processor(lowerCAmelCase_							,							*lowerCAmelCase_							,							**lowerCAmelCase_					)
								if text is not None:
													SCREAMING_SNAKE_CASE_	      =       self.tokenizer(lowerCAmelCase_							,							**lowerCAmelCase_					)

								if text is None:
													return inputs
								elif images is None:
													return encodings
								else:
													SCREAMING_SNAKE_CASE_	      =       encodings['''input_ids''']
													return inputs

			def _lowercase (	self     :     Any							,							*lowerCAmelCase_     :     Any							,							**lowerCAmelCase_     :     Dict					)					->					Dict:




								"""simple docstring"""






								return self.tokenizer.batch_decode(*lowerCAmelCase_							,							**lowerCAmelCase_					)

			def _lowercase (	self     :     Union[str, Any]							,							*lowerCAmelCase_     :     Any							,							**lowerCAmelCase_     :     int					)					->					int:




								"""simple docstring"""






								return self.tokenizer.decode(*lowerCAmelCase_							,							**lowerCAmelCase_					)

			@contextmanager
			def _lowercase (	self     :     Any					)					->					List[Any]:




								"""simple docstring"""






								warnings.warn(
								    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
								    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
								    '''your images inputs, or in a separate call.'''					)
								SCREAMING_SNAKE_CASE_	      =       True
								SCREAMING_SNAKE_CASE_	      =       self.tokenizer
								yield
								SCREAMING_SNAKE_CASE_	      =       self.image_processor
								SCREAMING_SNAKE_CASE_	      =       False

			@property
			def _lowercase (	self     :     Dict					)					->					Optional[int]:




								"""simple docstring"""






								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,							lowerCAmelCase_							,							)
								return self.image_processor_class




			@property
			def _lowercase (	self     :     Any					)					->					Tuple:




								"""simple docstring"""






								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,							lowerCAmelCase_							,							)
								return self.image_processor






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 						snake_case			(       unittest.TestCase	, lowerCAmelCase__	):
			'''simple docstring'''

			def _lowercase (	self     :     List[Any]					)					->					Optional[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       load_tool('''translation'''					)
								self.tool.setup()
								SCREAMING_SNAKE_CASE_	      =       load_tool('''translation'''							,							remote=lowerCAmelCase_					)

			def _lowercase (	self     :     Tuple					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.tool('''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)

			def _lowercase (	self     :     Optional[Any]					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.remote_tool('''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)

			def _lowercase (	self     :     Optional[int]					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.tool(text='''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)

			def _lowercase (	self     :     str					)					->					List[str]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       self.remote_tool(text='''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)

			def _lowercase (	self     :     Optional[int]					)					->					Dict:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       ['''Hey, what\'s up?''', '''English''', '''Spanish''']
								SCREAMING_SNAKE_CASE_	      =       self.tool(*lowerCAmelCase_					)

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

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

			def _lowercase (	self     :     Optional[int]					)					->					List[str]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       ['''Hey, what\'s up?''', '''English''', '''Spanish''']
								SCREAMING_SNAKE_CASE_	      =       self.tool(*lowerCAmelCase_					)

								if not isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
													SCREAMING_SNAKE_CASE_	      =       [outputs]

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

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




			def _lowercase (	self     :     Tuple					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       ['''Hey, what\'s up?''', '''English''', '''Spanish''']

								SCREAMING_SNAKE_CASE_	      =       []

								for _input, input_type in zip(lowerCAmelCase_							,							self.tool.inputs					):
													if isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
																		_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
								SCREAMING_SNAKE_CASE_	      =       self.tool(*lowerCAmelCase_					)

								if not isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
													SCREAMING_SNAKE_CASE_	      =       [outputs]

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


