


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     str     =					['''image_processor''', '''tokenizer''']
							_lowercase      :     Any     =					'''CLIPImageProcessor'''
							_lowercase      :     str     =					('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')

							def __init__(				self:      str       ,		UpperCamelCase_:      Any=None       ,		UpperCamelCase_:      Optional[int]=None       ,		**UpperCamelCase_:      List[str]				)     ->  Dict:





									"""simple docstring"""







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

									lowercase__				= 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__(UpperCamelCase_       ,		UpperCamelCase_				)

							def __call__(				self:      int       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      Tuple=None       ,		**UpperCamelCase_:      Union[str, Any]				)     ->  Tuple:





									"""simple docstring"""







									if text is None and images is None:
											raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

									if text is not None:
											lowercase__				= self.tokenizer(UpperCamelCase_       ,		return_tensors=UpperCamelCase_       ,		**UpperCamelCase_				)

									if images is not None:
											lowercase__				= self.image_processor(UpperCamelCase_       ,		return_tensors=UpperCamelCase_       ,		**UpperCamelCase_				)

									if text is not None and images is not None:
											lowercase__				= image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**UpperCamelCase_				)       ,		tensor_type=UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Any       ,		*UpperCamelCase_:      Optional[Any]       ,		**UpperCamelCase_:      int				)     ->  Union[str, Any]:





									"""simple docstring"""







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

							def  lowerCamelCase_					(				self:      int       ,		*UpperCamelCase_:      str       ,		**UpperCamelCase_:      List[Any]				)     ->  Optional[Any]:





									"""simple docstring"""







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


							@property
							def  lowerCamelCase_					(				self:      Optional[int]				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= self.tokenizer.model_input_names
									lowercase__				= self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)





import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class        _a		(unittest.TestCase						,  __magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       List[str]	  =						load_tool("""text-to-speech"""     )
						self.tool.setup()






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Tuple	  =						self.tool("""hey"""     )
						A__				:       Tuple	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Any	  =						self.tool("""hey"""     )
						A__				:       Optional[Any]	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )



