




from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image


__lowerCamelCase   :						Union[str, Any]				=			["text", "image", "audio"]







def 		lowercase__   (		__A:	List[str]				):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							[]

					for input_type in input_types:
										if input_type == "text":
															inputs.append('''Text input'''				)
										elif input_type == "image":
															inputs.append(
															    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''				).resize((5_1_2, 5_1_2)				)				)
										elif input_type == "audio":
															inputs.append(torch.ones(3_0_0_0				)				)
										elif isinstance(UpperCAmelCase__							,UpperCAmelCase__				):
															inputs.append(create_inputs(UpperCAmelCase__				)				)
										else:
															raise ValueError(F'''Invalid type requested: {input_type}'''				)

					return inputs







def 		lowercase__   (		__A:	List				):





					'''simple docstring'''






					__magic_name__							:	str    							=							[]

					for output in outputs:
										if isinstance(UpperCAmelCase__							,(str, AgentText)				):
															output_types.append('''text'''				)
										elif isinstance(UpperCAmelCase__							,(Image.Image, AgentImage)				):
															output_types.append('''image'''				)
										elif isinstance(UpperCAmelCase__							,(torch.Tensor, AgentAudio)				):
															output_types.append('''audio'''				)
										else:
															raise ValueError(F'''Invalid output: {output}'''				)

					return output_types


@is_tool_test
class    lowerCamelCase    :






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   str:
								self.assertTrue(hasattr(self.tool       ,						'''inputs'''  )  )
								self.assertTrue(hasattr(self.tool       ,						'''outputs'''  )  )

								__magic_name__							:	List[str]    							=							self.tool.inputs
								for _input in inputs:
													if isinstance(_input       ,						lowercase_  ):
																		for __input in _input:
																							self.assertTrue(__input in authorized_types  )
													else:
																		self.assertTrue(_input in authorized_types  )

								__magic_name__							:	Any    							=							self.tool.outputs
								for _output in outputs:
													self.assertTrue(_output in authorized_types  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   Dict:
								__magic_name__							:	Any    							=							create_inputs(self.tool.inputs  )
								__magic_name__							:	List[str]    							=							self.tool(*lowercase_  )

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

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

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Union[str, Any]:
								self.assertTrue(hasattr(self.tool       ,						'''description'''  )  )
								self.assertTrue(hasattr(self.tool       ,						'''default_checkpoint'''  )  )
								self.assertTrue(self.tool.description.startswith('''This is a tool that'''  )  )

			def 							UpperCAmelCase__       ( self			:   Dict  )						->   Any:
								__magic_name__							:	List[Any]    							=							create_inputs(self.tool.inputs  )
								__magic_name__							:	Optional[int]    							=							self.tool(*lowercase_  )

								if not isinstance(lowercase_       ,						lowercase_  ):
													__magic_name__							:	List[Any]    							=							[outputs]

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

								for output, output_type in zip(lowercase_       ,						self.tool.outputs  ):
													__magic_name__							:	int    							=							AGENT_TYPE_MAPPING[output_type]
													self.assertTrue(isinstance(lowercase_       ,						lowercase_  )  )


			def 							UpperCAmelCase__       ( self			:   Any  )						->   Union[str, Any]:
								__magic_name__							:	int    							=							create_inputs(self.tool.inputs  )

								__magic_name__							:	Dict    							=							[]

								for _input, input_type in zip(lowercase_       ,						self.tool.inputs  ):
													if isinstance(lowercase_       ,						lowercase_  ):
																		_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
								__magic_name__							:	Dict    							=							self.tool(*lowercase_  )

								if not isinstance(lowercase_       ,						lowercase_  ):
													__magic_name__							:	List[str]    							=							[outputs]

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[str]				=			{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Dict				=			[
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Dict				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



