





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


UpperCAmelCase__						:			str									=['''text''', '''image''', '''audio''']





def      _lowercase					(						_UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =[]

				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_12, 5_12)				)				)
								elif input_type == "audio":
												inputs.append(torch.ones(30_00				)				)
								elif isinstance(_UpperCAmelCase			,      _UpperCAmelCase				):
												inputs.append(create_inputs(_UpperCAmelCase				)				)
								else:
												raise ValueError(F"""Invalid type requested: {input_type}"""				)

				return inputs





def      _lowercase					(						_UpperCAmelCase				)						->  Optional[int]:
				lowerCamelCase	 =[]

				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       __A     :


					def _snake_case       (		self							):
									self.assertTrue(hasattr(self.tool   , """inputs"""							)							)
									self.assertTrue(hasattr(self.tool   , """outputs"""							)							)

									lowerCamelCase	 =self.tool.inputs
									for _input in inputs:
													if isinstance(_input   , UpperCAmelCase_							):
																	for __input in _input:
																					self.assertTrue(__input in authorized_types							)
													else:
																	self.assertTrue(_input in authorized_types							)

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


					def _snake_case       (		self							):
									lowerCamelCase	 =create_inputs(self.tool.inputs							)
									lowerCamelCase	 =self.tool(*UpperCAmelCase_							)

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

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


					def _snake_case       (		self							):
									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 _snake_case       (		self							):
									lowerCamelCase	 =create_inputs(self.tool.inputs							)
									lowerCamelCase	 =self.tool(*UpperCAmelCase_							)

									if not isinstance(UpperCAmelCase_   , UpperCAmelCase_							):
													lowerCamelCase	 =[outputs]

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

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


					def _snake_case       (		self							):
									lowerCamelCase	 =create_inputs(self.tool.inputs							)

									lowerCamelCase	 =[]

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

									if not isinstance(UpperCAmelCase_   , UpperCAmelCase_							):
													lowerCamelCase	 =[outputs]

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__						:			str									={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			List[Any]									=['''FNetTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Any									=['''FNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=[
							    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FNetForMaskedLM''',
							    '''FNetForMultipleChoice''',
							    '''FNetForNextSentencePrediction''',
							    '''FNetForPreTraining''',
							    '''FNetForQuestionAnswering''',
							    '''FNetForSequenceClassification''',
							    '''FNetForTokenClassification''',
							    '''FNetLayer''',
							    '''FNetModel''',
							    '''FNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[int]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





