
"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	       =      {
    '''configuration_albert''': ['''ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''AlbertConfig''', '''AlbertOnnxConfig'''],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE	       =      ['''AlbertTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE	       =      ['''AlbertTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE	       =      [
							    '''ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''AlbertForMaskedLM''',
							    '''AlbertForMultipleChoice''',
							    '''AlbertForPreTraining''',
							    '''AlbertForQuestionAnswering''',
							    '''AlbertForSequenceClassification''',
							    '''AlbertForTokenClassification''',
							    '''AlbertModel''',
							    '''AlbertPreTrainedModel''',
							    '''load_tf_weights_in_albert''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE	       =      [
							    '''TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFAlbertForMaskedLM''',
							    '''TFAlbertForMultipleChoice''',
							    '''TFAlbertForPreTraining''',
							    '''TFAlbertForQuestionAnswering''',
							    '''TFAlbertForSequenceClassification''',
							    '''TFAlbertForTokenClassification''',
							    '''TFAlbertMainLayer''',
							    '''TFAlbertModel''',
							    '''TFAlbertPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE	       =      [
							    '''FlaxAlbertForMaskedLM''',
							    '''FlaxAlbertForMultipleChoice''',
							    '''FlaxAlbertForPreTraining''',
							    '''FlaxAlbertForQuestionAnswering''',
							    '''FlaxAlbertForSequenceClassification''',
							    '''FlaxAlbertForTokenClassification''',
							    '''FlaxAlbertModel''',
							    '''FlaxAlbertPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_albert import AlbertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_albert_fast import AlbertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_albert import (
														    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AlbertForMaskedLM,
														    AlbertForMultipleChoice,
														    AlbertForPreTraining,
														    AlbertForQuestionAnswering,
														    AlbertForSequenceClassification,
														    AlbertForTokenClassification,
														    AlbertModel,
														    AlbertPreTrainedModel,
														    load_tf_weights_in_albert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_albert import (
														    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAlbertForMaskedLM,
														    TFAlbertForMultipleChoice,
														    TFAlbertForPreTraining,
														    TFAlbertForQuestionAnswering,
														    TFAlbertForSequenceClassification,
														    TFAlbertForTokenClassification,
														    TFAlbertMainLayer,
														    TFAlbertModel,
														    TFAlbertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_albert import (
														    FlaxAlbertForMaskedLM,
														    FlaxAlbertForMultipleChoice,
														    FlaxAlbertForPreTraining,
														    FlaxAlbertForQuestionAnswering,
														    FlaxAlbertForSequenceClassification,
														    FlaxAlbertForTokenClassification,
														    FlaxAlbertModel,
														    FlaxAlbertPreTrainedModel,
														)
else:
							import sys

							_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	       =      ["""text""", """image""", """audio"""]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       []

							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(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																					inputs.append(create_inputs(SCREAMING_SNAKE_CASE ) )
														else:
																					raise ValueError(F'''Invalid type requested: {input_type}''' )

							return inputs
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       []

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

							return output_types







@is_tool_test
class 							__magic_name__     :





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												self.assertTrue(hasattr(self.tool							,						"inputs"    )    )
												self.assertTrue(hasattr(self.tool							,						"outputs"    )    )

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

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





					def 							lowerCAmelCase      (				self   :							int    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

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

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





					def 							lowerCAmelCase      (				self   :							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 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

												if not isinstance(snake_case_							,						snake_case_    ):
																			__snake_case     			=       [outputs]

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

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





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )

												__snake_case     			=       []

												for _input, input_type in zip(snake_case_							,						self.tool.inputs    ):
																			if isinstance(snake_case_							,						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
												__snake_case     			=       self.tool(*snake_case_    )

												if not isinstance(snake_case_							,						snake_case_    ):
																			__snake_case     			=       [outputs]

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