





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			       =      ["""text""", """image""", """audio"""]




def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     Union[str, Any]	=		[]

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

       return inputs




def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     int	=		[]

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

       return output_types







@is_tool_test
class 			__A       :




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             self.assertTrue(hasattr(self.tool      ,							'''inputs'''))
             self.assertTrue(hasattr(self.tool      ,							'''outputs'''))

             _lowerCamelCase							:     Tuple	=		self.tool.inputs
             for _input in inputs:
                    if isinstance(_input      ,							__A):
                           for __input in _input:
                                  self.assertTrue(__input in authorized_types)
                    else:
                           self.assertTrue(_input in authorized_types)

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		create_inputs(self.tool.inputs)
             _lowerCamelCase							:     List[Any]	=		self.tool(*__A)

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

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


      def 				__snake_case			(		self):



             """simple docstring"""





             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):



             """simple docstring"""





             _lowerCamelCase							:     int	=		create_inputs(self.tool.inputs)
             _lowerCamelCase							:     int	=		self.tool(*__A)

             if not isinstance(__A      ,							__A):
                    _lowerCamelCase							:     Optional[Any]	=		[outputs]

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

             for output, output_type in zip(__A      ,							self.tool.outputs):
                    _lowerCamelCase							:     Any	=		AGENT_TYPE_MAPPING[output_type]
                    self.assertTrue(isinstance(__A      ,							__A))



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		create_inputs(self.tool.inputs)

             _lowerCamelCase							:     str	=		[]

             for _input, input_type in zip(__A      ,							self.tool.inputs):
                    if isinstance(__A      ,							__A):
                           _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							:     Union[str, Any]	=		self.tool(*__A)

             if not isinstance(__A      ,							__A):
                    _lowerCamelCase							:     int	=		[outputs]

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






from typing import TYPE_CHECKING

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


_lowerCamelCase			       =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

