'''simple docstring'''





import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar




class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   logging.get_logger()

         # the current default level is logging.WARNING
         UpperCAmelCase_           =   logging.get_verbosity()

         logging.set_verbosity_error()
         self.assertEqual(logger.getEffectiveLevel()   ,			logging.get_verbosity() )

         logging.set_verbosity_warning()
         self.assertEqual(logger.getEffectiveLevel()   ,			logging.get_verbosity() )

         logging.set_verbosity_info()
         self.assertEqual(logger.getEffectiveLevel()   ,			logging.get_verbosity() )

         logging.set_verbosity_debug()
         self.assertEqual(logger.getEffectiveLevel()   ,			logging.get_verbosity() )

         # restore to the original level
         logging.set_verbosity(__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   logging.get_verbosity()

         UpperCAmelCase_           =   logging.get_logger("transformers.models.bart.tokenization_bart" )
         UpperCAmelCase_           =   "Testing 1, 2, 3"

         # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
         if level_origin <= logging.WARNING:
              with CaptureLogger(__a ) as cl:
                   logger.warning(__a )
              self.assertEqual(cl.out   ,			msg + "\n" )

         # this is setting the level for all of `transformers.*` loggers
         logging.set_verbosity_error()

         # should not be able to log warnings
         with CaptureLogger(__a ) as cl:
              logger.warning(__a )
         self.assertEqual(cl.out   ,			"" )

         # should be able to log warnings again
         logging.set_verbosity_warning()
         with CaptureLogger(__a ) as cl:
              logger.warning(__a )
         self.assertEqual(cl.out   ,			msg + "\n" )

         # restore to the original level
         logging.set_verbosity(__a )



    @mockenv(TRANSFORMERS_VERBOSITY="error" )
    def 					_lowercase						(self    :    Optional[Any] ):
         # reset for the env var to take effect, next time some logger call is made
         transformers.utils.logging._reset_library_root_logger()
         # this action activates the env var
         UpperCAmelCase_           =   logging.get_logger("transformers.models.bart.tokenization_bart" )

         UpperCAmelCase_           =   os.getenv("TRANSFORMERS_VERBOSITY"   ,			__a )
         UpperCAmelCase_           =   logging.log_levels[env_level_str]

         UpperCAmelCase_           =   logging.get_verbosity()
         self.assertEqual(
             __a   ,			__a   ,			f"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""   ,			)

         # restore to the original level
         UpperCAmelCase_           =   ""
         transformers.utils.logging._reset_library_root_logger()



    @mockenv(TRANSFORMERS_VERBOSITY="super-error" )
    def 					_lowercase						(self    :    Optional[int] ):
         # reset for the env var to take effect, next time some logger call is made
         transformers.utils.logging._reset_library_root_logger()
         UpperCAmelCase_           =   logging.logging.getLogger()
         with CaptureLogger(__a ) as cl:
              # this action activates the env var
              logging.get_logger("transformers.models.bart.tokenization_bart" )
         self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error"   ,			cl.out )

         # no need to restore as nothing was changed






    def 					_lowercase						(self    :    Dict ):
         # testing `logger.warning_advice()`
         transformers.utils.logging._reset_library_root_logger()

         UpperCAmelCase_           =   logging.get_logger("transformers.models.bart.tokenization_bart" )
         UpperCAmelCase_           =   "Testing 1, 2, 3"

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1" ):
              # nothing should be logged as env var disables this method
              with CaptureLogger(__a ) as cl:
                   logger.warning_advice(__a )
              self.assertEqual(cl.out   ,			"" )

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="" ):
              # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
              with CaptureLogger(__a ) as cl:
                   logger.warning_advice(__a )
              self.assertEqual(cl.out   ,			msg + "\n" )





def 	lowerCAmelCase_				(					)    ->      Optional[Any]:


     '''simple docstring'''




     disable_progress_bar()
     assert are_progress_bars_disabled()

     enable_progress_bar()
     assert not are_progress_bars_disabled()






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


lowercase__       =['text', 'image', 'audio']




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 __a :  Optional[int]						=  []

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

 return inputs




def    __UpperCamelCase     (				lowerCAmelCase__							:				List  ):
 __a :  List[str]						=  []

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

 return output_types



@is_tool_test
class 		UpperCamelCase__  :
   def       lowerCAmelCase			(self :					Any   ):
    self.assertTrue(hasattr(self.tool					,				'''inputs'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''outputs'''   )   )

    __a :  Any						=  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   )

    __a :  Optional[int]						=  self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  Union[str, Any]						=  create_inputs(self.tool.inputs   )
    __a :  List[Any]						=  self.tool(*snake_case_   )

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

    self.assertListEqual(output_types(snake_case_   )					,				self.tool.outputs   )
   def       lowerCAmelCase			(self :					List[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   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )
    __a :  Union[str, Any]						=  self.tool(*snake_case_   )

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

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

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







   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )

    __a :  Dict						=  []

    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
    __a :  Optional[Any]						=  self.tool(*snake_case_   )

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

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