






'''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


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





def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       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_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 lowerCamelCase__      (						__lowerCamelCase	:							Dict							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =[]

   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  __magic_name__							:






       def       lowerCAmelCase		(      self)						->  Union[str, Any]:




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

          _UpperCAmelCase       :       Union[str, Any]							   =self.tool.inputs
          for _input in inputs:
             if isinstance(_input  ,							_SCREAMING_SNAKE_CASE):
                for __input in _input:
                   self.assertTrue(__input in authorized_types)
             else:
                self.assertTrue(_input in authorized_types)

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






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =create_inputs(self.tool.inputs)
          _UpperCAmelCase       :       Optional[int]							   =self.tool(*_SCREAMING_SNAKE_CASE)

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

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






       def       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''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       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =create_inputs(self.tool.inputs)
          _UpperCAmelCase       :       Dict							   =self.tool(*_SCREAMING_SNAKE_CASE)

          if not isinstance(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE):
             _UpperCAmelCase       :       Any							   =[outputs]

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

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






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =create_inputs(self.tool.inputs)

          _UpperCAmelCase       :       Dict							   =[]

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

          if not isinstance(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE):
             _UpperCAmelCase       :       List[Any]							   =[outputs]

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





'''simple docstring'''






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor






@require_torch
class  __magic_name__							(		unittest.TestCase    ):






       def       lowerCAmelCase		(      self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   ='ZinengTang/tvlt-base'
          _UpperCAmelCase       :       Dict							   =tempfile.mkdtemp()






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  Union[str, Any]:




          '''simple docstring'''
          return TvltImageProcessor.from_pretrained(self.checkpoint  ,							**snake_case)






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  Dict:




          '''simple docstring'''
          return TvltFeatureExtractor.from_pretrained(self.checkpoint  ,							**snake_case)






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          shutil.rmtree(self.tmpdirname)






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =self.get_image_processor()
          _UpperCAmelCase       :       Optional[Any]							   =self.get_feature_extractor()

          _UpperCAmelCase       :       str							   =TvltProcessor(image_processor=snake_case  ,							feature_extractor=snake_case)
          processor.save_pretrained(self.tmpdirname)
          _UpperCAmelCase       :       str							   =TvltProcessor.from_pretrained(self.tmpdirname)

          self.assertIsInstance(processor.feature_extractor  ,							snake_case)
          self.assertIsInstance(processor.image_processor  ,							snake_case)






       def       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =self.get_image_processor()
          _UpperCAmelCase       :       List[Any]							   =self.get_feature_extractor()

          _UpperCAmelCase       :       str							   =TvltProcessor(image_processor=snake_case  ,							feature_extractor=snake_case)

          _UpperCAmelCase       :       Optional[int]							   =np.ones([1_2_0_0_0])

          _UpperCAmelCase       :       str							   =feature_extractor(snake_case  ,							return_tensors='np')
          _UpperCAmelCase       :       Dict							   =processor(audio=snake_case  ,							return_tensors='np')

          for key in audio_dict.keys():
             self.assertAlmostEqual(audio_dict[key].sum()  ,							input_processor[key].sum()  ,							delta=1E-2)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.get_image_processor()
          _UpperCAmelCase       :       int							   =self.get_feature_extractor()

          _UpperCAmelCase       :       int							   =TvltProcessor(image_processor=snake_case  ,							feature_extractor=snake_case)

          _UpperCAmelCase       :       Union[str, Any]							   =np.ones([3, 2_2_4, 2_2_4])

          _UpperCAmelCase       :       Optional[Any]							   =image_processor(snake_case  ,							return_tensors='np')
          _UpperCAmelCase       :       List[Any]							   =processor(images=snake_case  ,							return_tensors='np')

          for key in image_dict.keys():
             self.assertAlmostEqual(image_dict[key].sum()  ,							input_processor[key].sum()  ,							delta=1E-2)






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =self.get_image_processor()
          _UpperCAmelCase       :       Dict							   =self.get_feature_extractor()

          _UpperCAmelCase       :       Optional[int]							   =TvltProcessor(image_processor=snake_case  ,							feature_extractor=snake_case)

          _UpperCAmelCase       :       Optional[int]							   =np.ones([1_2_0_0_0])
          _UpperCAmelCase       :       str							   =np.ones([3, 2_2_4, 2_2_4])

          _UpperCAmelCase       :       Optional[int]							   =processor(audio=snake_case  ,							images=snake_case)

          self.assertListEqual(list(inputs.keys())  ,							['audio_values', 'audio_mask', 'pixel_values', 'pixel_mask'])

          # test if it raises when no input is passed
          with pytest.raises(snake_case):
             processor()






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =self.get_image_processor()
          _UpperCAmelCase       :       Tuple							   =self.get_feature_extractor()

          _UpperCAmelCase       :       Dict							   =TvltProcessor(image_processor=snake_case  ,							feature_extractor=snake_case)

          self.assertListEqual(
              processor.model_input_names  ,							image_processor.model_input_names + feature_extractor.model_input_names  ,							msg='`processor` and `image_processor`+`feature_extractor` model input names do not match'  ,							)

