






'''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


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




def 						UpperCamelCase	(			a			)				->     Optional[int]:






     '''simple docstring'''
     __magic_name__						       =    []

     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((512, 512)			)			)
          elif input_type == "audio":
               inputs.append(torch.ones(3000			)			)
          elif isinstance(a		,						a			):
               inputs.append(create_inputs(a			)			)
          else:
               raise ValueError(F'''Invalid type requested: {input_type}'''			)

     return inputs




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    []

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

     return output_types






@is_tool_test
class   _SCREAMING_SNAKE_CASE   :



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

      __magic_name__						       =    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      )

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



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    create_inputs(self.tool.inputs      )
      __magic_name__						       =    self.tool(*a__      )

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

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



 def 			snake_case__  (   self		:   List[str]      ):
      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		:   Tuple      ):
      __magic_name__						       =    create_inputs(self.tool.inputs      )
      __magic_name__						       =    self.tool(*a__      )

      if not isinstance(a__			,				a__      ):
           __magic_name__						       =    [outputs]

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

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



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    create_inputs(self.tool.inputs      )

      __magic_name__						       =    []

      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
      __magic_name__						       =    self.tool(*a__      )

      if not isinstance(a__			,				a__      ):
           __magic_name__						       =    [outputs]

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









'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
      import torch






@is_pipeline_test
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 __SCREAMING_SNAKE_CASE					:Any      	=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Tuple			,				a__		:   int			,				a__		:   int      ):
      __magic_name__						       =    TextaTextGenerationPipeline(model=a__			,				tokenizer=a__      )
      return generator, ["Something to write", "Something else"]



 def 			snake_case__  (   self		:   List[str]			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    generator('''Something there'''      )
      self.assertEqual(a__			,				[{'''generated_text''': ANY(a__      )}]      )
      # These are encoder decoder, they don't just append to incoming string
      self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''      )      )

      __magic_name__						       =    generator(['''This is great !''', '''Something else''']			,				num_return_sequences=2			,				do_sample=a__      )
      self.assertEqual(
          a__			,				[
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
          ]			,				)

      __magic_name__						       =    generator(
          ['''This is great !''', '''Something else''']			,				num_return_sequences=2			,				batch_size=2			,				do_sample=a__      )
      self.assertEqual(
          a__			,				[
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
          ]			,				)

      with self.assertRaises(a__      ):
           generator(4      )



 @require_torch
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''pt'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )

      __magic_name__						       =    3
      __magic_name__						       =    generator(
          '''Something there'''			,				num_return_sequences=a__			,				num_beams=a__			,				)
      __magic_name__						       =    [
          {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
          {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
          {'''generated_text''': ''''''},
      ]
      self.assertEqual(a__			,				a__      )

      __magic_name__						       =    generator('''This is a test'''			,				do_sample=a__			,				num_return_sequences=2			,				return_tensors=a__      )
      self.assertEqual(
          a__			,				[
              {'''generated_token_ids''': ANY(torch.Tensor      )},
              {'''generated_token_ids''': ANY(torch.Tensor      )},
          ]			,				)
      __magic_name__						       =    generator.model.config.eos_token_id
      __magic_name__						       =    '''<pad>'''
      __magic_name__						       =    generator(
          ['''This is a test''', '''This is a second test''']			,				do_sample=a__			,				num_return_sequences=2			,				batch_size=2			,				return_tensors=a__			,				)
      self.assertEqual(
          a__			,				[
              [
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
              ],
              [
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
              ],
          ]			,				)



 @require_tf
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''tf'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )






