


'''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    :     int					=						["""text""", """image""", """audio"""]






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							List[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(__snake_case   ,						__snake_case					):
                     inputs.append(create_inputs(__snake_case					)					)
              else:
                     raise ValueError(f'Invalid type requested: {input_type}'					)

       return inputs






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=       []

       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 	UpperCamelCase__					:



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							int   			=       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    :							List[str]   			=       self.tool.outputs
            for _output in outputs:
                   self.assertTrue(_output in authorized_types   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       create_inputs(self.tool.inputs   )
            _lowerCAmelCase    :							Optional[int]   			=       self.tool(*__a   )

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

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







     def a	( 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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       create_inputs(self.tool.inputs   )
            _lowerCAmelCase    :							int   			=       self.tool(*__a   )

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

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

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       create_inputs(self.tool.inputs   )

            _lowerCAmelCase    :							List[Any]   			=       []

            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    :							Tuple   			=       self.tool(*__a   )

            if not isinstance(__a      ,	__a   ):
                   _lowerCAmelCase    :							str   			=       [outputs]

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





'''simple docstring'''







import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
       import jax.numpy as jnp

       from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'   )
            _lowerCAmelCase    :							Optional[Any]   			=       AutoTokenizer.from_pretrained('xlm-roberta-base'   )
            _lowerCAmelCase    :							Dict   			=       'The dog is cute and lives in the garden house'
            _lowerCAmelCase    :							List[str]   			=       jnp.array([tokenizer.encode(snake_case__   )]   )

            _lowerCAmelCase    :							Optional[int]   			=       (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
            _lowerCAmelCase    :							Tuple   			=       jnp.array(
                [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )

            _lowerCAmelCase    :							Union[str, Any]   			=       model(snake_case__   )['last_hidden_state']
            self.assertEqual(output.shape      ,	snake_case__   )
            # compare the actual values for a slice of last dim
            self.assertTrue(jnp.allclose(output[:, :, -1]      ,	snake_case__      ,	atol=1E-3   )   )

