






"""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_	:    Dict            =      ['''text''', '''image''', '''audio''']






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[]

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

       return inputs






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[]

       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_ :

  '''simple docstring'''





  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





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

         UpperCAmelCase	    =			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				)

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



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			create_inputs(self.tool.inputs				)
         UpperCAmelCase	    =			self.tool(*snake_case__				)

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

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



  def 		UpperCamelCase_				(      self				)			->			Optional[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 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			create_inputs(self.tool.inputs				)
         UpperCAmelCase	    =			self.tool(*snake_case__				)

         if not isinstance(snake_case__ ,		snake_case__				):
                UpperCAmelCase	    =			[outputs]

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

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



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			create_inputs(self.tool.inputs				)

         UpperCAmelCase	    =			[]

         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
         UpperCAmelCase	    =			self.tool(*snake_case__				)

         if not isinstance(snake_case__ ,		snake_case__				):
                UpperCAmelCase	    =			[outputs]

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








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Any            =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Tuple				=	'canine'



  def __init__(      self ,		snake_case__=7_68 ,		snake_case__=12 ,		snake_case__=12 ,		snake_case__=30_72 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=1_63_84 ,		snake_case__=16 ,		snake_case__=0.02 ,		snake_case__=1e-12 ,		snake_case__=0 ,		snake_case__=0xE_0_0_0 ,		snake_case__=0xE_0_0_1 ,		snake_case__=4 ,		snake_case__=4 ,		snake_case__=8 ,		snake_case__=1_63_84 ,		snake_case__=1_28 ,		**snake_case__ ,		)			->			int:



         """simple docstring"""





         super().__init__(pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		**snake_case__				)

         UpperCAmelCase	    =			max_position_embeddings
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         UpperCAmelCase	    =			intermediate_size
         UpperCAmelCase	    =			hidden_act
         UpperCAmelCase	    =			hidden_dropout_prob
         UpperCAmelCase	    =			attention_probs_dropout_prob
         UpperCAmelCase	    =			initializer_range
         UpperCAmelCase	    =			type_vocab_size
         UpperCAmelCase	    =			layer_norm_eps

         # Character config:
         UpperCAmelCase	    =			downsampling_rate
         UpperCAmelCase	    =			upsampling_kernel_size
         UpperCAmelCase	    =			num_hash_functions
         UpperCAmelCase	    =			num_hash_buckets
         UpperCAmelCase	    =			local_transformer_stride
