



"""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 	snake_case_  (				A_ :		List[str]						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			[]

       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_12, 5_12)						)						)
              elif input_type == "audio":
                     inputs.append(torch.ones(30_00						)						)
              elif isinstance(A_,    A_						):
                     inputs.append(create_inputs(A_						)						)
              else:
                     raise ValueError(F'''Invalid type requested: {input_type}'''						)

       return inputs






def 	snake_case_  (				A_ :		List						):


       '''simple docstring'''





       _lowerCamelCase						:    List[Any]         =			[]

       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 	__snake_case	:




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






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

         _lowerCamelCase						:    str         =			self.tool.inputs
         for _input in inputs:
                if isinstance(_input						, __lowerCAmelCase		):
                       for __input in _input:
                              self.assertTrue(__input in authorized_types		)
                else:
                       self.assertTrue(_input in authorized_types		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			create_inputs(self.tool.inputs		)
         _lowerCamelCase						:    Any         =			self.tool(*__lowerCAmelCase		)

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

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """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 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			create_inputs(self.tool.inputs		)
         _lowerCamelCase						:    str         =			self.tool(*__lowerCAmelCase		)

         if not isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                _lowerCamelCase						:    Any         =			[outputs]

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

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			create_inputs(self.tool.inputs		)

         _lowerCamelCase						:    Optional[int]         =			[]

         for _input, input_type in zip(__lowerCAmelCase						, self.tool.inputs		):
                if isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                       _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(*__lowerCAmelCase		)

         if not isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                _lowerCamelCase						:    List[Any]         =			[outputs]

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







"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       def wrapper(*A_ :		int,    **A_ :		List[str]						):
              _lowerCamelCase						:    Tuple         =			timeit.default_timer()
              _lowerCamelCase						:    Optional[Any]         =			func(*A_,    **A_						)
              _lowerCamelCase						:    Tuple         =			timeit.default_timer() - starttime
              return delta

       _lowerCamelCase						:    Optional[Any]         =			func.__name__

       return wrapper






def 	snake_case_  (				A_ :		dict,    A_ :		List[str]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			[]
       _lowerCamelCase						:    Optional[int]         =			seq_shapes or {}
       for i in range(A_						):
              _lowerCamelCase						:    Any         =			{}
              for col_id, (k, v) in enumerate(features.items()						):
                     if isinstance(A_,    _ArrayXD						):
                            _lowerCamelCase						:    str         =			np.random.rand(*v.shape						).astype(v.dtype						)
                     elif isinstance(A_,    datasets.Value						):
                            if v.dtype == "string":
                                   _lowerCamelCase						:    List[str]         =			'''The small grey turtle was surprisingly fast when challenged.'''
                            else:
                                   _lowerCamelCase						:    int         =			np.random.randint(10,    size=1						).astype(v.dtype						).item()
                     elif isinstance(A_,    datasets.Sequence						):
                            while isinstance(A_,    datasets.Sequence						):
                                   _lowerCamelCase						:    Union[str, Any]         =			v.feature
                            _lowerCamelCase						:    Dict         =			seq_shapes[k]
                            _lowerCamelCase						:    int         =			np.random.rand(*A_						).astype(v.dtype						)
                     _lowerCamelCase						:    Optional[Any]         =			data

              dummy_data.append((i, example)						)

       return dummy_data







def 	snake_case_  (				A_ :		List[str],    A_ :		Optional[int],    A_ :		Optional[int]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			generate_examples(A_,    num_examples=A_,    seq_shapes=A_						)

       with ArrowWriter(features=A_,    path=A_						) as writer:
              for key, record in dummy_data:
                     _lowerCamelCase						:    Optional[Any]         =			features.encode_example(A_						)
                     writer.write(A_						)

              _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			writer.finalize()

       if not num_final_examples == num_examples:
              raise ValueError(
                  F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''						)

       _lowerCamelCase						:    List[Any]         =			datasets.Dataset.from_file(filename=A_,    info=datasets.DatasetInfo(features=A_						)						)

       return dataset


