






'''simple docstring'''
from typing import List

import numpy as np




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     {key: len(_A							) for key, value in gen_kwargs.items() if isinstance(_A						,		_A							)}
   if len(set(lists_lengths.values()							)							) > 1:
      raise RuntimeError(
          (
              "Sharding is ambiguous for this dataset: "
              + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
              + "\n".join(F"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()							)
              + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
              + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
          )							)
   lowercase_	:    Optional[Any]           =     max(lists_lengths.values()						,		default=0							)
   return max(1						,		_A							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     []
   for group_idx in range(_A							):
      lowercase_	:    str           =     num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
      if num_shards_to_add == 0:
         break
      lowercase_	:    List[str]           =     shards_indices_per_group[-1].stop if shards_indices_per_group else 0
      lowercase_	:    Tuple           =     range(_A						,		start + num_shards_to_add							)
      shards_indices_per_group.append(_A							)
   return shards_indices_per_group




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    int           =     _number_of_shards_in_gen_kwargs(_A							)
   if num_shards == 1:
      return [dict(_A							)]
   else:
      lowercase_	:    Any           =     _distribute_shards(num_shards=_A						,		max_num_jobs=_A							)
      return [
          {
              key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
              if isinstance(_A						,		_A							)
              else value
              for key, value in gen_kwargs.items()
          }
          for group_idx in range(len(_A							)							)
      ]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return {
       key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
       if isinstance(gen_kwargs_list[0][key]						,		_A							)
       else gen_kwargs_list[0][key]
       for key in gen_kwargs_list[0]
   }





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[Any]           =     {len(_A							) for value in gen_kwargs.values() if isinstance(_A						,		_A							)}
   lowercase_	:    Any           =     {}
   for size in list_sizes:
      lowercase_	:    str           =     list(range(_A							)							)
      rng.shuffle(indices_per_size[size]							)
   # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
   lowercase_	:    Tuple           =     dict(_A							)
   for key, value in shuffled_kwargs.items():
      if isinstance(_A						,		_A							):
         lowercase_	:    Tuple           =     [value[i] for i in indices_per_size[len(_A							)]]
   return shuffled_kwargs








'''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


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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




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

   return inputs




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     []

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

   return output_types





@is_tool_test
class        _UpperCAmelCase						:




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






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

       lowercase_	:    Optional[Any]           =     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			)

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




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     create_inputs(self.tool.inputs			)
       lowercase_	:    List[str]           =     self.tool(*a			)

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

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




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''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  :       Any			):






       '''simple docstring'''






       lowercase_	:    Any           =     create_inputs(self.tool.inputs			)
       lowercase_	:    str           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    List[Any]           =     [outputs]

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

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





    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Dict           =     create_inputs(self.tool.inputs			)

       lowercase_	:    Optional[int]           =     []

       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
       lowercase_	:    Any           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    Any           =     [outputs]

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




