


'''simple docstring'''

import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types





class 					snake_case   (   unittest.TestCase      ,						lowercase      ):





  """simple docstring"""




  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  load_tool("translation"					)
   self.tool.setup()
   lowerCamelCase_								=  load_tool("translation"      ,  remote=UpperCamelCase					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.tool("Hey, what's up?"      ,  src_lang="English"      ,  tgt_lang="French"					)
   self.assertEqual(UpperCamelCase      ,  "- Hé, comment ça va?"					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.remote_tool("Hey, what's up?"      ,  src_lang="English"      ,  tgt_lang="French"					)
   self.assertEqual(UpperCamelCase      ,  "- Hé, comment ça va?"					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.tool(text="Hey, what's up?"      ,  src_lang="English"      ,  tgt_lang="French"					)
   self.assertEqual(UpperCamelCase      ,  "- Hé, comment ça va?"					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.remote_tool(text="Hey, what's up?"      ,  src_lang="English"      ,  tgt_lang="French"					)
   self.assertEqual(UpperCamelCase      ,  "- Hé, comment ça va?"					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  ["Hey, what's up?", "English", "Spanish"]
   lowerCamelCase_								=  self.tool(*UpperCamelCase					)

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

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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  ["Hey, what's up?", "English", "Spanish"]
   lowerCamelCase_								=  self.tool(*UpperCamelCase					)

   if not isinstance(UpperCamelCase      ,  UpperCamelCase					):
    lowerCamelCase_								=  [outputs]

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

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







  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  ["Hey, what's up?", "English", "Spanish"]

   lowerCamelCase_								=  []

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

   if not isinstance(UpperCamelCase      ,  UpperCamelCase					):
    lowerCamelCase_								=  [outputs]

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







'''simple docstring'''

import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
a_  :    Any      =			"""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
a_  :    Optional[Any]      =			"""\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
a_  :    List[str]      =			""""""

# TODO: Add the licence for the dataset here if you can find it
a_  :    Dict      =			""""""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
a_  :    Optional[int]      =			{
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}





class 					snake_case   (   datasets.GeneratorBasedBuilder      ):





  """simple docstring"""




  _lowerCamelCase		   =  datasets.Version("1.1.0"      )

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  _lowerCamelCase		   =  [
      datasets.BuilderConfig(name="first_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a first domain"      ),
      datasets.BuilderConfig(name="second_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a second domain"      ),
  ]

  _lowerCamelCase		   =  "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.






  def   snake_case		(   self					):







   """simple docstring"""
   # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
   if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option1": datasets.Value("string"					),
            "answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option2": datasets.Value("string"					),
            "second_domain_answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   return datasets.DatasetInfo(
       # This is the description that will appear on the datasets page.
       description=_DESCRIPTION      ,  features=UpperCamelCase      ,  homepage=_HOMEPAGE      ,  license=_LICENSE      ,  citation=_CITATION      ,  )






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
   # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

   # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
   # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
   # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
   lowerCamelCase_								=  _URLS[self.config.name]
   lowerCamelCase_								=  dl_manager.download_and_extract(UpperCamelCase					)
   return [
       datasets.SplitGenerator(
           name=datasets.Split.TRAIN      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "train.jsonl"					),
               "split": "train",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.VALIDATION      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "dev.jsonl"					),
               "split": "dev",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.TEST      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "test.jsonl"					),
               "split": "test"
           }      ,  ),
   ]







  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
   # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
   with open(UpperCamelCase      ,  encoding="utf-8"					) as f:
    for key, row in enumerate(UpperCamelCase					):
     lowerCamelCase_								=  json.loads(UpperCamelCase					)
     if self.config.name == "first_domain":
      # Yields examples as (key, example) tuples
      yield key, {
          "sentence": data["sentence"],
          "option1": data["option1"],
          "answer": "" if split == "test" else data["answer"],
      }
     else:
      yield key, {
          "sentence": data["sentence"],
          "option2": data["option2"],
          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
      }



