
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    """files""" , [
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ] , )
def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   str    		=      tmp_path_factory.mktemp("""dset_infos_dir"""   )
  if "full:README.md" in files:
    with open(dataset_infos_dir / """README.md""" , """w"""   ) as f:
      f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""   )
  if "empty:README.md" in files:
    with open(dataset_infos_dir / """README.md""" , """w"""   ) as f:
      f.write(""""""   )
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / """dataset_infos.json""" , """w"""   ) as f:
      f.write("""{\"default\": {\"dataset_size\": 42}}"""   )
  lowercase__  :   List[Any]    		=      DatasetInfosDict.from_directory(snake_case__   )
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    """dataset_info""" , [
        DatasetInfo(),
        DatasetInfo(
            description="""foo""" , features=Features({"""a""": Value("""int32"""   )}   ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=42 , ),
    ] , )
def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   str    		=      str(snake_case__   )
  dataset_info.write_to_directory(snake_case__   )
  lowercase__  :   Union[str, Any]    		=      DatasetInfo.from_directory(snake_case__   )
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(snake_case__ , """dataset_info.json"""   )   )

def 		UpperCamelCase	(      )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   Union[str, Any]    		=      DatasetInfo(
      description="""foo""" , citation="""bar""" , homepage="""https://foo.bar""" , license="""CC0""" , features=Features({"""a""": Value("""int32"""   )}   ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train""", """num_examples""": 42}] , download_checksums={} , download_size=13_37 , post_processing_size=4_42 , dataset_size=12_34 , size_in_bytes=13_37 + 4_42 + 12_34 , )
  lowercase__  :   List[str]    		=      dataset_info._to_yaml_dict()
  assert sorted(snake_case__   ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML   )
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str)   )
  lowercase__  :   Dict    		=      yaml.safe_dump(snake_case__   )
  lowercase__  :   Any    		=      yaml.safe_load(snake_case__   )
  assert dataset_info_yaml_dict == reloaded

def 		UpperCamelCase	(      )		->	Optional[Any]:


  '''simple docstring'''






  lowercase__  :   Tuple    		=      DatasetInfo()
  lowercase__  :   Union[str, Any]    		=      dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}



@pytest.mark.parametrize(
    """dataset_infos_dict""" , [
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}   ),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}   ),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo""" , features=Features({"""a""": Value("""int32"""   )}   ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=42 , )
            }   ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42   ),
                """v2""": DatasetInfo(dataset_size=13_37   ),
            }   ),
    ] , )
def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Optional[int]:


  '''simple docstring'''






  lowercase__  :   Dict    		=      str(snake_case__   )
  dataset_infos_dict.write_to_directory(snake_case__   )
  lowercase__  :   Union[str, Any]    		=      DatasetInfosDict.from_directory(snake_case__   )

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    lowercase__  :   Dict    		=      config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    lowercase__  :   List[Any]    		=      DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()   )
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(snake_case__ , """README.md"""   )   )



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Any        =			'dandelin/vilt-b32-finetuned-vqa'
 __lowerCAmelCase  :     Union[str, Any]        =			(
     'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
     'image containing the information, as well as a `question` which should be the question in English. It '
     'returns a text that is the answer to the question.'
 )
 __lowerCAmelCase  :     str        =			'image_qa'
 __lowerCAmelCase  :     int        =			AutoProcessor
 __lowerCAmelCase  :     Optional[Any]        =			AutoModelForVisualQuestionAnswering

 __lowerCAmelCase  :     str        =			['image', 'text']
 __lowerCAmelCase  :     Optional[int]        =			['text']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""vision"""])
   super().__init__(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.pre_processor(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  return_tensors="""pt""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   with torch.no_grad():
     return self.model(**SCREAMING_SNAKE_CASE_).logits



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Dict    		=      outputs.argmax(-1).item()
   return self.model.config.idalabel[idx]






