
"""simple docstring"""



import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase__        =					[
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]
def    _UpperCAmelCase (					__lowerCamelCase  :			Tuple=True						)	->   List[Any]:

  if with_config:
    return [
        {
            "testcase_name": d["dataset"] + "/" + d["config_name"],
            "dataset": d["dataset"],
            "config_name": d["config_name"],
        }
        for d in DATASETS_ON_HF_GCP
    ]
  else:
    return [
        {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
    ]

@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=A_      )      )
class  lowerCAmelCase__							(		A_      ):
     __a         =   None
     __a         =   None







     def 	lowercase      (				self     :     Optional[Any]       , _lowerCamelCase     :     List[str]       , _lowerCamelCase     :     Any     ):




       with TemporaryDirectory() as tmp_dir:
         _snake_case   	=			dataset_module_factory(lowerCAmelCase__       , cache_dir=lowerCAmelCase__     )

         _snake_case   	=			import_main_class(dataset_module.module_path       , dataset=lowerCAmelCase__     )

         _snake_case   	=			builder_cls(
             cache_dir=lowerCAmelCase__       , config_name=lowerCAmelCase__       , hash=dataset_module.hash       , )

         _snake_case   	=			'''/'''.join(
             [
                 HF_GCP_BASE_URL,
                 builder_instance._relative_data_dir(with_hash=lowerCAmelCase__     ).replace(os.sep       , '''/'''     ),
                 config.DATASET_INFO_FILENAME,
             ]     )
         _snake_case   	=			cached_path(lowerCAmelCase__       , cache_dir=lowerCAmelCase__     )
         self.assertTrue(os.path.exists(lowerCAmelCase__     )     )






@pytest.mark.integration
def    _UpperCAmelCase (					__lowerCamelCase  :			Any						)	->   List[Any]:
  _snake_case   	=			tmp_path_factory.mktemp('''test_hf_gcp'''						) / '''test_wikipedia_simple'''
  _snake_case   	=			dataset_module_factory('''wikipedia'''					,   cache_dir=__lowerCamelCase						)
  _snake_case   	=			import_main_class(dataset_module.module_path						)
  _snake_case   	=			builder_cls(
      cache_dir=__lowerCamelCase					,   config_name='''20220301.frr'''					,   hash=dataset_module.hash					,   )
  # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
  _snake_case   	=			None
  builder_instance.download_and_prepare()
  _snake_case   	=			builder_instance.as_dataset()
  assert ds

@pytest.mark.integration
def    _UpperCAmelCase (					__lowerCamelCase  :			Any						)	->   int:
  _snake_case   	=			dataset_module_factory('''wikipedia'''					,   cache_dir=__lowerCamelCase						)
  _snake_case   	=			import_main_class(dataset_module.module_path					,   dataset=__lowerCamelCase						)
  _snake_case   	=			builder_cls(
      cache_dir=__lowerCamelCase					,   config_name='''20220301.frr'''					,   hash=dataset_module.hash					,   )
  _snake_case   	=			builder_instance.as_streaming_dataset()
  assert ds
  assert isinstance(__lowerCamelCase					,   __lowerCamelCase						)
  assert "train" in ds
  assert isinstance(ds['''train''']					,   __lowerCamelCase						)
  assert next(iter(ds['''train''']						)						)










'''simple docstring'''

# 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

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	str									=       'Salesforce/blip-image-captioning-base'
       _snake_case      :	Union[str, Any]									=       (
           'This is a tool that generates a description of an image. It takes an input named `image` which should be the '
           'image to caption, and returns a text that contains the description in English.'
       )
       _snake_case      :	List[Any]									=       'image_captioner'
       _snake_case      :	Union[str, Any]									=       AutoModelForVisionaSeq

       _snake_case      :	Dict									=       ['image']
       _snake_case      :	Optional[int]									=       ['text']





       def __init__(				self  :						Union[str, Any]			,					*lowerCAmelCase__  :						Optional[Any]			,					**lowerCAmelCase__  :						Optional[int]	)       ->	Tuple:

            '''simple docstring'''




            requires_backends(self			,					['''vision''']	)
            super().__init__(*lowerCAmelCase__			,					**lowerCAmelCase__	)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						"Image"	)       ->	Optional[int]:

            '''simple docstring'''




            return self.pre_processor(images=lowerCAmelCase__			,					return_tensors='''pt'''	)





       def snake_case__		(				self  :						Any			,					lowerCAmelCase__  :						Union[str, Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            return self.model.generate(**lowerCAmelCase__	)







       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						List[str]	)       ->	List[str]:

            '''simple docstring'''




            return self.pre_processor.batch_decode(lowerCAmelCase__			,					skip_special_tokens=lowerCAmelCase__	)[0].strip()



