


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_      : Optional[int]  					=       [
    {'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        SCREAMING_SNAKE_CASE_						(			__A :			Tuple=True				)	->		Union[str, Any]:

       """simple docstring"""

       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=lowercase__			)			)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     List[Any]        =						None
      snake_case__				:     List[str]        =						None
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			List[str]:
             with TemporaryDirectory() as tmp_dir:
                    a_					:		Optional[Any]							=  dataset_module_factory(SCREAMING_SNAKE_CASE__     ,							cache_dir=SCREAMING_SNAKE_CASE__     )

                    a_					:		Optional[int]							=  import_main_class(dataset_module.module_path     ,							dataset=SCREAMING_SNAKE_CASE__     )

                    a_					:		DatasetBuilder							=  builder_cls(
                        cache_dir=SCREAMING_SNAKE_CASE__     ,							config_name=SCREAMING_SNAKE_CASE__     ,							hash=dataset_module.hash     ,							)

                    a_					:		Any							=  '/'.join(
                        [
                            HF_GCP_BASE_URL,
                            builder_instance._relative_data_dir(with_hash=SCREAMING_SNAKE_CASE__     ).replace(os.sep     ,							'/'     ),
                            config.DATASET_INFO_FILENAME,
                        ]     )
                    a_					:		Dict							=  cached_path(SCREAMING_SNAKE_CASE__     ,							cache_dir=SCREAMING_SNAKE_CASE__     )
                    self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE__     )     )







@pytest.mark.integration
def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		int:

       """simple docstring"""

       a_					:		List[str]							=  tmp_path_factory.mktemp('test_hf_gcp'				) / 'test_wikipedia_simple'
       a_					:		Union[str, Any]							=  dataset_module_factory('wikipedia'  ,    cache_dir=__A				)
       a_					:		Dict							=  import_main_class(dataset_module.module_path				)
       a_					:		DatasetBuilder							=  builder_cls(
           cache_dir=__A  ,    config_name='20220301.frr'  ,    hash=dataset_module.hash  ,    )
       # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
       a_					:		Optional[Any]							=  None
       builder_instance.download_and_prepare()
       a_					:		List[Any]							=  builder_instance.as_dataset()
       assert ds







@pytest.mark.integration
def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple				)	->		List[Any]:

       """simple docstring"""

       a_					:		Optional[int]							=  dataset_module_factory('wikipedia'  ,    cache_dir=__A				)
       a_					:		Any							=  import_main_class(dataset_module.module_path  ,    dataset=__A				)
       a_					:		DatasetBuilder							=  builder_cls(
           cache_dir=__A  ,    config_name='20220301.frr'  ,    hash=dataset_module.hash  ,    )
       a_					:		Tuple							=  builder_instance.as_streaming_dataset()
       assert ds
       assert isinstance(__A  ,    __A				)
       assert "train" in ds
       assert isinstance(ds['train']  ,    __A				)
       assert next(iter(ds['train']				)				)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_      : str  					=       logging.get_logger(__name__)

UpperCAmelCase_      : List[str]  					=       {
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Optional[int]        =						'''convbert'''
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_0_5_2_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       str=3_0_7_2     ,							SCREAMING_SNAKE_CASE__				:       Dict="gelu"     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.1     ,							SCREAMING_SNAKE_CASE__				:       List[str]=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0.02     ,							SCREAMING_SNAKE_CASE__				:       Any=1E-12     ,							SCREAMING_SNAKE_CASE__				:       int=1     ,							SCREAMING_SNAKE_CASE__				:       int=0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=9     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     ,							)		->			Any:
             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__     ,							bos_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

             a_					:		Tuple							=  vocab_size
             a_					:		List[str]							=  hidden_size
             a_					:		List[str]							=  num_hidden_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		Optional[int]							=  intermediate_size
             a_					:		int							=  hidden_act
             a_					:		Dict							=  hidden_dropout_prob
             a_					:		int							=  attention_probs_dropout_prob
             a_					:		str							=  max_position_embeddings
             a_					:		List[str]							=  type_vocab_size
             a_					:		List[str]							=  initializer_range
             a_					:		Tuple							=  layer_norm_eps
             a_					:		Optional[int]							=  embedding_size
             a_					:		List[Any]							=  head_ratio
             a_					:		List[Any]							=  conv_kernel_size
             a_					:		Tuple							=  num_groups
             a_					:		Tuple							=  classifier_dropout
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Mapping[str, Mapping[int, str]]:
             if self.task == "multiple-choice":
                    a_					:		Tuple							=  {0: 'batch', 1: 'choice', 2: 'sequence'}
             else:
                    a_					:		List[str]							=  {0: 'batch', 1: 'sequence'}
             return OrderedDict(
                 [
                     ('input_ids', dynamic_axis),
                     ('attention_mask', dynamic_axis),
                     ('token_type_ids', dynamic_axis),
                 ]     )


