
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:     Union[str, Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Tuple           =   {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""pegasus"""
  a__           =		["""past_key_values"""]
  a__           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=5_0265			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Any=12			,							UpperCamelCase__			: Union[str, Any]=4096			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=12			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[Any]=1024			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: str=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: Union[str, Any]=0.02			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Tuple=1			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       use_cache
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							is_encoder_decoder=UpperCamelCase__			,							decoder_start_token_id=UpperCamelCase__			,							forced_eos_token_id=UpperCamelCase__			,							**UpperCamelCase__			,							)
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    return self.encoder_attention_heads





  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self.d_model



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


__lowerCAmelCase			:     Any           =   [
    {'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 a__    (       A_=True					):






  '''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=_A     )     )
class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		None
  a__           =		None
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    with TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       dataset_module_factory(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )

      __magic_name__				     =       import_main_class(dataset_module.module_path			,							dataset=UpperCamelCase__    )

      __magic_name__				     =       builder_cls(
          cache_dir=UpperCamelCase__			,							config_name=UpperCamelCase__			,							hash=dataset_module.hash			,							)

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







@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       tmp_path_factory.mktemp("""test_hf_gcp"""					) / """test_wikipedia_simple"""
  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path					)
  __magic_name__				     =       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
  __magic_name__				     =       None
  builder_instance.download_and_prepare()
  __magic_name__				     =       builder_instance.as_dataset()
  assert ds





@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path,     dataset=A_					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  __magic_name__				     =       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"""]					)					)

