


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			=							[
    {'''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        snake_case_					(  snake_case=True					)  ->    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=__UpperCamelCase				)				)
class   __a      (   __UpperCamelCase				):
 __lowercase			:							Union[str, Any]       					=			None
 __lowercase			:							Union[str, Any]       					=			None
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        with TemporaryDirectory() as tmp_dir:
               lowercase__:       List[Any]        =       dataset_module_factory(lowerCAmelCase__    ,			cache_dir=lowerCAmelCase__			)

               lowercase__:       Any        =       import_main_class(dataset_module.module_path    ,			dataset=lowerCAmelCase__			)

               lowercase__:       DatasetBuilder        =       builder_cls(
                   cache_dir=lowerCAmelCase__    ,			config_name=lowerCAmelCase__    ,			hash=dataset_module.hash    ,			)

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





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




@pytest.mark.integration
def        snake_case_					(  snake_case					)  ->    List[Any]:
       lowercase__:       str        =       dataset_module_factory('wikipedia'      ,							cache_dir=snake_case					)
       lowercase__:       Any        =       import_main_class(dataset_module.module_path      ,							dataset=snake_case					)
       lowercase__:       DatasetBuilder        =       builder_cls(
           cache_dir=snake_case      ,							config_name='20220301.frr'      ,							hash=dataset_module.hash      ,							)
       lowercase__:       Tuple        =       builder_instance.as_streaming_dataset()
       assert ds
       assert isinstance(snake_case      ,							snake_case					)
       assert "train" in ds
       assert isinstance(ds['train']      ,							snake_case					)
       assert next(iter(ds['train']					)					)






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


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''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   __a      (   __UpperCamelCase				):
 __lowercase			:							Any       					=			'pegasus'
 __lowercase			:							Union[str, Any]       					=			['past_key_values']
 __lowercase			:							Any       					=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
 def __init__(       self    ,			lowerCAmelCase__=50_265    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=1    ,			lowerCAmelCase__=1    ,			**lowerCAmelCase__    ,			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       int        =       vocab_size
        lowercase__:       Optional[int]        =       max_position_embeddings
        lowercase__:       List[str]        =       d_model
        lowercase__:       Optional[Any]        =       encoder_ffn_dim
        lowercase__:       Optional[Any]        =       encoder_layers
        lowercase__:       Union[str, Any]        =       encoder_attention_heads
        lowercase__:       Optional[int]        =       decoder_ffn_dim
        lowercase__:       Tuple        =       decoder_layers
        lowercase__:       Union[str, Any]        =       decoder_attention_heads
        lowercase__:       Dict        =       dropout
        lowercase__:       List[str]        =       attention_dropout
        lowercase__:       List[str]        =       activation_dropout
        lowercase__:       Optional[int]        =       activation_function
        lowercase__:       Dict        =       init_std
        lowercase__:       Optional[Any]        =       encoder_layerdrop
        lowercase__:       List[str]        =       decoder_layerdrop
        lowercase__:       Union[str, Any]        =       use_cache
        lowercase__:       Any        =       encoder_layers
        lowercase__:       List[str]        =       scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase__    ,			eos_token_id=lowerCAmelCase__    ,			is_encoder_decoder=lowerCAmelCase__    ,			decoder_start_token_id=lowerCAmelCase__    ,			forced_eos_token_id=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.encoder_attention_heads
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.d_model


