

from typing import Mapping

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


_UpperCAmelCase		:   Any										=       logging.get_logger(__name__)

_UpperCAmelCase		:   Optional[int]										=       {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: List[str]       			=			'''t5'''
				__SCREAMING_SNAKE_CASE					: Any       			=			['''past_key_values''']
				__SCREAMING_SNAKE_CASE					: List[str]       			=			{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




				def __init__(					self      ,   snake_case=3_2128      ,   snake_case=512      ,   snake_case=64      ,   snake_case=2048      ,   snake_case=6      ,   snake_case=None      ,   snake_case=8      ,   snake_case=32      ,   snake_case=128      ,   snake_case=0.1      ,   snake_case=1e-6      ,   snake_case=1.0      ,   snake_case="relu"      ,   snake_case=True      ,   snake_case=True      ,   snake_case=0      ,   snake_case=1      ,   **snake_case      ,   ):
											snake_case_							=    vocab_size
											snake_case_							=    d_model
											snake_case_							=    d_kv
											snake_case_							=    d_ff
											snake_case_							=    num_layers
											snake_case_							=    (
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											snake_case_							=    num_heads
											snake_case_							=    relative_attention_num_buckets
											snake_case_							=    relative_attention_max_distance
											snake_case_							=    dropout_rate
											snake_case_							=    layer_norm_epsilon
											snake_case_							=    initializer_factor
											snake_case_							=    feed_forward_proj
											snake_case_							=    use_cache

											snake_case_							=    self.feed_forward_proj.split('-'       )
											snake_case_							=    act_info[-1]
											snake_case_							=    act_info[0] == 'gated'

											if len(snake_case       ) > 1 and act_info[0] != "gated" or len(snake_case       ) > 2:
																		raise ValueError(
																		    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
																		    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
																		    '\'gated-gelu\' or \'relu\''       )

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																		snake_case_							=    'gelu_new'

											super().__init__(
											    pad_token_id=snake_case      ,   eos_token_id=snake_case      ,   is_encoder_decoder=snake_case      ,   **snake_case      ,   )







class  lowercase				(       lowercase_			):




				@property
				def     a							(					self       ):
											snake_case_							=    {
											    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
											    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
											}
											if self.use_past:
																		snake_case_							=    'past_encoder_sequence + sequence'
																		snake_case_							=    {0: 'batch'}
																		snake_case_							=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
																		snake_case_							=    {0: 'batch', 1: 'decoder_sequence'}
																		snake_case_							=    {0: 'batch', 1: 'decoder_sequence'}

											if self.use_past:
																		self.fill_with_past_key_values_(snake_case      ,   direction='inputs'       )

											return common_inputs




				@property
				def     a							(					self       ):
											return 13







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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    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}}'      )
							snake_case_							=    DatasetInfosDict.from_directory(UpperCamelCase__      )
							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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    str(UpperCamelCase__      )
							dataset_info.write_to_directory(UpperCamelCase__      )
							snake_case_							=    DatasetInfo.from_directory(UpperCamelCase__      )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(UpperCamelCase__   ,							'dataset_info.json'      )      )





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    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=1337   ,							post_processing_size=442   ,							dataset_size=1234   ,							size_in_bytes=1337 + 442 + 1234   ,							)
							snake_case_							=    dataset_info._to_yaml_dict()
							assert sorted(UpperCamelCase__      ) == 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)      )
							snake_case_							=    yaml.safe_dump(UpperCamelCase__      )
							snake_case_							=    yaml.safe_load(UpperCamelCase__      )
							assert dataset_info_yaml_dict == reloaded





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    DatasetInfo()
							snake_case_							=    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=1337      ),
            }      ),
    ]   ,							)
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    str(UpperCamelCase__      )
							dataset_infos_dict.write_to_directory(UpperCamelCase__      )
							snake_case_							=    DatasetInfosDict.from_directory(UpperCamelCase__      )

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														snake_case_							=    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
														snake_case_							=    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(UpperCamelCase__   ,							'README.md'      )      )




