






'''simple docstring'''


from typing import Mapping

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


lowerCamelCase_				: str    	=							logging.get_logger(__name__)

lowerCamelCase_				: Tuple    	=							{
    '''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    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Any					     =					"t5"
			__a							:							List[str]					     =					["past_key_values"]
			__a							:							List[Any]					     =					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






			def __init__(   self							:						List[Any] ,      lowercase							:						List[str]=3_2_1_2_8 ,      lowercase							:						List[Any]=5_1_2 ,      lowercase							:						int=6_4 ,      lowercase							:						Any=2_0_4_8 ,      lowercase							:						List[Any]=6 ,      lowercase							:						Union[str, Any]=None ,      lowercase							:						str=8 ,      lowercase							:						Optional[int]=3_2 ,      lowercase							:						List[str]=1_2_8 ,      lowercase							:						Optional[Any]=0.1 ,      lowercase							:						Dict=1e-6 ,      lowercase							:						Optional[Any]=1.0 ,      lowercase							:						str="relu" ,      lowercase							:						List[str]=True ,      lowercase							:						Tuple=True ,      lowercase							:						Tuple=0 ,      lowercase							:						Any=1 ,      **lowercase							:						List[str] ,      )							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = vocab_size
									UpperCamelCase__         = d_model
									UpperCamelCase__         = d_kv
									UpperCamelCase__         = d_ff
									UpperCamelCase__         = num_layers
									UpperCamelCase__         = (
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									UpperCamelCase__         = num_heads
									UpperCamelCase__         = relative_attention_num_buckets
									UpperCamelCase__         = relative_attention_max_distance
									UpperCamelCase__         = dropout_rate
									UpperCamelCase__         = layer_norm_epsilon
									UpperCamelCase__         = initializer_factor
									UpperCamelCase__         = feed_forward_proj
									UpperCamelCase__         = use_cache

									UpperCamelCase__         = self.feed_forward_proj.split("""-"""			)
									UpperCamelCase__         = act_info[-1]
									UpperCamelCase__         = act_info[0] == """gated"""

									if len(lowercase			) > 1 and act_info[0] != "gated" or len(lowercase			) > 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":
															UpperCamelCase__         = """gelu_new"""

									super().__init__(
									    pad_token_id=lowercase ,      eos_token_id=lowercase ,      is_encoder_decoder=lowercase ,      **lowercase ,      )
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			@property
			def 				A      (   self							:						Dict			)							->    Mapping[str, Mapping[int, str]]:

									'''simple docstring'''







									UpperCamelCase__         = {
									    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
									    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
									}
									if self.use_past:
															UpperCamelCase__         = """past_encoder_sequence + sequence"""
															UpperCamelCase__         = {0: """batch"""}
															UpperCamelCase__         = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
									else:
															UpperCamelCase__         = {0: """batch""", 1: """decoder_sequence"""}
															UpperCamelCase__         = {0: """batch""", 1: """decoder_sequence"""}

									if self.use_past:
															self.fill_with_past_key_values_(lowercase ,      direction="""inputs"""			)

									return common_inputs







			@property
			def 				A      (   self							:						int			)							->    int:

									'''simple docstring'''







									return 1_3








'''simple docstring'''


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						pyspark.sql.DataFrame ,      lowercase							:						Optional[NamedSplit] = None ,      lowercase							:						Optional[Features] = None ,      lowercase							:						bool = True ,      lowercase							:						str = None ,      lowercase							:						bool = False ,      lowercase							:						str = None ,      lowercase							:						bool = True ,      lowercase							:						str = "arrow" ,      **lowercase							:						Any ,      )							->    Optional[int]:

									'''simple docstring'''







									super().__init__(
									    split=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      keep_in_memory=lowercase ,      streaming=lowercase ,      **lowercase ,      )
									UpperCamelCase__         = load_from_cache_file
									UpperCamelCase__         = file_format
									UpperCamelCase__         = Spark(
									    df=lowercase ,      features=lowercase ,      cache_dir=lowercase ,      working_dir=lowercase ,      **lowercase ,      )







			def 				A      (   self							:						int			)							->    Any:

									'''simple docstring'''







									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split			)
									UpperCamelCase__         = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=lowercase ,      file_format=self._file_format ,      )
									return self.builder.as_dataset(split=self.split			)
