




import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase_		:				Dict									=     logging.get_logger(__name__)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()			)}			)
						UpperCamelCase     						=					field(
						    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}			)
						UpperCamelCase     						=					field(
						    default=128					,						metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }					,						)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.task_name.lower()




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""train"""
						UpperCamelCase     						=					"""dev"""
						UpperCamelCase     						=					"""test"""




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					42
						UpperCamelCase     						=					42
						UpperCamelCase     						=					42

						def __init__(		self   :Optional[Any]    , __snake_case   :GlueDataTrainingArguments    , __snake_case   :PreTrainedTokenizerBase    , __snake_case   :Optional[int] = None    , __snake_case   :Union[str, Split] = Split.train    , __snake_case   :Optional[str] = None    , ):
										'''simple docstring'''

										warnings.warn(
										    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
										    """library. You can have a look at this example script for pointers: """
										    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""    , __snake_case    , )
										__magic_name__	:	List[Any]								=args
										__magic_name__	:	Optional[Any]								=glue_processors[args.task_name]()
										__magic_name__	:	Union[str, Any]								=glue_output_modes[args.task_name]
										if isinstance(__snake_case    , __snake_case ):
														try:
																		__magic_name__	:	Optional[Any]								=Split[mode]
														except KeyError:
																		raise KeyError("""mode is not a valid split name""" )
        # Load data features from cache or dataset file
										__magic_name__	:	Optional[int]								=os.path.join(
										    cache_dir if cache_dir is not None else args.data_dir    , f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"    , )
										__magic_name__	:	Dict								=self.processor.get_labels()
										if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
										    "RobertaTokenizer",
										    "RobertaTokenizerFast",
										    "XLMRobertaTokenizer",
										    "BartTokenizer",
										    "BartTokenizerFast",
										):
														# HACK(label indices are swapped in RoBERTa pretrained model)
														__magic_name__				, __magic_name__	:	List[Any]								=label_list[2], label_list[1]
										__magic_name__	:	Optional[int]								=label_list

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										__magic_name__	:	Union[str, Any]								=cached_features_file + """.lock"""
										with FileLock(__snake_case ):
														if os.path.exists(__snake_case ) and not args.overwrite_cache:
																		__magic_name__	:	List[str]								=time.time()
																		__magic_name__	:	List[Any]								=torch.load(__snake_case )
																		logger.info(
																		    f"Loading features from cached file {cached_features_file} [took %.3f s]"    , time.time() - start )
														else:
																		logger.info(f"Creating features from dataset file at {args.data_dir}" )

																		if mode == Split.dev:
																						__magic_name__	:	Union[str, Any]								=self.processor.get_dev_examples(args.data_dir )
																		elif mode == Split.test:
																						__magic_name__	:	Any								=self.processor.get_test_examples(args.data_dir )
																		else:
																						__magic_name__	:	Dict								=self.processor.get_train_examples(args.data_dir )
																		if limit_length is not None:
																						__magic_name__	:	Any								=examples[:limit_length]
																		__magic_name__	:	Any								=glue_convert_examples_to_features(
																		    __snake_case    , __snake_case    , max_length=args.max_seq_length    , label_list=__snake_case    , output_mode=self.output_mode    , )
																		__magic_name__	:	int								=time.time()
																		torch.save(self.features    , __snake_case )
																		# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																		logger.info(
																		    f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )

						def __len__(		self   :Tuple ):
										'''simple docstring'''

										return len(self.features )

						def __getitem__(		self   :List[str]    , __snake_case   :str ):
										'''simple docstring'''

										return self.features[i]


						def     A__						(		self   :int ):
										'''simple docstring'''

										return self.label_list










from typing import Mapping

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


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)

UpperCAmelCase_		:				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 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""t5"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

						def __init__(		self   :Optional[Any]    , __snake_case   :Optional[Any]=3_21_28    , __snake_case   :Optional[int]=5_12    , __snake_case   :str=64    , __snake_case   :int=20_48    , __snake_case   :Union[str, Any]=6    , __snake_case   :Union[str, Any]=None    , __snake_case   :Union[str, Any]=8    , __snake_case   :List[str]=32    , __snake_case   :Dict=1_28    , __snake_case   :List[Any]=0.1    , __snake_case   :int=1E-6    , __snake_case   :List[str]=1.0    , __snake_case   :Optional[int]="relu"    , __snake_case   :List[str]=True    , __snake_case   :Union[str, Any]=True    , __snake_case   :Union[str, Any]=0    , __snake_case   :str=1    , **__snake_case   :int    , ):
										'''simple docstring'''

										__magic_name__	:	str								=vocab_size
										__magic_name__	:	str								=d_model
										__magic_name__	:	Optional[Any]								=d_kv
										__magic_name__	:	int								=d_ff
										__magic_name__	:	Union[str, Any]								=num_layers
										__magic_name__	:	Tuple								=(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										__magic_name__	:	List[str]								=num_heads
										__magic_name__	:	Optional[Any]								=relative_attention_num_buckets
										__magic_name__	:	Union[str, Any]								=relative_attention_max_distance
										__magic_name__	:	Dict								=dropout_rate
										__magic_name__	:	Dict								=layer_norm_epsilon
										__magic_name__	:	List[str]								=initializer_factor
										__magic_name__	:	Any								=feed_forward_proj
										__magic_name__	:	Optional[int]								=use_cache

										__magic_name__	:	Tuple								=self.feed_forward_proj.split("""-""" )
										__magic_name__	:	int								=act_info[-1]
										__magic_name__	:	int								=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":
														__magic_name__	:	str								="""gelu_new"""

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




class 		__A      (			UpperCamelCase__			):

						@property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								={
										    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
										    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
										}
										if self.use_past:
														__magic_name__	:	List[str]								="""past_encoder_sequence + sequence"""
														__magic_name__	:	List[Any]								={0: """batch"""}
														__magic_name__	:	Union[str, Any]								={0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
														__magic_name__	:	int								={0: """batch""", 1: """decoder_sequence"""}
														__magic_name__	:	Tuple								={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   :Union[str, Any] ):
										'''simple docstring'''

										return 13




