



"""simple docstring"""







from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


A    =			logging.get_logger(__name__)


A    =			{
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[str] 							=     """codegen"""
		lowerCAmelCase_       :	Optional[int] 							=     {
		    """max_position_embeddings""": """n_positions""",
		    """hidden_size""": """n_embd""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}



		def __init__( self  :    int      ,							snake_case  :    Dict=50_400      ,							snake_case  :    Dict=2_048      ,							snake_case  :    str=2_048      ,							snake_case  :    int=4_096      ,							snake_case  :    Union[str, Any]=28      ,							snake_case  :    Optional[Any]=16      ,							snake_case  :    str=64      ,							snake_case  :    Tuple=None      ,							snake_case  :    Any="gelu_new"      ,							snake_case  :    Optional[Any]=0.0      ,							snake_case  :    Optional[Any]=0.0      ,							snake_case  :    Any=0.0      ,							snake_case  :    Any=1E-5      ,							snake_case  :    Dict=0.02      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Optional[Any]=50_256      ,							snake_case  :    List[Any]=50_256      ,							snake_case  :    Dict=False      ,							**snake_case  :    int      ,							)			->						List[Any]:






									'''simple docstring'''

									A         =					vocab_size
									A         =					n_ctx
									A         =					n_positions
									A         =					n_embd
									A         =					n_layer
									A         =					n_head
									A         =					n_inner
									A         =					rotary_dim
									A         =					activation_function
									A         =					resid_pdrop
									A         =					embd_pdrop
									A         =					attn_pdrop
									A         =					layer_norm_epsilon
									A         =					initializer_range
									A         =					use_cache

									A         =					bos_token_id
									A         =					eos_token_id

									super().__init__(
									    bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							tie_word_embeddings=snake_case      ,							**snake_case				)






class      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    Tuple      ,							snake_case  :    PretrainedConfig      ,							snake_case  :    str = "default"      ,							snake_case  :    List[PatchingSpec] = None      ,							snake_case  :    bool = False      ,							)			->						Tuple:






									'''simple docstring'''

									super().__init__(snake_case      ,							task=snake_case      ,							patching_specs=snake_case      ,							use_past=snake_case				)
									if not getattr(self._config      ,							'pad_token_id'      ,							snake_case				):
																# TODO: how to do that better?
																A         =					0



		@property
		def 					A_		( self  :    List[Any]				)			->						Mapping[str, Mapping[int, str]]:






									'''simple docstring'''

									A         =					OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}				)
									if self.use_past:
																self.fill_with_past_key_values_(snake_case      ,							direction='inputs'				)
																A         =					{0: 'batch', 1: 'past_sequence + sequence'}
									else:
																A         =					{0: 'batch', 1: 'sequence'}

									return common_inputs



		@property
		def 					A_		( self  :    List[Any]				)			->						int:






									'''simple docstring'''

									return self._config.n_layer



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






									'''simple docstring'''

									return self._config.n_head



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    PreTrainedTokenizer      ,							snake_case  :    int = -1      ,							snake_case  :    int = -1      ,							snake_case  :    bool = False      ,							snake_case  :    Optional[TensorType] = None      ,							)			->						Mapping[str, Any]:






									'''simple docstring'''

									A         =					super(snake_case      ,							self				).generate_dummy_inputs(
									    snake_case      ,							batch_size=snake_case      ,							seq_length=snake_case      ,							is_pair=snake_case      ,							framework=snake_case				)

									# We need to order the input in the way they appears in the forward()
									A         =					OrderedDict({'input_ids': common_inputs['input_ids']}				)

									# Need to add the past_keys
									if self.use_past:
																if not is_torch_available():
																							raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'				)
																else:
																							import torch

																							A      ,  A         =					common_inputs['input_ids'].shape
																							# Not using the same length for past_key_values
																							A         =					seqlen + 2
																							A         =					(
																							    batch,
																							    self.num_attention_heads,
																							    past_key_values_length,
																							    self._config.hidden_size // self.num_attention_heads,
																							)
																							A         =					[
																							    (torch.zeros(snake_case				), torch.zeros(snake_case				)) for _ in range(self.num_layers				)
																							]

									A         =					common_inputs['attention_mask']
									if self.use_past:
																A         =					ordered_inputs['attention_mask'].dtype
																A         =					torch.cat(
																    [ordered_inputs['attention_mask'], torch.ones(snake_case      ,							snake_case      ,							dtype=snake_case				)]      ,							dim=1				)

									return ordered_inputs



		@property
		def 					A_		( self  :    Any				)			->						int:






									'''simple docstring'''

									return 13








"""simple docstring"""







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

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

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


A    =			logging.get_logger(__name__)

A    =			list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A    =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class      UpperCAmelCase__		:
		lowerCAmelCase_       :	str 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Model type selected in the list: """ + """, """.join(UpperCamelCase     )}     )
		lowerCAmelCase_       :	str 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""}     )
		lowerCAmelCase_       :	int 							=     field(
		    default=1_28   ,metadata={
		        """help""": (
		            """The maximum total input sequence length after tokenization. Sequences longer """
		            """than this will be truncated, sequences shorter will be padded."""
		        )
		    }   ,)
		lowerCAmelCase_       :	int 							=     field(
		    default=1_28   ,metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""}   ,)
		lowerCAmelCase_       :	int 							=     field(
		    default=64   ,metadata={
		        """help""": (
		            """The maximum number of tokens for the question. Questions longer than this will """
		            """be truncated to this length."""
		        )
		    }   ,)
		lowerCAmelCase_       :	int 							=     field(
		    default=30   ,metadata={
		        """help""": (
		            """The maximum length of an answer that can be generated. This is needed because the start """
		            """and end predictions are not conditioned on one another."""
		        )
		    }   ,)
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""}     )
		lowerCAmelCase_       :	float 							=     field(
		    default=0.0   ,metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}     )
		lowerCAmelCase_       :	int 							=     field(
		    default=20   ,metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}     )
		lowerCAmelCase_       :	int 							=     field(
		    default=0   ,metadata={
		        """help""": (
		            """language id of input for language-specific xlm models (see"""
		            """ tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
		        )
		    }   ,)
		lowerCAmelCase_       :	int 							=     field(default=1   ,metadata={"""help""": """multiple threads for converting example to features"""}     )






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	str 							=     """train"""
		lowerCAmelCase_       :	Union[str, Any] 							=     """dev"""






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	SquadDataTrainingArguments
		lowerCAmelCase_       :	List[SquadFeatures]
		lowerCAmelCase_       :	Split
		lowerCAmelCase_       :	bool



		def __init__( self  :    List[Any]      ,							snake_case  :    SquadDataTrainingArguments      ,							snake_case  :    PreTrainedTokenizer      ,							snake_case  :    Optional[int] = None      ,							snake_case  :    Union[str, Split] = Split.train      ,							snake_case  :    Optional[bool] = False      ,							snake_case  :    Optional[str] = None      ,							snake_case  :    Optional[str] = "pt"      ,							)			->						Optional[Any]:






									'''simple docstring'''

									A         =					args
									A         =					is_language_sensitive
									A         =					SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
									if isinstance(snake_case      ,							snake_case				):
																try:
																							A         =					Split[mode]
																except KeyError:
																							raise KeyError('mode is not a valid split name'				)
									A         =					mode
									# Load data features from cache or dataset file
									A         =					'v2' if args.version_2_with_negative else 'v1'
									A         =					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}_{version_tag}"""      ,							)

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									A         =					cached_features_file + '.lock'
									with FileLock(snake_case				):
																if os.path.exists(snake_case				) and not args.overwrite_cache:
																							A         =					time.time()
																							A         =					torch.load(snake_case				)

																							# Legacy cache files have only features, while new cache files
																							# will have dataset and examples also.
																							A         =					self.old_features['features']
																							A         =					self.old_features.get('dataset'      ,							snake_case				)
																							A         =					self.old_features.get('examples'      ,							snake_case				)
																							logger.info(
																							    f"""Loading features from cached file {cached_features_file} [took %.3f s]"""      ,							time.time() - start				)

																							if self.dataset is None or self.examples is None:
																														logger.warning(
																														    f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
																														    ' future run'				)
																else:
																							if mode == Split.dev:
																														A         =					self.processor.get_dev_examples(args.data_dir				)
																							else:
																														A         =					self.processor.get_train_examples(args.data_dir				)

																							A      ,  A         =					squad_convert_examples_to_features(
																							    examples=self.examples      ,							tokenizer=snake_case      ,							max_seq_length=args.max_seq_length      ,							doc_stride=args.doc_stride      ,							max_query_length=args.max_query_length      ,							is_training=mode == Split.train      ,							threads=args.threads      ,							return_dataset=snake_case      ,							)

																							A         =					time.time()
																							torch.save(
																							    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}      ,							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  :    Any				)			->						Dict:






									'''simple docstring'''

									return len(self.features				)



		def __getitem__( self  :    Union[str, Any]      ,							snake_case  :    List[str]				)			->						Dict[str, torch.Tensor]:






									'''simple docstring'''

									A         =					self.features[i]

									A         =					torch.tensor(feature.input_ids      ,							dtype=torch.long				)
									A         =					torch.tensor(feature.attention_mask      ,							dtype=torch.long				)
									A         =					torch.tensor(feature.token_type_ids      ,							dtype=torch.long				)
									A         =					torch.tensor(feature.cls_index      ,							dtype=torch.long				)
									A         =					torch.tensor(feature.p_mask      ,							dtype=torch.float				)
									A         =					torch.tensor(feature.is_impossible      ,							dtype=torch.float				)

									A         =					{
									    'input_ids': input_ids,
									    'attention_mask': attention_mask,
									    'token_type_ids': token_type_ids,
									}

									if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
																del inputs["token_type_ids"]

									if self.args.model_type in ["xlnet", "xlm"]:
																inputs.update({'cls_index': cls_index, 'p_mask': p_mask}				)
																if self.args.version_2_with_negative:
																							inputs.update({'is_impossible': is_impossible}				)
																if self.is_language_sensitive:
																							inputs.update({'langs': (torch.ones(input_ids.shape      ,							dtype=torch.intaa				) * self.args.lang_id)}				)

									if self.mode == Split.train:
																A         =					torch.tensor(feature.start_position      ,							dtype=torch.long				)
																A         =					torch.tensor(feature.end_position      ,							dtype=torch.long				)
																inputs.update({'start_positions': start_positions, 'end_positions': end_positions}				)

									return inputs



