



"""simple docstring"""

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
			from fairscale.optim import OSS


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		*lowercase__   ,		**lowercase__       ):
											super().__init__(*lowercase__   ,		**lowercase__       )

											if config is None:
																assert isinstance(self.model   ,		lowercase__       ), (
																    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
																    f' {self.model.__class__}'
																)
																snake_case_					:		Optional[int] 					=					self.model.config
											else:
																snake_case_					:		Optional[int] 					=					config

											snake_case_					:		Any 					=					data_args
											snake_case_					:		Union[str, Any] 					=					self.config.tgt_vocab_size if isinstance(self.config   ,		lowercase__       ) else self.config.vocab_size

											if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
																assert self.config.pad_token_id is not None, (
																    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
																    " calculation or doing label smoothing."
																)

											if self.config.pad_token_id is None and self.config.eos_token_id is not None:
																logger.warning(
																    f'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
																    """ padding.."""       )

											if self.args.label_smoothing == 0:
																snake_case_					:		Optional[int] 					=					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
											else:
																# dynamically import label_smoothed_nll_loss
																from utils import label_smoothed_nll_loss

																snake_case_					:		Optional[int] 					=					label_smoothed_nll_loss







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if self.optimizer is None:
																snake_case_					:		str 					=					["""bias""", """LayerNorm.weight"""]
																snake_case_					:		str 					=					[
																    {
																        """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
																        """weight_decay""": self.args.weight_decay,
																    },
																    {
																        """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
																        """weight_decay""": 0.0,
																    },
																]
																snake_case_					:		Tuple 					=					Adafactor if self.args.adafactor else AdamW
																if self.args.adafactor:
																					snake_case_					:		Dict 					=					Adafactor
																					snake_case_					:		List[Any] 					=					{"""scale_parameter""": False, """relative_step""": False}
																else:
																					snake_case_					:		Optional[int] 					=					AdamW
																					snake_case_					:		Tuple 					=					{
																					    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
																					    """eps""": self.args.adam_epsilon,
																					}
																snake_case_					:		int 					=					self.args.learning_rate
																if self.sharded_ddp:
																					snake_case_					:		str 					=					OSS(
																					    params=lowercase__   ,		optim=lowercase__   ,		**lowercase__   ,		)
																else:
																					snake_case_					:		Any 					=					optimizer_cls(lowercase__   ,		**lowercase__       )

											if self.lr_scheduler is None:
																snake_case_					:		Optional[Any] 					=					self._get_lr_scheduler(lowercase__       )
											else:  # ignoring --lr_scheduler
																logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		str 					=					arg_to_scheduler[self.args.lr_scheduler]
											if self.args.lr_scheduler == "constant":
																snake_case_					:		Tuple 					=					schedule_func(self.optimizer       )
											elif self.args.lr_scheduler == "constant_w_warmup":
																snake_case_					:		Union[str, Any] 					=					schedule_func(self.optimizer   ,		num_warmup_steps=self.args.warmup_steps       )
											else:
																snake_case_					:		Optional[Any] 					=					schedule_func(
																    self.optimizer   ,		num_warmup_steps=self.args.warmup_steps   ,		num_training_steps=lowercase__       )
											return scheduler







						def 						__UpperCamelCase							(self       ):
											if isinstance(self.train_dataset   ,		torch.utils.data.IterableDataset       ):
																return None
											elif is_torch_tpu_available():
																return get_tpu_sampler(self.train_dataset       )
											else:
																if self.args.sortish_sampler:
																					self.train_dataset.make_sortish_sampler(
																					    self.args.per_device_train_batch_size   ,		distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)   ,		)

																return (
																    RandomSampler(self.train_dataset       )
																    if self.args.local_rank == -1
																    else DistributedSampler(self.train_dataset       )
																)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__       ):
											if self.args.label_smoothing == 0:
																if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																					# force training to ignore pad token
																					snake_case_					:		int 					=					model(**lowercase__   ,		use_cache=lowercase__       )[0]
																					snake_case_					:		Dict 					=					self.loss_fn(logits.view(-1   ,		logits.shape[-1]       )   ,		labels.view(-1       )       )
																else:
																					# compute usual loss via models
																					snake_case_					:		Union[str, Any] 					=					model(**lowercase__   ,		labels=lowercase__   ,		use_cache=lowercase__       )[:2]
											else:
																# compute label smoothed loss
																snake_case_					:		int 					=					model(**lowercase__   ,		use_cache=lowercase__       )[0]
																snake_case_					:		Optional[int] 					=					torch.nn.functional.log_softmax(lowercase__   ,		dim=-1       )
																snake_case_					:		Any 					=					self.loss_fn(lowercase__   ,		lowercase__   ,		self.args.label_smoothing   ,		ignore_index=self.config.pad_token_id       )
											return loss, logits







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					inputs.pop("""labels"""       )
											snake_case_					:		Union[str, Any] 					=					self._compute_loss(lowercase__   ,		lowercase__   ,		lowercase__       )
											return loss







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		):
											snake_case_					:		int 					=					self._prepare_inputs(lowercase__       )

											snake_case_					:		Dict 					=					{
											    """max_length""": self.data_args.val_max_target_length
											    if self.data_args is not None
											    else self.config.max_length,
											    """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
											}

											if self.args.predict_with_generate and not self.args.prediction_loss_only:
																snake_case_					:		int 					=					self.model.generate(
																    inputs["""input_ids"""]   ,		attention_mask=inputs["""attention_mask"""]   ,		**lowercase__   ,		)
																# in case the batch is shorter than max length, the output should be padded
																if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																					snake_case_					:		Optional[Any] 					=					self._pad_tensors_to_max_len(lowercase__   ,		gen_kwargs["""max_length"""]       )

											snake_case_					:		int 					=					inputs.pop("""labels"""       )
											with torch.no_grad():
																# compute loss on predict data
																snake_case_					:		str 					=					self._compute_loss(lowercase__   ,		lowercase__   ,		lowercase__       )

											snake_case_					:		List[Any] 					=					loss.mean().detach()
											if self.args.prediction_loss_only:
																return (loss, None, None)

											snake_case_					:		str 					=					generated_tokens if self.args.predict_with_generate else logits

											if labels.shape[-1] < gen_kwargs["max_length"]:
																snake_case_					:		Optional[Any] 					=					self._pad_tensors_to_max_len(lowercase__   ,		gen_kwargs["""max_length"""]       )

											return (loss, logits, labels)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											# If PAD token is not defined at least EOS token has to be defined
											snake_case_					:		Tuple 					=					self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

											if pad_token_id is None:
																raise ValueError(
																    """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
																    f' padded to `max_length`={max_length}'       )

											snake_case_					:		Dict 					=					pad_token_id * torch.ones(
											    (tensor.shape[0], max_length)   ,		dtype=tensor.dtype   ,		device=tensor.device       )
											snake_case_					:		List[Any] 					=					tensor
											return padded_tensor









"""simple docstring"""

import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


a_			   =					re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
a_			   =					10
a_			   =					256







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					if len(SCREAMING_SNAKE_CASE__			) < MIN_NUM_TOKENS:
										return None
					snake_case_					:		Union[str, Any] 					=					MinHash(num_perm=SCREAMING_SNAKE_CASE__			)
					for token in set(SCREAMING_SNAKE_CASE__			):
										min_hash.update(token.encode()			)
					return min_hash







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					return {t for t in NON_ALPHA.split(SCREAMING_SNAKE_CASE__			) if len(t.strip()			) > 0}







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		*,
						    lowercase__ = 0.85   ,		):
											snake_case_					:		Tuple 					=					duplication_jaccard_threshold
											snake_case_					:		Optional[Any] 					=					NUM_PERM
											snake_case_					:		Tuple 					=					MinHashLSH(threshold=self._duplication_jaccard_threshold   ,		num_perm=self._num_perm       )

											snake_case_					:		List[Any] 					=					defaultdict(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		int 					=					self._index.query(lowercase__       )
											if code_key in self._index.keys:
																print(f'Duplicate key {code_key}'       )
																return

											self._index.insert(lowercase__   ,		lowercase__       )
											if len(lowercase__       ) > 0:
																for base_duplicate in close_duplicates:
																					if base_duplicate in self._duplicate_clusters:
																										self._duplicate_clusters[base_duplicate].add(lowercase__       )
																										break
																else:
																					self._duplicate_clusters[close_duplicates[0]].add(lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		str 					=					[]
											for base, duplicates in self._duplicate_clusters.items():
																snake_case_					:		Optional[Any] 					=					[base] + list(lowercase__       )
																# reformat the cluster to be a list of dict
																snake_case_					:		Any 					=					[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
																duplicate_clusters.append(lowercase__       )
											return duplicate_clusters







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		int 					=					self.get_duplicate_clusters()
											with open(lowercase__   ,		"""w"""       ) as f:
																json.dump(lowercase__   ,		lowercase__       )







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					,			snake_case_					:		str 					=					element
					snake_case_					:		Tuple 					=					get_min_hash([t for t in NON_ALPHA.split(data["""content"""]			) if len(t.strip()			) > 0]			)
					if min_hash is not None:
										return (index, data["repo_name"], data["path"]), min_hash







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]			):



					"""simple docstring"""





					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash				,    ThreadedIterator(SCREAMING_SNAKE_CASE__				,    max_queue_size=1_0_0_0_0			)				,    chunksize=1_0_0				,    ):
															if data is not None:
																				yield data







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]				,    SCREAMING_SNAKE_CASE__ :  float			):



					"""simple docstring"""





					snake_case_					:		int 					=					DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE__			)

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(SCREAMING_SNAKE_CASE__			)			)				,    max_queue_size=1_0_0			)			):
										di.add(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

					# Returns a List[Cluster] where Cluster is List[str] with the filenames.
					return di.get_duplicate_clusters()







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					snake_case_					:		int 					=					get_tokens(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Tuple 					=					get_tokens(SCREAMING_SNAKE_CASE__			)
					return len(tokensa & tokensa			) / len(tokensa | tokensa			)


a_			   =					None







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					[]
					for elementa in cluster:
										snake_case_					:		Union[str, Any] 					=					_shared_dataset[elementa["""base_index"""]]["""content"""]
										for elementa in extremes:
															snake_case_					:		Any 					=					_shared_dataset[elementa["""base_index"""]]["""content"""]
															if jaccard_similarity(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															snake_case_					:		Union[str, Any] 					=					1
															extremes.append(SCREAMING_SNAKE_CASE__			)
					return extremes







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					global _shared_dataset
					snake_case_					:		str 					=					dataset
					snake_case_					:		int 					=					[]
					snake_case_					:		Optional[int] 					=					partial(_find_cluster_extremes_shared				,    jaccard_threshold=SCREAMING_SNAKE_CASE__			)
					with mp.Pool() as pool:
										for extremes in tqdm(
										    pool.imap_unordered(
										        SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    )				,    total=len(SCREAMING_SNAKE_CASE__			)				,    ):
															extremes_list.append(SCREAMING_SNAKE_CASE__			)
					return extremes_list







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]				,    SCREAMING_SNAKE_CASE__ :  float = 0.85			):



					"""simple docstring"""





					snake_case_					:		List[str] 					=					make_duplicate_clusters(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		str 					=					{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
					snake_case_					:		str 					=					{}
					snake_case_					:		Dict 					=					find_extremes(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					for extremes in extremes_clusters:
										for element in extremes:
															snake_case_					:		int 					=					element
					snake_case_					:		Optional[int] 					=					duplicate_indices - set(extreme_dict.keys()			)
					snake_case_					:		List[Any] 					=					dataset.filter(lambda SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			: idx not in remove_indices				,    with_indices=SCREAMING_SNAKE_CASE__			)

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															snake_case_					:		List[Any] 					=					element["""base_index"""] in extreme_dict
															if element["is_extreme"]:
																				snake_case_					:		str 					=					extreme_dict[element["""base_index"""]]["""copies"""]

					print(f'Original dataset size: {len(SCREAMING_SNAKE_CASE__			)}'			)
					print(f'Number of duplicate clusters: {len(SCREAMING_SNAKE_CASE__			)}'			)
					print(f'Files in duplicate cluster: {len(SCREAMING_SNAKE_CASE__			)}'			)
					print(f'Unique files in duplicate cluster: {len(SCREAMING_SNAKE_CASE__			)}'			)
					print(f'Filtered dataset size: {len(SCREAMING_SNAKE_CASE__			)}'			)

					return ds_filter, duplicate_clusters




