




"""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      =							1_0
a      =							2_5_6





def 							lowercase					(snake_case__   : List[str] )     ->  Optional[MinHash]:


				'''simple docstring'''






				if len(snake_case__ ) < MIN_NUM_TOKENS:
								return None
				lowerCAmelCase  =				MinHash(num_perm=snake_case__ )
				for token in set(snake_case__ ):
								min_hash.update(token.encode() )
				return min_hash





def 							lowercase					(snake_case__   : str )     ->  Set[str]:


				'''simple docstring'''






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






class       SCREAMING_SNAKE_CASE__					:


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

										lowerCAmelCase  =				defaultdict(lowerCAmelCase					)


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			MinHash					):
										lowerCAmelCase  =				self._index.query(lowerCAmelCase					)
										if code_key in self._index.keys:
														print(f'''Duplicate key {code_key}'''					)
														return

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


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




						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				self.get_duplicate_clusters()
										with open(lowerCAmelCase						,						"""w"""					) as f:
														json.dump(lowerCAmelCase						,						lowerCAmelCase					)





def 							lowercase					(snake_case__   : str )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase  =				element
				lowerCAmelCase  =				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 							lowercase					(snake_case__   : Type[Dataset] )     ->  Tuple:


				'''simple docstring'''






				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash      ,       ThreadedIterator(snake_case__      ,       max_queue_size=10_000 )      ,       chunksize=100      ,       ):
												if data is not None:
																yield data





def 							lowercase					(snake_case__   : Type[Dataset]      ,       snake_case__   : float )     ->  Tuple:


				'''simple docstring'''






				lowerCAmelCase  =				DuplicationIndex(duplication_jaccard_threshold=snake_case__ )

				for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(snake_case__ ) )      ,       max_queue_size=100 ) ):
								di.add(snake_case__      ,       snake_case__ )

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





def 							lowercase					(snake_case__   : str      ,       snake_case__   : str )     ->  float:


				'''simple docstring'''






				lowerCAmelCase  =				get_tokens(snake_case__ )
				lowerCAmelCase  =				get_tokens(snake_case__ )
				return len(tokensa & tokensa ) / len(tokensa | tokensa )


a      =							None





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : Union[str, Any] )     ->  Any:


				'''simple docstring'''






				lowerCAmelCase  =				[]
				for elementa in cluster:
								lowerCAmelCase  =				_shared_dataset[elementa["""base_index"""]]["""content"""]
								for elementa in extremes:
												lowerCAmelCase  =				_shared_dataset[elementa["""base_index"""]]["""content"""]
												if jaccard_similarity(snake_case__      ,       snake_case__ ) >= jaccard_threshold:
																elementa["copies"] += 1
																break
								else:
												lowerCAmelCase  =				1
												extremes.append(snake_case__ )
				return extremes





def 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : Dict      ,       snake_case__   : int )     ->  Dict:


				'''simple docstring'''






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






def 							lowercase					(snake_case__   : Type[Dataset]      ,       snake_case__   : float = 0.85 )     ->  Tuple[Type[Dataset], List[List[Dict]]]:


				'''simple docstring'''






				lowerCAmelCase  =				make_duplicate_clusters(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
				lowerCAmelCase  =				{}
				lowerCAmelCase  =				find_extremes(snake_case__      ,       snake_case__      ,       snake_case__ )
				for extremes in extremes_clusters:
								for element in extremes:
												lowerCAmelCase  =				element
				lowerCAmelCase  =				duplicate_indices - set(extreme_dict.keys() )
				lowerCAmelCase  =				dataset.filter(lambda snake_case__      ,       snake_case__ : idx not in remove_indices      ,       with_indices=snake_case__ )

				# update duplicate_clusters
				for cluster in duplicate_clusters:
								for element in cluster:
												lowerCAmelCase  =				element["""base_index"""] in extreme_dict
												if element["is_extreme"]:
																lowerCAmelCase  =				extreme_dict[element["""base_index"""]]["""copies"""]

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

				return ds_filter, duplicate_clusters











"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

a      =							1_6
a      =							3_2





def 							lowercase					(snake_case__   : Accelerator      ,       snake_case__   : int = 16 )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowerCAmelCase  =				load_dataset("""glue"""      ,       """mrpc""" )

				def tokenize_function(snake_case__   : List[Any] ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase  =				tokenizer(examples["""sentence1"""]      ,       examples["""sentence2"""]      ,       truncation=snake_case__      ,       max_length=snake_case__ )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								lowerCAmelCase  =				datasets.map(
								    snake_case__      ,       batched=snake_case__      ,       remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,       )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCAmelCase  =				tokenized_datasets.rename_column("""label"""      ,       """labels""" )

				def collate_fn(snake_case__   : Union[str, Any] ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase  =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCAmelCase  =				16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase  =				8
								else:
												lowerCAmelCase  =				None

								return tokenizer.pad(
								    snake_case__      ,       padding="""longest"""      ,       max_length=snake_case__      ,       pad_to_multiple_of=snake_case__      ,       return_tensors="""pt"""      ,       )

				# Instantiate dataloaders.
				lowerCAmelCase  =				DataLoader(
				    tokenized_datasets["""train"""]      ,       shuffle=snake_case__      ,       collate_fn=snake_case__      ,       batch_size=snake_case__ )
				lowerCAmelCase  =				DataLoader(
				    tokenized_datasets["""validation"""]      ,       shuffle=snake_case__      ,       collate_fn=snake_case__      ,       batch_size=snake_case__ )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		a      =							mocked_dataloaders  # noqa: F811





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,       snake_case__ ) == "1":
								lowerCAmelCase  =				2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								lowerCAmelCase  =				Accelerator(
								    cpu=args.cpu      ,       mixed_precision=args.mixed_precision      ,       log_with="""all"""      ,       project_dir=args.project_dir )
				else:
								lowerCAmelCase  =				Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase  =				config["""lr"""]
				lowerCAmelCase  =				int(config["""num_epochs"""] )
				lowerCAmelCase  =				int(config["""seed"""] )
				lowerCAmelCase  =				int(config["""batch_size"""] )
				set_seed(snake_case__ )

				lowerCAmelCase ,						lowerCAmelCase  =				get_dataloaders(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				evaluate.load("""glue"""      ,       """mrpc""" )

				# If the batch size is too big we use gradient accumulation
				lowerCAmelCase  =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCAmelCase  =				batch_size // MAX_GPU_BATCH_SIZE
								lowerCAmelCase  =				MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase  =				AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,       return_dict=snake_case__ )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				lowerCAmelCase  =				model.to(accelerator.device )

				# Instantiate optimizer
				lowerCAmelCase  =				AdamW(params=model.parameters()      ,       lr=snake_case__ )

				# Instantiate scheduler
				lowerCAmelCase  =				get_linear_schedule_with_warmup(
				    optimizer=snake_case__      ,       num_warmup_steps=100      ,       num_training_steps=(len(snake_case__ ) * num_epochs) // gradient_accumulation_steps      ,       )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				accelerator.prepare(
				    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								lowerCAmelCase  =				os.path.split(snake_case__ )[-1].split(""".""" )[0]
								accelerator.init_trackers(snake_case__      ,       snake_case__ )

				# Now we train the model
				for epoch in range(snake_case__ ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowerCAmelCase  =				0
								for step, batch in enumerate(snake_case__ ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCAmelCase  =				loss / gradient_accumulation_steps
												accelerator.backward(snake_case__ )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(snake_case__ ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device )
												with torch.no_grad():
																lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.logits.argmax(dim=-1 )
												lowerCAmelCase ,						lowerCAmelCase  =				accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
												metric.add_batch(
												    predictions=snake_case__      ,       references=snake_case__      ,       )

								lowerCAmelCase  =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''      ,       snake_case__ )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        """accuracy""": eval_metric["""accuracy"""],
												        """f1""": eval_metric["""f1"""],
												        """train_loss""": total_loss.item() / len(snake_case__ ),
												        """epoch""": epoch,
												    }      ,       step=snake_case__      ,       )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()





def 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				argparse.ArgumentParser(description="""Simple example of training script.""" )
				parser.add_argument(
				    """--mixed_precision"""      ,       type=snake_case__      ,       default=snake_case__      ,       choices=["""no""", """fp16""", """bf16""", """fp8"""]      ,       help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU."""      ,       )
				parser.add_argument("""--cpu"""      ,       action="""store_true"""      ,       help="""If passed, will train on the CPU.""" )
				parser.add_argument(
				    """--with_tracking"""      ,       action="""store_true"""      ,       help="""Whether to load in all available experiment trackers from the environment and use them for logging."""      ,       )
				parser.add_argument(
				    """--project_dir"""      ,       type=snake_case__      ,       default="""logs"""      ,       help="""Location on where to store experiment tracking logs` and relevent project information"""      ,       )
				lowerCAmelCase  =				parser.parse_args()
				lowerCAmelCase  =				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(snake_case__      ,       snake_case__ )


if __name__ == "__main__":
		main()





