


'''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


lowerCAmelCase   : List[str]             =				re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
lowerCAmelCase   : Union[str, Any]             =				1_0
lowerCAmelCase   : Optional[Any]             =				2_5_6




def 	_A  (       A							)  ->				Optional[MinHash]:
	if len(A							) < MIN_NUM_TOKENS:
		return None
	lowercase						:		List[Any]   					=		MinHash(num_perm=A							)
	for token in set(A							):
		min_hash.update(token.encode()							)
	return min_hash




def 	_A  (       A							)  ->				Set[str]:
	return {t for t in NON_ALPHA.split(A							) if len(t.strip()							) > 0}




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      *,
				    a_ = 0.85							,      )    ->  List[str]:
					lowercase						:		Any   					=		duplication_jaccard_threshold
					lowercase						:		str   					=		NUM_PERM
					lowercase						:		Union[str, Any]   					=		MinHashLSH(threshold=self._duplication_jaccard_threshold							,      num_perm=self._num_perm							)

					lowercase						:		Any   					=		defaultdict(a_							)

				def 	a__		(     self							,      a_							,      a_							)    ->  None:
					lowercase						:		Dict   					=		self._index.query(a_							)
					if code_key in self._index.keys:
						print(F'''Duplicate key {code_key}'''							)
						return

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

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


				def 	a__		(     self							,      a_							)    ->  None:
					lowercase						:		Tuple   					=		self.get_duplicate_clusters()
					with open(a_							,      "w"							) as f:
						json.dump(a_							,      a_							)




def 	_A  (       A							)  ->				Dict:
	lowercase					,		lowercase						:		List[str]   					=		element
	lowercase						:		int   					=		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 	_A  (       A							)  ->				Any:
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash							,ThreadedIterator(A							,max_queue_size=1_0_0_0_0							)							,chunksize=1_0_0							,):
			if data is not None:
				yield data




def 	_A  (       A							,A							)  ->				List[str]:
	lowercase						:		Dict   					=		DuplicationIndex(duplication_jaccard_threshold=A							)

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

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




def 	_A  (       A							,A							)  ->				float:
	lowercase						:		int   					=		get_tokens(A							)
	lowercase						:		Dict   					=		get_tokens(A							)
	return len(tokensa & tokensa							) / len(tokensa | tokensa							)


lowerCAmelCase   : List[str]             =				None




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	lowercase						:		int   					=		[]
	for elementa in cluster:
		lowercase						:		Any   					=		_shared_dataset[elementa["base_index"]]["content"]
		for elementa in extremes:
			lowercase						:		Optional[int]   					=		_shared_dataset[elementa["base_index"]]["content"]
			if jaccard_similarity(A							,A							) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			lowercase						:		List[Any]   					=		1
			extremes.append(A							)
	return extremes




def 	_A  (       A							,A							,A							)  ->				Optional[Any]:
	global _shared_dataset
	lowercase						:		Dict   					=		dataset
	lowercase						:		int   					=		[]
	lowercase						:		int   					=		partial(_find_cluster_extremes_shared							,jaccard_threshold=A							)
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        A							,A							,)							,total=len(A							)							,):
			extremes_list.append(A							)
	return extremes_list






def 	_A  (       A							,A = 0.85							)  ->				Tuple[Type[Dataset], List[List[Dict]]]:
	lowercase						:		Dict   					=		make_duplicate_clusters(A							,A							)
	lowercase						:		List[str]   					=		{x["base_index"] for cluster in duplicate_clusters for x in cluster}
	lowercase						:		Any   					=		{}
	lowercase						:		int   					=		find_extremes(A							,A							,A							)
	for extremes in extremes_clusters:
		for element in extremes:
			lowercase						:		str   					=		element
	lowercase						:		str   					=		duplicate_indices - set(extreme_dict.keys()							)
	lowercase						:		Any   					=		dataset.filter(lambda A							,A							: idx not in remove_indices							,with_indices=A							)

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

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

	return ds_filter, duplicate_clusters






'''simple docstring'''
import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowerCAmelCase   : Dict             =				logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      *a_							,      a_=None							,      a_=None							,      a_=None							,      **a_							)    ->  Any:
					super().__init__(*a_							,      **a_							)
					lowercase						:		Optional[Any]   					=		eval_examples
					lowercase						:		Any   					=		post_process_function
					lowercase						:		Dict   					=		quant_trainer_args
					lowercase						:		List[Any]   					=		1_2_8  # default number of calibration samples

				def 	a__		(     self							,      a_=None							)    ->  List[str]:
					if calib_dataset is None and self.calib_dataset is None:
						raise ValueError("Trainer: calibration requires an calib_dataset."							)
					lowercase						:		Dict   					=		calib_dataset if calib_dataset is not None else self.calib_dataset

					lowercase						:		Optional[int]   					=		self._remove_unused_columns(a_							,      description="Calibration"							)

					return DataLoader(
					    a_							,      batch_size=self.args.eval_batch_size							,      collate_fn=self.data_collator							,      drop_last=self.args.dataloader_drop_last							,      num_workers=self.args.dataloader_num_workers							,      pin_memory=self.args.dataloader_pin_memory							,      shuffle=a_							,      )

				def 	a__		(     self							,      a_=None							)    ->  str:
					lowercase						:		Optional[Any]   					=		self.train_dataset if calib_dataset is None else calib_dataset
					lowercase						:		Optional[Any]   					=		self.get_calib_dataloader(a_							)

					lowercase						:		Union[str, Any]   					=		self.model
					quant_trainer.configure_model(a_							,      self.quant_trainer_args							,      calib=a_							)
					model.eval()
					quant_trainer.enable_calibration(a_							)

					logger.info("***** Running calibration *****"							)
					logger.info(F'''  Num examples = {self.calib_num}'''							)
					logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''							)

					for step, inputs in enumerate(a_							):
						# Prediction step
						lowercase					,		lowercase					,		lowercase						:		Union[str, Any]   					=		self.prediction_step(a_							,      a_							,      prediction_loss_only=a_							)
						if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
							break

					quant_trainer.finish_calibration(a_							,      self.quant_trainer_args							)
					lowercase						:		List[Any]   					=		model

				def 	a__		(     self							,      a_=None							,      a_=None							,      a_=None							,      a_ = "eval"							)    ->  int:
					lowercase						:		List[str]   					=		self.eval_dataset if eval_dataset is None else eval_dataset
					lowercase						:		Union[str, Any]   					=		self.get_eval_dataloader(a_							)
					lowercase						:		Tuple   					=		self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowercase						:		List[str]   					=		self.compute_metrics
					lowercase						:		Dict   					=		None
					lowercase						:		int   					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
						lowercase						:		Tuple   					=		eval_loop(
						    a_							,      description="Evaluation"							,      prediction_loss_only=True if compute_metrics is None else None							,      ignore_keys=a_							,      )
					finally:
						lowercase						:		List[Any]   					=		compute_metrics

					if self.post_process_function is not None and self.compute_metrics is not None:
						lowercase						:		Union[str, Any]   					=		self.post_process_function(a_							,      a_							,      output.predictions							)
						lowercase						:		Union[str, Any]   					=		self.compute_metrics(a_							)

						# Prefix all keys with metric_key_prefix + '_'
						for key in list(metrics.keys()							):
							if not key.startswith(F'''{metric_key_prefix}_'''							):
								lowercase						:		Dict   					=		metrics.pop(a_							)

						self.log(a_							)
					else:
						lowercase						:		Tuple   					=		{}

					if self.args.tpu_metrics_debug or self.args.debug:
						# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
						xm.master_print(met.metrics_report()							)

					lowercase						:		int   					=		self.callback_handler.on_evaluate(self.args							,      self.state							,      self.control							,      a_							)
					return metrics

				def 	a__		(     self							,      a_							,      a_							,      a_=None							,      a_ = "test"							)    ->  Optional[int]:
					lowercase						:		Dict   					=		self.get_test_dataloader(a_							)

					# Temporarily disable metric computation, we will do it in the loop here.
					lowercase						:		List[str]   					=		self.compute_metrics
					lowercase						:		List[str]   					=		None
					lowercase						:		int   					=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
						lowercase						:		Optional[Any]   					=		eval_loop(
						    a_							,      description="Prediction"							,      prediction_loss_only=True if compute_metrics is None else None							,      ignore_keys=a_							,      )
					finally:
						lowercase						:		int   					=		compute_metrics

					if self.post_process_function is None or self.compute_metrics is None:
						return output

					lowercase						:		List[str]   					=		self.post_process_function(a_							,      a_							,      output.predictions							,      "predict"							)
					lowercase						:		Dict   					=		self.compute_metrics(a_							)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()							):
						if not key.startswith(F'''{metric_key_prefix}_'''							):
							lowercase						:		Union[str, Any]   					=		metrics.pop(a_							)

					return PredictionOutput(predictions=predictions.predictions							,      label_ids=predictions.label_ids							,      metrics=a_							)


				def 	a__		(     self							,      a_="./"							)    ->  Optional[Any]:
					lowercase						:		str   					=		self.eval_dataset
					lowercase						:		Dict   					=		self.get_eval_dataloader(a_							)

					lowercase						:		Any   					=		next(iter(a_							)							)

					# saving device - to make it consistent
					lowercase						:		List[Any]   					=		torch.device("cuda" if torch.cuda.is_available() else "cpu"							)

					# convert to tuple
					lowercase						:		Tuple   					=		tuple(v.to(a_							) for k, v in batch.items()							)

					logger.info("Converting model to be onnx compatible"							)
					from pytorch_quantization.nn import TensorQuantizer

					lowercase						:		Optional[Any]   					=		True

					lowercase						:		int   					=		self.model.to(a_							)

					model.eval()
					model.float()

					lowercase						:		Union[str, Any]   					=		model.module if hasattr(a_							,      "module"							) else model
					quant_trainer.configure_model(a_							,      self.quant_trainer_args							)

					lowercase						:		List[str]   					=		os.path.join(a_							,      "model.onnx"							)
					logger.info(F'''exporting model to {output_model_file}'''							)

					lowercase						:		Any   					=		{0: "batch_size", 1: "seq_len"}

					torch.onnx.export(
					    a_							,      a_							,      a_							,      export_params=a_							,      opset_version=1_3							,      do_constant_folding=a_							,      input_names=["input_ids", "attention_mask", "token_type_ids"]							,      output_names=["output_start_logits", "output_end_logits"]							,      dynamic_axes={
					        "input_ids": axes,
					        "attention_mask": axes,
					        "token_type_ids": axes,
					        "output_start_logits": axes,
					        "output_end_logits": axes,
					    }							,      verbose=a_							,      )
					logger.info("onnx export finished"							)


