"""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


_UpperCamelCase								=		re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_UpperCamelCase								=		10
_UpperCamelCase								=		2_56

def  _A(		lowerCAmelCase							):
							if len(lowerCAmelCase							) < MIN_NUM_TOKENS:
														return None
							A__       :     Optional[Any]								=			MinHash(num_perm=lowerCAmelCase							)
							for token in set(lowerCAmelCase							):
														min_hash.update(token.encode()							)
							return min_hash

def  _A(		lowerCAmelCase							):
							return {t for t in NON_ALPHA.split(lowerCAmelCase							) if len(t.strip()							) > 0}
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      *,
			    snake_case_ = 0.85   ,      ):







										'''simple docstring'''


										A__       :     Optional[int]								=			duplication_jaccard_threshold
										A__       :     int								=			NUM_PERM
										A__       :     List[str]								=			MinHashLSH(threshold=self._duplication_jaccard_threshold   ,      num_perm=self._num_perm							)

										A__       :     Union[str, Any]								=			defaultdict(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Dict								=			self._index.query(snake_case_							)
										if code_key in self._index.keys:
																	print(F'''Duplicate key {code_key}'''							)
																	return

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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			self.get_duplicate_clusters()
										with open(snake_case_   ,      """w"""							) as f:
																	json.dump(snake_case_   ,      snake_case_							)





def  _A(		lowerCAmelCase							):
							A__				,							A__       :     Optional[int]								=			element
							A__       :     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  _A(		lowerCAmelCase							):
							with mp.Pool() as pool:
														for data in pool.imap_unordered(
														    _compute_min_hash			,  ThreadedIterator(lowerCAmelCase			,  max_queue_size=1_0000							)			,  chunksize=100			,  ):
																					if data is not None:
																												yield data

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase							)

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

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

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     List[Any]								=			get_tokens(lowerCAmelCase							)
							A__       :     Any								=			get_tokens(lowerCAmelCase							)
							return len(tokensa & tokensa							) / len(tokensa | tokensa							)


_UpperCamelCase								=		None

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

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



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

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

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

							return ds_filter, duplicate_clusters






"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'sew'





			def __init__(			self   ,      snake_case_=32   ,      snake_case_=768   ,      snake_case_=12   ,      snake_case_=12   ,      snake_case_=3_072   ,      snake_case_=2   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.02   ,      snake_case_=1E-5   ,      snake_case_="group"   ,      snake_case_="gelu"   ,      snake_case_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,      snake_case_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,      snake_case_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,      snake_case_=False   ,      snake_case_=128   ,      snake_case_=16   ,      snake_case_=True   ,      snake_case_=0.05   ,      snake_case_=10   ,      snake_case_=2   ,      snake_case_=0.0   ,      snake_case_=10   ,      snake_case_=0   ,      snake_case_="mean"   ,      snake_case_=False   ,      snake_case_=False   ,      snake_case_=256   ,      snake_case_=0   ,      snake_case_=1   ,      snake_case_=2   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_   ,      pad_token_id=snake_case_   ,      bos_token_id=snake_case_   ,      eos_token_id=snake_case_							)
										A__       :     Dict								=			hidden_size
										A__       :     Dict								=			feat_extract_norm
										A__       :     str								=			feat_extract_activation
										A__       :     Optional[Any]								=			list(snake_case_							)
										A__       :     str								=			list(snake_case_							)
										A__       :     Any								=			list(snake_case_							)
										A__       :     Any								=			conv_bias
										A__       :     Any								=			num_conv_pos_embeddings
										A__       :     Any								=			num_conv_pos_embedding_groups
										A__       :     str								=			len(self.conv_dim							)
										A__       :     Tuple								=			num_hidden_layers
										A__       :     int								=			intermediate_size
										A__       :     Union[str, Any]								=			squeeze_factor
										A__       :     Union[str, Any]								=			hidden_act
										A__       :     List[str]								=			num_attention_heads
										A__       :     List[str]								=			hidden_dropout
										A__       :     Dict								=			attention_dropout
										A__       :     Tuple								=			activation_dropout
										A__       :     Optional[int]								=			feat_proj_dropout
										A__       :     Optional[Any]								=			final_dropout
										A__       :     int								=			layerdrop
										A__       :     List[Any]								=			layer_norm_eps
										A__       :     int								=			initializer_range
										A__       :     Dict								=			vocab_size

										if (
										    (len(self.conv_stride							) != self.num_feat_extract_layers)
										    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
										    or (len(self.conv_dim							) != self.num_feat_extract_layers)
										):
																	raise ValueError(
																	    """Configuration for convolutional layers is incorrect."""
																	    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
																	    F'''but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)'''
																	    F'''= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										A__       :     Optional[Any]								=			apply_spec_augment
										A__       :     int								=			mask_time_prob
										A__       :     Tuple								=			mask_time_length
										A__       :     Optional[Any]								=			mask_time_min_masks
										A__       :     Any								=			mask_feature_prob
										A__       :     List[Any]								=			mask_feature_length
										A__       :     Any								=			mask_feature_min_masks

										# ctc loss
										A__       :     str								=			ctc_loss_reduction
										A__       :     List[Any]								=			ctc_zero_infinity

										# sequence classification
										A__       :     Union[str, Any]								=			use_weighted_layer_sum
										A__       :     str								=			classifier_proj_size





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return functools.reduce(operator.mul   ,      self.conv_stride   ,      1							)





