





'''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_					:    str      	=			re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
lowerCAmelCase_					:    List[str]      	=			10
lowerCAmelCase_					:    Optional[Any]      	=			2_56






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






def  _lowerCamelCase				( lowercase						:      str						)	->	Set[str]:
				return {t for t in NON_ALPHA.split(lowercase						) if len(t.strip()						) > 0}
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					Dict					,				*,
				    __a     :					float = 0.85					,				):
								_a								= duplication_jaccard_threshold
								_a								= NUM_PERM
								_a								= MinHashLSH(threshold=self._duplication_jaccard_threshold					,				num_perm=self._num_perm					)

								_a								= defaultdict(__a					)




				def   UpperCamelCase__   (  self     :					Dict					,				__a     :					Tuple					,				__a     :					MinHash					):
								_a								= 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   UpperCamelCase__   (  self     :					str					):
								_a								= []
								for base, duplicates in self._duplicate_clusters.items():
												_a								= [base] + list(__a					)
												# reformat the cluster to be a list of dict
												_a								= [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
												duplicate_clusters.append(__a					)
								return duplicate_clusters




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					Any					):
								_a								= self.get_duplicate_clusters()
								with open(__a					,				"w"					) as f:
												json.dump(__a					,				__a					)






def  _lowerCamelCase				( lowercase						:      Optional[int]						)	->	Optional[Any]:
				_a	,					_a								= element
				_a								= 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  _lowerCamelCase				( lowercase						:      Type[Dataset]						)	->	Optional[int]:
				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash    ,							ThreadedIterator(lowercase    ,							max_queue_size=1_0000						)    ,							chunksize=100    ,							):
												if data is not None:
																yield data






def  _lowerCamelCase				( lowercase						:      Type[Dataset]    ,							lowercase						:      float						)	->	Union[str, Any]:
				_a								= DuplicationIndex(duplication_jaccard_threshold=lowercase						)

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

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






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      str						)	->	float:
				_a								= get_tokens(lowercase						)
				_a								= get_tokens(lowercase						)
				return len(tokensa & tokensa						) / len(tokensa | tokensa						)


lowerCAmelCase_					:    Dict      	=			None






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






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






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

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

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

				return ds_filter, duplicate_clusters







'''simple docstring'''







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


lowerCAmelCase_					:    Optional[Any]      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    List[str]      	=			{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='trocr'
				__a            =['past_key_values']
				__a            ={
				    'num_attention_heads': 'decoder_attention_heads',
				    'hidden_size': 'd_model',
				    'num_hidden_layers': 'decoder_layers',
				}




				def __init__(  self     :					Optional[int]					,				__a     :					Any=5_02_65					,				__a     :					Optional[int]=10_24					,				__a     :					List[Any]=12					,				__a     :					str=16					,				__a     :					int=40_96					,				__a     :					Optional[Any]="gelu"					,				__a     :					Union[str, Any]=5_12					,				__a     :					Dict=0.1					,				__a     :					List[str]=0.0					,				__a     :					Union[str, Any]=0.0					,				__a     :					Any=2					,				__a     :					Union[str, Any]=0.02					,				__a     :					Any=0.0					,				__a     :					List[str]=True					,				__a     :					Optional[Any]=False					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[Any]=True					,				__a     :					Any=1					,				__a     :					List[Any]=0					,				__a     :					Any=2					,				**__a     :					Optional[Any]					,				):
								_a								= vocab_size
								_a								= d_model
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= activation_function
								_a								= max_position_embeddings
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= init_std
								_a								= decoder_layerdrop
								_a								= use_cache
								_a								= scale_embedding
								_a								= use_learned_position_embeddings
								_a								= layernorm_embedding

								super().__init__(
								    pad_token_id=__a					,				bos_token_id=__a					,				eos_token_id=__a					,				decoder_start_token_id=__a					,				**__a					,				)
