


"""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


lowercase_			     =  re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase_			     =  1_0
lowercase_			     =  2_5_6

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

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


		'''simple docstring'''



		def __init__(  self							, *,
		    _a = 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							, _a							, _a      ):
				__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      ):
				__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							, _a      ):
				__a           =			self.get_duplicate_clusters()
				with open(_a							, '''w'''      ) as f:
						json.dump(_a							, _a      )







def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      int:
		__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        lowercase							(		lowerCAmelCase__ :     Type[Dataset]			) ->      str:
		with mp.Pool() as pool:
				for data in pool.imap_unordered(
				    _compute_min_hash    ,					ThreadedIterator(lowerCAmelCase__    ,					max_queue_size=10000			)    ,					chunksize=100    ,					):
						if data is not None:
								yield data

def        lowercase							(		lowerCAmelCase__ :     Type[Dataset]    ,					lowerCAmelCase__ :     float			) ->      Dict:
		__a           =			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        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     str			) ->      float:
		__a           =			get_tokens(lowerCAmelCase__			)
		__a           =			get_tokens(lowerCAmelCase__			)
		return len(tokensa & tokensa			) / len(tokensa | tokensa			)


lowercase_			     =  None

def        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Union[str, Any]			) ->      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(lowerCAmelCase__    ,					lowerCAmelCase__			) >= jaccard_threshold:
								elementa["copies"] += 1
								break
				else:
						__a           =			1
						extremes.append(lowerCAmelCase__			)
		return extremes

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Optional[int]			) ->      Optional[int]:
		global _shared_dataset
		__a           =			dataset
		__a           =			[]
		__a           =			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        lowercase							(		lowerCAmelCase__ :     Type[Dataset]    ,					lowerCAmelCase__ :     float = 0.85			) ->      Tuple[Type[Dataset], List[List[Dict]]]:
		__a           =			make_duplicate_clusters(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
		__a           =			{}
		__a           =			find_extremes(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
		for extremes in extremes_clusters:
				for element in extremes:
						__a           =			element
		__a           =			duplicate_indices - set(extreme_dict.keys()			)
		__a           =			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           =			element['''base_index'''] in extreme_dict
						if element["is_extreme"]:
								__a           =			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












from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFLEDForConditionalGeneration, TFLEDModel

@require_tf
class 		__lowerCAmelCase	:
							UpperCamelCase__					     =  LEDConfig
							UpperCamelCase__					     =  {}
							UpperCamelCase__					     =  '''gelu'''
							def __init__(						self						:Optional[int]		,      __magic_name__						:Dict		,      __magic_name__						:List[str]=13		,      __magic_name__						:Union[str, Any]=7		,      __magic_name__						:str=True		,      __magic_name__						:Union[str, Any]=False		,      __magic_name__						:Union[str, Any]=99		,      __magic_name__						:List[Any]=32		,      __magic_name__						:str=2		,      __magic_name__						:List[str]=4		,      __magic_name__						:str=37		,      __magic_name__						:Any=0.1		,      __magic_name__						:Dict=0.1		,      __magic_name__						:List[str]=20		,      __magic_name__						:Union[str, Any]=2		,      __magic_name__						:List[Any]=1		,      __magic_name__						:Optional[int]=0		,      __magic_name__						:Optional[int]=4		,      ):







								'''simple docstring'''







								a										=							parent
								a										=							batch_size
								a										=							seq_length
								a										=							is_training
								a										=							use_labels
								a										=							vocab_size
								a										=							hidden_size
								a										=							num_hidden_layers
								a										=							num_attention_heads
								a										=							intermediate_size

								a										=							hidden_dropout_prob
								a										=							attention_probs_dropout_prob
								a										=							max_position_embeddings
								a										=							eos_token_id
								a										=							pad_token_id
								a										=							bos_token_id
								a										=							attention_window

								# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
								# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
								# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
								# because its local attention only attends to `self.attention_window` and one before and one after
								a										=							self.attention_window + 2

								# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
								# the `test_attention_outputs` and `test_hidden_states_output` tests
								a										=							(
								    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
								)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								a										=							ids_tensor([self.batch_size, self.seq_length - 1]		,      self.vocab_size				)
								a										=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)		,      1				)
								a										=							tf.concat([input_ids, eos_tensor]		,      axis=1				)

								a										=							ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size				)

								a										=							self.config_cls(
								    vocab_size=self.vocab_size		,      d_model=self.hidden_size		,      encoder_layers=self.num_hidden_layers		,      decoder_layers=self.num_hidden_layers		,      encoder_attention_heads=self.num_attention_heads		,      decoder_attention_heads=self.num_attention_heads		,      encoder_ffn_dim=self.intermediate_size		,      decoder_ffn_dim=self.intermediate_size		,      dropout=self.hidden_dropout_prob		,      attention_dropout=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      eos_token_ids=[2]		,      bos_token_id=self.bos_token_id		,      pad_token_id=self.pad_token_id		,      decoder_start_token_id=self.pad_token_id		,      attention_window=self.attention_window		,      **self.config_updates		,      )
								a										=							prepare_led_inputs_dict(__magic_name__		,      __magic_name__		,      __magic_name__				)
								a										=							tf.concat(
								    [tf.zeros_like(__magic_name__				)[:, :-1], tf.ones_like(__magic_name__				)[:, -1:]]		,      axis=-1		,      )
								a										=							global_attention_mask
								return config, inputs_dict





							def       lowerCamelCase__							(						self						:Union[str, Any]		,      __magic_name__						:Optional[int]		,      __magic_name__						:List[Any]				):







								'''simple docstring'''







								a										=							TFLEDModel(config=__magic_name__				).get_decoder()
								a										=							inputs_dict["""input_ids"""]

								a										=							input_ids[:1, :]
								a										=							inputs_dict["""attention_mask"""][:1, :]
								a										=							1

								# first forward pass
								a										=							model(__magic_name__		,      attention_mask=__magic_name__		,      use_cache=__magic_name__				)

								a				, a										=							outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a										=							ids_tensor((self.batch_size, 3)		,      config.vocab_size				)
								a										=							tf.cast(ids_tensor((self.batch_size, 3)		,      2				)		,      tf.inta				)

								# append to next input_ids and
								a										=							tf.concat([input_ids, next_tokens]		,      axis=-1				)
								a										=							tf.concat([attention_mask, next_attn_mask]		,      axis=-1				)

								a										=							model(__magic_name__		,      attention_mask=__magic_name__				)[0]
								a										=							model(__magic_name__		,      attention_mask=__magic_name__		,      past_key_values=__magic_name__				)[0]

								self.parent.assertEqual(next_tokens.shape[1]		,      output_from_past.shape[1]				)

								# select random slice
								a										=							int(ids_tensor((1,)		,      output_from_past.shape[-1]				)				)
								a										=							output_from_no_past[:, -3:, random_slice_idx]
								a										=							output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__		,      __magic_name__		,      rtol=1E-3				)







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			)      ->       List[str]:
	if attention_mask is None:
		a										=							tf.cast(tf.math.not_equal(__lowerCamelCase							,			config.pad_token_id    )							,			tf.inta    )
	if decoder_attention_mask is None:
		a										=							tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape							,			dtype=tf.inta    ),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,			config.pad_token_id    )							,			tf.inta    ),
		    ]							,			axis=-1							,			)
	if head_mask is None:
		a										=							tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
	if decoder_head_mask is None:
		a										=							tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
	return {
	    "input_ids": input_ids,
	    "attention_mask": attention_mask,
	    "decoder_input_ids": decoder_input_ids,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	}

@require_tf
class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
							UpperCamelCase__					     =  (TFLEDForConditionalGeneration,) if is_tf_available() else ()
							UpperCamelCase__					     =  (
							    {
							        '''conversational''': TFLEDForConditionalGeneration,
							        '''feature-extraction''': TFLEDModel,
							        '''summarization''': TFLEDForConditionalGeneration,
							        '''text2text-generation''': TFLEDForConditionalGeneration,
							        '''translation''': TFLEDForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  False
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								a										=							TFLEDModelTester(self				)
								a										=							ConfigTester(self		,      config_class=__magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__				)
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a				, a										=							self.model_tester.prepare_config_and_inputs_for_common()
								a										=							tf.zeros_like(inputs_dict["""attention_mask"""]				)
								a										=							2
								a										=							tf.where(
								    tf.range(self.model_tester.seq_length				)[None, :] < num_global_attn_indices		,      1		,      inputs_dict["""global_attention_mask"""]		,      )

								a										=							True
								a										=							self.model_tester.seq_length
								a										=							self.model_tester.encoder_seq_length

								def check_decoder_attentions_output(__magic_name__						:int				):
									a										=							outputs.decoder_attentions
									self.assertEqual(len(__magic_name__				)		,      self.model_tester.num_hidden_layers				)
									self.assertListEqual(
									    list(decoder_attentions[0].shape[-3:]				)		,      [self.model_tester.num_attention_heads, seq_length, seq_length]		,      )

								def check_encoder_attentions_output(__magic_name__						:Any				):
									a										=							[t.numpy() for t in outputs.encoder_attentions]
									a										=							[t.numpy() for t in outputs.encoder_global_attentions]
									self.assertEqual(len(__magic_name__				)		,      self.model_tester.num_hidden_layers				)
									self.assertEqual(len(__magic_name__				)		,      self.model_tester.num_hidden_layers				)
									self.assertListEqual(
									    list(attentions[0].shape[-3:]				)		,      [self.model_tester.num_attention_heads, seq_length, seq_length]		,      )
									self.assertListEqual(
									    list(global_attentions[0].shape[-3:]				)		,      [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]		,      )

								for model_class in self.all_model_classes:
									a										=							True
									a										=							False
									a										=							False
									a										=							model_class(__magic_name__				)
									a										=							model(self._prepare_for_class(__magic_name__		,      __magic_name__				)				)
									a										=							len(__magic_name__				)
									self.assertEqual(config.output_hidden_states		,      __magic_name__				)
									check_encoder_attentions_output(__magic_name__				)

									if self.is_encoder_decoder:
										a										=							model_class(__magic_name__				)
										a										=							model(self._prepare_for_class(__magic_name__		,      __magic_name__				)				)
										self.assertEqual(config.output_hidden_states		,      __magic_name__				)
										check_decoder_attentions_output(__magic_name__				)

									# Check that output attentions can also be changed via the config
									del inputs_dict["output_attentions"]
									a										=							True
									a										=							model_class(__magic_name__				)
									a										=							model(self._prepare_for_class(__magic_name__		,      __magic_name__				)				)
									self.assertEqual(config.output_hidden_states		,      __magic_name__				)
									check_encoder_attentions_output(__magic_name__				)

									# Check attention is always last and order is fine
									a										=							True
									a										=							True
									a										=							model_class(__magic_name__				)
									a										=							model(self._prepare_for_class(__magic_name__		,      __magic_name__				)				)

									self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		,      len(__magic_name__				)				)
									self.assertEqual(model.config.output_hidden_states		,      __magic_name__				)
									check_encoder_attentions_output(__magic_name__				)
							@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								pass





							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								pass







def 		__A		(	__lowerCamelCase    )      ->       int:
	return tf.constant(__lowerCamelCase							,			dtype=tf.intaa    )


__UpperCamelCase					:       int    					=      1E-4



@slow
@require_tf
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""				).led

								# change to intended input here
								a										=							_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]				)
								a										=							_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]				)
								a										=							prepare_led_inputs_dict(model.config		,      __magic_name__		,      __magic_name__				)
								a										=							model(**__magic_name__				)[0]
								a										=							(1, 1024, 768)
								self.assertEqual(output.shape		,      __magic_name__				)
								# change to expected output here
								a										=							tf.convert_to_tensor(
								    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]		,      )
								tf.debugging.assert_near(output[:, :3, :3]		,      __magic_name__		,      atol=1E-3				)





							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""				)

								# change to intended input here
								a										=							_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]				)
								a										=							_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]				)
								a										=							prepare_led_inputs_dict(model.config		,      __magic_name__		,      __magic_name__				)
								a										=							model(**__magic_name__				)[0]
								a										=							(1, 1024, model.config.vocab_size)
								self.assertEqual(output.shape		,      __magic_name__				)
								# change to expected output here
								a										=							tf.convert_to_tensor(
								    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]		,      )
								tf.debugging.assert_near(output[:, :3, :3]		,      __magic_name__		,      atol=1E-3		,      rtol=1E-3				)

