






"""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    			=       10
lowercase    			=       256






def _lowerCAmelCase	(  __lowerCamelCase:List[str]   ):

						'''simple docstring'''






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






def _lowerCAmelCase	(  __lowerCamelCase:str   ):

						'''simple docstring'''






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

class 						A_							:







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

										__magic_name__            =     defaultdict(_a	)







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







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Any	)				->					Union[str, Any]:
										__magic_name__            =     self.get_duplicate_clusters()
										with open(_a						,    "w"	) as f:
																json.dump(_a						,    _a	)






def _lowerCAmelCase	(  __lowerCamelCase:Any   ):

						'''simple docstring'''






						__magic_name__      ,    __magic_name__            =     element
						__magic_name__            =     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	(  __lowerCamelCase:Type[Dataset]   ):

						'''simple docstring'''






						with mp.Pool() as pool:
												for data in pool.imap_unordered(
												    _compute_min_hash ,						ThreadedIterator(lowerCAmelCase__ ,						max_queue_size=1_0_0_0_0   ) ,						chunksize=1_0_0 ,						):
																		if data is not None:
																								yield data






def _lowerCAmelCase	(  __lowerCamelCase:Type[Dataset] ,						__lowerCamelCase:float   ):

						'''simple docstring'''






						__magic_name__            =     DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase__   )

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

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






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     get_tokens(lowerCAmelCase__   )
						__magic_name__            =     get_tokens(lowerCAmelCase__   )
						return len(tokensa & tokensa   ) / len(tokensa | tokensa   )


lowercase    			=       None






def _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:str   ):

						'''simple docstring'''






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






def _lowerCAmelCase	(  __lowerCamelCase:Dict ,						__lowerCamelCase:str ,						__lowerCamelCase:Any   ):

						'''simple docstring'''






						global _shared_dataset
						__magic_name__            =     dataset
						__magic_name__            =     []
						__magic_name__            =     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 _lowerCAmelCase	(  __lowerCamelCase:Type[Dataset] ,						__lowerCamelCase:float = 0.8_5   ):

						'''simple docstring'''






						__magic_name__            =     make_duplicate_clusters(lowerCAmelCase__ ,						lowerCAmelCase__   )
						__magic_name__            =     {x["base_index"] for cluster in duplicate_clusters for x in cluster}
						__magic_name__            =     {}
						__magic_name__            =     find_extremes(lowerCAmelCase__ ,						lowerCAmelCase__ ,						lowerCAmelCase__   )
						for extremes in extremes_clusters:
												for element in extremes:
																		__magic_name__            =     element
						__magic_name__            =     duplicate_indices - set(extreme_dict.keys()   )
						__magic_name__            =     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:
																		__magic_name__            =     element["base_index"] in extreme_dict
																		if element["is_extreme"]:
																								__magic_name__            =     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"""

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 						A_							:
				UpperCAmelCase__        =				LEDConfig
				UpperCAmelCase__        =				{}
				UpperCAmelCase__        =				'''gelu'''







				def __init__(							self							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						List[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						int=False						,    __lowerCamelCase							:						List[Any]=9_9						,    __lowerCamelCase							:						Any=3_2						,    __lowerCamelCase							:						Optional[Any]=2						,    __lowerCamelCase							:						Dict=4						,    __lowerCamelCase							:						int=3_7						,    __lowerCamelCase							:						Tuple=0.1						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[Any]=2_0						,    __lowerCamelCase							:						List[str]=2						,    __lowerCamelCase							:						str=1						,    __lowerCamelCase							:						List[Any]=0						,    __lowerCamelCase							:						Dict=4						,    )				->					Optional[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size

										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     eos_token_id
										__magic_name__            =     pad_token_id
										__magic_name__            =     bos_token_id
										__magic_name__            =     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
										__magic_name__            =     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
										__magic_name__            =     (
										    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
										)







				def     _snake_case					(							self							:						Dict	)				->					Optional[Any]:
										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length - 1]						,    self.vocab_size	)
										__magic_name__            =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)						,    1	)
										__magic_name__            =     tf.concat([input_ids, eos_tensor]						,    axis=1	)

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

										__magic_name__            =     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						,    )
										__magic_name__            =     prepare_led_inputs_dict(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     tf.concat(
										    [tf.zeros_like(__lowerCamelCase	)[:, :-1], tf.ones_like(__lowerCamelCase	)[:, -1:]]						,    axis=-1						,    )
										__magic_name__            =     global_attention_mask
										return config, inputs_dict







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Any	)				->					Union[str, Any]:
										__magic_name__            =     TFLEDModel(config=__lowerCamelCase	).get_decoder()
										__magic_name__            =     inputs_dict["input_ids"]

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

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

										__magic_name__      ,    __magic_name__            =     outputs.to_tuple()

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

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

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

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

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

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






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Dict=None ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Union[str, Any]=None ,						):

						'''simple docstring'''






						if attention_mask is None:
												__magic_name__            =     tf.cast(tf.math.not_equal(__lowerCamelCase ,						config.pad_token_id   ) ,						tf.inta   )
						if decoder_attention_mask is None:
												__magic_name__            =     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:
												__magic_name__            =     tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
						if decoder_head_mask is None:
												__magic_name__            =     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 						A_							(      snake_case_					,							snake_case_					,							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     _snake_case					(							self							:						int	)				->					Optional[int]:
										__magic_name__            =     TFLEDModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					Dict:
										self.config_tester.run_common_tests()







				def     _snake_case					(							self							:						Optional[int]	)				->					List[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]	)				->					str:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__            =     tf.zeros_like(inputs_dict["attention_mask"]	)
										__magic_name__            =     2
										__magic_name__            =     tf.where(
										    tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices						,    1						,    inputs_dict["global_attention_mask"]						,    )

										__magic_name__            =     True
										__magic_name__            =     self.model_tester.seq_length
										__magic_name__            =     self.model_tester.encoder_seq_length

										def check_decoder_attentions_output(__lowerCamelCase							:						int	):
																__magic_name__            =     outputs.decoder_attentions
																self.assertEqual(len(__lowerCamelCase	)						,    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(__lowerCamelCase							:						Any	):
																__magic_name__            =     [t.numpy() for t in outputs.encoder_attentions]
																__magic_name__            =     [t.numpy() for t in outputs.encoder_global_attentions]
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertEqual(len(__lowerCamelCase	)						,    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:
																__magic_name__            =     True
																__magic_name__            =     False
																__magic_name__            =     False
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																__magic_name__            =     len(__lowerCamelCase	)
																self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)

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

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

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

																self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,    len(__lowerCamelCase	)	)
																self.assertEqual(model.config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)







				@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."	)
				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										pass







				def     _snake_case					(							self							:						int	)				->					str:
										# TODO: Head-masking not yet implement
										pass






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return tf.constant(__lowerCamelCase ,						dtype=tf.intaa   )


lowercase    			=       1e-4


@slow
@require_tf
class 						A_							(      unittest.TestCase  ):







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[str]:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, 7_6_8)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     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]						,    __lowerCamelCase						,    atol=1e-3	)







				def     _snake_case					(							self							:						Any	)				->					Dict:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, model.config.vocab_size)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     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]						,    __lowerCamelCase						,    atol=1e-3						,    rtol=1e-3	)



