
import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							lowercase__	:Optional[Any]										=					"platform"
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class        lowercase	:
							lowercase_ : str       =PegasusConfig
							lowercase_ : str       ={}
							lowercase_ : List[Any]       ='''gelu'''




							def __init__(       self     ,A__     ,A__=1_3     ,A__=7     ,A__=True     ,A__=False     ,A__=9_9     ,A__=3_2     ,A__=5     ,A__=4     ,A__=3_7     ,A__=0.1     ,A__=0.1     ,A__=2_0     ,A__=2     ,A__=1     ,A__=0     ,):
												lowercase					=		parent
												lowercase					=		batch_size
												lowercase					=		seq_length
												lowercase					=		is_training
												lowercase					=		use_labels
												lowercase					=		vocab_size
												lowercase					=		hidden_size
												lowercase					=		num_hidden_layers
												lowercase					=		num_attention_heads
												lowercase					=		intermediate_size

												lowercase					=		hidden_dropout_prob
												lowercase					=		attention_probs_dropout_prob
												lowercase					=		max_position_embeddings
												lowercase					=		eos_token_id
												lowercase					=		pad_token_id
												lowercase					=		bos_token_id




							def 				A__   (       self):
												lowercase					=		ids_tensor([self.batch_size, self.seq_length - 1]     ,self.vocab_size).clip(3     ,self.vocab_size)
												lowercase					=		np.expand_dims(np.array([self.eos_token_id] * self.batch_size)     ,1)
												lowercase					=		np.concatenate([input_ids, eos_tensor]     ,axis=1)

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

												lowercase					=		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     ,**self.config_updates     ,)
												lowercase					=		prepare_pegasus_inputs_dict(A__     ,A__     ,A__)
												return config, inputs_dict




							def 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		2_0
												lowercase					=		model_class_name(A__)

												lowercase					=		model.encode(inputs_dict['''input_ids'''])

												lowercase			,		lowercase					=		(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

												lowercase					=		model.init_cache(decoder_input_ids.shape[0]     ,A__     ,A__)
												lowercase					=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,dtype='''i4''')

												lowercase					=		jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]     ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,)
												lowercase					=		model.decode(
												    decoder_input_ids[:, :-1]     ,A__     ,decoder_attention_mask=A__     ,past_key_values=A__     ,decoder_position_ids=A__     ,)

												lowercase					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype='''i4''')
												lowercase					=		model.decode(
												    decoder_input_ids[:, -1:]     ,A__     ,decoder_attention_mask=A__     ,past_key_values=outputs_cache.past_key_values     ,decoder_position_ids=A__     ,)

												lowercase					=		model.decode(A__     ,A__)

												lowercase					=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
												self.parent.assertTrue(diff < 1E-3     ,msg=f'Max diff is {diff}')





							def 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		2_0
												lowercase					=		model_class_name(A__)

												lowercase					=		model.encode(inputs_dict['''input_ids'''])

												lowercase			,		lowercase					=		(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

												lowercase					=		jnp.concatenate(
												    [
												        decoder_attention_mask,
												        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
												    ]     ,axis=-1     ,)

												lowercase					=		model.init_cache(decoder_input_ids.shape[0]     ,A__     ,A__)
												lowercase					=		jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]     ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,)

												lowercase					=		model.decode(
												    decoder_input_ids[:, :-1]     ,A__     ,decoder_attention_mask=A__     ,past_key_values=A__     ,decoder_position_ids=A__     ,)
												lowercase					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype='''i4''')
												lowercase					=		model.decode(
												    decoder_input_ids[:, -1:]     ,A__     ,past_key_values=outputs_cache.past_key_values     ,decoder_attention_mask=A__     ,decoder_position_ids=A__     ,)

												lowercase					=		model.decode(A__     ,A__     ,decoder_attention_mask=A__)

												lowercase					=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
												self.parent.assertTrue(diff < 1E-3     ,msg=f'Max diff is {diff}')



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None				,    ):
					'''simple docstring'''
					if attention_mask is None:
										lowercase					=		np.not_equal(lowerCAmelCase__				,    config.pad_token_id	).astype(np.inta	)
					if decoder_attention_mask is None:
										lowercase					=		np.concatenate(
										    [
										        np.ones(decoder_input_ids[:, :1].shape				,    dtype=np.inta	),
										        np.not_equal(decoder_input_ids[:, 1:]				,    config.pad_token_id	).astype(np.inta	),
										    ]				,    axis=-1				,    )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					}







@require_flax
class        lowercase	(      SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Union[str, Any]       =(
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							lowercase_ : str       =(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							lowercase_ : Union[str, Any]       =True
							lowercase_ : Optional[Any]       =False
							lowercase_ : Any       =False
							lowercase_ : Optional[int]       =False




							def 				A__   (       self):
												lowercase					=		FlaxPegasusModelTester(self)
												lowercase					=		ConfigTester(self     ,config_class=A__)




							def 				A__   (       self):
												self.config_tester.run_common_tests()




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward(A__     ,A__     ,A__)




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward_with_attn_mask(A__     ,A__     ,A__)




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__):
																						lowercase					=		self._prepare_for_class(A__     ,A__)
																						lowercase					=		model_class(A__)

																						@jax.jit
																						def encode_jitted(A__     ,A__=None     ,**A__):
																											return model.encode(input_ids=A__     ,attention_mask=A__)

																						with self.subTest('''JIT Enabled'''):
																											lowercase					=		encode_jitted(**A__).to_tuple()

																						with self.subTest('''JIT Disabled'''):
																											with jax.disable_jit():
																																lowercase					=		encode_jitted(**A__).to_tuple()

																						self.assertEqual(len(A__)     ,len(A__))
																						for jitted_output, output in zip(A__     ,A__):
																											self.assertEqual(jitted_output.shape     ,output.shape)




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__):
																						lowercase					=		model_class(A__)
																						lowercase					=		model.encode(inputs_dict['''input_ids''']     ,inputs_dict['''attention_mask'''])

																						lowercase					=		{
																						    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																						    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																						    '''encoder_outputs''': encoder_outputs,
																						}

																						@jax.jit
																						def decode_jitted(A__     ,A__     ,A__):
																											return model.decode(
																											    decoder_input_ids=A__     ,decoder_attention_mask=A__     ,encoder_outputs=A__     ,)

																						with self.subTest('''JIT Enabled'''):
																											lowercase					=		decode_jitted(**A__).to_tuple()

																						with self.subTest('''JIT Disabled'''):
																											with jax.disable_jit():
																																lowercase					=		decode_jitted(**A__).to_tuple()

																						self.assertEqual(len(A__)     ,len(A__))
																						for jitted_output, output in zip(A__     ,A__):
																											self.assertEqual(jitted_output.shape     ,output.shape)




							@slow
							def 				A__   (       self):
												for model_class_name in self.all_model_classes:
																	lowercase					=		model_class_name.from_pretrained('''google/pegasus-large'''     ,from_pt=A__)
																	lowercase					=		np.ones((1, 1))
																	lowercase					=		model(A__)
																	self.assertIsNotNone(A__)





							@slow
							def 				A__   (       self):
												lowercase					=		FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
												lowercase					=		PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')

												lowercase					=		[
												    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
												    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
												]

												lowercase					=		[
												    '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
												    '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
												]

												lowercase					=		tokenizer(A__     ,return_tensors='''np'''     ,truncation=A__     ,max_length=5_1_2     ,padding=A__)
												lowercase					=		model.generate(**A__     ,num_beams=2).sequences
												lowercase					=		tokenizer.batch_decode(A__     ,skip_special_tokens=A__)
												assert tgt_text == decoded






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__	:Optional[Any]										=					re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase__	:List[str]										=					10
lowercase__	:Tuple										=					256



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < MIN_NUM_TOKENS:
										return None
					lowercase					=		MinHash(num_perm=lowerCAmelCase__	)
					for token in set(lowerCAmelCase__	):
										min_hash.update(token.encode()	)
					return min_hash



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return {t for t in NON_ALPHA.split(lowerCAmelCase__	) if len(t.strip()	) > 0}







class        lowercase	:




							def __init__(       self     ,*,
							    A__ = 0.85     ,):
												lowercase					=		duplication_jaccard_threshold
												lowercase					=		NUM_PERM
												lowercase					=		MinHashLSH(threshold=self._duplication_jaccard_threshold     ,num_perm=self._num_perm)

												lowercase					=		defaultdict(A__)




							def 				A__   (       self     ,A__     ,A__):
												lowercase					=		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):
												lowercase					=		[]
												for base, duplicates in self._duplicate_clusters.items():
																	lowercase					=		[base] + list(A__)
																	# reformat the cluster to be a list of dict
																	lowercase					=		[{'''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__):
												lowercase					=		self.get_duplicate_clusters()
												with open(A__     ,'''w''') as f:
																	json.dump(A__     ,A__)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		element
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		get_tokens(lowerCAmelCase__	)
					lowercase					=		get_tokens(lowerCAmelCase__	)
					return len(tokensa & tokensa	) / len(tokensa | tokensa	)


lowercase__	:List[Any]										=					None



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



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					global _shared_dataset
					lowercase					=		dataset
					lowercase					=		[]
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.85	):
					'''simple docstring'''
					lowercase					=		make_duplicate_clusters(lowerCAmelCase__				,    lowerCAmelCase__	)
					lowercase					=		{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
					lowercase					=		{}
					lowercase					=		find_extremes(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)
					for extremes in extremes_clusters:
										for element in extremes:
															lowercase					=		element
					lowercase					=		duplicate_indices - set(extreme_dict.keys()	)
					lowercase					=		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:
															lowercase					=		element['''base_index'''] in extreme_dict
															if element["is_extreme"]:
																				lowercase					=		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




