
import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)







class        lowercase	:




							def __init__(       self     ,A__     ,A__=1_4     ,A__=7     ,A__=True     ,A__=True     ,A__=True     ,A__=True     ,A__=True     ,A__=9_9     ,A__=3_2     ,A__=5     ,A__=4     ,A__=3_7     ,A__="gelu"     ,A__=0.1     ,A__=0.1     ,A__=5_1_2     ,A__=1_6     ,A__=2     ,A__=0.02     ,A__=3     ,A__=4     ,A__=None     ,):
												lowercase					=		parent
												lowercase					=		batch_size
												lowercase					=		seq_length
												lowercase					=		is_training
												lowercase					=		use_token_type_ids
												lowercase					=		use_input_mask
												lowercase					=		use_labels
												lowercase					=		use_mc_token_ids
												lowercase					=		vocab_size
												lowercase					=		hidden_size
												lowercase					=		num_hidden_layers
												lowercase					=		num_attention_heads
												lowercase					=		intermediate_size
												lowercase					=		hidden_act
												lowercase					=		hidden_dropout_prob
												lowercase					=		attention_probs_dropout_prob
												lowercase					=		max_position_embeddings
												lowercase					=		type_vocab_size
												lowercase					=		type_sequence_label_size
												lowercase					=		initializer_range
												lowercase					=		num_labels
												lowercase					=		num_choices
												lowercase					=		scope
												lowercase					=		self.vocab_size - 1




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

												lowercase					=		None
												if self.use_input_mask:
																	lowercase					=		random_attention_mask([self.batch_size, self.seq_length])

												lowercase					=		None
												if self.use_token_type_ids:
																	lowercase					=		ids_tensor([self.batch_size, self.seq_length]     ,self.type_vocab_size)

												lowercase					=		None
												if self.use_mc_token_ids:
																	lowercase					=		ids_tensor([self.batch_size, self.num_choices]     ,self.seq_length)

												lowercase					=		None
												lowercase					=		None
												lowercase					=		None
												if self.use_labels:
																	lowercase					=		ids_tensor([self.batch_size]     ,self.type_sequence_label_size)
																	lowercase					=		ids_tensor([self.batch_size, self.seq_length]     ,self.num_labels)
																	lowercase					=		ids_tensor([self.batch_size]     ,self.num_choices)

												lowercase					=		self.get_config()

												lowercase					=		ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,2)

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												    token_type_ids,
												    mc_token_ids,
												    sequence_labels,
												    token_labels,
												    choice_labels,
												)




							def 				A__   (       self):
												return CTRLConfig(
												    vocab_size=self.vocab_size     ,n_embd=self.hidden_size     ,n_layer=self.num_hidden_layers     ,n_head=self.num_attention_heads     ,n_positions=self.max_position_embeddings     ,pad_token_id=self.pad_token_id     ,)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,*A__):
												lowercase					=		CTRLModel(config=A__)
												model.to(A__)
												model.eval()

												model(A__     ,token_type_ids=A__     ,head_mask=A__)
												model(A__     ,token_type_ids=A__)
												lowercase					=		model(A__)
												self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size))
												self.parent.assertEqual(len(result.past_key_values)     ,config.n_layer)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__     ,*A__):
												lowercase					=		CTRLLMHeadModel(A__)
												model.to(A__)
												model.eval()

												lowercase					=		model(A__     ,token_type_ids=A__     ,labels=A__)
												self.parent.assertEqual(result.loss.shape     ,())
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size))




							def 				A__   (       self):
												lowercase					=		self.prepare_config_and_inputs()

												(
												    (
												    lowercase
												)			,		(
												    lowercase
												)			,		(
												    lowercase
												)			,		(
												    lowercase
												)			,		(
												    lowercase
												)			,		(
												    lowercase
												)			,		(
												    lowercase
												)			,		(
												    lowercase
												)			,		(
												    lowercase
												)			,		
												)					=		config_and_inputs

												lowercase					=		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

												return config, inputs_dict





							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,*A__):
												lowercase					=		self.num_labels
												lowercase					=		CTRLForSequenceClassification(A__)
												model.to(A__)
												model.eval()
												lowercase					=		ids_tensor([self.batch_size]     ,self.type_sequence_label_size)
												lowercase					=		model(A__     ,token_type_ids=A__     ,labels=A__)
												self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels))







@require_torch
class        lowercase	(      SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Optional[int]       =(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							lowercase_ : List[Any]       =(CTRLLMHeadModel,) if is_torch_available() else ()
							lowercase_ : Union[str, Any]       =(
							    {
							        '''feature-extraction''': CTRLModel,
							        '''text-classification''': CTRLForSequenceClassification,
							        '''text-generation''': CTRLLMHeadModel,
							        '''zero-shot''': CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase_ : Optional[Any]       =True
							lowercase_ : List[str]       =False
							lowercase_ : Optional[int]       =False




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__     ,A__):
												if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																	# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																	# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																	# config could not be created.
																	return True

												return False




							def 				A__   (       self):
												lowercase					=		CTRLModelTester(self)
												lowercase					=		ConfigTester(self     ,config_class=A__     ,n_embd=3_7)




							def 				A__   (       self):
												super().tearDown()
												# clean-up as much as possible GPU memory occupied by PyTorch
												gc.collect()
												torch.cuda.empty_cache()




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




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_model(*A__)




							def 				A__   (       self):
												lowercase					=		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*A__)




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
							def 				A__   (       self):
												pass




							@slow
							def 				A__   (       self):
												for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase					=		CTRLModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)





							@unittest.skip('''The model doesn\'t support left padding''')  # and it's not used enough to be worth fixing :)
							def 				A__   (       self):
												pass







@require_torch
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												super().tearDown()
												# clean-up as much as possible GPU memory occupied by PyTorch
												gc.collect()
												torch.cuda.empty_cache()





							@slow
							def 				A__   (       self):
												lowercase					=		CTRLLMHeadModel.from_pretrained('''ctrl''')
												model.to(A__)
												lowercase					=		torch.tensor(
												    [[1_1_8_5_9, 0, 1_6_1_1, 8]]     ,dtype=torch.long     ,device=A__)  # Legal the president is
												lowercase					=		[
												    1_1_8_5_9,
												    0,
												    1_6_1_1,
												    8,
												    5,
												    1_5_0,
												    2_6_4_4_9,
												    2,
												    1_9,
												    3_4_8,
												    4_6_9,
												    3,
												    2_5_9_5,
												    4_8,
												    2_0_7_4_0,
												    2_4_6_5_3_3,
												    2_4_6_5_3_3,
												    1_9,
												    3_0,
												    5,
												]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

												lowercase					=		model.generate(A__     ,do_sample=A__)
												self.assertListEqual(output_ids[0].tolist()     ,A__)






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




