



import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
							from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor







class     __lowerCAmelCase					:



			def __init__(		self     :						Any				,	A     :						int				,	A     :						Tuple=13				,	A     :						List[str]=32				,	A     :						Union[str, Any]=2				,	A     :						Union[str, Any]=3				,	A     :						Union[str, Any]=16				,	A     :						List[Any]=[1, 2, 1]				,	A     :						Union[str, Any]=[2, 2, 4]				,	A     :						Optional[Any]=2				,	A     :						List[Any]=2.0				,	A     :						Tuple=True				,	A     :						Any=0.0				,	A     :						List[Any]=0.0				,	A     :						Tuple=0.1				,	A     :						str="gelu"				,	A     :						int=False				,	A     :						List[str]=True				,	A     :						Optional[int]=0.0_2				,	A     :						Optional[Any]=1E-5				,	A     :						Optional[Any]=True				,	A     :						Dict=None				,	A     :						Dict=True				,	A     :						Any=10				,	A     :						str=8				,	)     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     parent
						_UpperCAmelCase	 =     batch_size
						_UpperCAmelCase	 =     image_size
						_UpperCAmelCase	 =     patch_size
						_UpperCAmelCase	 =     num_channels
						_UpperCAmelCase	 =     embed_dim
						_UpperCAmelCase	 =     depths
						_UpperCAmelCase	 =     num_heads
						_UpperCAmelCase	 =     window_size
						_UpperCAmelCase	 =     mlp_ratio
						_UpperCAmelCase	 =     qkv_bias
						_UpperCAmelCase	 =     hidden_dropout_prob
						_UpperCAmelCase	 =     attention_probs_dropout_prob
						_UpperCAmelCase	 =     drop_path_rate
						_UpperCAmelCase	 =     hidden_act
						_UpperCAmelCase	 =     use_absolute_embeddings
						_UpperCAmelCase	 =     patch_norm
						_UpperCAmelCase	 =     layer_norm_eps
						_UpperCAmelCase	 =     initializer_range
						_UpperCAmelCase	 =     is_training
						_UpperCAmelCase	 =     scope
						_UpperCAmelCase	 =     use_labels
						_UpperCAmelCase	 =     type_sequence_label_size
						_UpperCAmelCase	 =     encoder_stride



			def 					_lowerCamelCase (		self     :						int)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

						_UpperCAmelCase	 =     None
						if self.use_labels:
									_UpperCAmelCase	 =     ids_tensor([self.batch_size]				,	self.type_sequence_label_size)

						_UpperCAmelCase	 =     self.get_config()

						return config, pixel_values, labels



			def 					_lowerCamelCase (		self     :						Tuple)     ->					List[Any]:



						"""simple docstring"""






						return SwinvaConfig(
						    image_size=self.image_size				,	patch_size=self.patch_size				,	num_channels=self.num_channels				,	embed_dim=self.embed_dim				,	depths=self.depths				,	num_heads=self.num_heads				,	window_size=self.window_size				,	mlp_ratio=self.mlp_ratio				,	qkv_bias=self.qkv_bias				,	hidden_dropout_prob=self.hidden_dropout_prob				,	attention_probs_dropout_prob=self.attention_probs_dropout_prob				,	drop_path_rate=self.drop_path_rate				,	hidden_act=self.hidden_act				,	use_absolute_embeddings=self.use_absolute_embeddings				,	path_norm=self.patch_norm				,	layer_norm_eps=self.layer_norm_eps				,	initializer_range=self.initializer_range				,	encoder_stride=self.encoder_stride				,	)



			def 					_lowerCamelCase (		self     :						List[Any]				,	A     :						List[Any]				,	A     :						int				,	A     :						List[str])     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     SwinvaModel(config=A)
						model.to(A)
						model.eval()
						_UpperCAmelCase	 =     model(A)

						_UpperCAmelCase	 =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
						_UpperCAmelCase	 =     int(config.embed_dim * 2 ** (len(config.depths) - 1))

						self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, expected_seq_len, expected_dim))



			def 					_lowerCamelCase (		self     :						int				,	A     :						Optional[Any]				,	A     :						Any				,	A     :						Union[str, Any])     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     SwinvaForMaskedImageModeling(config=A)
						model.to(A)
						model.eval()
						_UpperCAmelCase	 =     model(A)
						self.parent.assertEqual(
						    result.logits.shape				,	(self.batch_size, self.num_channels, self.image_size, self.image_size))

						# test greyscale images
						_UpperCAmelCase	 =     1
						_UpperCAmelCase	 =     SwinvaForMaskedImageModeling(A)
						model.to(A)
						model.eval()

						_UpperCAmelCase	 =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
						_UpperCAmelCase	 =     model(A)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, 1, self.image_size, self.image_size))



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						List[Any]				,	A     :						Optional[Any]				,	A     :						List[Any])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.type_sequence_label_size
						_UpperCAmelCase	 =     SwinvaForImageClassification(A)
						model.to(A)
						model.eval()
						_UpperCAmelCase	 =     model(A				,	labels=A)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.type_sequence_label_size))



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.prepare_config_and_inputs()
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     config_and_inputs
						_UpperCAmelCase	 =     {'pixel_values': pixel_values}
						return config, inputs_dict







@require_torch
class     __lowerCAmelCase					(    A       ,		A       ,		unittest.TestCase		):
			UpperCamelCase           =  (
			    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
			)
			UpperCamelCase           =  (
			    {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase           =  False
			UpperCamelCase           =  False
			UpperCamelCase           =  False
			UpperCamelCase           =  False



			def 					_lowerCamelCase (		self     :						Any)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     SwinvaModelTester(self)
						_UpperCAmelCase	 =     ConfigTester(self				,	config_class=A				,	embed_dim=37)



			def 					_lowerCamelCase (		self     :						Any)     ->					List[str]:



						"""simple docstring"""






						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()



			def 					_lowerCamelCase (		self     :						Any)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A)



			@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.')
			def 					_lowerCamelCase (		self     :						Optional[int])     ->					List[Any]:



						"""simple docstring"""






						pass



			@unittest.skip(reason='Swinv2 does not use inputs_embeds')
			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Dict:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     model_class(A)
									self.assertIsInstance(model.get_input_embeddings()				,	(nn.Module))
									_UpperCAmelCase	 =     model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A				,	nn.Linear))



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     model_class(A)
									_UpperCAmelCase	 =     inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_UpperCAmelCase	 =     [*signature.parameters.keys()]

									_UpperCAmelCase	 =     ['pixel_values']
									self.assertListEqual(arg_names[:1]				,	A)



			def 					_lowerCamelCase (		self     :						List[Any])     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()
						_UpperCAmelCase	 =     True

						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     True
									_UpperCAmelCase	 =     False
									_UpperCAmelCase	 =     True
									_UpperCAmelCase	 =     model_class(A)
									model.to(A)
									model.eval()
									with torch.no_grad():
												_UpperCAmelCase	 =     model(**self._prepare_for_class(A				,	A))
									_UpperCAmelCase	 =     outputs.attentions
									_UpperCAmelCase	 =     len(self.model_tester.depths)
									self.assertEqual(len(A)				,	A)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									_UpperCAmelCase	 =     True
									_UpperCAmelCase	 =     config.window_size**2
									_UpperCAmelCase	 =     model_class(A)
									model.to(A)
									model.eval()
									with torch.no_grad():
												_UpperCAmelCase	 =     model(**self._prepare_for_class(A				,	A))
									_UpperCAmelCase	 =     outputs.attentions
									self.assertEqual(len(A)				,	A)

									self.assertListEqual(
									    list(attentions[0].shape[-3:])				,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,	)
									_UpperCAmelCase	 =     len(A)

									# Check attention is always last and order is fine
									_UpperCAmelCase	 =     True
									_UpperCAmelCase	 =     True
									_UpperCAmelCase	 =     model_class(A)
									model.to(A)
									model.eval()
									with torch.no_grad():
												_UpperCAmelCase	 =     model(**self._prepare_for_class(A				,	A))

									if hasattr(self.model_tester				,	'num_hidden_states_types'):
												_UpperCAmelCase	 =     self.model_tester.num_hidden_states_types
									else:
												# also another +1 for reshaped_hidden_states
												_UpperCAmelCase	 =     2
									self.assertEqual(out_len + added_hidden_states				,	len(A))

									_UpperCAmelCase	 =     outputs.attentions

									self.assertEqual(len(A)				,	A)

									self.assertListEqual(
									    list(self_attentions[0].shape[-3:])				,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,	)



			def 					_lowerCamelCase (		self     :						str				,	A     :						str				,	A     :						Union[str, Any]				,	A     :						Optional[Any]				,	A     :						Optional[Any])     ->					Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     model_class(A)
						model.to(A)
						model.eval()

						with torch.no_grad():
									_UpperCAmelCase	 =     model(**self._prepare_for_class(A				,	A))

						_UpperCAmelCase	 =     outputs.hidden_states

						_UpperCAmelCase	 =     getattr(
						    self.model_tester				,	'expected_num_hidden_layers'				,	len(self.model_tester.depths) + 1)
						self.assertEqual(len(A)				,	A)

						# Swinv2 has a different seq_length
						_UpperCAmelCase	 =     (
						    config.patch_size
						    if isinstance(config.patch_size				,	collections.abc.Iterable)
						    else (config.patch_size, config.patch_size)
						)

						_UpperCAmelCase	 =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:])				,	[num_patches, self.model_tester.embed_dim]				,	)

						_UpperCAmelCase	 =     outputs.reshaped_hidden_states
						self.assertEqual(len(A)				,	A)

						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     reshaped_hidden_states[0].shape
						_UpperCAmelCase	 =     (
						    reshaped_hidden_states[0].view(A				,	A				,	height * width).permute(0				,	2				,	1)
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:])				,	[num_patches, self.model_tester.embed_dim]				,	)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					str:



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

						_UpperCAmelCase	 =     (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size				,	collections.abc.Iterable)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     True
									self.check_hidden_states_output(A				,	A				,	A				,	A)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									_UpperCAmelCase	 =     True

									self.check_hidden_states_output(A				,	A				,	A				,	A)



			def 					_lowerCamelCase (		self     :						Dict)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()
						_UpperCAmelCase	 =     3

						_UpperCAmelCase	 =     (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size				,	collections.abc.Iterable)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)
						_UpperCAmelCase	 =     (
						    config.patch_size
						    if isinstance(config.patch_size				,	collections.abc.Iterable)
						    else (config.patch_size, config.patch_size)
						)

						_UpperCAmelCase	 =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						_UpperCAmelCase	 =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     True
									self.check_hidden_states_output(A				,	A				,	A				,	(padded_height, padded_width))

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									_UpperCAmelCase	 =     True
									self.check_hidden_states_output(A				,	A				,	A				,	(padded_height, padded_width))



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*A)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A)



			@slow
			def 					_lowerCamelCase (		self     :						List[Any])     ->					List[str]:



						"""simple docstring"""






						for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_UpperCAmelCase	 =     SwinvaModel.from_pretrained(A)
									self.assertIsNotNone(A)



			def 					_lowerCamelCase (		self     :						str)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

						_UpperCAmelCase	 =     _config_zero_init(A)
						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     model_class(config=A)
									for name, param in model.named_parameters():
												if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
															self.assertIn(
															    ((param.data.mean() * 1E9).round() / 1E9).item()				,	[0.0, 1.0]				,	msg=F"Parameter {name} of model {model_class} seems not properly initialized"				,	)







@require_vision
@require_torch
class     __lowerCAmelCase					(    unittest.TestCase		):



			@cached_property
			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Optional[Any]:



						"""simple docstring"""






						return (
						    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256')
						    if is_vision_available()
						    else None
						)



			@slow
			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256').to(
						    A)
						_UpperCAmelCase	 =     self.default_image_processor

						_UpperCAmelCase	 =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						_UpperCAmelCase	 =     image_processor(images=A				,	return_tensors='pt').to(A)

						# forward pass
						with torch.no_grad():
									_UpperCAmelCase	 =     model(**A)

						# verify the logits
						_UpperCAmelCase	 =     torch.Size((1, 10_00))
						self.assertEqual(outputs.logits.shape				,	A)
						_UpperCAmelCase	 =     torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]).to(A)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]				,	A				,	atol=1E-4))





import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


UpperCAmelCase__     = re.compile(r"\s+")





def 							A			(  _UpperCAmelCase       :     Tuple					)       ->				str:







			'''simple docstring'''
			return {"hash": hashlib.mda(re.sub(_UpperCAmelCase							,     ''							,     example['content']					).encode('utf-8'					)					).hexdigest()}





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     [len(_UpperCAmelCase					) for line in example['content'].splitlines()]
			return {"line_mean": np.mean(_UpperCAmelCase					), "line_max": max(_UpperCAmelCase					)}





def 							A			(  _UpperCAmelCase       :     Any					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     np.mean([c.isalnum() for c in example['content']]					)
			return {"alpha_frac": alpha_frac}





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     List[Any]					)       ->				Dict:







			'''simple docstring'''
			if example["hash"] in uniques:
						uniques.remove(example['hash']					)
						return True
			else:
						return False





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Dict=5					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     ['auto-generated', 'autogenerated', 'automatically generated']
			_UpperCAmelCase	 =     example['content'].splitlines()
			for _, line in zip(range(_UpperCAmelCase					)							,     _UpperCAmelCase					):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Tuple=5							,     _UpperCAmelCase       :     Optional[int]=0.05					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     ['unit tests', 'test file', 'configuration file']
			_UpperCAmelCase	 =     example['content'].splitlines()
			_UpperCAmelCase	 =     0
			_UpperCAmelCase	 =     0
			# first test
			for _, line in zip(range(_UpperCAmelCase					)							,     _UpperCAmelCase					):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			_UpperCAmelCase	 =     example['content'].count('\n'					)
			_UpperCAmelCase	 =     int(coeff * nlines					)
			for line in lines:
						count_config += line.lower().count('config'					)
						count_test += line.lower().count('test'					)
						if count_config > threshold or count_test > threshold:
									return {"config_or_test": True}
			return {"config_or_test": False}





def 							A			(  _UpperCAmelCase       :     Any					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     ['def ', 'class ', 'for ', 'while ']
			_UpperCAmelCase	 =     example['content'].splitlines()
			for line in lines:
						for keyword in keywords:
									if keyword in line.lower():
												return {"has_no_keywords": False}
			return {"has_no_keywords": True}





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Optional[Any]=4					)       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     example['content'].splitlines()
			_UpperCAmelCase	 =     0
			for line in lines:
						counter += line.lower().count('='					)
						if counter > minimum:
									return {"has_few_assignments": False}
			return {"has_few_assignments": True}





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     tokenizer(example['content']							,     truncation=_UpperCAmelCase					)['input_ids']
			_UpperCAmelCase	 =     len(example['content']					) / len(_UpperCAmelCase					)
			return {"ratio": ratio}





def 							A			(  _UpperCAmelCase       :     Dict					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {}
			results.update(get_hash(_UpperCAmelCase					)					)
			results.update(line_stats(_UpperCAmelCase					)					)
			results.update(alpha_stats(_UpperCAmelCase					)					)
			results.update(char_token_ratio(_UpperCAmelCase					)					)
			results.update(is_autogenerated(_UpperCAmelCase					)					)
			results.update(is_config_or_test(_UpperCAmelCase					)					)
			results.update(has_no_keywords(_UpperCAmelCase					)					)
			results.update(has_few_assignments(_UpperCAmelCase					)					)
			return results





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[Any]					)       ->				Any:







			'''simple docstring'''
			if not check_uniques(_UpperCAmelCase							,     _UpperCAmelCase					):
						return False
			elif example["autogenerated"]:
						return False
			elif example["line_max"] > args.line_max:
						return False
			elif example["line_mean"] > args.line_mean:
						return False
			elif example["alpha_frac"] < args.alpha_frac:
						return False
			elif example["ratio"] < args.min_token_ratio:
						return False
			elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
						return False
			elif example["has_few_assignments"]:
						return False
			else:
						return True





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Any:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'rb'					) as f_in:
						with gzip.open(str(_UpperCAmelCase					) + '.gz'							,     'wb'							,     compresslevel=6					) as f_out:
									shutil.copyfileobj(_UpperCAmelCase							,     _UpperCAmelCase					)
			os.unlink(_UpperCAmelCase					)


# Settings
UpperCAmelCase__     = HfArgumentParser(PreprocessingArguments)
UpperCAmelCase__     = parser.parse_args()
if args.num_workers is None:
							UpperCAmelCase__     = multiprocessing.cpu_count()
UpperCAmelCase__     = AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
UpperCAmelCase__     = time.time()
UpperCAmelCase__     = load_dataset(args.dataset_name, split="train")
print(f"""Time to load dataset: {time.time()-t_start:.2f}""")

# Run preprocessing
UpperCAmelCase__     = time.time()
UpperCAmelCase__     = ds.map(preprocess, num_proc=args.num_workers)
print(f"""Time to preprocess dataset: {time.time()-t_start:.2f}""")

# Deduplicate hashes
UpperCAmelCase__     = set(ds.unique("hash"))
UpperCAmelCase__     = len(uniques) / len(ds)
print(f"""Fraction of duplicates: {1-frac:.2%}""")

# Deduplicate data and apply heuristics
UpperCAmelCase__     = time.time()
UpperCAmelCase__     = ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args})
print(f"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(f"""Size of filtered dataset: {len(ds_filter)}""")

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
							UpperCAmelCase__     = time.time()
							UpperCAmelCase__     ,						UpperCAmelCase__     = deduplicate_dataset(ds_filter, args.jaccard_threshold)
							print(f"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
							print(f"""Size of deduplicate dataset: {len(ds_filter)}""")

# Save data in batches of samples_per_file
UpperCAmelCase__     = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
							with open(output_dir / "duplicate_clusters.json", "w") as f:
														json.dump(duplicate_clusters, f)

UpperCAmelCase__     = output_dir / "data"
data_dir.mkdir(exist_ok=True)

UpperCAmelCase__     = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
							UpperCAmelCase__     = str(data_dir / f"""file-{file_number+1:012}.json""")
							UpperCAmelCase__     = min(len(ds_filter), index + args.samples_per_file)
							ds_filter.select(list(range(index, end_index))).to_json(file_path)
							compress_file(file_path)

print(f"""Time to save dataset: {time.time()-t_start:.2f}""")
