

from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.layoutlm.modeling_tf_layoutlm import (
				    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMForMaskedLM,
				    TFLayoutLMForQuestionAnswering,
				    TFLayoutLMForSequenceClassification,
				    TFLayoutLMForTokenClassification,
				    TFLayoutLMModel,
				)

class 						__UpperCamelCase    :




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						int=13		,	lowerCAmelCase	:						Any=7		,	lowerCAmelCase	:						Any=True		,	lowerCAmelCase	:						int=True		,	lowerCAmelCase	:						str=True		,	lowerCAmelCase	:						str=True		,	lowerCAmelCase	:						str=99		,	lowerCAmelCase	:						Dict=32		,	lowerCAmelCase	:						str=2		,	lowerCAmelCase	:						str=4		,	lowerCAmelCase	:						Any=37		,	lowerCAmelCase	:						Any="gelu"		,	lowerCAmelCase	:						List[str]=0.1		,	lowerCAmelCase	:						Optional[Any]=0.1		,	lowerCAmelCase	:						Union[str, Any]=512		,	lowerCAmelCase	:						int=16		,	lowerCAmelCase	:						int=2		,	lowerCAmelCase	:						Optional[int]=0.02		,	lowerCAmelCase	:						Optional[Any]=3		,	lowerCAmelCase	:						Optional[Any]=4		,	lowerCAmelCase	:						List[str]=None		,	lowerCAmelCase	:						Tuple=1_000		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			parent
		UpperCAmelCase_       				=			batch_size
		UpperCAmelCase_       				=			seq_length
		UpperCAmelCase_       				=			is_training
		UpperCAmelCase_       				=			use_input_mask
		UpperCAmelCase_       				=			use_token_type_ids
		UpperCAmelCase_       				=			use_labels
		UpperCAmelCase_       				=			vocab_size
		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			max_position_embeddings
		UpperCAmelCase_       				=			type_vocab_size
		UpperCAmelCase_       				=			type_sequence_label_size
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			num_labels
		UpperCAmelCase_       				=			num_choices
		UpperCAmelCase_       				=			scope
		UpperCAmelCase_       				=			range_bbox




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







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

		# convert bbox to numpy since TF does not support item assignment
		UpperCAmelCase_       				=			ids_tensor([self.batch_size, self.seq_length, 4]		,	self.range_bbox							).numpy()
		# Ensure that bbox is legal
		for i in range(bbox.shape[0]							):
			for j in range(bbox.shape[1]							):
				if bbox[i, j, 3] < bbox[i, j, 1]:
					UpperCAmelCase_       				=			bbox[i, j, 3]
					UpperCAmelCase_       				=			bbox[i, j, 1]
					UpperCAmelCase_       				=			t
				if bbox[i, j, 2] < bbox[i, j, 0]:
					UpperCAmelCase_       				=			bbox[i, j, 2]
					UpperCAmelCase_       				=			bbox[i, j, 0]
					UpperCAmelCase_       				=			t
		UpperCAmelCase_       				=			tf.convert_to_tensor(lowerCAmelCase							)

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

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

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

		UpperCAmelCase_       				=			LayoutLMConfig(
		    vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	initializer_range=self.initializer_range		,	)

		return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




	def       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMModel(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)
		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)
		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)
		self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)							)
		self.parent.assertEqual(result.pooler_output.shape		,	(self.batch_size, self.hidden_size)							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForMaskedLM(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase		,	labels=lowerCAmelCase							)
		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size)							)




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.num_labels
		UpperCAmelCase_       				=			TFLayoutLMForSequenceClassification(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)
		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)							)




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.num_labels
		UpperCAmelCase_       				=			TFLayoutLMForTokenClassification(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase		,	labels=lowerCAmelCase							)
		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.num_labels)							)




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForQuestionAnswering(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)
		self.parent.assertEqual(result.start_logits.shape		,	(self.batch_size, self.seq_length)							)
		self.parent.assertEqual(result.end_logits.shape		,	(self.batch_size, self.seq_length)							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.prepare_config_and_inputs()
		(
		    (
		    UpperCAmelCase_
		)				,   (
		    UpperCAmelCase_
		)				,   (
		    UpperCAmelCase_
		)				,   (
		    UpperCAmelCase_
		)				,   (
		    UpperCAmelCase_
		)				,   (
		    UpperCAmelCase_
		)				,   (
		    UpperCAmelCase_
		)				,   (
		    UpperCAmelCase_
		)				,   
		)       				=			config_and_inputs
		UpperCAmelCase_       				=			{
		    "input_ids": input_ids,
		    "bbox": bbox,
		    "token_type_ids": token_type_ids,
		    "attention_mask": input_mask,
		}
		return config, inputs_dict


@require_tf
class 						__UpperCamelCase    (					lowercase     ,     lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     (
	    (
	        TFLayoutLMModel,
	        TFLayoutLMForMaskedLM,
	        TFLayoutLMForTokenClassification,
	        TFLayoutLMForSequenceClassification,
	        TFLayoutLMForQuestionAnswering,
	    )
	    if is_tf_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE__    				=     (
	    {
	        'feature-extraction': TFLayoutLMModel,
	        'fill-mask': TFLayoutLMForMaskedLM,
	        'text-classification': TFLayoutLMForSequenceClassification,
	        'token-classification': TFLayoutLMForTokenClassification,
	        'zero-shot': TFLayoutLMForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE__    				=     False
	SCREAMING_SNAKE_CASE__    				=     True
	SCREAMING_SNAKE_CASE__    				=     10




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMModelTester(self							)
		UpperCAmelCase_       				=			ConfigTester(self		,	config_class=lowerCAmelCase		,	hidden_size=37							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		self.config_tester.run_common_tests()




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase							)




	@slow
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			UpperCAmelCase_       				=			TFLayoutLMModel.from_pretrained(lowerCAmelCase							)
			self.assertIsNotNone(lowerCAmelCase							)




	@unittest.skip("Onnx compliancy broke with TF 2.10"							)
	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		pass



def       __lowerCAmelCase      (						):
	# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
	# fmt: off
	UpperCAmelCase_       				=			tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]    )  # noqa: E231
	UpperCAmelCase_       				=			tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]    )  # noqa: E231
	UpperCAmelCase_       				=			tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]    )  # noqa: E231
	UpperCAmelCase_       				=			tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]    )  # noqa: E231
	# these are sequence labels (i.e. at the token level)
	UpperCAmelCase_       				=			tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]    )  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class 						__UpperCamelCase    (					unittest.TestCase						):




	@slow
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"							)

		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			prepare_layoutlm_batch_inputs()

		# forward pass
		UpperCAmelCase_       				=			model(input_ids=lowerCAmelCase		,	bbox=lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)

		# test the sequence output on [0, :3, :3]
		UpperCAmelCase_       				=			tf.convert_to_tensor(
		    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]		,	)

		self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]		,	lowerCAmelCase		,	atol=1e-3							)							)

		# test the pooled output on [1, :3]
		UpperCAmelCase_       				=			tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]							)

		self.assertTrue(np.allclose(outputs.pooler_output[1, :3]		,	lowerCAmelCase		,	atol=1e-3							)							)




	@slow
	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"		,	num_labels=2							)

		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			prepare_layoutlm_batch_inputs()

		# forward pass
		UpperCAmelCase_       				=			model(
		    input_ids=lowerCAmelCase		,	bbox=lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase		,	labels=tf.convert_to_tensor([1, 1]							)		,	)

		# test whether we get a loss as a scalar
		UpperCAmelCase_       				=			outputs.loss
		UpperCAmelCase_       				=			(2,)
		self.assertEqual(loss.shape		,	lowerCAmelCase							)

		# test the shape of the logits
		UpperCAmelCase_       				=			outputs.logits
		UpperCAmelCase_       				=			(2, 2)
		self.assertEqual(logits.shape		,	lowerCAmelCase							)




	@slow
	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"		,	num_labels=13							)

		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			prepare_layoutlm_batch_inputs()

		# forward pass
		UpperCAmelCase_       				=			model(
		    input_ids=lowerCAmelCase		,	bbox=lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase		,	labels=lowerCAmelCase							)

		# test the shape of the logits
		UpperCAmelCase_       				=			outputs.logits
		UpperCAmelCase_       				=			tf.convert_to_tensor((2, 25, 13)							)
		self.assertEqual(logits.shape		,	lowerCAmelCase							)




	@slow
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"							)

		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			prepare_layoutlm_batch_inputs()

		# forward pass
		UpperCAmelCase_       				=			model(input_ids=lowerCAmelCase		,	bbox=lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)

		# test the shape of the logits
		UpperCAmelCase_       				=			tf.convert_to_tensor((2, 25)							)
		self.assertEqual(outputs.start_logits.shape		,	lowerCAmelCase							)
		self.assertEqual(outputs.end_logits.shape		,	lowerCAmelCase							)


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


_a:    Optional[int] 							=			re.compile(r"""\s+""")



def       __lowerCAmelCase      (						A    ):
	return {"hash": hashlib.mda(re.sub(A       ,     ""       ,     example["content"]    ).encode("utf-8"    )    ).hexdigest()}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			[len(A    ) for line in example["content"].splitlines()]
	return {"line_mean": np.mean(A    ), "line_max": max(A    )}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			np.mean([c.isalnum() for c in example["content"]]    )
	return {"alpha_frac": alpha_frac}



def       __lowerCAmelCase      (						A       ,     A    ):
	if example["hash"] in uniques:
		uniques.remove(example["hash"]    )
		return True
	else:
		return False



def       __lowerCAmelCase      (						A       ,     A=5    ):
	UpperCAmelCase_       				=			["auto-generated", "autogenerated", "automatically generated"]
	UpperCAmelCase_       				=			example["content"].splitlines()
	for _, line in zip(range(A    )       ,     A    ):
		for keyword in keywords:
			if keyword in line.lower():
				return {"autogenerated": True}
	else:
		return {"autogenerated": False}



def       __lowerCAmelCase      (						A       ,     A=5       ,     A=0.05    ):
	UpperCAmelCase_       				=			["unit tests", "test file", "configuration file"]
	UpperCAmelCase_       				=			example["content"].splitlines()
	UpperCAmelCase_       				=			0
	UpperCAmelCase_       				=			0
	# first test
	for _, line in zip(range(A    )       ,     A    ):
		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       __lowerCAmelCase      (						A    ):
	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       __lowerCAmelCase      (						A       ,     A=4    ):
	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       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			tokenizer(example["content"]       ,     truncation=A    )["input_ids"]
	UpperCAmelCase_       				=			len(example["content"]    ) / len(A    )
	return {"ratio": ratio}



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			{}
	results.update(get_hash(A    )    )
	results.update(line_stats(A    )    )
	results.update(alpha_stats(A    )    )
	results.update(char_token_ratio(A    )    )
	results.update(is_autogenerated(A    )    )
	results.update(is_config_or_test(A    )    )
	results.update(has_no_keywords(A    )    )
	results.update(has_few_assignments(A    )    )
	return results



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	if not check_uniques(A       ,     A    ):
		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       __lowerCAmelCase      (						A    ):
	with open(A       ,     "rb"    ) as f_in:
		with gzip.open(str(A    ) + ".gz"       ,     "wb"       ,     compresslevel=6    ) as f_out:
			shutil.copyfileobj(A       ,     A    )
	os.unlink(A    )


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

# Load dataset
_a:    Union[str, Any] 							=			time.time()
_a:    List[str] 							=			load_dataset(args.dataset_name, split="""train""")
print(F'Time to load dataset: {time.time()-t_start:.2f}')

# Run preprocessing
_a:    List[str] 							=			time.time()
_a:    List[Any] 							=			ds.map(preprocess, num_proc=args.num_workers)
print(F'Time to preprocess dataset: {time.time()-t_start:.2f}')

# Deduplicate hashes
_a:    Union[str, Any] 							=			set(ds.unique("""hash"""))
_a:    Dict 							=			len(uniques) / len(ds)
print(F'Fraction of duplicates: {1-frac:.2%}')

# Deduplicate data and apply heuristics
_a:    Optional[Any] 							=			time.time()
_a:    Any 							=			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:
				_a:    Tuple 							=			time.time()
				_a							,			_a:    Dict 							=			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
_a:    List[str] 							=			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)

_a:    int 							=			output_dir / """data"""
data_dir.mkdir(exist_ok=True)

_a:    Dict 							=			time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
				_a:    int 							=			str(data_dir / F'file-{file_number+1:012}.json')
				_a:    int 							=			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}')