

'''simple docstring'''





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 						__UpperCamelCase    (					unittest.TestCase						):




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			0




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"							)
		self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCAmelCase_       				=			Path(lowerCAmelCase							) / "preprocessor_config.json"
			UpperCAmelCase_       				=			Path(lowerCAmelCase							) / "config.json"
			json.dump(
			    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}		,	open(lowerCAmelCase		,	"w"							)		,	)
			json.dump({"model_type": "clip"}		,	open(lowerCAmelCase		,	"w"							)							)

			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(lowerCAmelCase							)
			self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCAmelCase_       				=			Path(lowerCAmelCase							) / "preprocessor_config.json"
			UpperCAmelCase_       				=			Path(lowerCAmelCase							) / "config.json"
			json.dump(
			    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}		,	open(lowerCAmelCase		,	"w"							)		,	)
			json.dump({"model_type": "clip"}		,	open(lowerCAmelCase		,	"w"							)							)

			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(lowerCAmelCase							)
			self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCAmelCase_       				=			CLIPConfig()

			# Create a dummy config file with image_proceesor_type
			UpperCAmelCase_       				=			Path(lowerCAmelCase							) / "preprocessor_config.json"
			UpperCAmelCase_       				=			Path(lowerCAmelCase							) / "config.json"
			json.dump(
			    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}		,	open(lowerCAmelCase		,	"w"							)		,	)
			json.dump({"model_type": "clip"}		,	open(lowerCAmelCase		,	"w"							)							)

			# remove image_processor_type to make sure config.json alone is enough to load image processor locally
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(lowerCAmelCase							).to_dict()

			config_dict.pop("image_processor_type"							)
			UpperCAmelCase_       				=			CLIPImageProcessor(**lowerCAmelCase							)

			# save in new folder
			model_config.save_pretrained(lowerCAmelCase							)
			config.save_pretrained(lowerCAmelCase							)

			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(lowerCAmelCase							)

			# make sure private variable is not incorrectly saved
			UpperCAmelCase_       				=			json.loads(config.to_json_string()							)
			self.assertTrue("_processor_class" not in dict_as_saved							)

		self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCAmelCase_       				=			Path(lowerCAmelCase							) / "preprocessor_config.json"
			json.dump(
			    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}		,	open(lowerCAmelCase		,	"w"							)		,	)

			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(lowerCAmelCase							)
			self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		with self.assertRaisesRegex(
		    lowerCAmelCase		,	"clip-base is not a local folder and is not a valid model identifier"							):
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained("clip-base"							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		with self.assertRaisesRegex(
		    lowerCAmelCase		,	R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"							):
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(lowerCAmelCase		,	revision="aaaaaa"							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		with self.assertRaisesRegex(
		    lowerCAmelCase		,	"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."		,	):
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		with self.assertRaises(lowerCAmelCase							):
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"							)
		# If remote code is disabled, we can't load this config.
		with self.assertRaises(lowerCAmelCase							):
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(
			    "hf-internal-testing/test_dynamic_image_processor"		,	trust_remote_code=lowerCAmelCase							)

		UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(
		    "hf-internal-testing/test_dynamic_image_processor"		,	trust_remote_code=lowerCAmelCase							)
		self.assertEqual(image_processor.__class__.__name__		,	"NewImageProcessor"							)

		# Test image processor can be reloaded.
		with tempfile.TemporaryDirectory() as tmp_dir:
			image_processor.save_pretrained(lowerCAmelCase							)
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(lowerCAmelCase		,	trust_remote_code=lowerCAmelCase							)
		self.assertEqual(reloaded_image_processor.__class__.__name__		,	"NewImageProcessor"							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		try:
			AutoConfig.register("custom"		,	lowerCAmelCase							)
			AutoImageProcessor.register(lowerCAmelCase		,	lowerCAmelCase							)
			# Trying to register something existing in the Transformers library will raise an error
			with self.assertRaises(lowerCAmelCase							):
				AutoImageProcessor.register(lowerCAmelCase		,	lowerCAmelCase							)

			with tempfile.TemporaryDirectory() as tmpdirname:
				UpperCAmelCase_       				=			Path(lowerCAmelCase							) / "preprocessor_config.json"
				UpperCAmelCase_       				=			Path(lowerCAmelCase							) / "config.json"
				json.dump(
				    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}		,	open(lowerCAmelCase		,	"w"							)		,	)
				json.dump({"model_type": "clip"}		,	open(lowerCAmelCase		,	"w"							)							)

				UpperCAmelCase_       				=			CustomImageProcessor.from_pretrained(lowerCAmelCase							)

			# Now that the config is registered, it can be used as any other config with the auto-API
			with tempfile.TemporaryDirectory() as tmp_dir:
				image_processor.save_pretrained(lowerCAmelCase							)
				UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(lowerCAmelCase							)
				self.assertIsInstance(lowerCAmelCase		,	lowerCAmelCase							)

		finally:
			if "custom" in CONFIG_MAPPING._extra_content:
				del CONFIG_MAPPING._extra_content["custom"]
			if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
				del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		class 						__UpperCamelCase    (					lowercase						):
			SCREAMING_SNAKE_CASE__    				=     True

		try:
			AutoConfig.register("custom"		,	lowerCAmelCase							)
			AutoImageProcessor.register(lowerCAmelCase		,	lowerCAmelCase							)
			# If remote code is not set, the default is to use local
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained("hf-internal-testing/test_dynamic_image_processor"							)
			self.assertEqual(image_processor.__class__.__name__		,	"NewImageProcessor"							)
			self.assertTrue(image_processor.is_local							)

			# If remote code is disabled, we load the local one.
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(
			    "hf-internal-testing/test_dynamic_image_processor"		,	trust_remote_code=lowerCAmelCase							)
			self.assertEqual(image_processor.__class__.__name__		,	"NewImageProcessor"							)
			self.assertTrue(image_processor.is_local							)

			# If remote is enabled, we load from the Hub
			UpperCAmelCase_       				=			AutoImageProcessor.from_pretrained(
			    "hf-internal-testing/test_dynamic_image_processor"		,	trust_remote_code=lowerCAmelCase							)
			self.assertEqual(image_processor.__class__.__name__		,	"NewImageProcessor"							)
			self.assertTrue(not hasattr(lowerCAmelCase		,	"is_local"							)							)

		finally:
			if "custom" in CONFIG_MAPPING._extra_content:
				del CONFIG_MAPPING._extra_content["custom"]
			if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
				del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]


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}')