




"""simple docstring"""




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTMAEForPreTraining, ViTMAEModel
			from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class    UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_=13							,		UpperCamelCase_=30							,		UpperCamelCase_=2							,		UpperCamelCase_=3							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_=32							,		UpperCamelCase_=5							,		UpperCamelCase_=4							,		UpperCamelCase_=37							,		UpperCamelCase_="gelu"							,		UpperCamelCase_=0.1							,		UpperCamelCase_=0.1							,		UpperCamelCase_=10							,		UpperCamelCase_=0.0_2							,		UpperCamelCase_=3							,		UpperCamelCase_=0.6							,		UpperCamelCase_=None							,		)		->    List[Any]:
					__lowercase       :   Optional[Any]        =    parent
					__lowercase       :   Union[str, Any]        =    batch_size
					__lowercase       :   str        =    image_size
					__lowercase       :   int        =    patch_size
					__lowercase       :   Any        =    num_channels
					__lowercase       :   List[Any]        =    is_training
					__lowercase       :   Tuple        =    use_labels
					__lowercase       :   List[Any]        =    hidden_size
					__lowercase       :   Optional[Any]        =    num_hidden_layers
					__lowercase       :   int        =    num_attention_heads
					__lowercase       :   Optional[int]        =    intermediate_size
					__lowercase       :   int        =    hidden_act
					__lowercase       :   Tuple        =    hidden_dropout_prob
					__lowercase       :   Optional[Any]        =    attention_probs_dropout_prob
					__lowercase       :   Tuple        =    type_sequence_label_size
					__lowercase       :   int        =    initializer_range
					__lowercase       :   str        =    mask_ratio
					__lowercase       :   int        =    scope

					# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
					# (we add 1 for the [CLS] token)
					__lowercase       :   str        =    (image_size // patch_size) ** 2
					__lowercase       :   Any        =    int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )
		def      _lowerCamelCase				(		self    )		->    int:
					__lowercase       :   Any        =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

					__lowercase       :   Union[str, Any]        =    None
					if self.use_labels:
								__lowercase       :   Tuple        =    ids_tensor([self.batch_size]							,		self.type_sequence_label_size    )

					__lowercase       :   Any        =    self.get_config()

					return config, pixel_values, labels
		def      _lowerCamelCase				(		self    )		->    Any:
					return ViTMAEConfig(
					    image_size=self.image_size							,		patch_size=self.patch_size							,		num_channels=self.num_channels							,		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							,		is_decoder=snake_case_							,		initializer_range=self.initializer_range							,		mask_ratio=self.mask_ratio							,		)
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_    )		->    List[Any]:
					__lowercase       :   Optional[int]        =    ViTMAEModel(config=snake_case_    )
					model.to(snake_case_    )
					model.eval()
					__lowercase       :   int        =    model(snake_case_    )
					self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_    )		->    Dict:
					__lowercase       :   Any        =    ViTMAEForPreTraining(snake_case_    )
					model.to(snake_case_    )
					model.eval()
					__lowercase       :   int        =    model(snake_case_    )
					__lowercase       :   List[Any]        =    (self.image_size // self.patch_size) ** 2
					__lowercase       :   Optional[int]        =    self.patch_size**2 * self.num_channels
					self.parent.assertEqual(result.logits.shape							,		(self.batch_size, num_patches, expected_num_channels)    )

					# test greyscale images
					__lowercase       :   Tuple        =    1
					__lowercase       :   Any        =    ViTMAEForPreTraining(snake_case_    )
					model.to(snake_case_    )
					model.eval()
					__lowercase       :   List[Any]        =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
					__lowercase       :   int        =    model(snake_case_    )
					__lowercase       :   Optional[Any]        =    self.patch_size**2
					self.parent.assertEqual(result.logits.shape							,		(self.batch_size, num_patches, expected_num_channels)    )
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					__lowercase       :   Union[str, Any]        =    self.prepare_config_and_inputs()
					__lowercase       :   int        =    config_and_inputs
					__lowercase       :   int        =    {'pixel_values': pixel_values}
					return config, inputs_dict


@require_torch
class    UpperCAmelCase_  (       UpperCAmelCase__					, UpperCAmelCase__					, unittest.TestCase						):
		UpperCamelCase						 =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
		UpperCamelCase						 ={"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

		UpperCamelCase						 =False
		UpperCamelCase						 =False
		UpperCamelCase						 =False
		UpperCamelCase						 =False
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					__lowercase       :   str        =    ViTMAEModelTester(self    )
					__lowercase       :   str        =    ConfigTester(self							,		config_class=snake_case_							,		has_text_modality=snake_case_							,		hidden_size=37    )
		def      _lowerCamelCase				(		self    )		->    Optional[int]:
					self.config_tester.run_common_tests()
		@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''    )
		def      _lowerCamelCase				(		self    )		->    int:
					pass
		def      _lowerCamelCase				(		self    )		->    Tuple:
					__lowercase       :   Union[str, Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__lowercase       :   List[Any]        =    model_class(snake_case_    )
								self.assertIsInstance(model.get_input_embeddings()							,		(nn.Module)    )
								__lowercase       :   Any        =    model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(snake_case_							,		nn.Linear    )    )
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					__lowercase       :   Optional[Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowercase       :   List[str]        =    ['pixel_values']
								self.assertListEqual(arg_names[:1]							,		snake_case_    )
		def      _lowerCamelCase				(		self    )		->    int:
					__lowercase       :   Dict        =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*snake_case_    )
		def      _lowerCamelCase				(		self    )		->    Dict:
					__lowercase       :   int        =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*snake_case_    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_    )		->    List[str]:
					np.random.seed(2    )

					__lowercase       :   Dict        =    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2    )
					__lowercase       :   Any        =    np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
					__lowercase       :   Tuple        =    torch.from_numpy(snake_case_    )

					# Add `noise` argument.
					# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
					__lowercase       :   Tuple        =    pt_noise

					super().check_pt_tf_models(snake_case_							,		snake_case_							,		snake_case_    )
		def      _lowerCamelCase				(		self    )		->    Tuple:
					__lowercase       :   List[str]        =    self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__lowercase       :   Dict        =    model_class(snake_case_    )
								model.to(snake_case_    )
								model.eval()
								# make random mask reproducible
								torch.manual_seed(2    )
								with torch.no_grad():
											__lowercase       :   Optional[Any]        =    model(**self._prepare_for_class(snake_case_							,		snake_case_    )    )

								__lowercase       :   Optional[Any]        =    outputs[0].cpu().numpy()
								__lowercase       :   List[Any]        =    0

								with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(snake_case_    )
											__lowercase       :   Any        =    model_class.from_pretrained(snake_case_    )
											model.to(snake_case_    )
											# make random mask reproducible
											torch.manual_seed(2    )
											with torch.no_grad():
														__lowercase       :   List[str]        =    model(**self._prepare_for_class(snake_case_							,		snake_case_    )    )

											# Make sure we don't have nans
											__lowercase       :   Union[str, Any]        =    after_outputs[0].cpu().numpy()
											__lowercase       :   Union[str, Any]        =    0
											__lowercase       :   int        =    np.amax(np.abs(out_a - out_a    )    )
											self.assertLessEqual(snake_case_							,		1E-5    )
		@unittest.skip(
		    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'''    )
		def      _lowerCamelCase				(		self    )		->    Optional[Any]:
					pass
		@unittest.skip(
		    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'''    )
		def      _lowerCamelCase				(		self    )		->    Optional[int]:
					pass
		@unittest.skip(
		    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'''    )
		def      _lowerCamelCase				(		self    )		->    str:
					pass
		@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''    )
		def      _lowerCamelCase				(		self    )		->    str:
					pass
		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
		def      _lowerCamelCase				(		self    )		->    Dict:
					pass
		@slow
		def      _lowerCamelCase				(		self    )		->    int:

					for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase       :   int        =    ViTMAEModel.from_pretrained(snake_case_    )
								self.assertIsNotNone(snake_case_    )







def 			__UpperCAmelCase			(      ):
			__lowercase       :   List[Any]        =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
			return image


@require_torch
@require_vision
class    UpperCAmelCase_  (       unittest.TestCase						):
		@cached_property
		def      _lowerCamelCase				(		self    )		->    int:
					return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''    ) if is_vision_available() else None
		@slow
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					np.random.seed(2    )

					__lowercase       :   List[str]        =    ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''    ).to(snake_case_    )

					__lowercase       :   Tuple        =    self.default_image_processor
					__lowercase       :   Optional[Any]        =    prepare_img()
					__lowercase       :   List[str]        =    image_processor(images=snake_case_							,		return_tensors='''pt'''    ).to(snake_case_    )

					# prepare a noise vector that will be also used for testing the TF model
					# (this way we can ensure that the PT and TF models operate on the same inputs)
					__lowercase       :   List[str]        =    ViTMAEConfig()
					__lowercase       :   Optional[Any]        =    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
					__lowercase       :   List[Any]        =    np.random.uniform(size=(1, num_patches)    )

					# forward pass
					with torch.no_grad():
								__lowercase       :   int        =    model(**snake_case_							,		noise=torch.from_numpy(snake_case_    ).to(device=snake_case_    )    )

					# verify the logits
					__lowercase       :   Optional[Any]        =    torch.Size((1, 1_96, 7_68)    )
					self.assertEqual(outputs.logits.shape							,		snake_case_    )

					__lowercase       :   int        =    torch.tensor(
					    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]    )

					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]							,		expected_slice.to(snake_case_    )							,		atol=1E-4    )    )










"""simple docstring"""




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


lowerCamelCase_     :						Any						=					re.compile(r'\s+')





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5					):






  """simple docstring"""





  A_  :       Optional[int]										=  ['auto-generated', 'autogenerated', 'automatically generated']
  A_  :       List[str]										=  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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5 ,					_UpperCAmelCase=0.05					):






  """simple docstring"""





  A_  :       Any										=  ['unit tests', 'test file', 'configuration file']
  A_  :       Dict										=  example['content'].splitlines()
  A_  :       List[Any]										=  0
  A_  :       str										=  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
  A_  :       Tuple										=  example['content'].count('\n'					)
  A_  :       Tuple										=  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 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  ['def ', 'class ', 'for ', 'while ']
  A_  :       Tuple										=  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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=4					):






  """simple docstring"""





  A_  :       Union[str, Any]										=  example['content'].splitlines()
  A_  :       Any										=  0
  for line in lines:
    counter += line.lower().count('='					)
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  tokenizer(example['content'] ,					truncation=_UpperCAmelCase					)['input_ids']
  A_  :       Dict										=  len(example['content']					) / len(_UpperCAmelCase					)
  return {"ratio": ratio}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  {}
  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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """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 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """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
lowerCamelCase_     :						Optional[int]						=					HfArgumentParser(PreprocessingArguments)
lowerCamelCase_     :						Optional[Any]						=					parser.parse_args()
if args.num_workers is None:
   lowerCamelCase_     :						int						=					multiprocessing.cpu_count()
lowerCamelCase_     :						Tuple						=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
lowerCamelCase_     :						Optional[int]						=					time.time()
lowerCamelCase_     :						Tuple						=					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:
   lowerCamelCase_     :						Union[str, Any]						=					time.time()
   lowerCamelCase_					,					lowerCamelCase_     :						str						=					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
lowerCamelCase_     :						Tuple						=					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)

lowerCamelCase_     :						Optional[Any]						=					output_dir / 'data'
data_dir.mkdir(exist_ok=True)

lowerCamelCase_     :						List[str]						=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
   lowerCamelCase_     :						Optional[int]						=					str(data_dir / F"file-{file_number+1:012}.json")
   lowerCamelCase_     :						List[str]						=					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}")