


import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


__UpperCamelCase					:		Any	  =						re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
__UpperCamelCase					:		Tuple	  =						10
__UpperCamelCase					:		Union[str, Any]	  =						256
def      a_ (      _A  )			->			Optional[MinHash]:







				"""simple docstring"""
				if len(_A  ) < MIN_NUM_TOKENS:
								return None
				snake_case__									=     MinHash(num_perm=_A  )
				for token in set(_A  ):
								min_hash.update(token.encode()  )
				return min_hash
def      a_ (      _A  )			->			Set[str]:







				"""simple docstring"""
				return {t for t in NON_ALPHA.split(_A  ) if len(t.strip()  ) > 0}


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      int       ,	*,
							    UpperCamelCase:      float = 0.85       ,	)		->		List[Any]:
											snake_case__									=     duplication_jaccard_threshold
											snake_case__									=     NUM_PERM
											snake_case__									=     MinHashLSH(threshold=self._duplication_jaccard_threshold       ,	num_perm=self._num_perm						)

											snake_case__									=     defaultdict(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      MinHash						)		->		None:
											snake_case__									=     self._index.query(UpperCamelCase						)
											if code_key in self._index.keys:
															print(F'''Duplicate key {code_key}'''						)
															return

											self._index.insert(UpperCamelCase       ,	UpperCamelCase						)
											if len(UpperCamelCase						) > 0:
															for base_duplicate in close_duplicates:
																			if base_duplicate in self._duplicate_clusters:
																							self._duplicate_clusters[base_duplicate].add(UpperCamelCase						)
																							break
															else:
																			self._duplicate_clusters[close_duplicates[0]].add(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		List[List[Dict]]:
											snake_case__									=     []
											for base, duplicates in self._duplicate_clusters.items():
															snake_case__									=     [base] + list(UpperCamelCase						)
															# reformat the cluster to be a list of dict
															snake_case__									=     [{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
															duplicate_clusters.append(UpperCamelCase						)
											return duplicate_clusters


							def lowerCAmelCase_ (     self:      Optional[Any]       ,	UpperCamelCase:      int						)		->		None:
											snake_case__									=     self.get_duplicate_clusters()
											with open(UpperCamelCase       ,	'w'						) as f:
															json.dump(UpperCamelCase       ,	UpperCamelCase						)


def      a_ (      _A  )			->			Union[str, Any]:







				"""simple docstring"""
				snake_case__							,						snake_case__									=     element
				snake_case__									=     get_min_hash([t for t in NON_ALPHA.split(data['content']  ) if len(t.strip()  ) > 0]  )
				if min_hash is not None:
								return (index, data["repo_name"], data["path"]), min_hash
def      a_ (      _A  )			->			Any:







				"""simple docstring"""
				with mp.Pool() as pool:
								for data in pool.imap_unordered(
								    _compute_min_hash      ,   ThreadedIterator(_A      ,   max_queue_size=10000  )      ,   chunksize=100      ,   ):
												if data is not None:
																yield data
def      a_ (      _A      ,   _A  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     DuplicationIndex(duplication_jaccard_threshold=_A  )

				for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_A  )  )      ,   max_queue_size=100  )  ):
								di.add(_A      ,   _A  )

				# Returns a List[Cluster] where Cluster is List[str] with the filenames.
				return di.get_duplicate_clusters()
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     get_tokens(_A  )
				snake_case__									=     get_tokens(_A  )
				return len(tokensa & tokensa  ) / len(tokensa | tokensa  )


__UpperCamelCase					:		Union[str, Any]	  =						None
def      a_ (      _A      ,   _A  )			->			Dict:







				"""simple docstring"""
				snake_case__									=     []
				for elementa in cluster:
								snake_case__									=     _shared_dataset[elementa['base_index']]['content']
								for elementa in extremes:
												snake_case__									=     _shared_dataset[elementa['base_index']]['content']
												if jaccard_similarity(_A      ,   _A  ) >= jaccard_threshold:
																elementa["copies"] += 1
																break
								else:
												snake_case__									=     1
												extremes.append(_A  )
				return extremes
def      a_ (      _A      ,   _A      ,   _A  )			->			Optional[int]:







				"""simple docstring"""
				global _shared_dataset
				snake_case__									=     dataset
				snake_case__									=     []
				snake_case__									=     partial(_find_cluster_extremes_shared      ,   jaccard_threshold=_A  )
				with mp.Pool() as pool:
								for extremes in tqdm(
								    pool.imap_unordered(
								        _A      ,   _A      ,   )      ,   total=len(_A  )      ,   ):
												extremes_list.append(_A  )
				return extremes_list


def      a_ (      _A      ,   _A = 0.85  )			->			Tuple[Type[Dataset], List[List[Dict]]]:







				"""simple docstring"""
				snake_case__									=     make_duplicate_clusters(_A      ,   _A  )
				snake_case__									=     {x['base_index'] for cluster in duplicate_clusters for x in cluster}
				snake_case__									=     {}
				snake_case__									=     find_extremes(_A      ,   _A      ,   _A  )
				for extremes in extremes_clusters:
								for element in extremes:
												snake_case__									=     element
				snake_case__									=     duplicate_indices - set(extreme_dict.keys()  )
				snake_case__									=     dataset.filter(lambda _A      ,   _A  : idx not in remove_indices      ,   with_indices=_A  )

				# update duplicate_clusters
				for cluster in duplicate_clusters:
								for element in cluster:
												snake_case__									=     element['base_index'] in extreme_dict
												if element["is_extreme"]:
																snake_case__									=     extreme_dict[element['base_index']]['copies']

				print(f'''Original dataset size: {len(_A  )}'''  )
				print(f'''Number of duplicate clusters: {len(_A  )}'''  )
				print(f'''Files in duplicate cluster: {len(_A  )}'''  )
				print(f'''Unique files in duplicate cluster: {len(_A  )}'''  )
				print(f'''Filtered dataset size: {len(_A  )}'''  )

				return ds_filter, duplicate_clusters












import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__UpperCamelCase					:		List[Any]	  =						logging.get_logger(__name__)

__UpperCamelCase					:		str	  =						OrderedDict(
    [
        ("""align""", """EfficientNetImageProcessor"""),
        ("""beit""", """BeitImageProcessor"""),
        ("""bit""", """BitImageProcessor"""),
        ("""blip""", """BlipImageProcessor"""),
        ("""blip-2""", """BlipImageProcessor"""),
        ("""bridgetower""", """BridgeTowerImageProcessor"""),
        ("""chinese_clip""", """ChineseCLIPImageProcessor"""),
        ("""clip""", """CLIPImageProcessor"""),
        ("""clipseg""", """ViTImageProcessor"""),
        ("""conditional_detr""", """ConditionalDetrImageProcessor"""),
        ("""convnext""", """ConvNextImageProcessor"""),
        ("""convnextv2""", """ConvNextImageProcessor"""),
        ("""cvt""", """ConvNextImageProcessor"""),
        ("""data2vec-vision""", """BeitImageProcessor"""),
        ("""deformable_detr""", """DeformableDetrImageProcessor"""),
        ("""deit""", """DeiTImageProcessor"""),
        ("""deta""", """DetaImageProcessor"""),
        ("""detr""", """DetrImageProcessor"""),
        ("""dinat""", """ViTImageProcessor"""),
        ("""donut-swin""", """DonutImageProcessor"""),
        ("""dpt""", """DPTImageProcessor"""),
        ("""efficientformer""", """EfficientFormerImageProcessor"""),
        ("""efficientnet""", """EfficientNetImageProcessor"""),
        ("""flava""", """FlavaImageProcessor"""),
        ("""focalnet""", """BitImageProcessor"""),
        ("""git""", """CLIPImageProcessor"""),
        ("""glpn""", """GLPNImageProcessor"""),
        ("""groupvit""", """CLIPImageProcessor"""),
        ("""imagegpt""", """ImageGPTImageProcessor"""),
        ("""instructblip""", """BlipImageProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2ImageProcessor"""),
        ("""layoutlmv3""", """LayoutLMv3ImageProcessor"""),
        ("""levit""", """LevitImageProcessor"""),
        ("""mask2former""", """Mask2FormerImageProcessor"""),
        ("""maskformer""", """MaskFormerImageProcessor"""),
        ("""mgp-str""", """ViTImageProcessor"""),
        ("""mobilenet_v1""", """MobileNetV1ImageProcessor"""),
        ("""mobilenet_v2""", """MobileNetV2ImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevitv2""", """MobileViTImageProcessor"""),
        ("""nat""", """ViTImageProcessor"""),
        ("""oneformer""", """OneFormerImageProcessor"""),
        ("""owlvit""", """OwlViTImageProcessor"""),
        ("""perceiver""", """PerceiverImageProcessor"""),
        ("""pix2struct""", """Pix2StructImageProcessor"""),
        ("""poolformer""", """PoolFormerImageProcessor"""),
        ("""regnet""", """ConvNextImageProcessor"""),
        ("""resnet""", """ConvNextImageProcessor"""),
        ("""sam""", """SamImageProcessor"""),
        ("""segformer""", """SegformerImageProcessor"""),
        ("""swiftformer""", """ViTImageProcessor"""),
        ("""swin""", """ViTImageProcessor"""),
        ("""swin2sr""", """Swin2SRImageProcessor"""),
        ("""swinv2""", """ViTImageProcessor"""),
        ("""table-transformer""", """DetrImageProcessor"""),
        ("""timesformer""", """VideoMAEImageProcessor"""),
        ("""tvlt""", """TvltImageProcessor"""),
        ("""upernet""", """SegformerImageProcessor"""),
        ("""van""", """ConvNextImageProcessor"""),
        ("""videomae""", """VideoMAEImageProcessor"""),
        ("""vilt""", """ViltImageProcessor"""),
        ("""vit""", """ViTImageProcessor"""),
        ("""vit_hybrid""", """ViTHybridImageProcessor"""),
        ("""vit_mae""", """ViTImageProcessor"""),
        ("""vit_msn""", """ViTImageProcessor"""),
        ("""xclip""", """CLIPImageProcessor"""),
        ("""yolos""", """YolosImageProcessor"""),
    ]
)

__UpperCamelCase					:		Union[str, Any]	  =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def      a_ (      _A  )			->			Optional[int]:







				"""simple docstring"""
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												snake_case__									=     model_type_to_module_name(_A  )

												snake_case__									=     importlib.import_module(f'''.{module_name}'''      ,   'transformers.models'  )
												try:
																return getattr(_A      ,   _A  )
												except AttributeError:
																continue

				for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
								if getattr(_A      ,   '__name__'      ,   _A  ) == class_name:
												return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
				snake_case__									=     importlib.import_module('transformers'  )
				if hasattr(_A      ,   _A  ):
								return getattr(_A      ,   _A  )

				return None
def      a_ (      _A      ,   _A = None      ,   _A = False      ,   _A = False      ,   _A = None      ,   _A = None      ,   _A = None      ,   _A = False      ,   **_A      ,   )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     get_file_from_repo(
				    _A      ,   _A      ,   cache_dir=_A      ,   force_download=_A      ,   resume_download=_A      ,   proxies=_A      ,   use_auth_token=_A      ,   revision=_A      ,   local_files_only=_A      ,   )
				if resolved_config_file is None:
								logger.info(
								    'Could not locate the image processor configuration file, will try to use the model config instead.'  )
								return {}

				with open(_A      ,   encoding='utf-8'  ) as reader:
								return json.load(_A  )


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Optional[int]						)		->		Union[str, Any]:
											raise EnvironmentError(
											    'AutoImageProcessor is designed to be instantiated '
											    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'						)
							@classmethod
							@replace_list_option_in_docstrings(UpperCamelCase						)
							def lowerCAmelCase_ (     cls:      int       ,	UpperCamelCase:      int       ,	**UpperCamelCase:      str						)		->		Optional[Any]:
											snake_case__									=     kwargs.pop('config'       ,	UpperCamelCase						)
											snake_case__									=     kwargs.pop('trust_remote_code'       ,	UpperCamelCase						)
											snake_case__									=     True

											snake_case__							,						snake_case__									=     ImageProcessingMixin.get_image_processor_dict(UpperCamelCase       ,	**UpperCamelCase						)
											snake_case__									=     config_dict.get('image_processor_type'       ,	UpperCamelCase						)
											snake_case__									=     None
											if "AutoImageProcessor" in config_dict.get('auto_map'       ,	{}						):
															snake_case__									=     config_dict['auto_map']['AutoImageProcessor']

											# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
											# and if so, infer the image processor class from there.
											if image_processor_class is None and image_processor_auto_map is None:
															snake_case__									=     config_dict.pop('feature_extractor_type'       ,	UpperCamelCase						)
															if feature_extractor_class is not None:
																			logger.warning(
																			    'Could not find image processor class in the image processor config or the model config. Loading'
																			    ' based on pattern matching with the model\'s feature extractor configuration.'						)
																			snake_case__									=     feature_extractor_class.replace('FeatureExtractor'       ,	'ImageProcessor'						)
															if "AutoFeatureExtractor" in config_dict.get('auto_map'       ,	{}						):
																			snake_case__									=     config_dict['auto_map']['AutoFeatureExtractor']
																			snake_case__									=     feature_extractor_auto_map.replace('FeatureExtractor'       ,	'ImageProcessor'						)
																			logger.warning(
																			    'Could not find image processor auto map in the image processor config or the model config.'
																			    ' Loading based on pattern matching with the model\'s feature extractor configuration.'						)

        # If we don't find the image processor class in the image processor config, let's try the model config.
											if image_processor_class is None and image_processor_auto_map is None:
															if not isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase       ,	**UpperCamelCase						)
															# It could be in `config.image_processor_type``
															snake_case__									=     getattr(UpperCamelCase       ,	'image_processor_type'       ,	UpperCamelCase						)
															if hasattr(UpperCamelCase       ,	'auto_map'						) and "AutoImageProcessor" in config.auto_map:
																			snake_case__									=     config.auto_map['AutoImageProcessor']

											if image_processor_class is not None:
															snake_case__									=     image_processor_class_from_name(UpperCamelCase						)

											snake_case__									=     image_processor_auto_map is not None
											snake_case__									=     image_processor_class is not None or type(UpperCamelCase						) in IMAGE_PROCESSOR_MAPPING
											snake_case__									=     resolve_trust_remote_code(
											    UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)

											if has_remote_code and trust_remote_code:
															snake_case__									=     get_class_from_dynamic_module(
															    UpperCamelCase       ,	UpperCamelCase       ,	**UpperCamelCase						)
															snake_case__									=     kwargs.pop('code_revision'       ,	UpperCamelCase						)
															if os.path.isdir(UpperCamelCase						):
																			image_processor_class.register_for_auto_class()
															return image_processor_class.from_dict(UpperCamelCase       ,	**UpperCamelCase						)
											elif image_processor_class is not None:
															return image_processor_class.from_dict(UpperCamelCase       ,	**UpperCamelCase						)
											# Last try: we use the IMAGE_PROCESSOR_MAPPING.
											elif type(UpperCamelCase						) in IMAGE_PROCESSOR_MAPPING:
															snake_case__									=     IMAGE_PROCESSOR_MAPPING[type(UpperCamelCase						)]
															return image_processor_class.from_dict(UpperCamelCase       ,	**UpperCamelCase						)

											raise ValueError(
											    F'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
											    F'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
											    F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()						)}'''						)


							@staticmethod
							def lowerCAmelCase_ (     UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      int						)		->		Optional[Any]:
											IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase       ,	UpperCamelCase						)




