



"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
a_			   =					logging.get_logger(__name__)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					if "resnet-50" in model_name:
										snake_case_					:		Union[str, Any] 					=					ResNetConfig.from_pretrained("""microsoft/resnet-50"""			)
					elif "resnet-101" in model_name:
										snake_case_					:		Optional[int] 					=					ResNetConfig.from_pretrained("""microsoft/resnet-101"""			)
					else:
										raise ValueError("""Model name should include either resnet50 or resnet101"""			)

					snake_case_					:		List[str] 					=					DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE__				,    backbone_config=SCREAMING_SNAKE_CASE__			)

					# set label attributes
					snake_case_					:		Union[str, Any] 					=					"""panoptic""" in model_name
					if is_panoptic:
										snake_case_					:		Tuple 					=					2_5_0
					else:
										snake_case_					:		Optional[Any] 					=					9_1
										snake_case_					:		Union[str, Any] 					=					"""huggingface/label-files"""
										snake_case_					:		List[Any] 					=					"""coco-detection-id2label.json"""
										snake_case_					:		Any 					=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    repo_type="""dataset"""			)				,    """r"""			)			)
										snake_case_					:		Tuple 					=					{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
										snake_case_					:		List[Any] 					=					idalabel
										snake_case_					:		int 					=					{v: k for k, v in idalabel.items()}

					return config, is_panoptic







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					[]

					# stem
					# fmt: off
					rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")			)
					rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")			)
					rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")			)
					rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")			)
					rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")			)
					# stages
					for stage_idx in range(len(config.backbone_config.depths			)			):
										for layer_idx in range(config.backbone_config.depths[stage_idx]			):
															# shortcut
															if layer_idx == 0:
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
																				    )			)
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
																				    )			)
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
																				    )			)
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
																				    )			)
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
																				    )			)
															# 3 convs
															for i in range(3			):
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
																				    )			)
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
																				    )			)
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
																				    )			)
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
																				    )			)
																				rename_keys.append(
																				    (
																				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
																				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
																				    )			)
    # fmt: on

					for i in range(config.encoder_layers			):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append(
										    (
										        f'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
										        f'encoder.layers.{i}.self_attn.out_proj.weight',
										    )			)
										rename_keys.append(
										    (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')			)
										rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight')			)
										rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias')			)
										rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight')			)
										rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias')			)
										rename_keys.append(
										    (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')			)
										rename_keys.append(
										    (f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias')			)
										rename_keys.append(
										    (f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight')			)
										rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias')			)
										# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
										rename_keys.append(
										    (
										        f'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
										        f'decoder.layers.{i}.self_attn.out_proj.weight',
										    )			)
										rename_keys.append(
										    (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')			)
										rename_keys.append(
										    (
										        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
										        f'decoder.layers.{i}.encoder_attn.out_proj.weight',
										    )			)
										rename_keys.append(
										    (
										        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
										        f'decoder.layers.{i}.encoder_attn.out_proj.bias',
										    )			)
										rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight')			)
										rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias')			)
										rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight')			)
										rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias')			)
										rename_keys.append(
										    (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')			)
										rename_keys.append(
										    (f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias')			)
										rename_keys.append(
										    (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')			)
										rename_keys.append(
										    (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')			)
										rename_keys.append(
										    (f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight')			)
										rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias')			)

					# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
					rename_keys.extend(
					    [
					        ("""input_proj.weight""", """input_projection.weight"""),
					        ("""input_proj.bias""", """input_projection.bias"""),
					        ("""query_embed.weight""", """query_position_embeddings.weight"""),
					        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
					        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
					        ("""class_embed.weight""", """class_labels_classifier.weight"""),
					        ("""class_embed.bias""", """class_labels_classifier.bias"""),
					        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
					        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
					        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
					        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
					        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
					        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
					    ]			)

					return rename_keys







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Optional[int] 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Any 					=					val







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  str=False			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					""""""
					if is_panoptic:
										snake_case_					:		str 					=					"""detr."""

					# first: transformer encoder
					for i in range(6			):
										# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
										snake_case_					:		List[Any] 					=					state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'			)
										snake_case_					:		Union[str, Any] 					=					state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'			)
										# next, add query, keys and values (in that order) to the state dict
										snake_case_					:		List[Any] 					=					in_proj_weight[:2_5_6, :]
										snake_case_					:		Dict 					=					in_proj_bias[:2_5_6]
										snake_case_					:		List[Any] 					=					in_proj_weight[2_5_6:5_1_2, :]
										snake_case_					:		Optional[int] 					=					in_proj_bias[2_5_6:5_1_2]
										snake_case_					:		Dict 					=					in_proj_weight[-2_5_6:, :]
										snake_case_					:		Dict 					=					in_proj_bias[-2_5_6:]
					# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
					for i in range(6			):
										# read in weights + bias of input projection layer of self-attention
										snake_case_					:		Optional[Any] 					=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
										snake_case_					:		Optional[Any] 					=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'			)
										# next, add query, keys and values (in that order) to the state dict
										snake_case_					:		Any 					=					in_proj_weight[:2_5_6, :]
										snake_case_					:		Tuple 					=					in_proj_bias[:2_5_6]
										snake_case_					:		str 					=					in_proj_weight[2_5_6:5_1_2, :]
										snake_case_					:		Optional[int] 					=					in_proj_bias[2_5_6:5_1_2]
										snake_case_					:		Optional[int] 					=					in_proj_weight[-2_5_6:, :]
										snake_case_					:		str 					=					in_proj_bias[-2_5_6:]
										# read in weights + bias of input projection layer of cross-attention
										snake_case_					:		List[str] 					=					state_dict.pop(
										    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'			)
										snake_case_					:		Any 					=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'			)
										# next, add query, keys and values (in that order) of cross-attention to the state dict
										snake_case_					:		Dict 					=					in_proj_weight_cross_attn[:2_5_6, :]
										snake_case_					:		int 					=					in_proj_bias_cross_attn[:2_5_6]
										snake_case_					:		Dict 					=					in_proj_weight_cross_attn[2_5_6:5_1_2, :]
										snake_case_					:		Any 					=					in_proj_bias_cross_attn[2_5_6:5_1_2]
										snake_case_					:		Tuple 					=					in_proj_weight_cross_attn[-2_5_6:, :]
										snake_case_					:		int 					=					in_proj_bias_cross_attn[-2_5_6:]







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		str 					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					snake_case_					:		int 					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,    stream=SCREAMING_SNAKE_CASE__			).raw			)

					return im







@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]=None				,    SCREAMING_SNAKE_CASE__ :  List[Any]=False			):



					"""simple docstring"""





					snake_case_					,			snake_case_					:		Any 					=					get_detr_config(SCREAMING_SNAKE_CASE__			)

					# load original model from torch hub
					snake_case_					:		str 					=					{
					    """detr-resnet-50""": """detr_resnet50""",
					    """detr-resnet-101""": """detr_resnet101""",
					}
					logger.info(f'Converting model {model_name}...'			)
					snake_case_					:		List[str] 					=					torch.hub.load("""facebookresearch/detr"""				,    model_name_to_original_name[model_name]				,    pretrained=SCREAMING_SNAKE_CASE__			).eval()
					snake_case_					:		Dict 					=					detr.state_dict()
					# rename keys
					for src, dest in create_rename_keys(SCREAMING_SNAKE_CASE__			):
										if is_panoptic:
															snake_case_					:		Union[str, Any] 					=					"""detr.""" + src
										rename_key(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					# query, key and value matrices need special treatment
					read_in_q_k_v(SCREAMING_SNAKE_CASE__				,    is_panoptic=SCREAMING_SNAKE_CASE__			)
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					snake_case_					:		Union[str, Any] 					=					"""detr.model.""" if is_panoptic else """model."""
					for key in state_dict.copy().keys():
										if is_panoptic:
															if (
															    key.startswith("""detr"""			)
															    and not key.startswith("""class_labels_classifier"""			)
															    and not key.startswith("""bbox_predictor"""			)
															):
																				snake_case_					:		Optional[int] 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
																				snake_case_					:		Optional[Any] 					=					val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				snake_case_					:		int 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
																				snake_case_					:		Union[str, Any] 					=					val
															elif key.startswith("""bbox_attention"""			) or key.startswith("""mask_head"""			):
																				continue
															else:
																				snake_case_					:		Any 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
																				snake_case_					:		Optional[Any] 					=					val
										else:
															if not key.startswith("""class_labels_classifier"""			) and not key.startswith("""bbox_predictor"""			):
																				snake_case_					:		Dict 					=					state_dict.pop(SCREAMING_SNAKE_CASE__			)
																				snake_case_					:		Dict 					=					val

    # finally, create HuggingFace model and load state dict
					snake_case_					:		Optional[Any] 					=					DetrForSegmentation(SCREAMING_SNAKE_CASE__			) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__			)
					model.load_state_dict(SCREAMING_SNAKE_CASE__			)
					model.eval()

					# verify our conversion on an image
					snake_case_					:		int 					=					"""coco_panoptic""" if is_panoptic else """coco_detection"""
					snake_case_					:		List[Any] 					=					DetrImageProcessor(format=SCREAMING_SNAKE_CASE__			)

					snake_case_					:		int 					=					processor(images=prepare_img()				,    return_tensors="""pt"""			)
					snake_case_					:		Optional[Any] 					=					encoding["""pixel_values"""]

					snake_case_					:		Optional[Any] 					=					detr(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Optional[Any] 					=					model(SCREAMING_SNAKE_CASE__			)

					assert torch.allclose(outputs.logits				,    original_outputs["""pred_logits"""]				,    atol=1E-3			)
					assert torch.allclose(outputs.pred_boxes				,    original_outputs["""pred_boxes"""]				,    atol=1E-3			)
					if is_panoptic:
										assert torch.allclose(outputs.pred_masks				,    original_outputs["""pred_masks"""]				,    atol=1E-4			)
					print("""Looks ok!"""			)

					if pytorch_dump_folder_path is not None:
										# Save model and image processor
										logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'			)
										Path(SCREAMING_SNAKE_CASE__			).mkdir(exist_ok=SCREAMING_SNAKE_CASE__			)
										model.save_pretrained(SCREAMING_SNAKE_CASE__			)
										processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

					if push_to_hub:
										# Upload model and image processor to the hub
										logger.info("""Uploading PyTorch model and image processor to the hub..."""			)
										model.push_to_hub(f'nielsr/{model_name}'			)
										processor.push_to_hub(f'nielsr/{model_name}'			)


if __name__ == "__main__":
			a_			   =					argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name''',
			    default='''detr-resnet-50''',
			    type=str,
			    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
			    help='''Name of the DETR model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
			)
			parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
			a_			   =					parser.parse_args()
			convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""

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


a_			   =					re.compile('''[^A-Za-z_0-9]''')
# parameters used in DuplicationIndex
a_			   =					10
a_			   =					256







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					if len(SCREAMING_SNAKE_CASE__			) < MIN_NUM_TOKENS:
										return None
					snake_case_					:		Union[str, Any] 					=					MinHash(num_perm=SCREAMING_SNAKE_CASE__			)
					for token in set(SCREAMING_SNAKE_CASE__			):
										min_hash.update(token.encode()			)
					return min_hash







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					return {t for t in NON_ALPHA.split(SCREAMING_SNAKE_CASE__			) if len(t.strip()			) > 0}







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		*,
						    lowercase__ = 0.85   ,		):
											snake_case_					:		Tuple 					=					duplication_jaccard_threshold
											snake_case_					:		Optional[Any] 					=					NUM_PERM
											snake_case_					:		Tuple 					=					MinHashLSH(threshold=self._duplication_jaccard_threshold   ,		num_perm=self._num_perm       )

											snake_case_					:		List[Any] 					=					defaultdict(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		int 					=					self._index.query(lowercase__       )
											if code_key in self._index.keys:
																print(f'Duplicate key {code_key}'       )
																return

											self._index.insert(lowercase__   ,		lowercase__       )
											if len(lowercase__       ) > 0:
																for base_duplicate in close_duplicates:
																					if base_duplicate in self._duplicate_clusters:
																										self._duplicate_clusters[base_duplicate].add(lowercase__       )
																										break
																else:
																					self._duplicate_clusters[close_duplicates[0]].add(lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		str 					=					[]
											for base, duplicates in self._duplicate_clusters.items():
																snake_case_					:		Optional[Any] 					=					[base] + list(lowercase__       )
																# reformat the cluster to be a list of dict
																snake_case_					:		Any 					=					[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
																duplicate_clusters.append(lowercase__       )
											return duplicate_clusters







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		int 					=					self.get_duplicate_clusters()
											with open(lowercase__   ,		"""w"""       ) as f:
																json.dump(lowercase__   ,		lowercase__       )







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					,			snake_case_					:		str 					=					element
					snake_case_					:		Tuple 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]			):



					"""simple docstring"""





					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash				,    ThreadedIterator(SCREAMING_SNAKE_CASE__				,    max_queue_size=1_0_0_0_0			)				,    chunksize=1_0_0				,    ):
															if data is not None:
																				yield data







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]				,    SCREAMING_SNAKE_CASE__ :  float			):



					"""simple docstring"""





					snake_case_					:		int 					=					DuplicationIndex(duplication_jaccard_threshold=SCREAMING_SNAKE_CASE__			)

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(SCREAMING_SNAKE_CASE__			)			)				,    max_queue_size=1_0_0			)			):
										di.add(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

					# Returns a List[Cluster] where Cluster is List[str] with the filenames.
					return di.get_duplicate_clusters()







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					snake_case_					:		int 					=					get_tokens(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Tuple 					=					get_tokens(SCREAMING_SNAKE_CASE__			)
					return len(tokensa & tokensa			) / len(tokensa | tokensa			)


a_			   =					None







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					[]
					for elementa in cluster:
										snake_case_					:		Union[str, Any] 					=					_shared_dataset[elementa["""base_index"""]]["""content"""]
										for elementa in extremes:
															snake_case_					:		Any 					=					_shared_dataset[elementa["""base_index"""]]["""content"""]
															if jaccard_similarity(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															snake_case_					:		Union[str, Any] 					=					1
															extremes.append(SCREAMING_SNAKE_CASE__			)
					return extremes







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					global _shared_dataset
					snake_case_					:		str 					=					dataset
					snake_case_					:		int 					=					[]
					snake_case_					:		Optional[int] 					=					partial(_find_cluster_extremes_shared				,    jaccard_threshold=SCREAMING_SNAKE_CASE__			)
					with mp.Pool() as pool:
										for extremes in tqdm(
										    pool.imap_unordered(
										        SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    )				,    total=len(SCREAMING_SNAKE_CASE__			)				,    ):
															extremes_list.append(SCREAMING_SNAKE_CASE__			)
					return extremes_list







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Type[Dataset]				,    SCREAMING_SNAKE_CASE__ :  float = 0.85			):



					"""simple docstring"""





					snake_case_					:		List[str] 					=					make_duplicate_clusters(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		str 					=					{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
					snake_case_					:		str 					=					{}
					snake_case_					:		Dict 					=					find_extremes(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					for extremes in extremes_clusters:
										for element in extremes:
															snake_case_					:		int 					=					element
					snake_case_					:		Optional[int] 					=					duplicate_indices - set(extreme_dict.keys()			)
					snake_case_					:		List[Any] 					=					dataset.filter(lambda SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			: idx not in remove_indices				,    with_indices=SCREAMING_SNAKE_CASE__			)

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															snake_case_					:		List[Any] 					=					element["""base_index"""] in extreme_dict
															if element["is_extreme"]:
																				snake_case_					:		str 					=					extreme_dict[element["""base_index"""]]["""copies"""]

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

					return ds_filter, duplicate_clusters




