





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()
UpperCamelCase__           =			logging.get_logger(__name__)





def      _UpperCamelCase  (a__ :Union[str, Any]							):






							"""simple docstring"""





							if "resnet-50" in model_name:
														UpperCamelCase__					  =      ResNetConfig.from_pretrained("""microsoft/resnet-50"""							)
							elif "resnet-101" in model_name:
														UpperCamelCase__					  =      ResNetConfig.from_pretrained("""microsoft/resnet-101"""							)
							else:
														raise ValueError("""Model name should include either resnet50 or resnet101"""							)

							UpperCamelCase__					  =      DetrConfig(use_timm_backbone=__A ,      backbone_config=__A							)

							# set label attributes
							UpperCamelCase__					  =      """panoptic""" in model_name
							if is_panoptic:
														UpperCamelCase__					  =      250
							else:
														UpperCamelCase__					  =      91
														UpperCamelCase__					  =      """huggingface/label-files"""
														UpperCamelCase__					  =      """coco-detection-id2label.json"""
														UpperCamelCase__					  =      json.load(open(hf_hub_download(__A ,      __A ,      repo_type="""dataset"""							) ,      """r"""							)							)
														UpperCamelCase__					  =      {int(__A							): v for k, v in idalabel.items()}
														UpperCamelCase__					  =      idalabel
														UpperCamelCase__					  =      {v: k for k, v in idalabel.items()}

							return config, is_panoptic





def      _UpperCamelCase  (a__ :Any							):






							"""simple docstring"""





							UpperCamelCase__					  =      []

							# 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      _UpperCamelCase  (a__ :Dict ,      a__ :Union[str, Any] ,      a__ :Dict							):






							"""simple docstring"""





							UpperCamelCase__					  =      state_dict.pop(__A							)
							UpperCamelCase__					  =      val





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :int=False							):






							"""simple docstring"""





							UpperCamelCase__					  =      """"""
							if is_panoptic:
														UpperCamelCase__					  =      """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)
														UpperCamelCase__					  =      state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""							)
														UpperCamelCase__					  =      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
														UpperCamelCase__					  =      in_proj_weight[:256, :]
														UpperCamelCase__					  =      in_proj_bias[:256]
														UpperCamelCase__					  =      in_proj_weight[256:512, :]
														UpperCamelCase__					  =      in_proj_bias[256:512]
														UpperCamelCase__					  =      in_proj_weight[-256:, :]
														UpperCamelCase__					  =      in_proj_bias[-256:]
							# 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
														UpperCamelCase__					  =      state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
														UpperCamelCase__					  =      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
														UpperCamelCase__					  =      in_proj_weight[:256, :]
														UpperCamelCase__					  =      in_proj_bias[:256]
														UpperCamelCase__					  =      in_proj_weight[256:512, :]
														UpperCamelCase__					  =      in_proj_bias[256:512]
														UpperCamelCase__					  =      in_proj_weight[-256:, :]
														UpperCamelCase__					  =      in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														UpperCamelCase__					  =      state_dict.pop(
														    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""							)
														UpperCamelCase__					  =      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
														UpperCamelCase__					  =      in_proj_weight_cross_attn[:256, :]
														UpperCamelCase__					  =      in_proj_bias_cross_attn[:256]
														UpperCamelCase__					  =      in_proj_weight_cross_attn[256:512, :]
														UpperCamelCase__					  =      in_proj_bias_cross_attn[256:512]
														UpperCamelCase__					  =      in_proj_weight_cross_attn[-256:, :]
														UpperCamelCase__					  =      in_proj_bias_cross_attn[-256:]





def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCamelCase__					  =      Image.open(requests.get(__A ,      stream=__A							).raw							)

							return im





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






							"""simple docstring"""





							UpperCamelCase__  ,  UpperCamelCase__					  =      get_detr_config(__A							)

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

    # finally, create HuggingFace model and load state dict
							UpperCamelCase__					  =      DetrForSegmentation(__A							) if is_panoptic else DetrForObjectDetection(__A							)
							model.load_state_dict(__A							)
							model.eval()

							# verify our conversion on an image
							UpperCamelCase__					  =      """coco_panoptic""" if is_panoptic else """coco_detection"""
							UpperCamelCase__					  =      DetrImageProcessor(format=__A							)

							UpperCamelCase__					  =      processor(images=prepare_img() ,      return_tensors="""pt"""							)
							UpperCamelCase__					  =      encoding["""pixel_values"""]

							UpperCamelCase__					  =      detr(__A							)
							UpperCamelCase__					  =      model(__A							)

							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(__A							).mkdir(exist_ok=__A							)
														model.save_pretrained(__A							)
														processor.save_pretrained(__A							)

							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__":
					UpperCamelCase__           =			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.")
					UpperCamelCase__           =			parser.parse_args()
					convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







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


UpperCamelCase__		 =     re.compile(r'''\s+''')







def 						lowerCamelCase__      ( __A    :int		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Any		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :str	,__A    :int		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :int=5		):


       """simple docstring"""




       __snake_case       	=							["""auto-generated""", """autogenerated""", """automatically generated"""]
       __snake_case       	=							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    :int	,__A    :int=5	,__A    :str=0.05		):


       """simple docstring"""




       __snake_case       	=							["""unit tests""", """test file""", """configuration file"""]
       __snake_case       	=							example["""content"""].splitlines()
       __snake_case       	=							0
       __snake_case       	=							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
       __snake_case       	=							example["""content"""].count("""\n"""		)
       __snake_case       	=							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    :Dict		):


       """simple docstring"""




       __snake_case       	=							["""def """, """class """, """for """, """while """]
       __snake_case       	=							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    :Dict	,__A    :List[str]=4		):


       """simple docstring"""




       __snake_case       	=							example["""content"""].splitlines()
       __snake_case       	=							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    :str		):


       """simple docstring"""




       __snake_case       	=							tokenizer(example["""content"""]	,truncation=__A		)["""input_ids"""]
       __snake_case       	=							len(example["""content"""]		) / len(__A		)
       return {"ratio": ratio}







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							{}
       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    :int	,__A    :str	,__A    :Dict		):


       """simple docstring"""




       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    :str		):


       """simple docstring"""




       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
UpperCamelCase__		 =     HfArgumentParser(PreprocessingArguments)
UpperCamelCase__		 =     parser.parse_args()
if args.num_workers is None:
    UpperCamelCase__		 =     multiprocessing.cpu_count()
UpperCamelCase__		 =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

# Deduplicate hashes
UpperCamelCase__		 =     set(ds.unique('''hash'''))
UpperCamelCase__		 =     len(uniques) / len(ds)
print(F'Fraction of duplicates: {1-frac:.2%}')

# Deduplicate data and apply heuristics
UpperCamelCase__		 =     time.time()
UpperCamelCase__		 =     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:
    UpperCamelCase__		 =     time.time()
    UpperCamelCase__ ,UpperCamelCase__		 =     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
UpperCamelCase__		 =     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)

UpperCamelCase__		 =     output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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

