

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      				=     re.compile(R'''\s+''')


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=5      ):

					"""simple docstring"""






					__lowerCAmelCase					=							['auto-generated', 'autogenerated', 'automatically generated']
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=5		,					UpperCAmelCase__=0.05      ):

					"""simple docstring"""






					__lowerCAmelCase					=							['unit tests', 'test file', 'configuration file']
					__lowerCAmelCase					=							example['content'].splitlines()
					__lowerCAmelCase					=							0
					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							example['content'].count('\n'      )
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							['def ', 'class ', 'for ', 'while ']
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=4      ):

					"""simple docstring"""






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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							tokenizer(example['content']		,					truncation=UpperCAmelCase__      )['input_ids']
					__lowerCAmelCase					=							len(example['content']      ) / len(UpperCAmelCase__      )
					return {"ratio": ratio}


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							{}
					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        __lowercase    (  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        __lowercase    (  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      				=     HfArgumentParser(PreprocessingArguments)
lowerCamelCase      				=     parser.parse_args()
if args.num_workers is None:
		lowerCamelCase      				=     multiprocessing.cpu_count()
lowerCamelCase      				=     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
lowerCamelCase      				=     time.time()
lowerCamelCase      				=     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      				=     time.time()
		lowerCamelCase				,				lowerCamelCase      				=     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      				=     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      				=     output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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









import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if "img_encoder.pos_embed" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.pos_embed'		,					'vision_model.embeddings.position_embeddings'      )
					if "img_encoder.patch_embed.proj" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.patch_embed.proj'		,					'vision_model.embeddings.patch_embeddings.projection'      )
					if "img_encoder.patch_embed.norm" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.patch_embed.norm'		,					'vision_model.embeddings.layernorm'      )
					if "img_encoder.layers" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.layers'		,					'vision_model.encoder.stages'      )
					if "blocks" in name and "res" not in name:
										__lowerCAmelCase					=							name.replace('blocks'		,					'layers'      )
					if "attn" in name and "pre_assign" not in name:
										__lowerCAmelCase					=							name.replace('attn'		,					'self_attn'      )
					if "proj" in name and "self_attn" in name and "text" not in name:
										__lowerCAmelCase					=							name.replace('proj'		,					'out_proj'      )
					if "pre_assign_attn.attn.proj" in name:
										__lowerCAmelCase					=							name.replace('pre_assign_attn.attn.proj'		,					'pre_assign_attn.attn.out_proj'      )
					if "norm1" in name:
										__lowerCAmelCase					=							name.replace('norm1'		,					'layer_norm1'      )
					if "norm2" in name and "pre_assign" not in name:
										__lowerCAmelCase					=							name.replace('norm2'		,					'layer_norm2'      )
					if "img_encoder.norm" in name:
										__lowerCAmelCase					=							name.replace('img_encoder.norm'		,					'vision_model.layernorm'      )
					# text encoder
					if "text_encoder.token_embedding" in name:
										__lowerCAmelCase					=							name.replace('text_encoder.token_embedding'		,					'text_model.embeddings.token_embedding'      )
					if "text_encoder.positional_embedding" in name:
										__lowerCAmelCase					=							name.replace('text_encoder.positional_embedding'		,					'text_model.embeddings.position_embedding.weight'      )
					if "text_encoder.transformer.resblocks." in name:
										__lowerCAmelCase					=							name.replace('text_encoder.transformer.resblocks.'		,					'text_model.encoder.layers.'      )
					if "ln_1" in name:
										__lowerCAmelCase					=							name.replace('ln_1'		,					'layer_norm1'      )
					if "ln_2" in name:
										__lowerCAmelCase					=							name.replace('ln_2'		,					'layer_norm2'      )
					if "c_fc" in name:
										__lowerCAmelCase					=							name.replace('c_fc'		,					'fc1'      )
					if "c_proj" in name:
										__lowerCAmelCase					=							name.replace('c_proj'		,					'fc2'      )
					if "text_encoder" in name:
										__lowerCAmelCase					=							name.replace('text_encoder'		,					'text_model'      )
					if "ln_final" in name:
										__lowerCAmelCase					=							name.replace('ln_final'		,					'final_layer_norm'      )
					# projection layers
					if "img_projector.linear_hidden." in name:
										__lowerCAmelCase					=							name.replace('img_projector.linear_hidden.'		,					'visual_projection.'      )
					if "img_projector.linear_out." in name:
										__lowerCAmelCase					=							name.replace('img_projector.linear_out.'		,					'visual_projection.3.'      )
					if "text_projector.linear_hidden" in name:
										__lowerCAmelCase					=							name.replace('text_projector.linear_hidden'		,					'text_projection'      )
					if "text_projector.linear_out" in name:
										__lowerCAmelCase					=							name.replace('text_projector.linear_out'		,					'text_projection.3'      )

					return name


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase					=							orig_state_dict.pop(UpperCAmelCase__      )

										if "qkv" in key:
															# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															__lowerCAmelCase					=							key.split('.'      )
															__lowerCAmelCase,	__lowerCAmelCase					=							int(key_split[2]      ), int(key_split[4]      )
															__lowerCAmelCase					=							config.vision_config.hidden_size
															if "weight" in key:
																				__lowerCAmelCase					=							val[:dim, :]
																				__lowerCAmelCase					=							val[dim : dim * 2, :]
																				__lowerCAmelCase					=							val[-dim:, :]
															else:
																				__lowerCAmelCase					=							val[:dim]
																				__lowerCAmelCase					=							val[dim : dim * 2]
																				__lowerCAmelCase					=							val[-dim:]
										elif "in_proj" in key:
															# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
															# we need to split them up into separate matrices/vectors
															__lowerCAmelCase					=							key.split('.'      )
															__lowerCAmelCase					=							int(key_split[3]      )
															__lowerCAmelCase					=							config.text_config.hidden_size
															if "weight" in key:
																				__lowerCAmelCase					=							val[:dim, :]
																				__lowerCAmelCase					=							val[
																				    dim : dim * 2, :
																				]
																				__lowerCAmelCase					=							val[-dim:, :]
															else:
																				__lowerCAmelCase					=							val[:dim]
																				__lowerCAmelCase					=							val[dim : dim * 2]
																				__lowerCAmelCase					=							val[-dim:]
										else:
															__lowerCAmelCase					=							rename_key(UpperCAmelCase__      )
															# squeeze if necessary
															if (
															    "text_projection.0" in new_name
															    or "text_projection.3" in new_name
															    or "visual_projection.0" in new_name
															    or "visual_projection.3" in new_name
															):
																				__lowerCAmelCase					=							val.squeeze_()
															else:
																				__lowerCAmelCase					=							val

					return orig_state_dict


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							'http://images.cocodataset.org/val2017/000000039769.jpg'
					__lowerCAmelCase					=							Image.open(requests.get(UpperCAmelCase__		,					stream=UpperCAmelCase__      ).raw      )
					return im


@torch.no_grad()
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__="groupvit-gcc-yfcc"		,					UpperCAmelCase__=False      ):

					"""simple docstring"""






					__lowerCAmelCase					=							GroupViTConfig()
					__lowerCAmelCase					=							GroupViTModel(UpperCAmelCase__      ).eval()

					__lowerCAmelCase					=							torch.load(UpperCAmelCase__		,					map_location='cpu'      )['model']
					__lowerCAmelCase					=							convert_state_dict(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase,	__lowerCAmelCase					=							model.load_state_dict(UpperCAmelCase__		,					strict=UpperCAmelCase__      )
					assert missing_keys == ["text_model.embeddings.position_ids"]
					assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCAmelCase__      ) == 0)

					# verify result
					__lowerCAmelCase					=							CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'      )
					__lowerCAmelCase					=							prepare_img()
					__lowerCAmelCase					=							processor(text=['a photo of a cat', 'a photo of a dog']		,					images=UpperCAmelCase__		,					padding=UpperCAmelCase__		,					return_tensors='pt'      )

					with torch.no_grad():
										__lowerCAmelCase					=							model(**UpperCAmelCase__      )

					if model_name == "groupvit-gcc-yfcc":
										__lowerCAmelCase					=							torch.tensor([[13.3523, 6.3629]]      )
					elif model_name == "groupvit-gcc-redcaps":
										__lowerCAmelCase					=							torch.tensor([[16.1873, 8.6230]]      )
					else:
										raise ValueError(F"""Model name {model_name} not supported."""      )
					assert torch.allclose(outputs.logits_per_image		,					UpperCAmelCase__		,					atol=1E-3      )

					processor.save_pretrained(UpperCAmelCase__      )
					model.save_pretrained(UpperCAmelCase__      )
					print('Successfully saved processor and model to'		,					UpperCAmelCase__      )

					if push_to_hub:
										print('Pushing to the hub...'      )
										processor.push_to_hub(UpperCAmelCase__		,					organization='nielsr'      )
										model.push_to_hub(UpperCAmelCase__		,					organization='nielsr'      )


if __name__ == "__main__":
		lowerCamelCase      				=     argparse.ArgumentParser()
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
		)
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
		parser.add_argument(
		    '''--model_name''',
		    default='''groupvit-gccy-fcc''',
		    type=str,
		    help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
		)
		parser.add_argument(
		    '''--push_to_hub''',
		    action='''store_true''',
		    help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
		)
		lowerCamelCase      				=     parser.parse_args()

		convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)






