





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




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Optional[MinHash]:
	if len(UpperCAmelCase_							) < MIN_NUM_TOKENS:
		return None
	snake_case__	       =   MinHash(num_perm=UpperCAmelCase_							)
	for token in set(UpperCAmelCase_							):
		min_hash.update(token.encode()							)
	return min_hash




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Set[str]:
	return {t for t in NON_ALPHA.split(UpperCAmelCase_							) if len(t.strip()							) > 0}






class 				__snake_case     :





	def __init__(		self      ,       *,
	    UpperCamelCase_ = 0.8_5      ,       )      ->		str:
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		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   _snake_case   (		self		)      ->		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   _snake_case   (		self      ,       UpperCamelCase_		)      ->		None:
		snake_case__	       =   self.get_duplicate_clusters()
		with open(UpperCamelCase_      ,       'w'		) as f:
			json.dump(UpperCamelCase_      ,       UpperCamelCase_		)





def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Union[str, Any]:
	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 						__lowerCamelCase			(	UpperCAmelCase_							)   ->List[str]:
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash					,				ThreadedIterator(UpperCAmelCase_					,				max_queue_size=1_00_00							)					,				chunksize=1_00					,				):
			if data is not None:
				yield data




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	snake_case__	       =   DuplicationIndex(duplication_jaccard_threshold=UpperCAmelCase_							)

	for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(UpperCAmelCase_							)							)					,				max_queue_size=1_00							)							):
		di.add(UpperCAmelCase_					,				UpperCAmelCase_							)

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




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->float:
	snake_case__	       =   get_tokens(UpperCAmelCase_							)
	snake_case__	       =   get_tokens(UpperCAmelCase_							)
	return len(tokensa & tokensa							) / len(tokensa | tokensa							)


a__					:			Optional[Any]          =     None




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Optional[int]:
	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(UpperCAmelCase_					,				UpperCAmelCase_							) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			snake_case__	       =   1
			extremes.append(UpperCAmelCase_							)
	return extremes




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Optional[int]:
	global _shared_dataset
	snake_case__	       =   dataset
	snake_case__	       =   []
	snake_case__	       =   partial(_find_cluster_extremes_shared					,				jaccard_threshold=UpperCAmelCase_							)
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        UpperCAmelCase_					,				UpperCAmelCase_					,				)					,				total=len(UpperCAmelCase_							)					,				):
			extremes_list.append(UpperCAmelCase_							)
	return extremes_list







def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = 0.85							)   ->Tuple[Type[Dataset], List[List[Dict]]]:
	snake_case__	       =   make_duplicate_clusters(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   {x['base_index'] for cluster in duplicate_clusters for x in cluster}
	snake_case__	       =   {}
	snake_case__	       =   find_extremes(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	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 UpperCAmelCase_					,				UpperCAmelCase_							: idx not in remove_indices					,				with_indices=UpperCAmelCase_							)

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

	return ds_filter, duplicate_clusters










'''simple docstring'''




import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None							)   ->List[str]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	snake_case__	       =   nn.Parameter(UpperCAmelCase_							)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
		snake_case__	       =   nn.Parameter(UpperCAmelCase_							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)

	set_param(
	    torch_layer.self_attention.query_key					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)
	snake_case__	       =   np.asarray(weights[3]							)

	set_param(
	    torch_layer.self_attention.query					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.key					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# layernorm 1
	snake_case__	       =   weights[0][0][0]
	snake_case__	       =   np.asarray(layer_norm_a[0]							)
	snake_case__	       =   np.asarray(layer_norm_a[1]							)
	set_param(
	    torch_block.attention.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# lsh weights + output
	snake_case__	       =   weights[0][1]
	if len(UpperCAmelCase_							) < 4:
		set_layer_weights_in_torch_lsh(UpperCAmelCase_					,				torch_block.attention					,				UpperCAmelCase_							)
	else:
		set_layer_weights_in_torch_local(UpperCAmelCase_					,				torch_block.attention					,				UpperCAmelCase_							)

	# intermediate weighs
	snake_case__	       =   weights[2][0][1][2]

	# Chunked Feed Forward
	if len(UpperCAmelCase_							) == 4:
		snake_case__	       =   intermediate_weights[2]

	# layernorm 2
	snake_case__	       =   np.asarray(intermediate_weights[0][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[0][1]							)
	set_param(
	    torch_block.feed_forward.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate dense
	snake_case__	       =   np.asarray(intermediate_weights[1][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[1][1]							)
	set_param(
	    torch_block.feed_forward.dense.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate out
	snake_case__	       =   np.asarray(intermediate_weights[4][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[4][1]							)
	set_param(
	    torch_block.feed_forward.output.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# reformer model
	snake_case__	       =   torch_model.reformer

	# word embeds
	snake_case__	       =   np.asarray(weights[1]							)
	set_param(
	    torch_model_reformer.embeddings.word_embeddings					,				torch.tensor(UpperCAmelCase_							)					,				)

	if isinstance(weights[3]					,				UpperCAmelCase_							):
		snake_case__	       =   torch_model_reformer.embeddings.position_embeddings
		for emb_idx in range(len(position_embeddings.weights							)							):
			snake_case__	       =   np.asarray(weights[3][emb_idx][0]							)
			assert (
			    position_embeddings.weights[emb_idx].shape == emb_weights.shape
			), f'''{position_embeddings[emb_idx]} emb does not match'''
			snake_case__	       =   nn.Parameter(torch.tensor(UpperCAmelCase_							)							)

	snake_case__	       =   weights[5]
	assert len(torch_model_reformer.encoder.layers							) * 4 == len(
	    UpperCAmelCase_							), "HF and trax model do not have the same number of layers"
	for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers							):
		snake_case__	       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	# output layer norm
	snake_case__	       =   np.asarray(weights[7][0]							)
	snake_case__	       =   np.asarray(weights[7][1]							)
	set_param(
	    torch_model_reformer.encoder.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# output embeddings
	snake_case__	       =   np.asarray(weights[9][0]							)
	snake_case__	       =   np.asarray(weights[9][1]							)
	set_param(
	    torch_model.lm_head.decoder					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# Initialise PyTorch model
	snake_case__	       =   ReformerConfig.from_json_file(UpperCAmelCase_							)
	print(f'''Building PyTorch model from configuration: {config}'''							)
	snake_case__	       =   ReformerModelWithLMHead(UpperCAmelCase_							)

	with open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   pickle.load(UpperCAmelCase_							)['weights']

	set_model_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				config.hidden_size							)

	# Save pytorch-model
	print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
	torch.save(model.state_dict()					,				UpperCAmelCase_							)


if __name__ == "__main__":
			a__					:			Optional[int]          =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			a__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



