
"""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


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



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



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

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    *,
							    _lowerCamelCase = 0.85  ,    )			->							int:




										'''simple docstring'''
										__lowercase       =       duplication_jaccard_threshold
										__lowercase       =       NUM_PERM
										__lowercase       =       MinHashLSH(threshold=self._duplication_jaccard_threshold  ,    num_perm=self._num_perm       )

										__lowercase       =       defaultdict(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							None:




										'''simple docstring'''
										__lowercase       =       self._index.query(_lowerCamelCase       )
										if code_key in self._index.keys:
													print(f'''Duplicate key {code_key}'''       )
													return

										self._index.insert(_lowerCamelCase  ,    _lowerCamelCase       )
										if len(_lowerCamelCase       ) > 0:
													for base_duplicate in close_duplicates:
																if base_duplicate in self._duplicate_clusters:
																			self._duplicate_clusters[base_duplicate].add(_lowerCamelCase       )
																			break
													else:
																self._duplicate_clusters[close_duplicates[0]].add(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[List[Dict]]:




										'''simple docstring'''
										__lowercase       =       []
										for base, duplicates in self._duplicate_clusters.items():
													__lowercase       =       [base] + list(_lowerCamelCase       )
													# reformat the cluster to be a list of dict
													__lowercase       =       [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
													duplicate_clusters.append(_lowerCamelCase       )
										return duplicate_clusters





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							None:




										'''simple docstring'''
										__lowercase       =       self.get_duplicate_clusters()
										with open(_lowerCamelCase  ,    "w"       ) as f:
													json.dump(_lowerCamelCase  ,    _lowerCamelCase       )





def      lowerCAmelCase__			(		__magic_name__      )						->Optional[Any]:
			__lowercase   ,					__lowercase       =       element
			__lowercase       =       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__			(		__magic_name__      )						->Any:
			with mp.Pool() as pool:
						for data in pool.imap_unordered(
						    _compute_min_hash			,   ThreadedIterator(__magic_name__			,   max_queue_size=1_0_0_0_0      )			,   chunksize=1_0_0			,   ):
									if data is not None:
												yield data



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Any:
			__lowercase       =       DuplicationIndex(duplication_jaccard_threshold=__magic_name__      )

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

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



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       get_tokens(__magic_name__      )
			__lowercase       =       get_tokens(__magic_name__      )
			return len(tokensa & tokensa      ) / len(tokensa | tokensa      )


_lowercase							=				None



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Optional[int]:
			__lowercase       =       []
			for elementa in cluster:
						__lowercase       =       _shared_dataset[elementa["base_index"]]["content"]
						for elementa in extremes:
									__lowercase       =       _shared_dataset[elementa["base_index"]]["content"]
									if jaccard_similarity(__magic_name__			,   __magic_name__      ) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									__lowercase       =       1
									extremes.append(__magic_name__      )
			return extremes



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







def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__ = 0.85      )						->Tuple[Type[Dataset], List[List[Dict]]]:
			__lowercase       =       make_duplicate_clusters(__magic_name__			,   __magic_name__      )
			__lowercase       =       {x["base_index"] for cluster in duplicate_clusters for x in cluster}
			__lowercase       =       {}
			__lowercase       =       find_extremes(__magic_name__			,   __magic_name__			,   __magic_name__      )
			for extremes in extremes_clusters:
						for element in extremes:
									__lowercase       =       element
			__lowercase       =       duplicate_indices - set(extreme_dict.keys()      )
			__lowercase       =       dataset.filter(lambda __magic_name__			,   __magic_name__      : idx not in remove_indices			,   with_indices=__magic_name__      )

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

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

			return ds_filter, duplicate_clusters







"""simple docstring"""






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


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



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->str:
			__lowercase       =       os.path.abspath(__magic_name__      )
			logger.info(F'''Converting TensorFlow checkpoint from {tf_path}'''      )
			# Load weights from TF model
			__lowercase       =       tf.train.list_variables(__magic_name__      )
			__lowercase       =       []
			__lowercase       =       []
			__lowercase       =       []
			for full_name, shape in init_vars:
						# logger.info(f"Loading TF weight {name} with shape {shape}")
						__lowercase       =       full_name.split("/"      )
						if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
									logger.info(F'''Skipping non-model layer {full_name}'''      )
									continue
						if "optimizer" in full_name:
									logger.info(F'''Skipping optimization layer {full_name}'''      )
									continue
						if name[0] == "model":
									# ignore initial 'model'
									__lowercase       =       name[1:]
						# figure out how many levels deep the name is
						__lowercase       =       0
						for _name in name:
									if _name.startswith("layer_with_weights"      ):
												depth += 1
									else:
												break
						layer_depth.append(__magic_name__      )
						# read data
						__lowercase       =       tf.train.load_variable(__magic_name__			,   __magic_name__      )
						names.append("/".join(__magic_name__      )      )
						arrays.append(__magic_name__      )
			logger.info(F'''Read a total of {len(__magic_name__      ):,} layers'''      )

			# Sanity check
			if len(set(__magic_name__      )      ) != 1:
						raise ValueError(F'''Found layer names with different depths (layer depth {list(set(__magic_name__      )      )})'''      )
			__lowercase       =       list(set(__magic_name__      )      )[0]
			if layer_depth != 1:
						raise ValueError(
						    "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
						    " heads."      )

			# convert layers
			logger.info("Converting weights..."      )
			for full_name, array in zip(__magic_name__			,   __magic_name__      ):
						__lowercase       =       full_name.split("/"      )
						__lowercase       =       model
						__lowercase       =       []
						for i, m_name in enumerate(__magic_name__      ):
									if m_name == ".ATTRIBUTES":
												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
												break
									if m_name.startswith("layer_with_weights"      ):
												__lowercase       =       int(m_name.split("-"      )[-1]      )
												if layer_num <= 2:
															# embedding layers
															# layer_num 0: word_embeddings
															# layer_num 1: position_embeddings
															# layer_num 2: token_type_embeddings
															continue
												elif layer_num == 3:
															# embedding LayerNorm
															trace.extend(["embeddings", "LayerNorm"]      )
															__lowercase       =       getattr(__magic_name__			,   "embeddings"      )
															__lowercase       =       getattr(__magic_name__			,   "LayerNorm"      )
												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
															# encoder layers
															trace.extend(["encoder", "layer", str(layer_num - 4      )]      )
															__lowercase       =       getattr(__magic_name__			,   "encoder"      )
															__lowercase       =       getattr(__magic_name__			,   "layer"      )
															__lowercase       =       pointer[layer_num - 4]
												elif layer_num == config.num_hidden_layers + 4:
															# pooler layer
															trace.extend(["pooler", "dense"]      )
															__lowercase       =       getattr(__magic_name__			,   "pooler"      )
															__lowercase       =       getattr(__magic_name__			,   "dense"      )
									elif m_name == "embeddings":
												trace.append("embeddings"      )
												__lowercase       =       getattr(__magic_name__			,   "embeddings"      )
												if layer_num == 0:
															trace.append("word_embeddings"      )
															__lowercase       =       getattr(__magic_name__			,   "word_embeddings"      )
												elif layer_num == 1:
															trace.append("position_embeddings"      )
															__lowercase       =       getattr(__magic_name__			,   "position_embeddings"      )
												elif layer_num == 2:
															trace.append("token_type_embeddings"      )
															__lowercase       =       getattr(__magic_name__			,   "token_type_embeddings"      )
												else:
															raise ValueError(F'''Unknown embedding layer with name {full_name}'''      )
												trace.append("weight"      )
												__lowercase       =       getattr(__magic_name__			,   "weight"      )
									elif m_name == "_attention_layer":
												# self-attention layer
												trace.extend(["attention", "self"]      )
												__lowercase       =       getattr(__magic_name__			,   "attention"      )
												__lowercase       =       getattr(__magic_name__			,   "self"      )
									elif m_name == "_attention_layer_norm":
												# output attention norm
												trace.extend(["attention", "output", "LayerNorm"]      )
												__lowercase       =       getattr(__magic_name__			,   "attention"      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
												__lowercase       =       getattr(__magic_name__			,   "LayerNorm"      )
									elif m_name == "_attention_output_dense":
												# output attention dense
												trace.extend(["attention", "output", "dense"]      )
												__lowercase       =       getattr(__magic_name__			,   "attention"      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
												__lowercase       =       getattr(__magic_name__			,   "dense"      )
									elif m_name == "_output_dense":
												# output dense
												trace.extend(["output", "dense"]      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
												__lowercase       =       getattr(__magic_name__			,   "dense"      )
									elif m_name == "_output_layer_norm":
												# output dense
												trace.extend(["output", "LayerNorm"]      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
												__lowercase       =       getattr(__magic_name__			,   "LayerNorm"      )
									elif m_name == "_key_dense":
												# attention key
												trace.append("key"      )
												__lowercase       =       getattr(__magic_name__			,   "key"      )
									elif m_name == "_query_dense":
												# attention query
												trace.append("query"      )
												__lowercase       =       getattr(__magic_name__			,   "query"      )
									elif m_name == "_value_dense":
												# attention value
												trace.append("value"      )
												__lowercase       =       getattr(__magic_name__			,   "value"      )
									elif m_name == "_intermediate_dense":
												# attention intermediate dense
												trace.extend(["intermediate", "dense"]      )
												__lowercase       =       getattr(__magic_name__			,   "intermediate"      )
												__lowercase       =       getattr(__magic_name__			,   "dense"      )
									elif m_name == "_output_layer_norm":
												# output layer norm
												trace.append("output"      )
												__lowercase       =       getattr(__magic_name__			,   "output"      )
									# weights & biases
									elif m_name in ["bias", "beta"]:
												trace.append("bias"      )
												__lowercase       =       getattr(__magic_name__			,   "bias"      )
									elif m_name in ["kernel", "gamma"]:
												trace.append("weight"      )
												__lowercase       =       getattr(__magic_name__			,   "weight"      )
									else:
												logger.warning(F'''Ignored {m_name}'''      )
        # for certain layers reshape is necessary
						__lowercase       =       ".".join(__magic_name__      )
						if re.match(R"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"			,   __magic_name__      ) or re.match(
						    R"(\S+)\.attention\.output\.dense\.weight"			,   __magic_name__      ):
									__lowercase       =       array.reshape(pointer.data.shape      )
						if "kernel" in full_name:
									__lowercase       =       array.transpose()
						if pointer.shape == array.shape:
									__lowercase       =       torch.from_numpy(__magic_name__      )
						else:
									raise ValueError(
									    F'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
									    F''' {array.shape}'''      )
						logger.info(F'''Successfully set variable {full_name} to PyTorch layer {trace}'''      )
			return model



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->str:
			# Instantiate model
			logger.info(F'''Loading model based on config from {config_path}...'''      )
			__lowercase       =       BertConfig.from_json_file(__magic_name__      )
			__lowercase       =       BertModel(__magic_name__      )

			# Load weights from checkpoint
			logger.info(F'''Loading weights from checkpoint {tf_checkpoint_path}...'''      )
			load_tfa_weights_in_bert(__magic_name__			,   __magic_name__			,   __magic_name__      )

			# Save pytorch-model
			logger.info(F'''Saving PyTorch model to {pytorch_dump_path}...'''      )
			torch.save(model.state_dict()			,   __magic_name__      )


if __name__ == "__main__":
					_lowercase							=				argparse.ArgumentParser()
					parser.add_argument(
					    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
					)
					parser.add_argument(
					    '''--bert_config_file''',
					    type=str,
					    required=True,
					    help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_path''',
					    type=str,
					    required=True,
					    help='''Path to the output PyTorch model (must include filename).''',
					)
					_lowercase							=				parser.parse_args()
					convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





