
'''simple docstring'''



import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig



def  lowercase__(		_UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : Any=0				)->   Any:






					"""simple docstring"""

					if name is None:
										_UpperCamelCase											=   None
					else:
										_UpperCamelCase											=   "." * max(0 ,   spaces - 2				) + "# {:" + str(50 - spaces				) + "s}"
										_UpperCamelCase											=   fmt.format(_UpperCamelCase				)

					# Print and recurse (if needed).
					if isinstance(_UpperCamelCase ,   _UpperCamelCase				):
										if msg is not None:
															print(_UpperCamelCase				)
										for k in val.keys():
															recursive_print(_UpperCamelCase ,   val[k] ,   spaces + 2				)
					elif isinstance(_UpperCamelCase ,   torch.Tensor				):
										print(_UpperCamelCase ,   ":" ,   val.size()				)
					else:
										print(_UpperCamelCase ,   ":" ,   _UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : str ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : List[Any] ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : str				)->   Tuple:






					"""simple docstring"""

					_UpperCamelCase											=   param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										_UpperCamelCase											=   (num_heads, hidden_size, num_splits) + input_shape[1:]
										_UpperCamelCase											=   param.view(*_UpperCamelCase				)
										_UpperCamelCase											=   param.transpose(0 ,   2				)
										_UpperCamelCase											=   param.transpose(1 ,   2				).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										_UpperCamelCase											=   (num_heads, num_splits, hidden_size) + input_shape[1:]
										_UpperCamelCase											=   param.view(*_UpperCamelCase				)
										_UpperCamelCase											=   param.transpose(0 ,   1				).contiguous()
					_UpperCamelCase											=   param.view(*_UpperCamelCase				)
					return param



def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : Optional[int]				)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   {}

					# old versions did not store training args
					_UpperCamelCase											=   input_state_dict.get("args" ,   _UpperCamelCase				)
					if ds_args is not None:
										# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
										# from pprint import pprint
										# pprint(vars(ds_args))

										_UpperCamelCase											=   ds_args.tokenizer_type
										_UpperCamelCase											=   ds_args.padded_vocab_size
										_UpperCamelCase											=   ds_args.max_position_embeddings
										_UpperCamelCase											=   ds_args.hidden_size
										_UpperCamelCase											=   ds_args.num_layers
										_UpperCamelCase											=   ds_args.num_attention_heads
										_UpperCamelCase											=   ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

					# The number of heads.
					_UpperCamelCase											=   config.num_attention_heads
					# The hidden_size per head.
					_UpperCamelCase											=   config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										_UpperCamelCase											=   input_state_dict["checkpoint_version"]
					else:
										_UpperCamelCase											=   0.0

					# The model.
					_UpperCamelCase											=   input_state_dict["model"]
					# The language model.
					_UpperCamelCase											=   model["language_model"]
					# The embeddings.
					_UpperCamelCase											=   lm["embedding"]

					# The word embeddings.
					_UpperCamelCase											=   embeddings["word_embeddings"]["weight"]
					# Truncate the embedding table to vocab_size rows.
					_UpperCamelCase											=   word_embeddings[: config.vocab_size, :]
					# Store the word embeddings.
					_UpperCamelCase											=   word_embeddings

					# The position embeddings.
					_UpperCamelCase											=   embeddings["position_embeddings"]["weight"]
					assert pos_embeddings.size(0				) == config.max_position_embeddings and pos_embeddings.size(1				) == config.hidden_size
					# Store the position embeddings.
					_UpperCamelCase											=   pos_embeddings

					# The token-type embeddings.
					_UpperCamelCase											=   embeddings["tokentype_embeddings"]["weight"]
					# Store the position embeddings.
					_UpperCamelCase											=   tokentype_embeddings

					# The transformer.
					_UpperCamelCase											=   lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

					# The regex to extract layer names.
					_UpperCamelCase											=   re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"				)

					# The simple map of names for "automated" rules.
					_UpperCamelCase											=   {
					    "attention.dense": ".attention.output.dense.",
					    "self_attention.dense": ".attention.output.dense.",
					    "mlp.dense_h_to_4h": ".intermediate.dense.",
					    "mlp.dense_4h_to_h": ".output.dense.",
					}

					# Keep track of the attention/query/value tensor.
					_UpperCamelCase											=   None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										_UpperCamelCase											=   layer_re.match(_UpperCamelCase				)

										# Stop if that's not a layer
										if m is None:
															break

										# The index of the layer.
										_UpperCamelCase											=   int(m.group(1				)				)
										# The name of the operation.
										_UpperCamelCase											=   m.group(2				)
										# Is it a weight or a bias?
										_UpperCamelCase											=   m.group(3				)

										# The name of the layer.
										_UpperCamelCase											=   f"bert.encoder.layer.{layer_idx}"

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("layernorm"				):
															_UpperCamelCase											=   "attention.ln" if op_name.startswith("input"				) else "ln"
															_UpperCamelCase											=   val

										# Transpose the QKV matrix.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "weight":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															_UpperCamelCase											=   fix_query_key_value_ordering(_UpperCamelCase ,   _UpperCamelCase ,   3 ,   _UpperCamelCase ,   _UpperCamelCase				)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															_UpperCamelCase											=   out_val

										# Transpose the bias.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "bias":
															# Make sure we read the weight tensor.
															assert attention_qkv_weight is not None, ""

															# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
															_UpperCamelCase											=   attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															_UpperCamelCase											=   attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															_UpperCamelCase											=   attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															_UpperCamelCase											=   fix_query_key_value_ordering(_UpperCamelCase ,   _UpperCamelCase ,   3 ,   _UpperCamelCase ,   _UpperCamelCase				)
															# Split the bias.
															_UpperCamelCase											=   out_val[0 * config.hidden_size : 1 * config.hidden_size]
															_UpperCamelCase											=   out_val[1 * config.hidden_size : 2 * config.hidden_size]
															_UpperCamelCase											=   out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															_UpperCamelCase											=   q
															_UpperCamelCase											=   q_bias
															_UpperCamelCase											=   k
															_UpperCamelCase											=   k_bias
															_UpperCamelCase											=   v
															_UpperCamelCase											=   v_bias

															# Clear the stored tensor.
															_UpperCamelCase											=   None

										# Copy weights and biases as is.
										elif weight_or_bias in ["weight", "bias"]:
															_UpperCamelCase											=   megatron_to_transformers[op_name]
															_UpperCamelCase											=   val

    # The final layernorm.
					_UpperCamelCase											=   transformer["final_layernorm.weight"]
					_UpperCamelCase											=   transformer["final_layernorm.bias"]

					# The pooler.
					_UpperCamelCase											=   lm["pooler"]

					# Store the matrix and the bias.
					_UpperCamelCase											=   pooler["dense.weight"]
					_UpperCamelCase											=   pooler["dense.bias"]

					# The LM head from Megatron (for RACE).
					_UpperCamelCase											=   model["lm_head"]

					# The transform matrix.
					_UpperCamelCase											=   lm_head["dense.weight"]
					_UpperCamelCase											=   lm_head["dense.bias"]

					# The transform LN.
					_UpperCamelCase											=   lm_head["layernorm.weight"]
					_UpperCamelCase											=   lm_head["layernorm.bias"]

					# For the decoder, we replicate the weights.
					_UpperCamelCase											=   word_embeddings
					_UpperCamelCase											=   lm_head["bias"]

					# The classifier from Megatron (for MLNI).
					_UpperCamelCase											=   model["binary_head"]

					# Store the classifier.
					_UpperCamelCase											=   binary_head["weight"]
					_UpperCamelCase											=   binary_head["bias"]

					# It should be done!
					return output_state_dict



def  lowercase__(		)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure" ,   action="store_true"				)
					parser.add_argument("path_to_checkpoint" ,   type=_UpperCamelCase ,   help="Path to the ZIP file containing the checkpoint"				)
					parser.add_argument(
					    "--config_file" ,   default="" ,   type=_UpperCamelCase ,   help="An optional config json file describing the pre-trained model." ,   )
					_UpperCamelCase											=   parser.parse_args()

					# Extract the basename.
					_UpperCamelCase											=   os.path.dirname(args.path_to_checkpoint				)

					# Load the model.
					# the .zip is very optional, let's keep it for backward compatibility
					print(f"Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\""				)
					if args.path_to_checkpoint.endswith(".zip"				):
										with zipfile.ZipFile(args.path_to_checkpoint ,   "r"				) as checkpoint:
															with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"				) as pytorch_dict:
																				_UpperCamelCase											=   torch.load(_UpperCamelCase ,   map_location="cpu"				)
					else:
										_UpperCamelCase											=   torch.load(args.path_to_checkpoint ,   map_location="cpu"				)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										_UpperCamelCase											=   MegatronBertConfig()

										# different megatron-bert-*-345m models have different vocab sizes, so override the default
										# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
										_UpperCamelCase											=   input_state_dict["model"]["lm_head"]["bias"].numel()
					else:
										_UpperCamelCase											=   MegatronBertConfig.from_json_file(args.config_file				)

					# Convert.
					print("Converting"				)
					_UpperCamelCase											=   convert_megatron_checkpoint(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(_UpperCamelCase ,   _UpperCamelCase				)

					# Store the config to file.
					print("Saving config"				)
					config.save_pretrained(_UpperCamelCase				)

					# Store the state_dict to file.
					_UpperCamelCase											=   os.path.join(_UpperCamelCase ,   "pytorch_model.bin"				)
					print(f"Saving checkpoint to \"{output_checkpoint_file}\""				)
					torch.save(_UpperCamelCase ,   _UpperCamelCase				)


####################################################################################################

if __name__ == "__main__":
			main()

####################################################################################################










'''simple docstring'''



import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

class     A_ (  tf.keras.layers.Layer  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_			,     A_ = None			,     A_ = None			,     A_ = None			,     A_ = "longest"			,     A_ = True			,     A_ = 5_12			,     A_ = None			,     A_ = True			,     A_ = True			,     A_ = True			,     **A_			,     ):
								super().__init__()
								if use_fast_bert_tokenizer:
													_UpperCamelCase											=   FastBertTokenizer(
													    A_			,     token_out_type=tf.intaa			,     lower_case_nfd_strip_accents=A_			,     **A_   )
								else:
													_UpperCamelCase											=   tf.lookup.StaticVocabularyTable(
													    tf.lookup.KeyValueTensorInitializer(
													        keys=A_			,     key_dtype=tf.string			,     values=tf.range(tf.size(A_			,     out_type=tf.intaa   )			,     dtype=tf.intaa   )			,     value_dtype=tf.intaa			,     )			,     num_oov_buckets=1			,     )
													_UpperCamelCase											=   BertTokenizerLayer(
													    A_			,     token_out_type=tf.intaa			,     lower_case=A_			,     **A_   )

								_UpperCamelCase											=   vocab_list
								_UpperCamelCase											=   do_lower_case
								_UpperCamelCase											=   cls_token_id or vocab_list.index("[CLS]"   )
								_UpperCamelCase											=   sep_token_id or vocab_list.index("[SEP]"   )
								_UpperCamelCase											=   pad_token_id or vocab_list.index("[PAD]"   )
								_UpperCamelCase											=   ShrinkLongestTrimmer(max_length - 3			,     axis=1   )  # Allow room for special tokens
								_UpperCamelCase											=   max_length
								_UpperCamelCase											=   padding
								_UpperCamelCase											=   truncation
								_UpperCamelCase											=   pad_to_multiple_of
								_UpperCamelCase											=   return_token_type_ids
								_UpperCamelCase											=   return_attention_mask




			@classmethod
			def       a	(					cls			,     A_			,     **A_   ):  # noqa: F821
								_UpperCamelCase											=   kwargs.pop("do_lower_case"			,     A_   )
								_UpperCamelCase											=   tokenizer.do_lower_case if do_lower_case is None else do_lower_case
								_UpperCamelCase											=   kwargs.pop("cls_token_id"			,     A_   )
								_UpperCamelCase											=   tokenizer.cls_token_id if cls_token_id is None else cls_token_id
								_UpperCamelCase											=   kwargs.pop("sep_token_id"			,     A_   )
								_UpperCamelCase											=   tokenizer.sep_token_id if sep_token_id is None else sep_token_id
								_UpperCamelCase											=   kwargs.pop("pad_token_id"			,     A_   )
								_UpperCamelCase											=   tokenizer.pad_token_id if pad_token_id is None else pad_token_id

								_UpperCamelCase											=   tokenizer.get_vocab()
								_UpperCamelCase											=   sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,     key=lambda A_   : x[1]   )
								_UpperCamelCase											=   [entry[0] for entry in vocab]
								return cls(
								    vocab_list=A_			,     do_lower_case=A_			,     cls_token_id=A_			,     sep_token_id=A_			,     pad_token_id=A_			,     **A_			,     )




			@classmethod
			def       a	(					cls			,     A_			,     *A_			,     **A_   ):
								try:
													_UpperCamelCase											=   BertTokenizer.from_pretrained(A_			,     *A_			,     **A_   )
								except:  # noqa: E722
													from .tokenization_bert_fast import BertTokenizerFast

													_UpperCamelCase											=   BertTokenizerFast.from_pretrained(A_			,     *A_			,     **A_   )
								return cls.from_tokenizer(A_			,     **A_   )




			def       a	(					self			,     A_   ):
								if self.do_lower_case:
													_UpperCamelCase											=   case_fold_utfa(A_   )
								_UpperCamelCase											=   self.tf_tokenizer.tokenize(A_   )
								return tokens.merge_dims(1			,     -1   )




			def       a	(					self			,     A_			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     A_=None			,     ):
								if padding is None:
													_UpperCamelCase											=   self.padding
								if padding not in ("longest", "max_length"):
													raise ValueError("Padding must be either 'longest' or 'max_length'!"   )
								if max_length is not None and text_pair is not None:
													# Because we have to instantiate a Trimmer to do it properly
													raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"   )
								if max_length is None:
													_UpperCamelCase											=   self.max_length
								if truncation is None:
													_UpperCamelCase											=   self.truncation
								if pad_to_multiple_of is None:
													_UpperCamelCase											=   self.pad_to_multiple_of
								if return_token_type_ids is None:
													_UpperCamelCase											=   self.return_token_type_ids
								if return_attention_mask is None:
													_UpperCamelCase											=   self.return_attention_mask
								if not isinstance(A_			,     tf.Tensor   ):
													_UpperCamelCase											=   tf.convert_to_tensor(A_   )
								if text_pair is not None and not isinstance(A_			,     tf.Tensor   ):
													_UpperCamelCase											=   tf.convert_to_tensor(A_   )
								if text_pair is not None:
													if text.shape.rank > 1:
																		raise ValueError("text argument should not be multidimensional when a text pair is supplied!"   )
													if text_pair.shape.rank > 1:
																		raise ValueError("text_pair should not be multidimensional!"   )
								if text.shape.rank == 2:
													_UpperCamelCase				,	_UpperCamelCase											=   text[:, 0], text[:, 1]
								_UpperCamelCase											=   self.unpaired_tokenize(A_   )
								if text_pair is None:  # Unpaired text
													if truncation:
																		_UpperCamelCase											=   text[:, : max_length - 2]  # Allow room for special tokens
													_UpperCamelCase				,	_UpperCamelCase											=   combine_segments(
													    (text,)			,     start_of_sequence_id=self.cls_token_id			,     end_of_segment_id=self.sep_token_id   )
								else:  # Paired text
													_UpperCamelCase											=   self.unpaired_tokenize(A_   )
													if truncation:
																		_UpperCamelCase				,	_UpperCamelCase											=   self.paired_trimmer.trim([text, text_pair]   )
													_UpperCamelCase				,	_UpperCamelCase											=   combine_segments(
													    (text, text_pair)			,     start_of_sequence_id=self.cls_token_id			,     end_of_segment_id=self.sep_token_id   )
								if padding == "longest":
													_UpperCamelCase											=   input_ids.bounding_shape(axis=1   )
													if pad_to_multiple_of is not None:
																		# No ceiling division in tensorflow, so we negate floordiv instead
																		_UpperCamelCase											=   pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,     A_   ))
								else:
													_UpperCamelCase											=   max_length

								_UpperCamelCase				,	_UpperCamelCase											=   pad_model_inputs(A_			,     max_seq_length=A_			,     pad_value=self.pad_token_id   )
								_UpperCamelCase											=   {"input_ids": input_ids}
								if return_attention_mask:
													_UpperCamelCase											=   attention_mask
								if return_token_type_ids:
													_UpperCamelCase				,	_UpperCamelCase											=   pad_model_inputs(
													    A_			,     max_seq_length=A_			,     pad_value=self.pad_token_id   )
													_UpperCamelCase											=   token_type_ids
								return output




			def       a	(					self   ):
								return {
								    "vocab_list": self.vocab_list,
								    "do_lower_case": self.do_lower_case,
								    "cls_token_id": self.cls_token_id,
								    "sep_token_id": self.sep_token_id,
								    "pad_token_id": self.pad_token_id,
								}



