

"""simple docstring"""





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
				if is_torch_available():
								import torch
				if is_tf_available():
								import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


_SCREAMING_SNAKE_CASE							:	int										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Optional[int]										=			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_SCREAMING_SNAKE_CASE							:	Tuple										=			{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
}

_SCREAMING_SNAKE_CASE							:	str										=			{
    """Salesforce/codegen-350M-mono""": 2_0_4_8,
}



@lru_cache()
def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     List[str]								=(
					    list(range(ord('''!'''					)   ,				ord('''~'''					) + 1					)					) + list(range(ord('''¡'''					)   ,				ord('''¬'''					) + 1					)					) + list(range(ord('''®'''					)   ,				ord('''ÿ'''					) + 1					)					)
					)
					UpperCamelCase__		:     Optional[int]								=bs[:]
					UpperCamelCase__		:     List[Any]								=0
					for b in range(2**8					):
										if b not in bs:
															bs.append(UpperCAmelCase					)
															cs.append(2**8 + n					)
															n += 1
					UpperCamelCase__		:     Union[str, Any]								=[chr(UpperCAmelCase					) for n in cs]
					return dict(zip(UpperCAmelCase   ,				UpperCAmelCase					)					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[Any]					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=set()
					UpperCamelCase__		:     Optional[int]								=word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)					)
										UpperCamelCase__		:     Optional[Any]								=char
					return pairs







class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE_      =    PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE_      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE_      =    ['input_ids', 'attention_mask']






			def __init__(				self  :       Tuple    ,	lowercase_  :       Union[str, Any]    ,	lowercase_  :       str    ,	lowercase_  :       Optional[Any]="replace"    ,	lowercase_  :       Optional[Any]="<|endoftext|>"    ,	lowercase_  :       List[Any]="<|endoftext|>"    ,	lowercase_  :       List[str]="<|endoftext|>"    ,	lowercase_  :       str=None    ,	lowercase_  :       str=False    ,	lowercase_  :       Optional[Any]=False    ,	**lowercase_  :       Dict    ,	):
								UpperCamelCase__		:     Union[str, Any]								=AddedToken(lowercase_    ,	lstrip=lowercase_    ,	rstrip=lowercase_    ) if isinstance(lowercase_    ,	lowercase_    ) else bos_token
								UpperCamelCase__		:     Tuple								=AddedToken(lowercase_    ,	lstrip=lowercase_    ,	rstrip=lowercase_    ) if isinstance(lowercase_    ,	lowercase_    ) else eos_token
								UpperCamelCase__		:     List[str]								=AddedToken(lowercase_    ,	lstrip=lowercase_    ,	rstrip=lowercase_    ) if isinstance(lowercase_    ,	lowercase_    ) else unk_token
								UpperCamelCase__		:     Tuple								=AddedToken(lowercase_    ,	lstrip=lowercase_    ,	rstrip=lowercase_    ) if isinstance(lowercase_    ,	lowercase_    ) else pad_token
								super().__init__(
								    errors=lowercase_    ,	unk_token=lowercase_    ,	bos_token=lowercase_    ,	eos_token=lowercase_    ,	pad_token=lowercase_    ,	add_prefix_space=lowercase_    ,	add_bos_token=lowercase_    ,	**lowercase_    ,	)
								UpperCamelCase__		:     List[Any]								=add_bos_token

								with open(lowercase_    ,	encoding='''utf-8'''    ) as vocab_handle:
													UpperCamelCase__		:     List[str]								=json.load(lowercase_    )
								UpperCamelCase__		:     Tuple								={v: k for k, v in self.encoder.items()}
								UpperCamelCase__		:     Union[str, Any]								=errors  # how to handle errors in decoding
								UpperCamelCase__		:     Dict								=bytes_to_unicode()
								UpperCamelCase__		:     List[str]								={v: k for k, v in self.byte_encoder.items()}
								with open(lowercase_    ,	encoding='''utf-8'''    ) as merges_handle:
													UpperCamelCase__		:     Tuple								=merges_handle.read().split('''\n'''    )[1:-1]
								UpperCamelCase__		:     str								=[tuple(merge.split()    ) for merge in bpe_merges]
								UpperCamelCase__		:     Any								=dict(zip(lowercase_    ,	range(len(lowercase_    )    )    )    )
								UpperCamelCase__		:     Tuple								={}
								UpperCamelCase__		:     Optional[Any]								=add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								UpperCamelCase__		:     Dict								=re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''    )






			@property
			def  _lowerCAmelCase      (				self  :       List[Any]    ):
								return len(self.encoder    )






			def  _lowerCAmelCase      (				self  :       List[Any]    ):
								return dict(self.encoder    ,	**self.added_tokens_encoder    )






			def  _lowerCAmelCase      (				self  :       List[str]    ,	lowercase_  :       Union[str, Any]    ):
								if token in self.cache:
													return self.cache[token]
								UpperCamelCase__		:     Dict								=tuple(lowercase_    )
								UpperCamelCase__		:     Union[str, Any]								=get_pairs(lowercase_    )

								if not pairs:
													return token

								while True:
													UpperCamelCase__		:     List[str]								=min(lowercase_    ,	key=lambda lowercase_    : self.bpe_ranks.get(lowercase_    ,	float('''inf'''    )    )    )
													if bigram not in self.bpe_ranks:
																		break
													UpperCamelCase__	,      UpperCamelCase__		:     str								=bigram
													UpperCamelCase__		:     Tuple								=[]
													UpperCamelCase__		:     Tuple								=0
													while i < len(lowercase_    ):
																		try:
																							UpperCamelCase__		:     Optional[Any]								=word.index(lowercase_    ,	lowercase_    )
																		except ValueError:
																							new_word.extend(word[i:]    )
																							break
																		else:
																							new_word.extend(word[i:j]    )
																							UpperCamelCase__		:     Union[str, Any]								=j

																		if word[i] == first and i < len(lowercase_    ) - 1 and word[i + 1] == second:
																							new_word.append(first + second    )
																							i += 2
																		else:
																							new_word.append(word[i]    )
																							i += 1
													UpperCamelCase__		:     Any								=tuple(lowercase_    )
													UpperCamelCase__		:     Optional[int]								=new_word
													if len(lowercase_    ) == 1:
																		break
													else:
																		UpperCamelCase__		:     Optional[Any]								=get_pairs(lowercase_    )
								UpperCamelCase__		:     Optional[Any]								=''' '''.join(lowercase_    )
								UpperCamelCase__		:     Optional[Any]								=word
								return word






			def  _lowerCAmelCase      (				self  :       Tuple    ,	lowercase_  :       Union[str, Any]    ,	lowercase_  :       List[Any]=None    ):
								if self.add_bos_token:
													UpperCamelCase__		:     Tuple								=[self.bos_token_id]
								else:
													UpperCamelCase__		:     Optional[int]								=[]

								UpperCamelCase__		:     int								=bos_token_ids + token_ids_a

								if token_ids_a is None:
													return output

								return output + bos_token_ids + token_ids_a






			def  _lowerCAmelCase      (				self  :       str    ,	lowercase_  :       Tuple    ):
								UpperCamelCase__		:     Optional[int]								=[]
								for token in re.findall(self.pat    ,	lowercase_    ):
													UpperCamelCase__		:     int								=''''''.join(
													    self.byte_encoder[b] for b in token.encode('''utf-8'''    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase_    ).split(''' '''    )    )
								return bpe_tokens






			def  _lowerCAmelCase      (				self  :       Any    ,	lowercase_  :       Tuple    ):
								return self.encoder.get(lowercase_    ,	self.encoder.get(self.unk_token    )    )






			def  _lowerCAmelCase      (				self  :       Tuple    ,	lowercase_  :       Dict    ):
								return self.decoder.get(lowercase_    )






			def  _lowerCAmelCase      (				self  :       int    ,	lowercase_  :       int    ):
								UpperCamelCase__		:     int								=''''''.join(lowercase_    )
								UpperCamelCase__		:     Optional[Any]								=bytearray([self.byte_decoder[c] for c in text]    ).decode('''utf-8'''    ,	errors=self.errors    )
								return text






			def  _lowerCAmelCase      (				self  :       Optional[int]    ,	lowercase_  :       str    ,	lowercase_  :       Optional[str] = None    ):
								if not os.path.isdir(lowercase_    ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
													return
								UpperCamelCase__		:     List[Any]								=os.path.join(
								    lowercase_    ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
								UpperCamelCase__		:     List[str]								=os.path.join(
								    lowercase_    ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

								with open(lowercase_    ,	'''w'''    ,	encoding='''utf-8'''    ) as f:
													f.write(json.dumps(self.encoder    ,	indent=2    ,	sort_keys=lowercase_    ,	ensure_ascii=lowercase_    ) + '''\n'''    )

								UpperCamelCase__		:     Tuple								=0
								with open(lowercase_    ,	'''w'''    ,	encoding='''utf-8'''    ) as writer:
													writer.write('''#version: 0.2\n'''    )
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,	key=lambda lowercase_    : kv[1]    ):
																		if index != token_index:
																							logger.warning(
																							    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																							    ''' Please check that the tokenizer is not corrupted!'''    )
																							UpperCamelCase__		:     Tuple								=token_index
																		writer.write(''' '''.join(lowercase_    ) + '''\n'''    )
																		index += 1

								return vocab_file, merge_file






			def  _lowerCAmelCase      (				self  :       Dict    ,	lowercase_  :       Tuple    ,	lowercase_  :       str=False    ,	**lowercase_  :       List[Any]    ):
								UpperCamelCase__		:     Optional[int]								=kwargs.pop('''add_prefix_space'''    ,	self.add_prefix_space    )
								if is_split_into_words or add_prefix_space:
													UpperCamelCase__		:     Optional[Any]								=''' ''' + text
								return (text, kwargs)






			def  _lowerCAmelCase      (				self  :       Optional[int]    ,	lowercase_  :       Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = None    ,	lowercase_  :       Optional[List[str]] = None    ,	**lowercase_  :       Any    ,	):
								UpperCamelCase__		:     List[str]								=super()._decode(
								    token_ids=lowercase_    ,	skip_special_tokens=lowercase_    ,	clean_up_tokenization_spaces=lowercase_    ,	**lowercase_    ,	)

								if truncate_before_pattern is not None and len(lowercase_    ) > 0:
													UpperCamelCase__		:     List[str]								=self.truncate(lowercase_    ,	lowercase_    )

								return decoded_text






			def  _lowerCAmelCase      (				self  :       Tuple    ,	lowercase_  :       Optional[int]    ,	lowercase_  :       List[Any]    ):
								def find_re(lowercase_  :       Tuple    ,	lowercase_  :       Any    ,	lowercase_  :       Optional[int]    ):
													UpperCamelCase__		:     int								=pattern.search(lowercase_    ,	lowercase_    )
													return m.start() if m else -1

								UpperCamelCase__		:     List[Any]								=[re.compile(lowercase_    ,	re.MULTILINE    ) for pattern in truncate_before_pattern]

								UpperCamelCase__		:     Dict								=list(re.finditer('''^print'''    ,	lowercase_    ,	re.MULTILINE    )    )

								if len(lowercase_    ) > 1:
													UpperCamelCase__		:     Optional[Any]								=completion[: prints[1].start()]

								UpperCamelCase__		:     Dict								=list(re.finditer('''^def'''    ,	lowercase_    ,	re.MULTILINE    )    )

								if len(lowercase_    ) > 1:
													UpperCamelCase__		:     str								=completion[: defs[1].start()]

								UpperCamelCase__		:     Tuple								=0

								UpperCamelCase__		:     List[Any]								=[
								    pos for pos in [find_re(lowercase_    ,	lowercase_    ,	lowercase_    ) for terminal in terminals] if pos != -1
								]

								if len(lowercase_    ) > 0:
													return completion[: min(lowercase_    )]
								else:
													return completion





"""simple docstring"""





# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]   ,				UpperCAmelCase  :				Tuple   ,				UpperCAmelCase  :				Tuple=0					):


					'''simple docstring'''






					if name is None:
										UpperCamelCase__		:     Any								=None
					else:
										UpperCamelCase__		:     str								='''.''' * max(0   ,				spaces - 2					) + '''# {:''' + str(50 - spaces					) + '''s}'''
										UpperCamelCase__		:     int								=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 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				Union[str, Any]   ,				UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				List[Any]					):


					'''simple docstring'''






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



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[int]   ,				UpperCAmelCase  :				Tuple   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     int								={}

					# old versions did not store training args
					UpperCamelCase__		:     Optional[int]								=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__		:     List[str]								=ds_args.tokenizer_type
										UpperCamelCase__		:     str								=ds_args.padded_vocab_size
										UpperCamelCase__		:     List[str]								=ds_args.max_position_embeddings
										UpperCamelCase__		:     Any								=ds_args.hidden_size
										UpperCamelCase__		:     Optional[Any]								=ds_args.num_layers
										UpperCamelCase__		:     Optional[Any]								=ds_args.num_attention_heads
										UpperCamelCase__		:     Optional[Any]								=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__		:     List[str]								=config.num_attention_heads
					# The hidden_size per head.
					UpperCamelCase__		:     Union[str, Any]								=config.hidden_size // heads
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										UpperCamelCase__		:     Optional[Any]								=input_state_dict['''checkpoint_version''']
					else:
										UpperCamelCase__		:     int								=0.0

					# The model.
					UpperCamelCase__		:     Optional[int]								=input_state_dict['''model''']
					# The language model.
					UpperCamelCase__		:     int								=model['''language_model''']
					# The embeddings.
					UpperCamelCase__		:     Any								=lm['''embedding''']

					# The word embeddings.
					UpperCamelCase__		:     Union[str, Any]								=embeddings['''word_embeddings''']['''weight''']
					# Truncate the embedding table to vocab_size rows.
					UpperCamelCase__		:     Optional[Any]								=word_embeddings[: config.vocab_size, :]
					# Store the word embeddings.
					UpperCamelCase__		:     List[str]								=word_embeddings

					# The position embeddings.
					UpperCamelCase__		:     Dict								=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__		:     List[str]								=pos_embeddings

					# The token-type embeddings.
					UpperCamelCase__		:     str								=embeddings['''tokentype_embeddings''']['''weight''']
					# Store the position embeddings.
					UpperCamelCase__		:     List[str]								=tokentype_embeddings

					# The transformer.
					UpperCamelCase__		:     Optional[int]								=lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

					# The regex to extract layer names.
					UpperCamelCase__		:     int								=re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''					)

					# The simple map of names for "automated" rules.
					UpperCamelCase__		:     List[Any]								={
					    '''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__		:     Tuple								=None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										UpperCamelCase__		:     Union[str, Any]								=layer_re.match(UpperCAmelCase					)

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

										# The index of the layer.
										UpperCamelCase__		:     int								=int(m.group(1					)					)
										# The name of the operation.
										UpperCamelCase__		:     List[str]								=m.group(2					)
										# Is it a weight or a bias?
										UpperCamelCase__		:     int								=m.group(3					)

										# The name of the layer.
										UpperCamelCase__		:     str								=F'''bert.encoder.layer.{layer_idx}'''

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith('''layernorm'''					):
															UpperCamelCase__		:     int								='''attention.ln''' if op_name.startswith('''input'''					) else '''ln'''
															UpperCamelCase__		:     Tuple								=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__		:     Any								=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__		:     Tuple								=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__		:     Tuple								=attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															UpperCamelCase__		:     Tuple								=attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															UpperCamelCase__		:     str								=attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															UpperCamelCase__		:     int								=fix_query_key_value_ordering(UpperCAmelCase   ,				UpperCAmelCase   ,				3   ,				UpperCAmelCase   ,				UpperCAmelCase					)
															# Split the bias.
															UpperCamelCase__		:     Any								=out_val[0 * config.hidden_size : 1 * config.hidden_size]
															UpperCamelCase__		:     Any								=out_val[1 * config.hidden_size : 2 * config.hidden_size]
															UpperCamelCase__		:     List[Any]								=out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															UpperCamelCase__		:     int								=q
															UpperCamelCase__		:     Dict								=q_bias
															UpperCamelCase__		:     List[Any]								=k
															UpperCamelCase__		:     Optional[int]								=k_bias
															UpperCamelCase__		:     Tuple								=v
															UpperCamelCase__		:     Optional[Any]								=v_bias

															# Clear the stored tensor.
															UpperCamelCase__		:     Optional[Any]								=None

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

    # The final layernorm.
					UpperCamelCase__		:     Dict								=transformer['''final_layernorm.weight''']
					UpperCamelCase__		:     List[str]								=transformer['''final_layernorm.bias''']

					# The pooler.
					UpperCamelCase__		:     str								=lm['''pooler''']

					# Store the matrix and the bias.
					UpperCamelCase__		:     Optional[Any]								=pooler['''dense.weight''']
					UpperCamelCase__		:     Tuple								=pooler['''dense.bias''']

					# The LM head from Megatron (for RACE).
					UpperCamelCase__		:     Tuple								=model['''lm_head''']

					# The transform matrix.
					UpperCamelCase__		:     Optional[Any]								=lm_head['''dense.weight''']
					UpperCamelCase__		:     Union[str, Any]								=lm_head['''dense.bias''']

					# The transform LN.
					UpperCamelCase__		:     Optional[int]								=lm_head['''layernorm.weight''']
					UpperCamelCase__		:     Dict								=lm_head['''layernorm.bias''']

					# For the decoder, we replicate the weights.
					UpperCamelCase__		:     Any								=word_embeddings
					UpperCamelCase__		:     Optional[Any]								=lm_head['''bias''']

					# The classifier from Megatron (for MLNI).
					UpperCamelCase__		:     Union[str, Any]								=model['''binary_head''']

					# Store the classifier.
					UpperCamelCase__		:     Union[str, Any]								=binary_head['''weight''']
					UpperCamelCase__		:     int								=binary_head['''bias''']

					# It should be done!
					return output_state_dict



def 							_lowerCAmelCase   (    ):


					'''simple docstring'''






					UpperCamelCase__		:     Dict								=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__		:     Dict								=parser.parse_args()

					# Extract the basename.
					UpperCamelCase__		:     Optional[int]								=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__		:     Optional[int]								=torch.load(UpperCAmelCase   ,				map_location='''cpu'''					)
					else:
										UpperCamelCase__		:     Optional[Any]								=torch.load(args.path_to_checkpoint   ,				map_location='''cpu'''					)

					if args.config_file == "":
										# Default config of megatron-bert 345m
										UpperCamelCase__		:     Union[str, Any]								=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__		:     Tuple								=input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
					else:
										UpperCamelCase__		:     Any								=MegatronBertConfig.from_json_file(args.config_file					)

					# Convert.
					print('''Converting'''					)
					UpperCamelCase__		:     str								=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__		:     Union[str, Any]								=os.path.join(UpperCAmelCase   ,				'''pytorch_model.bin'''					)
					print(F'''Saving checkpoint to "{output_checkpoint_file}"'''					)
					torch.save(UpperCAmelCase   ,				UpperCAmelCase					)


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

if __name__ == "__main__":
				main()

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


