




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

require_version('''tokenizers>=0.13.3''')

if is_sentencepiece_available():
						from .tokenization_llama import LlamaTokenizer
else:
						__a  :		str		     =   None

__a  :		str		     =   logging.get_logger(__name__)
__a  :		Optional[Any]		     =   {'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

__a     ,	__a  :		Any		     =   '''[INST]''', '''[/INST]'''
__a     ,	__a  :		Union[str, Any]		     =   '''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
__a  :		Any		     =   '''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : List[str]	  =					VOCAB_FILES_NAMES
	a     : Tuple	  =					LlamaTokenizer
	a     : List[str]	  =					"""left"""
	a     : Union[str, Any]	  =					["""input_ids""", """attention_mask"""]

	def __init__(					self	,				lowerCamelCase=None	,				lowerCamelCase=None	,				lowerCamelCase=False	,				lowerCamelCase="<unk>"	,				lowerCamelCase="<s>"	,				lowerCamelCase="</s>"	,				lowerCamelCase=True	,				lowerCamelCase=False	,				**lowerCamelCase	,				) ->      Any:





								"""simple docstring"""


								super().__init__(
								    vocab_file=lowerCamelCase	,				tokenizer_file=lowerCamelCase	,				clean_up_tokenization_spaces=lowerCamelCase	,				unk_token=lowerCamelCase	,				bos_token=lowerCamelCase	,				eos_token=lowerCamelCase	,				**lowerCamelCase	,				)
								lowercase__  :	List[str]        =							add_bos_token
								lowercase__  :	Union[str, Any]        =							add_eos_token
								self.update_post_processor()

								lowercase__  :	Optional[int]        =							vocab_file
								lowercase__  :	Union[str, Any]        =							False if not self.vocab_file else True

	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								lowercase__  :	Any        =							self.bos_token
								lowercase__  :	Dict        =							self.bos_token_id

								lowercase__  :	Optional[int]        =							self.eos_token
								lowercase__  :	Optional[Any]        =							self.eos_token_id

								lowercase__  :	Tuple        =							f"""{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}"""
								lowercase__  :	int        =							f"""{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}"""

								lowercase__  :	str        =							[]
								if self.add_bos_token:
															special_tokens.append((bos, bos_token_id)    )
								if self.add_eos_token:
															special_tokens.append((eos, eos_token_id)    )
								lowercase__  :	List[Any]        =							processors.TemplateProcessing(
								    single=lowerCamelCase	,				pair=lowerCamelCase	,				special_tokens=lowerCamelCase    )

	@property
	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								return self._add_eos_token

	@property
	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


								return self._add_bos_token

	@add_eos_token.setter
	def    __a     (					self	,				lowerCamelCase    ) ->      List[str]:





								"""simple docstring"""


								lowercase__  :	Tuple        =							value
								self.update_post_processor()

	@add_bos_token.setter
	def    __a     (					self	,				lowerCamelCase    ) ->      Any:





								"""simple docstring"""


								lowercase__  :	Any        =							value
								self.update_post_processor()

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase = None    ) ->      Tuple[str]:





								"""simple docstring"""


								if not self.can_save_slow_tokenizer:
															raise ValueError(
															    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
															    "tokenizer."    )

								if not os.path.isdir(lowerCamelCase    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
															return
								lowercase__  :	Union[str, Any]        =							os.path.join(
								    lowerCamelCase	,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

								if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowerCamelCase    ):
															copyfile(self.vocab_file	,				lowerCamelCase    )

								return (out_vocab_file,)







	def    __a     (					self	,				lowerCamelCase    ) ->      str:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							list(conversation.iter_texts()    )
								if not all([is_user for is_user, msg in dialogue[::2]]    ) or not all(
								    [not is_user for is_user, msg in dialogue[1::2]]    ):
															raise ValueError(
															    "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"    )

								lowercase__  :	int        =							[]
								if len(conversation.past_user_inputs    ) > 0:
															if not conversation.past_user_inputs[0].startswith(lowerCamelCase    ) or E_SYS not in conversation.past_user_inputs[0]:
																						lowercase__  :	List[Any]        =							(
																						    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																						)
								elif not dialogue[0][1].startswith(lowerCamelCase    ) or E_SYS not in dialogue[0][1]:
															lowercase__  :	Optional[int]        =							(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

								dialog_tokens += sum(
								    [
								        [self.bos_token_id]
								        + self.encode(
								            f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """	,				add_special_tokens=lowerCamelCase    )
								        + [self.eos_token_id]
								        for prompt, answer in zip(dialogue[::2]	,				dialogue[1::2]    )
								    ]	,				[]	,				)
								if not (dialogue[-1][0]):
															raise ValueError(f"""Last message must be from user, got {dialogue[-1]["role"]}"""    )
								dialog_tokens += [self.bos_token_id] + self.encode(
								    f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""	,				add_special_tokens=lowerCamelCase    )
								return dialog_tokens





# 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  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=0    )      ->			Any:
							# Format the message.
							if name is None:
														lowercase__  :	Any        =							None
							else:
														lowercase__  :	int        =							"." * max(0    ,spaces - 2    ) + "# {:" + str(50 - spaces    ) + "s}"
														lowercase__  :	str        =							fmt.format(SCREAMING_SNAKE_CASE_    )

							# Print and recurse (if needed).
							if isinstance(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ):
														if msg is not None:
																					print(SCREAMING_SNAKE_CASE_    )
														for k in val.keys():
																					recursive_print(SCREAMING_SNAKE_CASE_    ,val[k]    ,spaces + 2    )
							elif isinstance(SCREAMING_SNAKE_CASE_    ,torch.Tensor    ):
														print(SCREAMING_SNAKE_CASE_    ,":"    ,val.size()    )
							else:
														print(SCREAMING_SNAKE_CASE_    ,":"    ,SCREAMING_SNAKE_CASE_    )
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Dict:
							# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
							# for compatibility with later versions of NVIDIA Megatron-LM.
							# The inverse operation is performed inside Megatron-LM to read checkpoints:
							# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
							# If param is the weight tensor of the self-attention block, the returned tensor
							# will have to be transposed one more time to be read by HuggingFace BERT.
							lowercase__  :	Optional[Any]        =							param.size()
							if checkpoint_version == 1.0:
														# version 1.0 stores [num_heads * hidden_size * num_splits, :]
														lowercase__  :	List[str]        =							(num_heads, hidden_size, num_splits) + input_shape[1:]
														lowercase__  :	List[Any]        =							param.view(*SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Optional[Any]        =							param.transpose(0    ,2    )
														lowercase__  :	List[str]        =							param.transpose(1    ,2    ).contiguous()
							elif checkpoint_version >= 2.0:
														# other versions store [num_heads * num_splits * hidden_size, :]
														lowercase__  :	Any        =							(num_heads, num_splits, hidden_size) + input_shape[1:]
														lowercase__  :	str        =							param.view(*SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Any        =							param.transpose(0    ,1    ).contiguous()
							lowercase__  :	List[Any]        =							param.view(*SCREAMING_SNAKE_CASE_    )
							return param
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Optional[int]:
							# The converted output model.
							lowercase__  :	List[str]        =							{}

							# old versions did not store training args
							lowercase__  :	List[str]        =							input_state_dict.get("args"    ,SCREAMING_SNAKE_CASE_    )
							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))

														lowercase__  :	List[Any]        =							ds_args.tokenizer_type
														lowercase__  :	Tuple        =							ds_args.padded_vocab_size
														lowercase__  :	Optional[int]        =							ds_args.max_position_embeddings
														lowercase__  :	Any        =							ds_args.hidden_size
														lowercase__  :	List[str]        =							ds_args.num_layers
														lowercase__  :	List[str]        =							ds_args.num_attention_heads
														lowercase__  :	List[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.
							lowercase__  :	Optional[int]        =							config.num_attention_heads
							# The hidden_size per head.
							lowercase__  :	int        =							config.hidden_size // heads
							# Megatron-LM checkpoint version
							if "checkpoint_version" in input_state_dict.keys():
														lowercase__  :	List[Any]        =							input_state_dict["checkpoint_version"]
							else:
														lowercase__  :	Optional[int]        =							0.0

							# The model.
							lowercase__  :	Any        =							input_state_dict["model"]
							# The language model.
							lowercase__  :	int        =							model["language_model"]
							# The embeddings.
							lowercase__  :	str        =							lm["embedding"]

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

							# The position embeddings.
							lowercase__  :	Optional[Any]        =							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.
							lowercase__  :	Tuple        =							pos_embeddings

							# The token-type embeddings.
							lowercase__  :	List[str]        =							embeddings["tokentype_embeddings"]["weight"]
							# Store the position embeddings.
							lowercase__  :	Tuple        =							tokentype_embeddings

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

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

							# The simple map of names for "automated" rules.
							lowercase__  :	int        =							{
							    "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.
							lowercase__  :	int        =							None

							# Extract the layers.
							for key, val in transformer.items():
														# Match the name.
														lowercase__  :	List[str]        =							layer_re.match(SCREAMING_SNAKE_CASE_    )

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

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

														# The name of the layer.
														lowercase__  :	Tuple        =							F"""bert.encoder.layer.{layer_idx}"""

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("layernorm"    ):
																					lowercase__  :	Optional[Any]        =							"attention.ln" if op_name.startswith("input"    ) else "ln"
																					lowercase__  :	Optional[int]        =							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, ""

																					lowercase__  :	List[Any]        =							fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,3    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
																					# Store the tensor as we need the bias as well to interleave QKV and biases.
																					lowercase__  :	Union[str, Any]        =							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.
																					lowercase__  :	List[Any]        =							attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																					lowercase__  :	Optional[int]        =							attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																					lowercase__  :	List[Any]        =							attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

																					lowercase__  :	Any        =							fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,3    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
																					# Split the bias.
																					lowercase__  :	Dict        =							out_val[0 * config.hidden_size : 1 * config.hidden_size]
																					lowercase__  :	List[str]        =							out_val[1 * config.hidden_size : 2 * config.hidden_size]
																					lowercase__  :	str        =							out_val[2 * config.hidden_size : 3 * config.hidden_size]

																					# Store.
																					lowercase__  :	int        =							q
																					lowercase__  :	Optional[Any]        =							q_bias
																					lowercase__  :	List[str]        =							k
																					lowercase__  :	str        =							k_bias
																					lowercase__  :	List[str]        =							v
																					lowercase__  :	Optional[Any]        =							v_bias

																					# Clear the stored tensor.
																					lowercase__  :	Union[str, Any]        =							None

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

    # The final layernorm.
							lowercase__  :	str        =							transformer["final_layernorm.weight"]
							lowercase__  :	Dict        =							transformer["final_layernorm.bias"]

							# The pooler.
							lowercase__  :	Union[str, Any]        =							lm["pooler"]

							# Store the matrix and the bias.
							lowercase__  :	Dict        =							pooler["dense.weight"]
							lowercase__  :	List[Any]        =							pooler["dense.bias"]

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

							# The transform matrix.
							lowercase__  :	Optional[int]        =							lm_head["dense.weight"]
							lowercase__  :	Union[str, Any]        =							lm_head["dense.bias"]

							# The transform LN.
							lowercase__  :	int        =							lm_head["layernorm.weight"]
							lowercase__  :	Union[str, Any]        =							lm_head["layernorm.bias"]

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

							# The classifier from Megatron (for MLNI).
							lowercase__  :	Optional[Any]        =							model["binary_head"]

							# Store the classifier.
							lowercase__  :	Tuple        =							binary_head["weight"]
							lowercase__  :	Any        =							binary_head["bias"]

							# It should be done!
							return output_state_dict
def  snake_case_					(		)      ->			str:
							# Create the argument parser.
							lowercase__  :	Optional[Any]        =							argparse.ArgumentParser()
							parser.add_argument("--print-checkpoint-structure"    ,action="store_true"    )
							parser.add_argument("path_to_checkpoint"    ,type=SCREAMING_SNAKE_CASE_    ,help="Path to the ZIP file containing the checkpoint"    )
							parser.add_argument(
							    "--config_file"    ,default=""    ,type=SCREAMING_SNAKE_CASE_    ,help="An optional config json file describing the pre-trained model."    ,)
							lowercase__  :	List[str]        =							parser.parse_args()

							# Extract the basename.
							lowercase__  :	Optional[Any]        =							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:
																												lowercase__  :	Optional[Any]        =							torch.load(SCREAMING_SNAKE_CASE_    ,map_location="cpu"    )
							else:
														lowercase__  :	str        =							torch.load(args.path_to_checkpoint    ,map_location="cpu"    )

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

							# Convert.
							print("Converting"    )
							lowercase__  :	Optional[int]        =							convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

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

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

							# Store the state_dict to file.
							lowercase__  :	Tuple        =							os.path.join(SCREAMING_SNAKE_CASE_    ,"pytorch_model.bin"    )
							print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
							torch.save(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )


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

if __name__ == "__main__":
						main()

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