






# 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     _a					(       UpperCamelCase_   :			Tuple      ,				UpperCamelCase_   :			str      ,				UpperCamelCase_   :			List[str]=0				)	->				Optional[int]:
						"""simple docstring"""







						if name is None:
												lowerCAmelCase__	 =  None
						else:
												lowerCAmelCase__	 =  "." * max(0      ,				spaces - 2				) + "# {:" + str(50 - spaces				) + "s}"
												lowerCAmelCase__	 =  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     _a					(       UpperCamelCase_   :			Optional[int]      ,				UpperCamelCase_   :			Union[str, Any]      ,				UpperCamelCase_   :			Optional[int]      ,				UpperCamelCase_   :			Dict      ,				UpperCamelCase_   :			Optional[int]				)	->				Union[str, Any]:
						"""simple docstring"""







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


def     _a					(       UpperCamelCase_   :			List[Any]      ,				UpperCamelCase_   :			Tuple      ,				UpperCamelCase_   :			int				)	->				Tuple:
						"""simple docstring"""







						lowerCAmelCase__	 =  {}

						# old versions did not store training args
						lowerCAmelCase__	 =  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))

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

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

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

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

						# The position embeddings.
						lowerCAmelCase__	 =  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.
						lowerCAmelCase__	 =  pos_embeddings

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

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

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

						# The simple map of names for "automated" rules.
						lowerCAmelCase__	 =  {
						    "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.
						lowerCAmelCase__	 =  None

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

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

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

												# The name of the layer.
												lowerCAmelCase__	 =  F"bert.encoder.layer.{layer_idx}"

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

																		lowerCAmelCase__	 =  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.
																		lowerCAmelCase__	 =  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.
																		lowerCAmelCase__	 =  attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																		lowerCAmelCase__	 =  attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																		lowerCAmelCase__	 =  attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

																		# Clear the stored tensor.
																		lowerCAmelCase__	 =  None

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

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

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

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

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

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

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

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

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

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

						# It should be done!
						return output_state_dict


def     _a					(       )	->				List[str]:
						"""simple docstring"""







						lowerCAmelCase__	 =  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."      ,				)
						lowerCAmelCase__	 =  parser.parse_args()

						# Extract the basename.
						lowerCAmelCase__	 =  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:
																								lowerCAmelCase__	 =  torch.load(UpperCamelCase_      ,				map_location="cpu"				)
						else:
												lowerCAmelCase__	 =  torch.load(args.path_to_checkpoint      ,				map_location="cpu"				)

						if args.config_file == "":
												# Default config of megatron-bert 345m
												lowerCAmelCase__	 =  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
												lowerCAmelCase__	 =  input_state_dict["model"]["lm_head"]["bias"].numel()
						else:
												lowerCAmelCase__	 =  MegatronBertConfig.from_json_file(args.config_file				)

						# Convert.
						print("Converting"				)
						lowerCAmelCase__	 =  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.
						lowerCAmelCase__	 =  os.path.join(UpperCamelCase_      ,				"pytorch_model.bin"				)
						print(F"Saving checkpoint to \"{output_checkpoint_file}\""				)
						torch.save(UpperCamelCase_      ,				UpperCamelCase_				)


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

if __name__ == "__main__":
	main()

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














import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


a_							=      logging.get_logger(__name__)

a_							=      {
    '''facebook/musicgen-small''': '''https://huggingface.co/facebook/musicgen-small/resolve/main/config.json''',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}


class        lowercase__       (     _UpperCAmelCase		):
	a_             ="""musicgen_decoder"""
	a_             =["""past_key_values"""]

	def __init__(							self						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=24						,      __UpperCAmelCase=4096						,      __UpperCAmelCase=16						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=True						,      __UpperCAmelCase="gelu"						,      __UpperCAmelCase=1024						,      __UpperCAmelCase=0.1						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.02						,      __UpperCAmelCase=False						,      __UpperCAmelCase=4						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=None						,      __UpperCAmelCase=False						,      **__UpperCAmelCase						,      )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  vocab_size
							lowerCAmelCase__	 =  max_position_embeddings
							lowerCAmelCase__	 =  hidden_size
							lowerCAmelCase__	 =  ffn_dim
							lowerCAmelCase__	 =  num_hidden_layers
							lowerCAmelCase__	 =  num_attention_heads
							lowerCAmelCase__	 =  dropout
							lowerCAmelCase__	 =  attention_dropout
							lowerCAmelCase__	 =  activation_dropout
							lowerCAmelCase__	 =  activation_function
							lowerCAmelCase__	 =  initializer_factor
							lowerCAmelCase__	 =  layerdrop
							lowerCAmelCase__	 =  use_cache
							lowerCAmelCase__	 =  scale_embedding  # scale factor will be sqrt(d_model) if True
							lowerCAmelCase__	 =  num_codebooks
							super().__init__(
							    pad_token_id=__UpperCAmelCase						,      bos_token_id=__UpperCAmelCase						,      eos_token_id=__UpperCAmelCase						,      tie_word_embeddings=__UpperCAmelCase						,      **__UpperCAmelCase						,      )





class        lowercase__       (     _UpperCAmelCase		):
	a_             ="""musicgen"""
	a_             =True

	def __init__(							self						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							super().__init__(**__UpperCAmelCase    )
							if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
													raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config"    )

							lowerCAmelCase__	 =  kwargs.pop("text_encoder"    )
							lowerCAmelCase__	 =  text_encoder_config.pop("model_type"    )

							lowerCAmelCase__	 =  kwargs.pop("audio_encoder"    )
							lowerCAmelCase__	 =  audio_encoder_config.pop("model_type"    )

							lowerCAmelCase__	 =  kwargs.pop("decoder"    )

							lowerCAmelCase__	 =  AutoConfig.for_model(__UpperCAmelCase						,      **__UpperCAmelCase    )
							lowerCAmelCase__	 =  AutoConfig.for_model(__UpperCAmelCase						,      **__UpperCAmelCase    )
							lowerCAmelCase__	 =  MusicgenDecoderConfig(**__UpperCAmelCase    )
							lowerCAmelCase__	 =  True

	@classmethod
	def 						UpperCAmelCase					(							cls						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      **__UpperCAmelCase						,      )->   Tuple:







							'''simple docstring'''





							return cls(
							    text_encoder=text_encoder_config.to_dict()						,      audio_encoder=audio_encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__UpperCAmelCase						,      )


	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  copy.deepcopy(self.__dict__    )
							lowerCAmelCase__	 =  self.text_encoder.to_dict()
							lowerCAmelCase__	 =  self.audio_encoder.to_dict()
							lowerCAmelCase__	 =  self.decoder.to_dict()
							lowerCAmelCase__	 =  self.__class__.model_type
							return output






