




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=0.999    ,SCREAMING_SNAKE_CASE_="cosine"    ,)      ->			Optional[int]:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(SCREAMING_SNAKE_CASE_    ):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(SCREAMING_SNAKE_CASE_    ):
																					return math.exp(t * -12.0    )

							else:
														raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

							lowercase__  :	List[str]        =							[]
							for i in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	Optional[Any]        =							i / num_diffusion_timesteps
														lowercase__  :	List[str]        =							(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_    ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )    )
							return torch.tensor(SCREAMING_SNAKE_CASE_    ,dtype=torch.floataa    )







class 				UpperCAmelCase(					snake_case_   ,     snake_case_					):



	"""simple docstring"""






	a     : List[Any]	  =					[e.name for e in KarrasDiffusionSchedulers]
	a     : List[str]	  =					2

	@register_to_config
	def __init__(					self	,				lowerCamelCase = 1000	,				lowerCamelCase = 0.0_00_85	,				lowerCamelCase = 0.0_12	,				lowerCamelCase = "linear"	,				lowerCamelCase = None	,				lowerCamelCase = "epsilon"	,				lowerCamelCase = "linspace"	,				lowerCamelCase = 0	,				) ->      List[Any]:





								"""simple docstring"""


								if trained_betas is not None:
															lowercase__  :	Tuple        =							torch.tensor(lowerCamelCase	,				dtype=torch.floataa    )
								elif beta_schedule == "linear":
															lowercase__  :	Tuple        =							torch.linspace(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				dtype=torch.floataa    )
								elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															lowercase__  :	Any        =							(
															    torch.linspace(beta_start**0.5	,				beta_end**0.5	,				lowerCamelCase	,				dtype=torch.floataa    ) ** 2
															)
								elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															lowercase__  :	Any        =							betas_for_alpha_bar(lowerCamelCase    )
								else:
															raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""    )

								lowercase__  :	Union[str, Any]        =							1.0 - self.betas
								lowercase__  :	Union[str, Any]        =							torch.cumprod(self.alphas	,				dim=0    )

								#  set all values
								self.set_timesteps(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase=None    ) ->      Dict:





								"""simple docstring"""


								if schedule_timesteps is None:
															lowercase__  :	Any        =							self.timesteps

								lowercase__  :	Tuple        =							(schedule_timesteps == timestep).nonzero()

								# The sigma index that is taken for the **very** first `step`
								# is always the second index (or the last index if there is only 1)
								# This way we can ensure we don't accidentally skip a sigma in
								# case we start in the middle of the denoising schedule (e.g. for image-to-image)
								if len(self._index_counter    ) == 0:
															lowercase__  :	Union[str, Any]        =							1 if len(lowerCamelCase    ) > 1 else 0
								else:
															lowercase__  :	Tuple        =							timestep.cpu().item() if torch.is_tensor(lowerCamelCase    ) else timestep
															lowercase__  :	int        =							self._index_counter[timestep_int]

								return indices[pos].item()

	@property
	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				) ->      torch.FloatTensor:





								"""simple docstring"""


								lowercase__  :	Tuple        =							self.index_for_timestep(lowerCamelCase    )

								if self.state_in_first_order:
															lowercase__  :	int        =							self.sigmas[step_index]
								else:
															lowercase__  :	str        =							self.sigmas_interpol[step_index - 1]

								lowercase__  :	Union[str, Any]        =							sample / ((sigma**2 + 1) ** 0.5)
								return sample

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase = None	,				lowerCamelCase = None	,				) ->      Optional[Any]:





								"""simple docstring"""


								lowercase__  :	List[str]        =							num_inference_steps

								lowercase__  :	int        =							num_train_timesteps or self.config.num_train_timesteps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
															lowercase__  :	List[Any]        =							np.linspace(0	,				num_train_timesteps - 1	,				lowerCamelCase	,				dtype=lowerCamelCase    )[::-1].copy()
								elif self.config.timestep_spacing == "leading":
															lowercase__  :	Dict        =							num_train_timesteps // self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															lowercase__  :	str        =							(np.arange(0	,				lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase    )
															timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
															lowercase__  :	int        =							num_train_timesteps / self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															lowercase__  :	str        =							(np.arange(lowerCamelCase	,				0	,				-step_ratio    )).round().copy().astype(lowerCamelCase    )
															timesteps -= 1
								else:
															raise ValueError(
															    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

								lowercase__  :	int        =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
								lowercase__  :	str        =							torch.from_numpy(np.log(lowerCamelCase    )    ).to(lowerCamelCase    )

								lowercase__  :	Tuple        =							np.interp(lowerCamelCase	,				np.arange(0	,				len(lowerCamelCase    )    )	,				lowerCamelCase    )
								lowercase__  :	List[str]        =							np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
								lowercase__  :	Optional[int]        =							torch.from_numpy(lowerCamelCase    ).to(device=lowerCamelCase    )

								# compute up and down sigmas
								lowercase__  :	List[Any]        =							sigmas.roll(-1    )
								lowercase__  :	int        =							0.0
								lowercase__  :	Optional[int]        =							(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
								lowercase__  :	Optional[int]        =							(sigmas_next**2 - sigmas_up**2) ** 0.5
								lowercase__  :	Optional[Any]        =							0.0

								# compute interpolated sigmas
								lowercase__  :	Tuple        =							sigmas.log().lerp(sigmas_down.log()	,				0.5    ).exp()
								lowercase__  :	Any        =							0.0

								# set sigmas
								lowercase__  :	List[str]        =							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
								lowercase__  :	List[Any]        =							torch.cat(
								    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )
								lowercase__  :	List[Any]        =							torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2    ), sigmas_up[-1:]]    )
								lowercase__  :	List[str]        =							torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2    ), sigmas_down[-1:]]    )

								if str(lowerCamelCase    ).startswith("mps"    ):
															# mps does not support float64
															lowercase__  :	List[str]        =							torch.from_numpy(lowerCamelCase    ).to(lowerCamelCase	,				dtype=torch.floataa    )
								else:
															lowercase__  :	Union[str, Any]        =							torch.from_numpy(lowerCamelCase    ).to(lowerCamelCase    )

								lowercase__  :	str        =							self.sigma_to_t(lowerCamelCase    ).to(lowerCamelCase	,				dtype=timesteps.dtype    )
								lowercase__  :	Any        =							torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])	,				dim=-1    ).flatten()

								lowercase__  :	str        =							torch.cat([timesteps[:1], interleaved_timesteps]    )

								lowercase__  :	Optional[int]        =							None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								lowercase__  :	Dict        =							defaultdict(lowerCamelCase    )

	def    __a     (					self	,				lowerCamelCase    ) ->      List[str]:





								"""simple docstring"""


								lowercase__  :	List[str]        =							sigma.log()

								# get distribution
								lowercase__  :	str        =							log_sigma - self.log_sigmas[:, None]

								# get sigmas range
								lowercase__  :	int        =							dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
								lowercase__  :	Tuple        =							low_idx + 1

								lowercase__  :	Optional[int]        =							self.log_sigmas[low_idx]
								lowercase__  :	int        =							self.log_sigmas[high_idx]

								# interpolate sigmas
								lowercase__  :	Union[str, Any]        =							(low - log_sigma) / (low - high)
								lowercase__  :	Tuple        =							w.clamp(0	,				1    )

								# transform interpolation to time range
								lowercase__  :	List[Any]        =							(1 - w) * low_idx + w * high_idx
								lowercase__  :	List[str]        =							t.view(sigma.shape    )
								return t

	@property
	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								return self.sample is None

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase = None	,				lowerCamelCase = True	,				) ->      Union[SchedulerOutput, Tuple]:





								"""simple docstring"""


								lowercase__  :	Dict        =							self.index_for_timestep(lowerCamelCase    )

								# advance index counter by 1
								lowercase__  :	Any        =							timestep.cpu().item() if torch.is_tensor(lowerCamelCase    ) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
															lowercase__  :	Optional[int]        =							self.sigmas[step_index]
															lowercase__  :	str        =							self.sigmas_interpol[step_index]
															lowercase__  :	Union[str, Any]        =							self.sigmas_up[step_index]
															lowercase__  :	int        =							self.sigmas_down[step_index - 1]
								else:
															# 2nd order / KPDM2's method
															lowercase__  :	Dict        =							self.sigmas[step_index - 1]
															lowercase__  :	Optional[Any]        =							self.sigmas_interpol[step_index - 1]
															lowercase__  :	List[Any]        =							self.sigmas_up[step_index - 1]
															lowercase__  :	int        =							self.sigmas_down[step_index - 1]

								# currently only gamma=0 is supported. This usually works best anyways.
								# We can support gamma in the future but then need to scale the timestep before
								# passing it to the model which requires a change in API
								lowercase__  :	Tuple        =							0
								lowercase__  :	List[str]        =							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								lowercase__  :	Optional[int]        =							model_output.device
								lowercase__  :	str        =							randn_tensor(model_output.shape	,				dtype=model_output.dtype	,				device=lowerCamelCase	,				generator=lowerCamelCase    )

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
															lowercase__  :	Optional[int]        =							sigma_hat if self.state_in_first_order else sigma_interpol
															lowercase__  :	Optional[Any]        =							sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
															lowercase__  :	Dict        =							sigma_hat if self.state_in_first_order else sigma_interpol
															lowercase__  :	Union[str, Any]        =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
															    sample / (sigma_input**2 + 1)
															)
								elif self.config.prediction_type == "sample":
															raise NotImplementedError("prediction_type not implemented yet: sample"    )
								else:
															raise ValueError(
															    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

								if self.state_in_first_order:
															# 2. Convert to an ODE derivative for 1st order
															lowercase__  :	List[str]        =							(sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															lowercase__  :	Optional[int]        =							sigma_interpol - sigma_hat

															# store for 2nd order step
															lowercase__  :	Tuple        =							sample
															lowercase__  :	Optional[int]        =							dt
															lowercase__  :	str        =							sample + derivative * dt
								else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															lowercase__  :	Optional[int]        =							(sample - pred_original_sample) / sigma_interpol
															# 3. delta timestep
															lowercase__  :	Optional[Any]        =							sigma_down - sigma_hat

															lowercase__  :	List[Any]        =							self.sample
															lowercase__  :	Dict        =							None

															lowercase__  :	Optional[int]        =							sample + derivative * dt
															lowercase__  :	Tuple        =							prev_sample + noise * sigma_up

								if not return_dict:
															return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase    )

	def    __a     (					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				) ->      torch.FloatTensor:





								"""simple docstring"""


								lowercase__  :	int        =							self.sigmas.to(device=original_samples.device	,				dtype=original_samples.dtype    )
								if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase    ):
															# mps does not support float64
															lowercase__  :	List[str]        =							self.timesteps.to(original_samples.device	,				dtype=torch.floataa    )
															lowercase__  :	List[Any]        =							timesteps.to(original_samples.device	,				dtype=torch.floataa    )
								else:
															lowercase__  :	Dict        =							self.timesteps.to(original_samples.device    )
															lowercase__  :	Any        =							timesteps.to(original_samples.device    )

								lowercase__  :	Any        =							[self.index_for_timestep(lowerCamelCase	,				lowerCamelCase    ) for t in timesteps]

								lowercase__  :	Tuple        =							sigmas[step_indices].flatten()
								while len(sigma.shape    ) < len(original_samples.shape    ):
															lowercase__  :	Dict        =							sigma.unsqueeze(-1    )

								lowercase__  :	Optional[int]        =							original_samples + noise * sigma
								return noisy_samples







	def __len__(					self    ) ->      List[str]:





								"""simple docstring"""


								return self.config.num_train_timesteps





# 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()

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