





'''simple docstring'''







import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)






def 					_lowerCamelCase							(					lowercase		: Optional[Any]       ,				lowercase		: Tuple       ,				lowercase		: List[Any]		)      ->      List[Any]:







				if isinstance(__UpperCAmelCase       ,				torch.Tensor		):
								return image
				elif isinstance(__UpperCAmelCase       ,				PIL.Image.Image		):
								_a				      =					[image]

				if isinstance(image[0]       ,				PIL.Image.Image		):
								_a				      =					[np.array(i.resize((w, h)       ,				resample=PIL_INTERPOLATION["lanczos"]		)		)[None, :] for i in image]
								_a				      =					np.concatenate(__UpperCAmelCase       ,				axis=0		)
								_a				      =					np.array(__UpperCAmelCase		).astype(np.floataa		) / 2_55.0
								_a				      =					image.transpose(0       ,				3       ,				1       ,				2		)
								_a				      =					2.0 * image - 1.0
								_a				      =					torch.from_numpy(__UpperCAmelCase		)
				elif isinstance(image[0]       ,				torch.Tensor		):
								_a				      =					torch.cat(__UpperCAmelCase       ,				dim=0		)
				return image






def 					_lowerCamelCase							(					lowercase		: Dict       ,				lowercase		: int       ,				lowercase		: str       ,				lowercase		: Union[str, Any]=0.99_95		)      ->      Optional[Any]:







				if not isinstance(__UpperCAmelCase       ,				np.ndarray		):
								_a				      =					True
								_a				      =					va.device
								_a				      =					va.cpu().numpy()
								_a				      =					va.cpu().numpy()

				_a				      =					np.sum(va * va / (np.linalg.norm(__UpperCAmelCase		) * np.linalg.norm(__UpperCAmelCase		))		)
				if np.abs(__UpperCAmelCase		) > DOT_THRESHOLD:
								_a				      =					(1 - t) * va + t * va
				else:
								_a				      =					np.arccos(__UpperCAmelCase		)
								_a				      =					np.sin(__UpperCAmelCase		)
								_a				      =					theta_a * t
								_a				      =					np.sin(__UpperCAmelCase		)
								_a				      =					np.sin(theta_a - theta_t		) / sin_theta_a
								_a				      =					sin_theta_t / sin_theta_a
								_a				      =					sa * va + sa * va

				if inputs_are_torch:
								_a				      =					torch.from_numpy(__UpperCAmelCase		).to(__UpperCAmelCase		)

				return va






def 					_lowerCamelCase							(					lowercase		: Union[str, Any]       ,				lowercase		: int		)      ->      Dict:
				_a				      =					F.normalize(__UpperCAmelCase       ,				dim=-1		)
				_a				      =					F.normalize(__UpperCAmelCase       ,				dim=-1		)
				return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)






def 					_lowerCamelCase							(					lowercase		: Union[str, Any]       ,				lowercase		: str		)      ->      List[Any]:







				for param in model.parameters():
								_a				      =					value



class 							__SCREAMING_SNAKE_CASE    (lowerCamelCase_ ):






				"""simple docstring"""







				def __init__(    self	:   List[str]   ,							__a	:   AutoencoderKL   ,							__a	:   CLIPTextModel   ,							__a	:   CLIPModel   ,							__a	:   CLIPTokenizer   ,							__a	:   UNetaDConditionModel   ,							__a	:   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]   ,							__a	:   CLIPFeatureExtractor   ,							__a	:   Union[str, Any]=None   ,							__a	:   Optional[int]=None   ,							__a	:   Any=None   ,							):
								super().__init__()
								self.register_modules(
								    vae=a_   ,							text_encoder=a_   ,							clip_model=a_   ,							tokenizer=a_   ,							unet=a_   ,							scheduler=a_   ,							feature_extractor=a_   ,							coca_model=a_   ,							coca_tokenizer=a_   ,							coca_transform=a_   ,							)
								_a				      =					(
								    feature_extractor.size
								    if isinstance(feature_extractor.size   ,							a_   )
								    else feature_extractor.size['shortest_edge']
								)
								_a				      =					transforms.Normalize(mean=feature_extractor.image_mean   ,							std=feature_extractor.image_std   )
								set_requires_grad(self.text_encoder   ,							a_   )
								set_requires_grad(self.clip_model   ,							a_   )




				def     UpperCamelCase__  (    self	:   List[Any]   ,							__a	:   Optional[Union[str, int]] = "auto"   ):
								if slice_size == "auto":
												# half the attention head size is usually a good trade-off between
												# speed and memory
												_a				      =					self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(a_   )




				def     UpperCamelCase__  (    self	:   Optional[int]   ):
								self.enable_attention_slicing(a_   )




				def     UpperCamelCase__  (    self	:   str   ):
								set_requires_grad(self.vae   ,							a_   )




				def     UpperCamelCase__  (    self	:   Any   ):
								set_requires_grad(self.vae   ,							a_   )




				def     UpperCamelCase__  (    self	:   Optional[Any]   ):
								set_requires_grad(self.unet   ,							a_   )




				def     UpperCamelCase__  (    self	:   Union[str, Any]   ):
								set_requires_grad(self.unet   ,							a_   )




				def     UpperCamelCase__  (    self	:   Optional[int]   ,							__a	:   str   ,							__a	:   Tuple   ,							__a	:   int   ):
								# get the original timestep using init_timestep
								_a				      =					min(int(num_inference_steps * strength   )   ,							a_   )

								_a				      =					max(num_inference_steps - init_timestep   ,							0   )
								_a				      =					self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start




				def     UpperCamelCase__  (    self	:   int   ,							__a	:   Any   ,							__a	:   str   ,							__a	:   Optional[Any]   ,							__a	:   Any   ,							__a	:   List[str]   ,							__a	:   Optional[Any]=None   ):
								if not isinstance(a_   ,							torch.Tensor   ):
												raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(a_   )}'   )

								_a				      =					image.to(device=a_   ,							dtype=a_   )

								if isinstance(a_   ,							a_   ):
												_a				      =					[
												    self.vae.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(a_   )
												]
												_a				      =					torch.cat(a_   ,							dim=0   )
								else:
												_a				      =					self.vae.encode(a_   ).latent_dist.sample(a_   )

								# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								_a				      =					0.18215 * init_latents
								_a				      =					init_latents.repeat_interleave(a_   ,							dim=0   )

								_a				      =					randn_tensor(init_latents.shape   ,							generator=a_   ,							device=a_   ,							dtype=a_   )

								# get latents
								_a				      =					self.scheduler.add_noise(a_   ,							a_   ,							a_   )
								_a				      =					init_latents

								return latents




				def     UpperCamelCase__  (    self	:   Optional[int]   ,							__a	:   Any   ):
								_a				      =					self.coca_transform(a_   ).unsqueeze(0   )
								with torch.no_grad(), torch.cuda.amp.autocast():
												_a				      =					self.coca_model.generate(transformed_image.to(device=self.device   ,							dtype=self.coca_model.dtype   )   )
								_a				      =					self.coca_tokenizer.decode(generated[0].cpu().numpy()   )
								return generated.split("<end_of_text>"   )[0].replace("<start_of_text>"   ,							""   ).rstrip(" .,"   )




				def     UpperCamelCase__  (    self	:   Any   ,							__a	:   Optional[Any]   ,							__a	:   Optional[int]   ):
								_a				      =					self.feature_extractor.preprocess(a_   )
								_a				      =					torch.from_numpy(clip_image_input["pixel_values"][0]   ).unsqueeze(0   ).to(self.device   ).half()
								_a				      =					self.clip_model.get_image_features(a_   )
								_a				      =					image_embeddings_clip / image_embeddings_clip.norm(p=2   ,							dim=-1   ,							keepdim=a_   )
								_a				      =					image_embeddings_clip.repeat_interleave(a_   ,							dim=0   )
								return image_embeddings_clip




				@torch.enable_grad()
				def     UpperCamelCase__  (    self	:   Dict   ,							__a	:   Tuple   ,							__a	:   Dict   ,							__a	:   int   ,							__a	:   Union[str, Any]   ,							__a	:   str   ,							__a	:   List[Any]   ,							__a	:   Tuple   ,							):
								_a				      =					latents.detach().requires_grad_()

								_a				      =					self.scheduler.scale_model_input(a_   ,							a_   )

								# predict the noise residual
								_a				      =					self.unet(a_   ,							a_   ,							encoder_hidden_states=a_   ).sample

								if isinstance(self.scheduler   ,							(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
												_a				      =					self.scheduler.alphas_cumprod[timestep]
												_a				      =					1 - alpha_prod_t
												# compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												_a				      =					(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

												_a				      =					torch.sqrt(a_   )
												_a				      =					pred_original_sample * (fac) + latents * (1 - fac)
								elif isinstance(self.scheduler   ,							a_   ):
												_a				      =					self.scheduler.sigmas[index]
												_a				      =					latents - sigma * noise_pred
								else:
												raise ValueError(f'scheduler type {type(self.scheduler   )} not supported'   )

								# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								_a				      =					1 / 0.18215 * sample
								_a				      =					self.vae.decode(a_   ).sample
								_a				      =					(image / 2 + 0.5).clamp(0   ,							1   )

								_a				      =					transforms.Resize(self.feature_extractor_size   )(a_   )
								_a				      =					self.normalize(a_   ).to(latents.dtype   )

								_a				      =					self.clip_model.get_image_features(a_   )
								_a				      =					image_embeddings_clip / image_embeddings_clip.norm(p=2   ,							dim=-1   ,							keepdim=a_   )

								_a				      =					spherical_dist_loss(a_   ,							a_   ).mean() * clip_guidance_scale

								_a				      =					-torch.autograd.grad(a_   ,							a_   )[0]

								if isinstance(self.scheduler   ,							a_   ):
												_a				      =					latents.detach() + grads * (sigma**2)
												_a				      =					noise_pred_original
								else:
												_a				      =					noise_pred_original - torch.sqrt(a_   ) * grads
								return noise_pred, latents




				@torch.no_grad()
				def __call__(    self	:   Tuple   ,							__a	:   Union[torch.FloatTensor, PIL.Image.Image]   ,							__a	:   Union[torch.FloatTensor, PIL.Image.Image]   ,							__a	:   Optional[str] = None   ,							__a	:   Optional[str] = None   ,							__a	:   Optional[int] = 5_12   ,							__a	:   Optional[int] = 5_12   ,							__a	:   float = 0.6   ,							__a	:   Optional[int] = 50   ,							__a	:   Optional[float] = 7.5   ,							__a	:   Optional[int] = 1   ,							__a	:   float = 0.0   ,							__a	:   Optional[float] = 1_00   ,							__a	:   Optional[torch.Generator] = None   ,							__a	:   Optional[str] = "pil"   ,							__a	:   bool = True   ,							__a	:   float = 0.8   ,							__a	:   float = 0.1   ,							__a	:   float = 0.1   ,							):
								if isinstance(a_   ,							a_   ) and len(a_   ) != batch_size:
												raise ValueError(f'You have passed {batch_size} batch_size, but only {len(a_   )} generators.'   )

								if height % 8 != 0 or width % 8 != 0:
												raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'   )

								if isinstance(a_   ,							torch.Generator   ) and batch_size > 1:
												_a				      =					[generator] + [None] * (batch_size - 1)

								_a				      =					[
								    ('model', self.coca_model is None),
								    ('tokenizer', self.coca_tokenizer is None),
								    ('transform', self.coca_transform is None),
								]
								_a				      =					[x[0] for x in coca_is_none if x[1]]
								_a				      =					', '.join(a_   )
								# generate prompts with coca model if prompt is None
								if content_prompt is None:
												if len(a_   ):
																raise ValueError(
																    f'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
																    f'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'   )
												_a				      =					self.get_image_description(a_   )
								if style_prompt is None:
												if len(a_   ):
																raise ValueError(
																    f'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
																    f' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'   )
												_a				      =					self.get_image_description(a_   )

								# get prompt text embeddings for content and style
								_a				      =					self.tokenizer(
								    a_   ,							padding="max_length"   ,							max_length=self.tokenizer.model_max_length   ,							truncation=a_   ,							return_tensors="pt"   ,							)
								_a				      =					self.text_encoder(content_text_input.input_ids.to(self.device   )   )[0]

								_a				      =					self.tokenizer(
								    a_   ,							padding="max_length"   ,							max_length=self.tokenizer.model_max_length   ,							truncation=a_   ,							return_tensors="pt"   ,							)
								_a				      =					self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

								_a				      =					slerp(a_   ,							a_   ,							a_   )

								# duplicate text embeddings for each generation per prompt
								_a				      =					text_embeddings.repeat_interleave(a_   ,							dim=0   )

								# set timesteps
								_a				      =					'offset' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
								_a				      =					{}
								if accepts_offset:
												_a				      =					1

								self.scheduler.set_timesteps(a_   ,							**a_   )
								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								self.scheduler.timesteps.to(self.device   )

								_a				      =					self.get_timesteps(a_   ,							a_   ,							self.device   )
								_a				      =					timesteps[:1].repeat(a_   )

								# Preprocess image
								_a				      =					preprocess(a_   ,							a_   ,							a_   )
								_a				      =					self.prepare_latents(
								    a_   ,							a_   ,							a_   ,							text_embeddings.dtype   ,							self.device   ,							a_   )

								_a				      =					preprocess(a_   ,							a_   ,							a_   )
								_a				      =					self.prepare_latents(
								    a_   ,							a_   ,							a_   ,							text_embeddings.dtype   ,							self.device   ,							a_   )

								_a				      =					slerp(a_   ,							a_   ,							a_   )

								if clip_guidance_scale > 0:
												_a				      =					self.get_clip_image_embeddings(a_   ,							a_   )
												_a				      =					self.get_clip_image_embeddings(a_   ,							a_   )
												_a				      =					slerp(
												    a_   ,							a_   ,							a_   )

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								_a				      =					guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
												_a				      =					content_text_input.input_ids.shape[-1]
												_a				      =					self.tokenizer([""]   ,							padding="max_length"   ,							max_length=a_   ,							return_tensors="pt"   )
												_a				      =					self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
												# duplicate unconditional embeddings for each generation per prompt
												_a				      =					uncond_embeddings.repeat_interleave(a_   ,							dim=0   )

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												_a				      =					torch.cat([uncond_embeddings, text_embeddings]   )

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								_a				      =					(batch_size, self.unet.config.in_channels, height // 8, width // 8)
								_a				      =					text_embeddings.dtype
								if latents is None:
												if self.device.type == "mps":
																# randn does not work reproducibly on mps
																_a				      =					torch.randn(a_   ,							generator=a_   ,							device="cpu"   ,							dtype=a_   ).to(
																    self.device   )
												else:
																_a				      =					torch.randn(a_   ,							generator=a_   ,							device=self.device   ,							dtype=a_   )
								else:
												if latents.shape != latents_shape:
																raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'   )
												_a				      =					latents.to(self.device   )

								# scale the initial noise by the standard deviation required by the scheduler
								_a				      =					latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								_a				      =					'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
								_a				      =					{}
								if accepts_eta:
												_a				      =					eta

								# check if the scheduler accepts generator
								_a				      =					'generator' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
								if accepts_generator:
												_a				      =					generator

								with self.progress_bar(total=a_   ):
												for i, t in enumerate(a_   ):
																# expand the latents if we are doing classifier free guidance
																_a				      =					torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
																_a				      =					self.scheduler.scale_model_input(a_   ,							a_   )

																# predict the noise residual
																_a				      =					self.unet(a_   ,							a_   ,							encoder_hidden_states=a_   ).sample

																# perform classifier free guidance
																if do_classifier_free_guidance:
																				_a				      =					noise_pred.chunk(2   )
																				_a				      =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																# perform clip guidance
																if clip_guidance_scale > 0:
																				_a				      =					(
																				    text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
																				)
																				_a				      =					self.cond_fn(
																				    a_   ,							a_   ,							a_   ,							a_   ,							a_   ,							a_   ,							a_   ,							)

																# compute the previous noisy sample x_t -> x_t-1
																_a				      =					self.scheduler.step(a_   ,							a_   ,							a_   ,							**a_   ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								_a				      =					1 / 0.18215 * latents
								_a				      =					self.vae.decode(a_   ).sample

								_a				      =					(image / 2 + 0.5).clamp(0   ,							1   )
								_a				      =					image.cpu().permute(0   ,							2   ,							3   ,							1   ).numpy()

								if output_type == "pil":
												_a				      =					self.numpy_to_pil(a_   )

								if not return_dict:
												return (image, None)

								return StableDiffusionPipelineOutput(images=a_   ,							nsfw_content_detected=a_   )





'''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_gpt2/convert_megatron_gpt2_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 AutoTokenizer, GPTaConfig







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=0					)				->					Dict:





			"""simple docstring"""







			if name is None:
						snake_case:    Any      							=None
			else:
						snake_case:    Any      							='.' * max(0				,							spaces - 2					) + '# {:' + str(50 - spaces					) + 's}'
						snake_case:    Optional[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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Optional[Any]      							={}

			# old versions did not store training args
			snake_case:    Dict      							=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))

						snake_case:    List[Any]      							=ds_args.padded_vocab_size
						snake_case:    List[Any]      							=ds_args.max_position_embeddings
						snake_case:    str      							=ds_args.hidden_size
						snake_case:    Any      							=ds_args.num_layers
						snake_case:    Dict      							=ds_args.num_attention_heads
						snake_case:    Dict      							=ds_args.ffn_hidden_size
						# pprint(config)

			# The number of heads.
			snake_case:    Any      							=config.n_head
			# The hidden_size per head.
			snake_case:    Union[str, Any]      							=config.n_embd // config.n_head
			# Megatron-LM checkpoint version
			if "checkpoint_version" in input_state_dict.keys():
						snake_case:    Any      							=input_state_dict['checkpoint_version']
			else:
						snake_case:    Optional[int]      							=0.0

			# The model.
			snake_case:    List[str]      							=input_state_dict['model']
			# The language model.
			snake_case:    List[Any]      							=model['language_model']
			# The embeddings.
			snake_case:    Union[str, Any]      							=lm['embedding']

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

			# The position embeddings.
			snake_case:    str      							=embeddings['position_embeddings']['weight']
			# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
			snake_case:    Dict      							=pos_embeddings.size(0					)
			if n_positions != config.n_positions:
						raise ValueError(
						    f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''					)
			# Store the position embeddings.
			snake_case:    Any      							=pos_embeddings

			# The transformer.
			snake_case:    Union[str, Any]      							=lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

			# The regex to extract layer names.
			snake_case:    Union[str, Any]      							=re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'					)

			# The simple map of names for "automated" rules.
			snake_case:    List[str]      							={
			    'attention.dense': '.attn.c_proj.',
			    'self_attention.dense': '.attn.c_proj.',
			    'mlp.dense_h_to_4h': '.mlp.c_fc.',
			    'mlp.dense_4h_to_h': '.mlp.c_proj.',
			}

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

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

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

						# The name of the layer.
						snake_case:    Tuple      							=f'''transformer.h.{layer_idx}'''

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith('layernorm'					):
									snake_case:    Union[str, Any]      							='ln_1' if op_name.startswith('input'					) else 'ln_2'
									snake_case:    List[str]      							=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":
									# Insert a tensor of 1x1xDxD bias.
									snake_case:    Optional[Any]      							=torch.tril(torch.ones((n_positions, n_positions)				,							dtype=torch.floataa					)					).view(
									    1				,							1				,							__UpperCAmelCase				,							__UpperCAmelCase					)
									snake_case:    int      							=causal_mask

									# Insert a "dummy" tensor for masked_bias.
									snake_case:    Dict      							=torch.tensor(-1e4				,							dtype=torch.floataa					)
									snake_case:    Optional[Any]      							=masked_bias

									snake_case:    Dict      							=fix_query_key_value_ordering(__UpperCAmelCase				,							__UpperCAmelCase				,							3				,							__UpperCAmelCase				,							__UpperCAmelCase					)
									# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
									snake_case:    Dict      							=out_val.transpose(0				,							1					).contiguous()
									# Store.
									snake_case:    Optional[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":
									snake_case:    Dict      							=fix_query_key_value_ordering(__UpperCAmelCase				,							__UpperCAmelCase				,							3				,							__UpperCAmelCase				,							__UpperCAmelCase					)
									# Store. No change of shape.
									snake_case:    str      							=out_val

						# Transpose the weights.
						elif weight_or_bias == "weight":
									snake_case:    Optional[int]      							=megatron_to_transformers[op_name]
									snake_case:    str      							=val.transpose(0				,							1					)

						# Copy the bias.
						elif weight_or_bias == "bias":
									snake_case:    int      							=megatron_to_transformers[op_name]
									snake_case:    Dict      							=val

    # DEBUG.
			assert config.n_layer == layer_idx + 1

			# The final layernorm.
			snake_case:    Optional[int]      							=transformer['final_layernorm.weight']
			snake_case:    Optional[Any]      							=transformer['final_layernorm.bias']

			# For LM head, transformers' wants the matrix to weight embeddings.
			snake_case:    Union[str, Any]      							=word_embeddings

			# It should be done!
			return output_state_dict







def a_     (    )				->					Tuple:





			"""simple docstring"""







			snake_case:    List[str]      							=argparse.ArgumentParser()
			parser.add_argument('--print-checkpoint-structure'				,							action='store_true'					)
			parser.add_argument(
			    'path_to_checkpoint'				,							type=__UpperCAmelCase				,							help='Path to the checkpoint file (.zip archive or direct .pt file)'				,							)
			parser.add_argument(
			    '--config_file'				,							default=''				,							type=__UpperCAmelCase				,							help='An optional config json file describing the pre-trained model.'				,							)
			snake_case:    List[Any]      							=parser.parse_args()

			# Extract the basename.
			snake_case:    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:
												snake_case:    List[Any]      							=torch.load(__UpperCAmelCase				,							map_location='cpu'					)
			else:
						snake_case:    Dict      							=torch.load(args.path_to_checkpoint				,							map_location='cpu'					)

			snake_case:    Optional[Any]      							=input_state_dict.get('args'				,							__UpperCAmelCase					)

			# Read the config, or default to the model released by NVIDIA.
			if args.config_file == "":
						if ds_args is not None:
									if ds_args.bias_gelu_fusion:
												snake_case:    List[Any]      							='gelu_fast'
									elif ds_args.openai_gelu:
												snake_case:    Optional[int]      							='gelu_new'
									else:
												snake_case:    Any      							='gelu'
						else:
									# in the very early days this used to be "gelu_new"
									snake_case:    Dict      							='gelu_new'

						# Spell out all parameters in case the defaults change.
						snake_case:    Union[str, Any]      							=GPTaConfig(
						    vocab_size=5_02_57				,							n_positions=10_24				,							n_embd=10_24				,							n_layer=24				,							n_head=16				,							n_inner=40_96				,							activation_function=__UpperCAmelCase				,							resid_pdrop=0.1				,							embd_pdrop=0.1				,							attn_pdrop=0.1				,							layer_norm_epsilon=1e-5				,							initializer_range=0.02				,							summary_type='cls_index'				,							summary_use_proj=__UpperCAmelCase				,							summary_activation=__UpperCAmelCase				,							summary_proj_to_labels=__UpperCAmelCase				,							summary_first_dropout=0.1				,							scale_attn_weights=__UpperCAmelCase				,							use_cache=__UpperCAmelCase				,							bos_token_id=5_02_56				,							eos_token_id=5_02_56				,							)
			else:
						snake_case:    Optional[Any]      							=GPTaConfig.from_json_file(args.config_file					)

			snake_case:    int      							=['GPT2LMHeadModel']

			# Convert.
			print('Converting'					)
			snake_case:    str      							=convert_megatron_checkpoint(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)

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

			# Add tokenizer class info to config
			# see https://github.com/huggingface/transformers/issues/13906)
			if ds_args is not None:
						snake_case:    Dict      							=ds_args.tokenizer_type
						if tokenizer_type == "GPT2BPETokenizer":
									snake_case:    Tuple      							='gpt2'
						elif tokenizer_type == "PretrainedFromHF":
									snake_case:    int      							=ds_args.tokenizer_name_or_path
						else:
									raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''					)
			else:
						snake_case:    Optional[Any]      							='gpt2'

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained(__UpperCAmelCase					)
			snake_case:    Any      							=type(__UpperCAmelCase					).__name__
			snake_case:    Optional[Any]      							=tokenizer_class

			# Store the config to file.
			print('Saving config'					)
			config.save_pretrained(__UpperCAmelCase					)

			# Save tokenizer based on args
			print(f'''Adding {tokenizer_class} tokenizer files'''					)
			tokenizer.save_pretrained(__UpperCAmelCase					)

			# Store the state_dict to file.
			snake_case:    int      							=os.path.join(__UpperCAmelCase				,							'pytorch_model.bin'					)
			print(f'''Saving checkpoint to "{output_checkpoint_file}"'''					)
			torch.save(__UpperCAmelCase				,							__UpperCAmelCase					)


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

if __name__ == "__main__":
	main()

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




