






"""simple docstring"""







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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput


class    __UpperCAmelCase(       __UpperCamelCase  ,  __UpperCamelCase							):




						"""simple docstring"""




						__lowerCamelCase    	= 1




						@register_to_config
						def __init__(       self	,  snake_case__ = 1000	,  snake_case__ = None      ):
												'''simple docstring'''
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(snake_case__      )

												# standard deviation of the initial noise distribution
												lowercase__    :				Tuple=	1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												lowercase__    :				int=	4

												# running values
												lowercase__    :				List[Any]=	[]




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__ = None      ):
												'''simple docstring'''
												lowercase__    :				Dict=	num_inference_steps
												lowercase__    :				Tuple=	torch.linspace(1	,  0	,  num_inference_steps + 1      )[:-1]
												lowercase__    :				Dict=	torch.cat([steps, torch.tensor([0.0]      )]      )

												if self.config.trained_betas is not None:
																		lowercase__    :				Any=	torch.tensor(self.config.trained_betas	,  dtype=torch.floataa      )
												else:
																		lowercase__    :				List[Any]=	torch.sin(steps * math.pi / 2      ) ** 2

												lowercase__    :				Optional[Any]=	(1.0 - self.betas**2) ** 0.5

												lowercase__    :				List[str]=	(torch.atana(self.betas	,  self.alphas      ) / math.pi * 2)[:-1]
												lowercase__    :				Tuple=	timesteps.to(snake_case__      )

												lowercase__    :				Union[str, Any]=	[]




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__ = True	,  ):
												'''simple docstring'''
												if self.num_inference_steps is None:
																		raise ValueError(
																		    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"      )

												lowercase__    :				Dict=	(self.timesteps == timestep).nonzero().item()
												lowercase__    :				Optional[Any]=	timestep_index + 1

												lowercase__    :				Optional[int]=	sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(snake_case__      )

												if len(self.ets      ) == 1:
																		lowercase__    :				Optional[Any]=	self.ets[-1]
												elif len(self.ets      ) == 2:
																		lowercase__    :				Union[str, Any]=	(3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets      ) == 3:
																		lowercase__    :				List[str]=	(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																		lowercase__    :				Optional[Any]=	(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												lowercase__    :				Union[str, Any]=	self._get_prev_sample(snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      )

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  *snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												return sample




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Dict=	self.alphas[timestep_index]
												lowercase__    :				int=	self.betas[timestep_index]

												lowercase__    :				Tuple=	self.alphas[prev_timestep_index]
												lowercase__    :				int=	self.betas[prev_timestep_index]

												lowercase__    :				Optional[int]=	(sample - sigma * ets) / max(snake_case__	,  1e-8      )
												lowercase__    :				Optional[int]=	next_alpha * pred + ets * next_sigma

												return prev_sample




						def __len__(       self      ):
												'''simple docstring'''
												return self.config.num_train_timesteps














"""simple docstring"""







import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
a			:    List[str]				 =		logging.get_logger(__name__)

a			:    List[Any]				 =		[
    ("""bert.bert""", """visual_bert"""),
    ("""bert.cls""", """cls"""),
    ("""bert.classifier""", """cls"""),
    ("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
    ("""position_embeddings_visual""", """visual_position_embeddings"""),
    ("""projection""", """visual_projection"""),
]

a			:    Dict				 =		[
    """nlvr2_coco_pre_trained.th""",
    """nlvr2_fine_tuned.th""",
    """nlvr2_pre_trained.th""",
    """vcr_coco_pre_train.th""",
    """vcr_fine_tune.th""",
    """vcr_pre_train.th""",
    """vqa_coco_pre_trained.th""",
    """vqa_fine_tuned.th""",
    """vqa_pre_trained.th""",
]





def 						lowercase__(A						)    ->Optional[int]:




						"""simple docstring"""



						lowercase__    :				Any=	torch.load(A ,		map_location="cpu"						)
						return sd





def 						lowercase__(A ,		A ,		A=rename_keys_prefix						)    ->List[str]:




						"""simple docstring"""



						lowercase__    :				int=	OrderedDict()
						lowercase__    :				Optional[Any]=	torch.arange(config.max_position_embeddings						).expand((1, -1)						)
						# detector_d = OrderedDict()
						for key in d:
												if "detector" in key:
																		# detector_d[key.replace('detector.','')] = d[key]
																		continue
												lowercase__    :				Union[str, Any]=	key
												for name_pair in rename_keys_prefix:
																		lowercase__    :				str=	new_key.replace(name_pair[0] ,		name_pair[1]						)
												lowercase__    :				Union[str, Any]=	d[key]
												if key == "bert.cls.predictions.decoder.weight":
																		# Old bert code didn't have `decoder.bias`, but was added separately
																		lowercase__    :				Optional[int]=	new_d["cls.predictions.bias"]
						return new_d





@torch.no_grad()
def 						lowercase__(A ,		A						)    ->str:




						"""simple docstring"""



						assert (
						    checkpoint_path.split("/"						)[-1] in ACCEPTABLE_CHECKPOINTS
						), f'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

						# Get Config
						if "pre" in checkpoint_path:
												lowercase__    :				Union[str, Any]=	"pretraining"
												if "vcr" in checkpoint_path:
																		lowercase__    :				str=	{"visual_embedding_dim": 512}
												elif "vqa_advanced" in checkpoint_path:
																		lowercase__    :				Optional[Any]=	{"visual_embedding_dim": 2_048}
												elif "vqa" in checkpoint_path:
																		lowercase__    :				int=	{"visual_embedding_dim": 2_048}
												elif "nlvr" in checkpoint_path:
																		lowercase__    :				Tuple=	{"visual_embedding_dim": 1_024}
												else:
																		raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.'''						)
						else:
												if "vcr" in checkpoint_path:
																		lowercase__    :				int=	{"visual_embedding_dim": 512}
																		lowercase__    :				int=	"multichoice"
												elif "vqa_advanced" in checkpoint_path:
																		lowercase__    :				Dict=	{"visual_embedding_dim": 2_048}
																		lowercase__    :				Optional[Any]=	"vqa_advanced"
												elif "vqa" in checkpoint_path:
																		lowercase__    :				Optional[int]=	{"visual_embedding_dim": 2_048, "num_labels": 3_129}
																		lowercase__    :				List[str]=	"vqa"
												elif "nlvr" in checkpoint_path:
																		lowercase__    :				Dict=	{
																		    "visual_embedding_dim": 1_024,
																		    "num_labels": 2,
																		}
																		lowercase__    :				Any=	"nlvr"

						lowercase__    :				List[Any]=	VisualBertConfig(**A						)

						# Load State Dict
						lowercase__    :				Union[str, Any]=	load_state_dict(A						)

						lowercase__    :				List[str]=	get_new_dict(A ,		A						)

						if model_type == "pretraining":
												lowercase__    :				Optional[Any]=	VisualBertForPreTraining(A						)
						elif model_type == "vqa":
												lowercase__    :				Any=	VisualBertForQuestionAnswering(A						)
						elif model_type == "nlvr":
												lowercase__    :				Union[str, Any]=	VisualBertForVisualReasoning(A						)
						elif model_type == "multichoice":
												lowercase__    :				str=	VisualBertForMultipleChoice(A						)

						model.load_state_dict(A						)
						# Save Checkpoints
						Path(A						).mkdir(exist_ok=A						)
						model.save_pretrained(A						)


if __name__ == "__main__":
			a			:    int				 =		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
			parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
			a			:    Dict				 =		parser.parse_args()
			convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)






