






"""simple docstring"""







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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  ):
												'''simple docstring'''
												super().__init__()
												self.register_modules(transformer=snake_case__	,  vae=snake_case__	,  scheduler=snake_case__      )

												# create a imagenet -> id dictionary for easier use
												lowercase__    :				int=	{}
												if idalabel is not None:
																		for key, value in idalabel.items():
																								for label in value.split(","      ):
																														lowercase__    :				Tuple=	int(snake_case__      )
																		lowercase__    :				Union[str, Any]=	dict(sorted(self.labels.items()      )      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''

												if not isinstance(snake_case__	,  snake_case__      ):
																		lowercase__    :				List[Any]=	list(snake_case__      )

												for l in label:
																		if l not in self.labels:
																								raise ValueError(
																								    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''      )

												return [self.labels[l] for l in label]




						@torch.no_grad()
						def __call__(       self	,  snake_case__	,  snake_case__ = 4.0	,  snake_case__ = None	,  snake_case__ = 50	,  snake_case__ = "pil"	,  snake_case__ = True	,  ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	len(snake_case__      )
												lowercase__    :				Optional[int]=	self.transformer.config.sample_size
												lowercase__    :				List[str]=	self.transformer.config.in_channels

												lowercase__    :				Any=	randn_tensor(
												    shape=(batch_size, latent_channels, latent_size, latent_size)	,  generator=snake_case__	,  device=self.device	,  dtype=self.transformer.dtype	,  )
												lowercase__    :				Any=	torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

												lowercase__    :				Tuple=	torch.tensor(snake_case__	,  device=self.device      ).reshape(-1      )
												lowercase__    :				Any=	torch.tensor([1000] * batch_size	,  device=self.device      )
												lowercase__    :				Tuple=	torch.cat([class_labels, class_null]	,  0      ) if guidance_scale > 1 else class_labels

												# set step values
												self.scheduler.set_timesteps(snake_case__      )

												for t in self.progress_bar(self.scheduler.timesteps      ):
																		if guidance_scale > 1:
																								lowercase__    :				List[str]=	latent_model_input[: len(snake_case__      ) // 2]
																								lowercase__    :				int=	torch.cat([half, half]	,  dim=0      )
																		lowercase__    :				Union[str, Any]=	self.scheduler.scale_model_input(snake_case__	,  snake_case__      )

																		lowercase__    :				Optional[int]=	t
																		if not torch.is_tensor(snake_case__      ):
																								# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																								# This would be a good case for the `match` statement (Python 3.10+)
																								lowercase__    :				List[str]=	latent_model_input.device.type == "mps"
																								if isinstance(snake_case__	,  snake_case__      ):
																														lowercase__    :				int=	torch.floataa if is_mps else torch.floataa
																								else:
																														lowercase__    :				Dict=	torch.intaa if is_mps else torch.intaa
																								lowercase__    :				Tuple=	torch.tensor([timesteps]	,  dtype=snake_case__	,  device=latent_model_input.device      )
																		elif len(timesteps.shape      ) == 0:
																								lowercase__    :				Dict=	timesteps[None].to(latent_model_input.device      )
																		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																		lowercase__    :				int=	timesteps.expand(latent_model_input.shape[0]      )
																		# predict noise model_output
																		lowercase__    :				Union[str, Any]=	self.transformer(
																		    snake_case__	,  timestep=snake_case__	,  class_labels=snake_case__      ).sample

																		# perform guidance
																		if guidance_scale > 1:
																								lowercase__,     lowercase__    :				Tuple=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																								lowercase__,     lowercase__    :				Union[str, Any]=	torch.split(snake_case__	,  len(snake_case__      ) // 2	,  dim=0      )

																								lowercase__    :				str=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																								lowercase__    :				Dict=	torch.cat([half_eps, half_eps]	,  dim=0      )

																								lowercase__    :				Optional[int]=	torch.cat([eps, rest]	,  dim=1      )

																		# learned sigma
																		if self.transformer.config.out_channels // 2 == latent_channels:
																								lowercase__,     lowercase__    :				Union[str, Any]=	torch.split(snake_case__	,  snake_case__	,  dim=1      )
																		else:
																								lowercase__    :				int=	noise_pred

																		# compute previous image: x_t -> x_t-1
																		lowercase__    :				List[Any]=	self.scheduler.step(snake_case__	,  snake_case__	,  snake_case__      ).prev_sample

												if guidance_scale > 1:
																		lowercase__,     lowercase__    :				Any=	latent_model_input.chunk(2	,  dim=0      )
												else:
																		lowercase__    :				str=	latent_model_input

												lowercase__    :				Dict=	1 / self.vae.config.scaling_factor * latents
												lowercase__    :				Any=	self.vae.decode(snake_case__      ).sample

												lowercase__    :				Tuple=	(samples / 2 + 0.5).clamp(0	,  1      )

												# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
												lowercase__    :				List[Any]=	samples.cpu().permute(0	,  2	,  3	,  1      ).float().numpy()

												if output_type == "pil":
																		lowercase__    :				Optional[Any]=	self.numpy_to_pil(snake_case__      )

												if not return_dict:
																		return (samples,)

												return ImagePipelineOutput(images=snake_case__      )














"""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)






