
'''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()
lowerCamelCase_    =		logging.get_logger(__name__)

lowerCamelCase_    =		[
    ('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'),
]

lowerCamelCase_    =		[
    '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 A				(__lowerCamelCase				:Any		):
						_lowerCAmelCase  						=			torch.load(__snake_case			,     map_location="""cpu"""		)
						return sd


def A				(__lowerCamelCase				:Tuple			,     __lowerCamelCase				:int			,     __lowerCamelCase				:Any=rename_keys_prefix		):
						_lowerCAmelCase  						=			OrderedDict()
						_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			key
												for name_pair in rename_keys_prefix:
																		_lowerCAmelCase  						=			new_key.replace(name_pair[0]			,     name_pair[1]		)
												_lowerCAmelCase  						=			d[key]
												if key == "bert.cls.predictions.decoder.weight":
																		# Old bert code didn't have `decoder.bias`, but was added separately
																		_lowerCAmelCase  						=			new_d["cls.predictions.bias"]
						return new_d


@torch.no_grad()
def A				(__lowerCamelCase				:Any			,     __lowerCamelCase				:Optional[int]		):
						assert (
						    checkpoint_path.split("""/"""		)[-1] in ACCEPTABLE_CHECKPOINTS
						), f'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'

						# Get Config
						if "pre" in checkpoint_path:
												_lowerCAmelCase  						=			"pretraining"
												if "vcr" in checkpoint_path:
																		_lowerCAmelCase  						=			{"visual_embedding_dim": 512}
												elif "vqa_advanced" in checkpoint_path:
																		_lowerCAmelCase  						=			{"visual_embedding_dim": 2048}
												elif "vqa" in checkpoint_path:
																		_lowerCAmelCase  						=			{"visual_embedding_dim": 2048}
												elif "nlvr" in checkpoint_path:
																		_lowerCAmelCase  						=			{"visual_embedding_dim": 1024}
												else:
																		raise NotImplementedError(f'No implementation found for `{checkpoint_path}`.'		)
						else:
												if "vcr" in checkpoint_path:
																		_lowerCAmelCase  						=			{"visual_embedding_dim": 512}
																		_lowerCAmelCase  						=			"multichoice"
												elif "vqa_advanced" in checkpoint_path:
																		_lowerCAmelCase  						=			{"visual_embedding_dim": 2048}
																		_lowerCAmelCase  						=			"vqa_advanced"
												elif "vqa" in checkpoint_path:
																		_lowerCAmelCase  						=			{"visual_embedding_dim": 2048, "num_labels": 3129}
																		_lowerCAmelCase  						=			"vqa"
												elif "nlvr" in checkpoint_path:
																		_lowerCAmelCase  						=			{
																		    "visual_embedding_dim": 1024,
																		    "num_labels": 2,
																		}
																		_lowerCAmelCase  						=			"nlvr"

						_lowerCAmelCase  						=			VisualBertConfig(**__snake_case		)

						# Load State Dict
						_lowerCAmelCase  						=			load_state_dict(__snake_case		)

						_lowerCAmelCase  						=			get_new_dict(__snake_case			,     __snake_case		)

						if model_type == "pretraining":
												_lowerCAmelCase  						=			VisualBertForPreTraining(__snake_case		)
						elif model_type == "vqa":
												_lowerCAmelCase  						=			VisualBertForQuestionAnswering(__snake_case		)
						elif model_type == "nlvr":
												_lowerCAmelCase  						=			VisualBertForVisualReasoning(__snake_case		)
						elif model_type == "multichoice":
												_lowerCAmelCase  						=			VisualBertForMultipleChoice(__snake_case		)

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


if __name__ == "__main__":
						lowerCamelCase_    =		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.""")
						lowerCamelCase_    =		parser.parse_args()
						convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()

												# make sure scheduler can always be converted to DDIM
												_lowerCAmelCase  						=			DDIMScheduler.from_config(scheduler.config				)

												self.register_modules(unet=_lowercase   ,       scheduler=_lowercase				)




						@torch.no_grad()
						def __call__( self   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = 0.0   ,       _lowercase = 50   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if isinstance(self.unet.config.sample_size   ,       _lowercase				):
																		_lowerCAmelCase  						=			(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		_lowerCAmelCase  						=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(_lowercase   ,       _lowercase				) and len(_lowercase				) != batch_size:
																		raise ValueError(
																		    F'You have passed a list of generators of length {len(_lowercase				)}, but requested an effective batch'
																		    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

												_lowerCAmelCase  						=			randn_tensor(_lowercase   ,       generator=_lowercase   ,       device=self.device   ,       dtype=self.unet.dtype				)

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

												for t in self.progress_bar(self.scheduler.timesteps				):
																		# 1. predict noise model_output
																		_lowerCAmelCase  						=			self.unet(_lowercase   ,       _lowercase				).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		_lowerCAmelCase  						=			self.scheduler.step(
																		    _lowercase   ,       _lowercase   ,       _lowercase   ,       eta=_lowercase   ,       use_clipped_model_output=_lowercase   ,       generator=_lowercase				).prev_sample

												_lowerCAmelCase  						=			(image / 2 + 0.5).clamp(0   ,       1				)
												_lowerCAmelCase  						=			image.cpu().permute(0   ,       2   ,       3   ,       1				).numpy()
												if output_type == "pil":
																		_lowerCAmelCase  						=			self.numpy_to_pil(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)
