

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()
_UpperCAmelCase		:   Optional[Any]										=       logging.get_logger(__name__)

_UpperCAmelCase		:   str										=       [
    ('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'),
]

_UpperCAmelCase		:   List[Any]										=       [
    '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 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    torch.load(_SCREAMING_SNAKE_CASE   ,							map_location='cpu'      )
							return sd





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__=rename_keys_prefix      ):







							'''simple docstring'''






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





@torch.no_grad()
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







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

							snake_case_							=    VisualBertConfig(**_SCREAMING_SNAKE_CASE      )

							# Load State Dict
							snake_case_							=    load_state_dict(_SCREAMING_SNAKE_CASE      )

							snake_case_							=    get_new_dict(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE      )

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

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


if __name__ == "__main__":
					_UpperCAmelCase		:   Optional[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.""")
					_UpperCAmelCase		:   Any										=       parser.parse_args()
					convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''



import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class 							__UpperCamelCase				(		unittest.TestCase					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       Dict					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'							)

						# using the PNDM scheduler by default
						__a			:       str					    =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,				revision='onnx'						,				safety_checker=__a						,				feature_extractor=__a						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 1E-2



