import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase	: str    		=  logging.get_logger(__name__)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							'''huggingface/label-files'''
						SCREAMING_SNAKE_CASE							:					Any           =							'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE							:					Any           =							json.load(open(hf_hub_download(_lowercase	,						_lowercase	,						repo_type='''dataset'''		)	,						'''r'''		)		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{int(_lowercase		): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE							:					Dict           =							{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:					str           =							'''std_conv''' if '''bit''' in model_name else False

						# note that when using BiT as backbone for ViT-hybrid checkpoints,
						# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
						# config.conv_layer = "std_conv_same"
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							BitConfig(
						    conv_layer=_lowercase	,						num_labels=1_000	,						idalabel=_lowercase	,						labelaid=_lowercase	,						)

						return config
def    A			(					_lowercase		):
						if "stem.conv" in name:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							name.replace('''stem.conv'''	,						'''bit.embedder.convolution'''		)
						if "blocks" in name:
												SCREAMING_SNAKE_CASE							:					Tuple           =							name.replace('''blocks'''	,						'''layers'''		)
						if "head.fc" in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							name.replace('''head.fc'''	,						'''classifier.1'''		)
						if name.startswith('''norm'''		):
												SCREAMING_SNAKE_CASE							:					str           =							'''bit.''' + name
						if "bit" not in name and "classifier" not in name:
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''bit.encoder.''' + name

						return name
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Any           =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						SCREAMING_SNAKE_CASE							:					List[str]           =							Image.open(requests.get(_lowercase	,						stream=_lowercase		).raw		)
						return im
@torch.no_grad()
def    A			(					_lowercase	,						_lowercase	,						_lowercase=False		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							get_config(_lowercase		)

						# load original model from timm
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_model(_lowercase	,						pretrained=_lowercase		)
						timm_model.eval()

						# load state_dict of original model
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							timm_model.state_dict()
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:					Dict           =							state_dict.pop(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							val.squeeze() if '''head''' in key else val

						# load HuggingFace model
						SCREAMING_SNAKE_CASE							:					str           =							BitForImageClassification(_lowercase		)
						model.eval()
						model.load_state_dict(_lowercase		)

						# create image processor
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							create_transform(**resolve_data_config({}	,						model=_lowercase		)		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							transform.transforms

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{
						    '''bilinear''': PILImageResampling.BILINEAR,
						    '''bicubic''': PILImageResampling.BICUBIC,
						    '''nearest''': PILImageResampling.NEAREST,
						}

						SCREAMING_SNAKE_CASE							:					Tuple           =							BitImageProcessor(
						    do_resize=_lowercase	,						size={'''shortest_edge''': timm_transforms[0].size}	,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]	,						do_center_crop=_lowercase	,						crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}	,						do_normalize=_lowercase	,						image_mean=timm_transforms[-1].mean.tolist()	,						image_std=timm_transforms[-1].std.tolist()	,						)

						SCREAMING_SNAKE_CASE							:					Any           =							prepare_img()
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							transform(_lowercase		).unsqueeze(0		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							processor(_lowercase	,						return_tensors='''pt'''		).pixel_values

						# verify pixel values
						assert torch.allclose(_lowercase	,						_lowercase		)

						# verify logits
						with torch.no_grad():
												SCREAMING_SNAKE_CASE							:					Dict           =							model(_lowercase		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							outputs.logits

						print('''Logits:'''	,						logits[0, :3]		)
						print('''Predicted class:'''	,						model.config.idalabel[logits.argmax(-1		).item()]		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							timm_model(_lowercase		)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(_lowercase	,						outputs.logits	,						atol=1e-3		)
						print('''Looks ok!'''		)

						if pytorch_dump_folder_path is not None:
												Path(_lowercase		).mkdir(exist_ok=_lowercase		)
												print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""		)
												model.save_pretrained(_lowercase		)
												processor.save_pretrained(_lowercase		)

						if push_to_hub:
												print(f"""Pushing model {model_name} and processor to the hub"""		)
												model.push_to_hub(f"""ybelkada/{model_name}"""		)
												processor.push_to_hub(f"""ybelkada/{model_name}"""		)


if __name__ == "__main__":
					__UpperCamelCase	: Any    		=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='resnetv2_50x1_bitm',
					    type=str,
					    help='Name of the BiT timm model you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)
					parser.add_argument(
					    '--push_to_hub',
					    action='store_true',
					    help='Whether to push the model to the hub.',
					)

					__UpperCamelCase	: Optional[int]    		=  parser.parse_args()
					convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCamelCase	: str    		=  False





class       lowercase__		(     unittest.TestCase):
						pass






@nightly
@require_torch_gpu
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							generator.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''cyberpunk 2077'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							pipe.dual_guided(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''					,	).images

												SCREAMING_SNAKE_CASE							:					Tuple           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					int           =							np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''A painting of a squirrel eating a burger '''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Any           =							pipe.text_to_image(
												    prompt=UpperCamelCase__					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					List[str]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Any           =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe.image_variation(UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1





