





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()
snake_case_     :       List[Any]						=  logging.get_logger(__name__)





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple   ) ->	List[Any]:
						UpperCAmelCase_			:      Optional[Any]     						=  """huggingface/label-files"""
						UpperCAmelCase_			:      Optional[Any]     						=  """imagenet-1k-id2label.json"""
						UpperCAmelCase_			:      str     						=  json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			repo_type='''dataset'''   ),			'''r'''   )   )
						UpperCAmelCase_			:      Tuple     						=  {int(SCREAMING_SNAKE_CASE__   ): v for k, v in idalabel.items()}
						UpperCAmelCase_			:      Optional[Any]     						=  {v: k for k, v in idalabel.items()}

						UpperCAmelCase_			:      List[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"
						UpperCAmelCase_			:      Optional[int]     						=  BitConfig(
						    conv_layer=SCREAMING_SNAKE_CASE__,			num_labels=1000,			idalabel=SCREAMING_SNAKE_CASE__,			labelaid=SCREAMING_SNAKE_CASE__,			)

						return config





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	int:
						if "stem.conv" in name:
												UpperCAmelCase_			:      int     						=  name.replace('''stem.conv''',			'''bit.embedder.convolution'''   )
						if "blocks" in name:
												UpperCAmelCase_			:      Optional[int]     						=  name.replace('''blocks''',			'''layers'''   )
						if "head.fc" in name:
												UpperCAmelCase_			:      Union[str, Any]     						=  name.replace('''head.fc''',			'''classifier.1'''   )
						if name.startswith('''norm'''   ):
												UpperCAmelCase_			:      Optional[int]     						=  """bit.""" + name
						if "bit" not in name and "classifier" not in name:
												UpperCAmelCase_			:      Any     						=  """bit.encoder.""" + name

						return name





def        lowerCamelCase_      (  ) ->	Any:
						UpperCAmelCase_			:      List[Any]     						=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase_			:      List[Any]     						=  Image.open(requests.get(SCREAMING_SNAKE_CASE__,			stream=SCREAMING_SNAKE_CASE__   ).raw   )
						return im





@torch.no_grad()
def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			Optional[Any]=False   ) ->	Any:
						UpperCAmelCase_			:      List[Any]     						=  get_config(SCREAMING_SNAKE_CASE__   )

						# load original model from timm
						UpperCAmelCase_			:      str     						=  create_model(SCREAMING_SNAKE_CASE__,			pretrained=SCREAMING_SNAKE_CASE__   )
						timm_model.eval()

						# load state_dict of original model
						UpperCAmelCase_			:      int     						=  timm_model.state_dict()
						for key in state_dict.copy().keys():
												UpperCAmelCase_			:      Tuple     						=  state_dict.pop(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Any     						=  val.squeeze() if """head""" in key else val

						# load HuggingFace model
						UpperCAmelCase_			:      int     						=  BitForImageClassification(SCREAMING_SNAKE_CASE__   )
						model.eval()
						model.load_state_dict(SCREAMING_SNAKE_CASE__   )

						# create image processor
						UpperCAmelCase_			:      Optional[Any]     						=  create_transform(**resolve_data_config({},			model=SCREAMING_SNAKE_CASE__   )   )
						UpperCAmelCase_			:      Tuple     						=  transform.transforms

						UpperCAmelCase_			:      List[str]     						=  {
						    """bilinear""": PILImageResampling.BILINEAR,
						    """bicubic""": PILImageResampling.BICUBIC,
						    """nearest""": PILImageResampling.NEAREST,
						}

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

						UpperCAmelCase_			:      Optional[int]     						=  prepare_img()
						UpperCAmelCase_			:      List[str]     						=  transform(SCREAMING_SNAKE_CASE__   ).unsqueeze(0   )
						UpperCAmelCase_			:      List[Any]     						=  processor(SCREAMING_SNAKE_CASE__,			return_tensors='''pt'''   ).pixel_values

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

						# verify logits
						with torch.no_grad():
												UpperCAmelCase_			:      str     						=  model(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      List[str]     						=  outputs.logits

						print('''Logits:''',			logits[0, :3]   )
						print('''Predicted class:''',			model.config.idalabel[logits.argmax(-1   ).item()]   )
						UpperCAmelCase_			:      str     						=  timm_model(SCREAMING_SNAKE_CASE__   )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(SCREAMING_SNAKE_CASE__,			outputs.logits,			atol=1E-3   )
						print('''Looks ok!'''   )

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

						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__":
					snake_case_     :       int						=  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.",
					)

					snake_case_     :       Dict						=  parser.parse_args()
					convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''







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


snake_case_     :       List[str]						=  False


class 					__a      (unittest.TestCase     ):
							pass





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

							def     UpperCAmelCase__	(							self							:     int   )					->       str:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

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

													UpperCAmelCase_			:      Optional[int]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Union[str, Any]     						=  pipe.dual_guided(
													    prompt='''first prompt'''       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=2       ,     output_type='''numpy'''       ,     ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(__magic_name__   )
																			UpperCAmelCase_			:      Optional[int]     						=  VersatileDiffusionPipeline.from_pretrained(__magic_name__       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Any     						=  generator.manual_seed(0   )
													UpperCAmelCase_			:      Dict     						=  pipe.dual_guided(
													    prompt='''first prompt'''       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     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     UpperCAmelCase__	(							self							:     str   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Union[str, Any]     						=  '''cyberpunk 2077'''
													UpperCAmelCase_			:      Union[str, Any]     						=  load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''   )
													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe.dual_guided(
													    prompt=__magic_name__       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=50       ,     output_type='''numpy'''       ,     ).images

													UpperCAmelCase_			:      List[str]     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      Union[str, Any]     						=  np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]   )

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

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

													UpperCAmelCase_			:      Tuple     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      Any     						=  np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]   )

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

													UpperCAmelCase_			:      Tuple     						=  pipe.image_variation(__magic_name__       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Optional[Any]     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      List[str]     						=  np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]   )

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





