
'''simple docstring'''



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()
lowerCamelCase_    =		logging.get_logger(__name__)


def A				(__lowerCamelCase				:Dict		):
						_lowerCAmelCase  						=			"""huggingface/label-files"""
						_lowerCAmelCase  						=			"""imagenet-1k-id2label.json"""
						_lowerCAmelCase  						=			json.load(open(hf_hub_download(__lowerCamelCase			,     __lowerCamelCase			,     repo_type="""dataset"""		)			,     """r"""		)		)
						_lowerCAmelCase  						=			{int(__lowerCamelCase		): v for k, v in idalabel.items()}
						_lowerCAmelCase  						=			{v: k for k, v in idalabel.items()}

						_lowerCAmelCase  						=			"""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"
						_lowerCAmelCase  						=			BitConfig(
						    conv_layer=__lowerCamelCase			,     num_labels=1000			,     idalabel=__lowerCamelCase			,     labelaid=__lowerCamelCase			,     )

						return config


def A				(__lowerCamelCase				:Tuple		):
						if "stem.conv" in name:
												_lowerCAmelCase  						=			name.replace("""stem.conv"""			,     """bit.embedder.convolution"""		)
						if "blocks" in name:
												_lowerCAmelCase  						=			name.replace("""blocks"""			,     """layers"""		)
						if "head.fc" in name:
												_lowerCAmelCase  						=			name.replace("""head.fc"""			,     """classifier.1"""		)
						if name.startswith("""norm"""		):
												_lowerCAmelCase  						=			"""bit.""" + name
						if "bit" not in name and "classifier" not in name:
												_lowerCAmelCase  						=			"""bit.encoder.""" + name

						return name


def A				():
						_lowerCAmelCase  						=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						_lowerCAmelCase  						=			Image.open(requests.get(__lowerCamelCase			,     stream=__lowerCamelCase		).raw		)
						return im


@torch.no_grad()
def A				(__lowerCamelCase				:Dict			,     __lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Any=False		):
						_lowerCAmelCase  						=			get_config(__lowerCamelCase		)

						# load original model from timm
						_lowerCAmelCase  						=			create_model(__lowerCamelCase			,     pretrained=__lowerCamelCase		)
						timm_model.eval()

						# load state_dict of original model
						_lowerCAmelCase  						=			timm_model.state_dict()
						for key in state_dict.copy().keys():
												_lowerCAmelCase  						=			state_dict.pop(__lowerCamelCase		)
												_lowerCAmelCase  						=			val.squeeze() if """head""" in key else val

						# load HuggingFace model
						_lowerCAmelCase  						=			BitForImageClassification(__lowerCamelCase		)
						model.eval()
						model.load_state_dict(__lowerCamelCase		)

						# create image processor
						_lowerCAmelCase  						=			create_transform(**resolve_data_config({}			,     model=__lowerCamelCase		)		)
						_lowerCAmelCase  						=			transform.transforms

						_lowerCAmelCase  						=			{
						    """bilinear""": PILImageResampling.BILINEAR,
						    """bicubic""": PILImageResampling.BICUBIC,
						    """nearest""": PILImageResampling.NEAREST,
						}

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

						_lowerCAmelCase  						=			prepare_img()
						_lowerCAmelCase  						=			transform(__lowerCamelCase		).unsqueeze(0		)
						_lowerCAmelCase  						=			processor(__lowerCamelCase			,     return_tensors="""pt"""		).pixel_values

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

						# verify logits
						with torch.no_grad():
												_lowerCAmelCase  						=			model(__lowerCamelCase		)
												_lowerCAmelCase  						=			outputs.logits

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

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

						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__":
						lowerCamelCase_    =		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.""",
						)

						lowerCamelCase_    =		parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


'''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				)
