


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       =							logging.get_logger(__name__)


def  _snake_case       (					A	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     '''huggingface/label-files'''
							lowerCAmelCase__	       =     '''imagenet-1k-id2label.json'''
							lowerCAmelCase__	       =     json.load(open(hf_hub_download(A			,   A			,   repo_type='''dataset'''	)			,   '''r'''	)	)
							lowerCAmelCase__	       =     {int(A	): 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=A			,   num_labels=1000			,   idalabel=A			,   labelaid=A			,   )

							return config


def  _snake_case       (					A	)						->   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  _snake_case       (					)						->   List[Any]:
							lowerCAmelCase__	       =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							lowerCAmelCase__	       =     Image.open(requests.get(A			,   stream=A	).raw	)
							return im


@torch.no_grad()
def  _snake_case       (					A			,   A			,   A=False	)						->   Tuple:
							lowerCAmelCase__	       =     get_config(A	)

							# load original model from timm
							lowerCAmelCase__	       =     create_model(A			,   pretrained=A	)
							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(A	)
														lowerCAmelCase__	       =     val.squeeze() if '''head''' in key else val

							# load HuggingFace model
							lowerCAmelCase__	       =     BitForImageClassification(A	)
							model.eval()
							model.load_state_dict(A	)

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

							lowerCAmelCase__	       =     {
							    '''bilinear''': PILImageResampling.BILINEAR,
							    '''bicubic''': PILImageResampling.BICUBIC,
							    '''nearest''': PILImageResampling.NEAREST,
							}

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

							lowerCAmelCase__	       =     prepare_img()
							lowerCAmelCase__	       =     transform(A	).unsqueeze(0	)
							lowerCAmelCase__	       =     processor(A			,   return_tensors='''pt'''	).pixel_values

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

							# verify logits
							with torch.no_grad():
														lowerCAmelCase__	       =     model(A	)
														lowerCAmelCase__	       =     outputs.logits

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

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

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



'''simple docstring'''






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       torch.FloatTensor




class 		a__    (							a__    ,    a__  ):





							'''simple docstring'''






							@register_to_config
							def __init__(		self ,   lowerCamelCase_ = 6_55_36 ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 0 ,   lowerCamelCase_ = "fourier" ,   lowerCamelCase_ = True ,   lowerCamelCase_ = False ,   lowerCamelCase_ = 0.0 ,   lowerCamelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,   lowerCamelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,   lowerCamelCase_ = "UNetMidBlock1D" ,   lowerCamelCase_ = None ,   lowerCamelCase_ = (32, 32, 64) ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 8 ,   lowerCamelCase_ = 1 ,   lowerCamelCase_ = False ,   )	->							Optional[int]:
														super().__init__()
														lowerCAmelCase__	       =     sample_size

														# time
														if time_embedding_type == "fourier":
																					lowerCAmelCase__	       =     GaussianFourierProjection(
																					    embedding_size=8 ,   set_W_to_weight=lowerCamelCase_ ,   log=lowerCamelCase_ ,   flip_sin_to_cos=lowerCamelCase_       )
																					lowerCAmelCase__	       =     2 * block_out_channels[0]
														elif time_embedding_type == "positional":
																					lowerCAmelCase__	       =     Timesteps(
																					    block_out_channels[0] ,   flip_sin_to_cos=lowerCamelCase_ ,   downscale_freq_shift=lowerCamelCase_       )
																					lowerCAmelCase__	       =     block_out_channels[0]

														if use_timestep_embedding:
																					lowerCAmelCase__	       =     block_out_channels[0] * 4
																					lowerCAmelCase__	       =     TimestepEmbedding(
																					    in_channels=lowerCamelCase_ ,   time_embed_dim=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   out_dim=block_out_channels[0] ,   )

														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None

														# down
														lowerCAmelCase__	       =     in_channels
														for i, down_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     block_out_channels[i]

																					if i == 0:
																												input_channel += extra_in_channels

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_down_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_downsample=not is_final_block or downsample_each_block ,   )
																					self.down_blocks.append(lowerCamelCase_       )

														# mid
														lowerCAmelCase__	       =     get_mid_block(
														    lowerCamelCase_ ,   in_channels=block_out_channels[-1] ,   mid_channels=block_out_channels[-1] ,   out_channels=block_out_channels[-1] ,   embed_dim=block_out_channels[0] ,   num_layers=lowerCamelCase_ ,   add_downsample=lowerCamelCase_ ,   )

														# up
														lowerCAmelCase__	       =     list(reversed(lowerCamelCase_       )       )
														lowerCAmelCase__	       =     reversed_block_out_channels[0]
														if out_block_type is None:
																					lowerCAmelCase__	       =     out_channels
														else:
																					lowerCAmelCase__	       =     block_out_channels[0]

														for i, up_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     (
																					    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase_       ) - 1 else final_upsample_channels
																					)

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_up_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_upsample=not is_final_block ,   )
																					self.up_blocks.append(lowerCamelCase_       )
																					lowerCAmelCase__	       =     output_channel

														# out
														lowerCAmelCase__	       =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,   32       )
														lowerCAmelCase__	       =     get_out_block(
														    out_block_type=lowerCamelCase_ ,   num_groups_out=lowerCamelCase_ ,   embed_dim=block_out_channels[0] ,   out_channels=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   fc_dim=block_out_channels[-1] // 4 ,   )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = True ,   )	->							Union[UNetaDOutput, Tuple]:
														lowerCAmelCase__	       =     timestep
														if not torch.is_tensor(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     torch.tensor([timesteps] ,   dtype=torch.long ,   device=sample.device       )
														elif torch.is_tensor(lowerCamelCase_       ) and len(timesteps.shape       ) == 0:
																					lowerCAmelCase__	       =     timesteps[None].to(sample.device       )

														lowerCAmelCase__	       =     self.time_proj(lowerCamelCase_       )
														if self.config.use_timestep_embedding:
																					lowerCAmelCase__	       =     self.time_mlp(lowerCamelCase_       )
														else:
																					lowerCAmelCase__	       =     timestep_embed[..., None]
																					lowerCAmelCase__	       =     timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
																					lowerCAmelCase__	       =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

														# 2. down
														lowerCAmelCase__	       =     ()
														for downsample_block in self.down_blocks:
																					lowerCAmelCase__       , lowerCAmelCase__	       =     downsample_block(hidden_states=lowerCamelCase_ ,   temb=lowerCamelCase_       )
																					down_block_res_samples += res_samples

														# 3. mid
														if self.mid_block:
																					lowerCAmelCase__	       =     self.mid_block(lowerCamelCase_ ,   lowerCamelCase_       )

														# 4. up
														for i, upsample_block in enumerate(self.up_blocks       ):
																					lowerCAmelCase__	       =     down_block_res_samples[-1:]
																					lowerCAmelCase__	       =     down_block_res_samples[:-1]
																					lowerCAmelCase__	       =     upsample_block(lowerCamelCase_ ,   res_hidden_states_tuple=lowerCamelCase_ ,   temb=lowerCamelCase_       )

														# 5. post-process
														if self.out_block:
																					lowerCAmelCase__	       =     self.out_block(lowerCamelCase_ ,   lowerCamelCase_       )

														if not return_dict:
																					return (sample,)

														return UNetaDOutput(sample=lowerCamelCase_       )