






'''simple docstring'''

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 							a_		:
  lowercase							= 42

  # setable values
  lowercase							= 42
  lowercase							= 42
  lowercase							= None

  @classmethod
  def     A__		( cls     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE )    -> Tuple:



        """simple docstring"""






        return cls(common=SCREAMING_SNAKE_CASE_     ,						init_noise_sigma=SCREAMING_SNAKE_CASE_     ,						timesteps=SCREAMING_SNAKE_CASE_ )






@dataclass
class 							a_		(     __lowercase							):
  lowercase							= 42






class 							a_		(     __lowercase      ,			__lowercase							):
  lowercase							= [e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase							= 42

  @property
  def     A__		( self )    -> Union[str, Any]:



        """simple docstring"""






        return True

  @register_to_config
  def __init__( self     ,						_SCREAMING_SNAKE_CASE = 1000     ,						_SCREAMING_SNAKE_CASE = 0.0_0_0_1     ,						_SCREAMING_SNAKE_CASE = 0.0_2     ,						_SCREAMING_SNAKE_CASE = "linear"     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = "fixed_small"     ,						_SCREAMING_SNAKE_CASE = True     ,						_SCREAMING_SNAKE_CASE = "epsilon"     ,						_SCREAMING_SNAKE_CASE = jnp.floataa     ,						)    -> str:



        """simple docstring"""






        UpperCamelCase							    =   dtype

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE = None )    -> List[str]:



        """simple docstring"""






        if common is None:
              UpperCamelCase							    =   CommonSchedulerState.create(self )

        # standard deviation of the initial noise distribution
        UpperCamelCase							    =   jnp.array(1.0     ,						dtype=self.dtype )

        UpperCamelCase							    =   jnp.arange(0     ,						self.config.num_train_timesteps ).round()[::-1]

        return DDPMSchedulerState.create(
            common=SCREAMING_SNAKE_CASE_     ,						init_noise_sigma=SCREAMING_SNAKE_CASE_     ,						timesteps=SCREAMING_SNAKE_CASE_     ,						)

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None )    -> List[Any]:



        """simple docstring"""






        return sample

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = () )    -> Any:



        """simple docstring"""






        UpperCamelCase							    =   self.config.num_train_timesteps // num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # rounding to avoid issues when num_inference_step is power of 3
        UpperCamelCase							    =   (jnp.arange(0     ,						SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1]

        return state.replace(
            num_inference_steps=SCREAMING_SNAKE_CASE_     ,						timesteps=SCREAMING_SNAKE_CASE_     ,						)

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=None )    -> Tuple:



        """simple docstring"""






        UpperCamelCase							    =   state.common.alphas_cumprod[t]
        UpperCamelCase							    =   jnp.where(t > 0     ,						state.common.alphas_cumprod[t - 1]     ,						jnp.array(1.0     ,						dtype=self.dtype ) )

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        UpperCamelCase							    =   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

        if variance_type is None:
              UpperCamelCase							    =   self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small":
              UpperCamelCase							    =   jnp.clip(SCREAMING_SNAKE_CASE_     ,						a_min=1e-20 )
        # for rl-diffuser https://arxiv.org/abs/2205.09991
        elif variance_type == "fixed_small_log":
              UpperCamelCase							    =   jnp.log(jnp.clip(SCREAMING_SNAKE_CASE_     ,						a_min=1e-20 ) )
        elif variance_type == "fixed_large":
              UpperCamelCase							    =   state.common.betas[t]
        elif variance_type == "fixed_large_log":
              # Glide max_log
              UpperCamelCase							    =   jnp.log(state.common.betas[t] )
        elif variance_type == "learned":
              return predicted_variance
        elif variance_type == "learned_range":
              UpperCamelCase							    =   variance
              UpperCamelCase							    =   state.common.betas[t]
              UpperCamelCase							    =   (predicted_variance + 1) / 2
              UpperCamelCase							    =   frac * max_log + (1 - frac) * min_log

        return variance

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None     ,						_SCREAMING_SNAKE_CASE = True     ,						)    -> Union[str, Any]:



        """simple docstring"""






        UpperCamelCase							    =   timestep

        if key is None:
              UpperCamelCase							    =   jax.random.PRNGKey(0 )

        if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
              UpperCamelCase    ,UpperCamelCase							    =   jnp.split(SCREAMING_SNAKE_CASE_     ,						sample.shape[1]     ,						axis=1 )
        else:
              UpperCamelCase							    =   None

        # 1. compute alphas, betas
        UpperCamelCase							    =   state.common.alphas_cumprod[t]
        UpperCamelCase							    =   jnp.where(t > 0     ,						state.common.alphas_cumprod[t - 1]     ,						jnp.array(1.0     ,						dtype=self.dtype ) )
        UpperCamelCase							    =   1 - alpha_prod_t
        UpperCamelCase							    =   1 - alpha_prod_t_prev

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
              UpperCamelCase							    =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
              UpperCamelCase							    =   model_output
        elif self.config.prediction_type == "v_prediction":
              UpperCamelCase							    =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
        else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
                  """ for the FlaxDDPMScheduler.""" )

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
              UpperCamelCase							    =   jnp.clip(SCREAMING_SNAKE_CASE_     ,						-1     ,						1 )

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        UpperCamelCase							    =   (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
        UpperCamelCase							    =   state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        UpperCamelCase							    =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        def random_variance():
              UpperCamelCase							    =   jax.random.split(SCREAMING_SNAKE_CASE_     ,						num=1 )
              UpperCamelCase							    =   jax.random.normal(SCREAMING_SNAKE_CASE_     ,						shape=model_output.shape     ,						dtype=self.dtype )
              return (self._get_variance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						predicted_variance=SCREAMING_SNAKE_CASE_ ) ** 0.5) * noise

        UpperCamelCase							    =   jnp.where(t > 0     ,						random_variance()     ,						jnp.zeros(model_output.shape     ,						dtype=self.dtype ) )

        UpperCamelCase							    =   pred_prev_sample + variance

        if not return_dict:
              return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_     ,						state=SCREAMING_SNAKE_CASE_ )

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						)    -> Any:



        """simple docstring"""






        return add_noise_common(state.common     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_ )

  def     A__		( self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						)    -> List[str]:



        """simple docstring"""






        return get_velocity_common(state.common     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_ )


  def __len__( self )    -> Optional[Any]:



        """simple docstring"""






        return self.config.num_train_timesteps


"""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 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			'''huggingface/label-files'''
		SCREAMING_SNAKE_CASE_    			=			'''imagenet-1k-id2label.json'''
		SCREAMING_SNAKE_CASE_    			=			json.load(open(hf_hub_download(__a,				__a,				repo_type='''dataset'''     ),				'''r'''     )     )
		SCREAMING_SNAKE_CASE_    			=			{int(__a     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_    			=			{v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE_    			=			'''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_    			=			BitConfig(
		    conv_layer=__a,				num_labels=1_000,				idalabel=__a,				labelaid=__a,				)

		return config



def 							_lowerCamelCase			(			__a     ):
		if "stem.conv" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''stem.conv''',				'''bit.embedder.convolution'''     )
		if "blocks" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''blocks''',				'''layers'''     )
		if "head.fc" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''head.fc''',				'''classifier.1'''     )
		if name.startswith('''norm'''     ):
				SCREAMING_SNAKE_CASE_    			=			'''bit.''' + name
		if "bit" not in name and "classifier" not in name:
				SCREAMING_SNAKE_CASE_    			=			'''bit.encoder.''' + name

		return name



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     )
		return im



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=False     ):
		SCREAMING_SNAKE_CASE_    			=			get_config(__a     )

		# load original model from timm
		SCREAMING_SNAKE_CASE_    			=			create_model(__a,				pretrained=__a     )
		timm_model.eval()

		# load state_dict of original model
		SCREAMING_SNAKE_CASE_    			=			timm_model.state_dict()
		for key in state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			val.squeeze() if '''head''' in key else val

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_    			=			BitForImageClassification(__a     )
		model.eval()
		model.load_state_dict(__a     )

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

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

		SCREAMING_SNAKE_CASE_    			=			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(),				)

		SCREAMING_SNAKE_CASE_    			=			prepare_img()
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     )
		SCREAMING_SNAKE_CASE_    			=			processor(__a,				return_tensors='''pt'''     ).pixel_values

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

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_    			=			model(__a     )
				SCREAMING_SNAKE_CASE_    			=			outputs.logits

		print('''Logits:''',				logits[0, :3]     )
		print('''Predicted class:''',				model.config.idalabel[logits.argmax(-1     ).item()]     )
		SCREAMING_SNAKE_CASE_    			=			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__":
						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)