


"""simple docstring"""






import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__				:  Tuple						= logging.get_logger()


@dataclass
class 	A_       :





  """simple docstring"""

  lowercase		:	nn.Module
  lowercase		:	List[nn.Module]   =						field(default_factory=_UpperCAmelCase   )
  lowercase		:	list   =						field(default_factory=_UpperCAmelCase   )
  lowercase		:	Dict[str, nn.Module]   =						field(default_factory=_UpperCAmelCase   )
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[str]:
     a   :	List[str]						=    len(list(m.modules()						)						) == 1 or isinstance(__UpperCAmelCase  ,					nn.Convad						) or isinstance(__UpperCAmelCase  ,					nn.BatchNormad						)
     if has_not_submodules:
        self.traced.append(__UpperCAmelCase						)
        a   :	Optional[int]						=    m
  def __call__(						self  ,					__UpperCAmelCase						)				->					List[str]:
     for name, m in self.module.named_modules():
        self.handles.append(m.register_forward_hook(partial(self._forward_hook  ,					name=__UpperCAmelCase						)						)						)
     self.module(__UpperCAmelCase						)
     [x.remove() for x in self.handles]
     return self






  @property
  def 							lowercase_     (						self						)				->					Tuple:
     # check the len of the state_dict keys to see if we have learnable params
     return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()						)						) > 0}



class 	A_       (     nn.Module   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase						)				->					Tuple:
     super().__init__()

     a   :	List[Tuple[str, nn.Module]]						=    []
     # - get the stem
     feature_blocks.append(('conv1', model.stem)						)
     # - get all the feature blocks
     for k, v in model.trunk_output.named_children():
        assert k.startswith('block'						), f'Unexpected layer name {k}'
        a   :	Dict						=    len(__UpperCAmelCase						) + 1
        feature_blocks.append((f'res{block_index}', v)						)

     a   :	List[Any]						=    nn.ModuleDict(__UpperCAmelCase						)






  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Any:
     return get_trunk_forward_outputs(
         __UpperCAmelCase  ,					out_feat_keys=__UpperCAmelCase  ,					feature_blocks=self._feature_blocks  ,					)



class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Tuple:
     return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]




def        A_					(					UpperCAmelCase__				)     ->     Dict[str, str]:
   a   :	List[str]						=    RegNetConfig(depths=[2, 7, 17, 1]       ,   hidden_sizes=[8, 8, 8, 8]       ,   groups_width=8				)
   if "in1k" in model_name:
      a   :	List[Any]						=    RegNetForImageClassification(UpperCAmelCase__				)
   else:
      a   :	Tuple						=    RegNetModel(UpperCAmelCase__				)
   # create from model (with small weights)
   a   :	Any						=    FakeRegNetVisslWrapper(
       RegNet(FakeRegNetParams(depth=27       ,   group_width=1010       ,   w_a=1744       ,   w_a=6_2_0.8_3       ,   w_m=2.5_2				)				)				)

   with torch.no_grad():
      a   :	Tuple						=    from_model.eval()
      a   :	int						=    our_model.eval()

      a   :	Optional[int]						=    torch.randn((1, 3, 32, 32)				)
      # trace both
      a   :	Any						=    Tracker(UpperCAmelCase__				)
      a   :	Tuple						=    dest_tracker(UpperCAmelCase__				).parametrized

      pprint(dest_tracker.nameamodule				)
      a   :	Tuple						=    Tracker(UpperCAmelCase__				)
      a   :	Any						=    src_tracker(UpperCAmelCase__				).parametrized

   # convert the keys -> module dict to keys -> params
   def to_params_dict(UpperCAmelCase__				):
      a   :	Optional[int]						=    OrderedDict()
      for name, module in dict_with_modules.items():
         for param_name, param in module.state_dict().items():
            a   :	List[Any]						=    param
      return params_dict

   a   :	Optional[int]						=    {}

   a   :	str						=    to_params_dict(UpperCAmelCase__				)
   a   :	Optional[int]						=    to_params_dict(UpperCAmelCase__				)

   for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,   dst_state_dict.items()				):
      a   :	Optional[int]						=    dest_key
      logger.info(F'{src_key} -> {dest_key}'				)
   # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
   if "in1k" in model_name:
      a   :	List[Any]						=    'classifier.1.weight'
      a   :	Any						=    'classifier.1.bias'

   return from_to_ours_keys




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__ = None       ,   UpperCAmelCase__ = True				)     ->     Dict:
   a   :	Dict						=    'imagenet-1k-id2label.json'
   a   :	Any						=    1000

   a   :	Tuple						=    'huggingface/label-files'
   a   :	str						=    num_labels
   a   :	int						=    json.load(open(cached_download(hf_hub_url(UpperCAmelCase__       ,   UpperCAmelCase__       ,   repo_type='dataset'				)				)       ,   'r'				)				)
   a   :	str						=    {int(UpperCAmelCase__				): v for k, v in idalabel.items()}

   a   :	Dict						=    idalabel
   a   :	int						=    {v: k for k, v in idalabel.items()}

   a   :	Any						=    partial(UpperCAmelCase__       ,   num_labels=UpperCAmelCase__       ,   idalabel=UpperCAmelCase__       ,   labelaid=UpperCAmelCase__				)

   a   :	Tuple						=    {
       'regnet-y-10b-seer': ImageNetPreTrainedConfig(
           depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010				),
       # finetuned on imagenet
       'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
           depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010				),
   }

   # add seer weights logic
   def load_using_classy_vision(UpperCAmelCase__				) -> Tuple[Dict, Dict]:
      a   :	Optional[Any]						=    torch.hub.load_state_dict_from_url(UpperCAmelCase__       ,   model_dir=str(UpperCAmelCase__				)       ,   map_location='cpu'				)
      # check if we have a head, if yes add it
      a   :	Tuple						=    files['classy_state_dict']['base_model']['model']
      return model_state_dict["trunk"], model_state_dict["heads"]

   a   :	List[Any]						=    {
       'regnet-y-10b-seer': partial(
           UpperCAmelCase__       ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,   ),
       'regnet-y-10b-seer-in1k': partial(
           UpperCAmelCase__       ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'       ,   ),
   }

   a   :	Union[str, Any]						=    get_from_to_our_keys(UpperCAmelCase__				)

   if not (save_directory / F'{model_name}.pth').exists():
      logger.info('Loading original state_dict.'				)
      a	, a   :	Optional[Any]						=    names_to_from_model[model_name]()
      a   :	Optional[Any]						=    from_state_dict_trunk
      if "in1k" in model_name:
         # add the head
         a   :	List[Any]						=    {**from_state_dict_trunk, **from_state_dict_head}
      logger.info('Done!'				)

      a   :	int						=    {}

      a   :	Optional[int]						=    list(from_state_dict.keys()				)
      a   :	List[str]						=    r'\.block.-part.'
      # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
      for key in from_state_dict.keys():
         # remove the weird "block[0,1]-part" from the key
         a   :	Tuple						=    re.sub(UpperCAmelCase__       ,   ''       ,   UpperCAmelCase__				)
         # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
         a   :	Optional[Any]						=    from_to_ours_keys[src_key]
         # store the parameter with our key
         a   :	int						=    from_state_dict[key]
         not_used_keys.remove(UpperCAmelCase__				)
      # check that all keys have been updated
      assert len(UpperCAmelCase__				) == 0, F'Some keys where not used {",".join(UpperCAmelCase__				)}'

      logger.info(F'The following keys were not used: {",".join(UpperCAmelCase__				)}'				)

      # save our state dict to disk
      torch.save(UpperCAmelCase__       ,   save_directory / F'{model_name}.pth'				)

      del converted_state_dict
   else:
      logger.info('The state_dict was already stored on disk.'				)
   if push_to_hub:
      logger.info(F'Token is {os.environ["HF_TOKEN"]}'				)
      logger.info('Loading our model.'				)
      # create our model
      a   :	List[str]						=    names_to_config[model_name]
      a   :	Tuple						=    RegNetModel
      if "in1k" in model_name:
         a   :	Union[str, Any]						=    RegNetForImageClassification
      a   :	Optional[int]						=    our_model_func(UpperCAmelCase__				)
      # place our model to the meta device (so remove all the weights)
      our_model.to(torch.device('meta'				)				)
      logger.info('Loading state_dict in our model.'				)
      # load state dict
      a   :	Dict						=    our_model.state_dict().keys()
      PreTrainedModel._load_pretrained_model_low_mem(
          UpperCAmelCase__       ,   UpperCAmelCase__       ,   [save_directory / F'{model_name}.pth']				)
      logger.info('Finally, pushing!'				)
      # push it to hub
      our_model.push_to_hub(
          repo_path_or_name=save_directory / model_name       ,   commit_message='Add model'       ,   output_dir=save_directory / model_name       ,   )
      a   :	Optional[Any]						=    384
      # we can use the convnext one
      a   :	Optional[int]						=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,   size=UpperCAmelCase__				)
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / model_name       ,   commit_message='Add image processor'       ,   output_dir=save_directory / model_name       ,   )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__				:  Any						= argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default=None,
     type=str,
     help=(
         "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
         " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=Path,
     required=True,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument(
     "--push_to_hub",
     default=True,
     type=bool,
     required=False,
     help="If True, push model and image processor to the hub.",
 )

 SCREAMING_SNAKE_CASE__				:  str						= parser.parse_args()

 SCREAMING_SNAKE_CASE__				:  Path						= args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









"""simple docstring"""






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

import flax
import jax.numpy as jnp
from scipy import integrate

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


@flax.struct.dataclass
class 	A_       :





  """simple docstring"""

  lowercase		:	CommonSchedulerState

  # setable values
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	Optional[int]   =						None

  # running values
  lowercase		:	Optional[jnp.ndarray]   =						None
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     return cls(common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase						)



@dataclass
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	LMSDiscreteSchedulerState



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	int   =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase		:	jnp.dtype
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     return True
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = jnp.floataa  ,					)				->					Union[str, Any]:
     a   :	Dict						=    dtype
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					LMSDiscreteSchedulerState:
     if common is None:
        a   :	Dict						=    CommonSchedulerState.create(self						)

     a   :	List[Any]						=    jnp.arange(0  ,					self.config.num_train_timesteps						).round()[::-1]
     a   :	Dict						=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     a   :	Union[str, Any]						=    sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					jnp.ndarray:
     ((a)	, )   :	Optional[int]						=    jnp.where(state.timesteps == timestep  ,					size=1						)
     a   :	Optional[Any]						=    step_index[0]

     a   :	Union[str, Any]						=    state.sigmas[step_index]
     a   :	Optional[int]						=    sample / ((sigma**2 + 1) ** 0.5)
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:

     def lms_derivative(__UpperCAmelCase						):
        a   :	Optional[Any]						=    1.0
        for k in range(__UpperCAmelCase						):
           if current_order == k:
              continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

     a   :	Union[str, Any]						=    integrate.quad(__UpperCAmelCase  ,					state.sigmas[t]  ,					state.sigmas[t + 1]  ,					epsrel=1E-4						)[0]

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = ()						)				->					LMSDiscreteSchedulerState:
     a   :	Optional[int]						=    jnp.linspace(self.config.num_train_timesteps - 1  ,					0  ,					__UpperCAmelCase  ,					dtype=self.dtype						)

     a   :	Dict						=    jnp.floor(__UpperCAmelCase						).astype(jnp.intaa						)
     a   :	str						=    jnp.ceil(__UpperCAmelCase						).astype(jnp.intaa						)

     a   :	List[Any]						=    jnp.mod(__UpperCAmelCase  ,					1.0						)

     a   :	Optional[int]						=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     a   :	int						=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     a   :	Optional[int]						=    jnp.concatenate([sigmas, jnp.array([0.0]  ,					dtype=self.dtype						)]						)

     a   :	Any						=    timesteps.astype(jnp.intaa						)

     # initial running values
     a   :	Optional[int]						=    jnp.zeros((0,) + shape  ,					dtype=self.dtype						)

     return state.replace(
         timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					num_inference_steps=__UpperCAmelCase  ,					derivatives=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 4  ,					__UpperCAmelCase = True  ,					)				->					Union[FlaxLMSSchedulerOutput, Tuple]:
     if state.num_inference_steps is None:
        raise ValueError(
            'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

     a   :	Dict						=    state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        a   :	Any						=    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a   :	str						=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     else:
        raise ValueError(
            f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'						)

     # 2. Convert to an ODE derivative
     a   :	str						=    (sample - pred_original_sample) / sigma
     a   :	int						=    state.replace(derivatives=jnp.append(state.derivatives  ,					__UpperCAmelCase						)						)
     if len(state.derivatives						) > order:
        a   :	Any						=    state.replace(derivatives=jnp.delete(state.derivatives  ,					0						)						)

     # 3. Compute linear multistep coefficients
     a   :	Any						=    min(timestep + 1  ,					__UpperCAmelCase						)
     a   :	Dict						=    [self.get_lms_coefficient(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

     # 4. Compute previous sample based on the derivatives path
     a   :	int						=    sample + sum(
         coeff * derivative for coeff, derivative in zip(__UpperCAmelCase  ,					reversed(state.derivatives						)						)						)

     if not return_dict:
        return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase  ,					state=__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					jnp.ndarray:
     a   :	Dict						=    state.sigmas[timesteps].flatten()
     a   :	Union[str, Any]						=    broadcast_to_shape_from_left(__UpperCAmelCase  ,					noise.shape						)

     a   :	List[Any]						=    original_samples + noise * sigma

     return noisy_samples






  def __len__(						self						)				->					List[Any]:
     return self.config.num_train_timesteps





