





"""simple docstring"""




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowerCamelCase_     :						Union[str, Any]						=					'scheduler_config.json'

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Optional[Any]	    =   1
      lowercase_      : str	    =   2
      lowercase_      : List[str]	    =   3
      lowercase_      : List[Any]	    =   4
      lowercase_      : int	    =   5


@dataclass
class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : jnp.ndarray


class  _UpperCAmelCase  :



      '''simple docstring'''
      lowercase_      : int	    =   SCHEDULER_CONFIG_NAME
      lowercase_      : str	    =   ["""dtype"""]
      lowercase_      : List[Any]	    =   []
      lowercase_      : Any	    =   True

      @classmethod
      def 					lowerCamelCase_			(     cls					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_=False					,      **snake_case_					,      ):

        """simple docstring"""

        A_						,       A_  :       str										=  cls.load_config(
            pretrained_model_name_or_path=snake_case_					,      subfolder=snake_case_					,      return_unused_kwargs=snake_case_					,      **snake_case_					,      )
        A_						,       A_  :       int										=  cls.from_config(snake_case_					,      return_unused_kwargs=snake_case_					,      **snake_case_  )

        if hasattr(snake_case_					,      'create_state'  ) and getattr(snake_case_					,      'has_state'					,      snake_case_  ):
          A_  :       Optional[int]										=  scheduler.create_state()

        if return_unused_kwargs:
          return scheduler, state, unused_kwargs

        return scheduler, state

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = False					,      **snake_case_  ):

        """simple docstring"""

        self.save_config(save_directory=snake_case_					,      push_to_hub=snake_case_					,      **snake_case_  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return self._get_compatibles()

      @classmethod
      def 					lowerCamelCase_			(     cls  ):

        """simple docstring"""

        A_  :       int										=  list(set([cls.__name__] + cls._compatibles  )  )
        A_  :       Dict										=  importlib.import_module(__name__.split('.'  )[0]  )
        A_  :       Tuple										=  [
            getattr(snake_case_					,      snake_case_  ) for c in compatible_classes_str if hasattr(snake_case_					,      snake_case_  )
        ]
        return compatible_classes





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  assert len(_UpperCAmelCase					) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCAmelCase					) - x.ndim)					) ,					_UpperCAmelCase					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=0.999 ,					_UpperCAmelCase=jnp.floataa					):






  """simple docstring"""





  def alpha_bar(_UpperCAmelCase					):
    return math.cos((time_step + 0.008) / 1.008 * math.pi / 2					) ** 2

  A_  :       List[Any]										=  []
  for i in range(_UpperCAmelCase					):
    A_  :       Optional[Any]										=  i / num_diffusion_timesteps
    A_  :       Optional[int]										=  (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar(_UpperCAmelCase					) / alpha_bar(_UpperCAmelCase					) ,					_UpperCAmelCase					)					)
  return jnp.array(_UpperCAmelCase ,					dtype=_UpperCAmelCase					)

@flax.struct.dataclass
class  _UpperCAmelCase  :



      '''simple docstring'''
      lowercase_      : jnp.ndarray
      lowercase_      : jnp.ndarray
      lowercase_      : jnp.ndarray

      @classmethod
      def 					lowerCamelCase_			(     cls					,      snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  scheduler.config

        if config.trained_betas is not None:
          A_  :       str										=  jnp.asarray(config.trained_betas					,      dtype=scheduler.dtype  )
        elif config.beta_schedule == "linear":
          A_  :       str										=  jnp.linspace(config.beta_start					,      config.beta_end					,      config.num_train_timesteps					,      dtype=scheduler.dtype  )
        elif config.beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          A_  :       int										=  (
              jnp.linspace(
                  config.beta_start**0.5					,      config.beta_end**0.5					,      config.num_train_timesteps					,      dtype=scheduler.dtype  )
              ** 2
          )
        elif config.beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          A_  :       Optional[Any]										=  betas_for_alpha_bar(config.num_train_timesteps					,      dtype=scheduler.dtype  )
        else:
          raise NotImplementedError(
              F"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""  )

        A_  :       List[Any]										=  1.0 - betas

        A_  :       Optional[int]										=  jnp.cumprod(snake_case_					,      axis=0  )

        return cls(
            alphas=snake_case_					,      betas=snake_case_					,      alphas_cumprod=snake_case_					,      )





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Union[str, Any]										=  state.alphas_cumprod

  A_  :       Tuple										=  alphas_cumprod[timesteps] ** 0.5
  A_  :       str										=  sqrt_alpha_prod.flatten()
  A_  :       str										=  broadcast_to_shape_from_left(_UpperCAmelCase ,					original_samples.shape					)

  A_  :       Union[str, Any]										=  (1 - alphas_cumprod[timesteps]) ** 0.5
  A_  :       List[str]										=  sqrt_one_minus_alpha_prod.flatten()
  A_  :       List[Any]										=  broadcast_to_shape_from_left(_UpperCAmelCase ,					original_samples.shape					)

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_						,       A_  :       List[Any]										=  get_sqrt_alpha_prod(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
  A_  :       str										=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_						,       A_  :       int										=  get_sqrt_alpha_prod(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
  A_  :       Any										=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity






"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_     :						Optional[Any]						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  A_  :       Optional[Any]										=  []

  # fmt: off
  # stem:
  rename_keys.append(('cls_token', 'vit.embeddings.cls_token')					)
  rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')					)

  rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')					)
  rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')					)

  # backbone
  rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')					)
  rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')					)
  rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')					)

  for stage_idx in range(len(config.backbone_config.depths					)					):
    for layer_idx in range(config.backbone_config.depths[stage_idx]					):
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")					)
      rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")					)

    rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")					)
    rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")					)
    rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")					)

  # transformer encoder
  for i in range(config.num_hidden_layers					):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")					)
    rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")					)
    rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")					)
    rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")					)
    rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")					)
    rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")					)
    rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")					)
    rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")					)
    rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")					)
    rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")					)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('norm.weight', 'layernorm.weight'),
            ('norm.bias', 'layernorm.bias'),
            ('pre_logits.fc.weight', 'pooler.dense.weight'),
            ('pre_logits.fc.bias', 'pooler.dense.bias'),
        ]					)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    A_  :       List[str]										=  [(pair[0], pair[1][4:]) if pair[1].startswith('vit'					) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('norm.weight', 'vit.layernorm.weight'),
            ('norm.bias', 'vit.layernorm.bias'),
            ('head.weight', 'classifier.weight'),
            ('head.bias', 'classifier.bias'),
        ]					)
  # fmt: on

  return rename_keys





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  for i in range(config.num_hidden_layers					):
    if base_model:
      A_  :       List[str]										=  ''
    else:
      A_  :       Dict										=  'vit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    A_  :       str										=  state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""					)
    A_  :       List[Any]										=  state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""					)
    # next, add query, keys and values (in that order) to the state dict
    A_  :       List[Any]										=  in_proj_weight[
        : config.hidden_size, :
    ]
    A_  :       Tuple										=  in_proj_bias[: config.hidden_size]
    A_  :       Union[str, Any]										=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    A_  :       Dict										=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    A_  :       Optional[Any]										=  in_proj_weight[
        -config.hidden_size :, :
    ]
    A_  :       Tuple										=  in_proj_bias[-config.hidden_size :]





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_UpperCAmelCase ,					_UpperCAmelCase					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  dct.pop(_UpperCAmelCase					)
  A_  :       Optional[int]										=  val





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Optional[int]										=  'http://images.cocodataset.org/val2017/000000039769.jpg'
  A_  :       int										=  Image.open(requests.get(_UpperCAmelCase ,					stream=_UpperCAmelCase					).raw					)
  return im





@torch.no_grad()
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  A_  :       List[Any]										=  BitConfig(
      global_padding='same' ,					layer_type='bottleneck' ,					depths=(3, 4, 9) ,					out_features=['stage3'] ,					embedding_dynamic_padding=_UpperCAmelCase ,					)
  A_  :       Optional[int]										=  ViTHybridConfig(backbone_config=_UpperCAmelCase ,					image_size=384 ,					num_labels=1000					)
  A_  :       Union[str, Any]										=  False

  # load original model from timm
  A_  :       List[Any]										=  timm.create_model(_UpperCAmelCase ,					pretrained=_UpperCAmelCase					)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  A_  :       Tuple										=  timm_model.state_dict()
  if base_model:
    remove_classification_head_(_UpperCAmelCase					)
  A_  :       Any										=  create_rename_keys(_UpperCAmelCase ,					_UpperCAmelCase					)
  for src, dest in rename_keys:
    rename_key(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)
  read_in_q_k_v(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

  A_  :       Union[str, Any]										=  'huggingface/label-files'
  A_  :       Dict										=  'imagenet-1k-id2label.json'
  A_  :       List[str]										=  json.load(open(hf_hub_download(_UpperCAmelCase ,					_UpperCAmelCase ,					repo_type='dataset'					) ,					'r'					)					)
  A_  :       str										=  {int(_UpperCAmelCase					): v for k, v in idalabel.items()}
  A_  :       Any										=  idalabel
  A_  :       Optional[int]										=  {v: k for k, v in idalabel.items()}

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    A_  :       List[Any]										=  ViTHybridModel(_UpperCAmelCase					).eval()
  else:
    A_  :       str										=  ViTHybridForImageClassification(_UpperCAmelCase					).eval()
  model.load_state_dict(_UpperCAmelCase					)

  # create image processor
  A_  :       Dict										=  create_transform(**resolve_data_config({} ,					model=_UpperCAmelCase					)					)
  A_  :       List[str]										=  transform.transforms

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

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

  A_  :       Optional[Any]										=  prepare_img()
  A_  :       Any										=  transform(_UpperCAmelCase					).unsqueeze(0					)
  A_  :       Dict										=  processor(_UpperCAmelCase ,					return_tensors='pt'					).pixel_values

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

  # verify logits
  with torch.no_grad():
    A_  :       List[Any]										=  model(_UpperCAmelCase					)
    A_  :       List[str]										=  outputs.logits

  print('Predicted class:' ,					logits.argmax(-1					).item()					)
  if base_model:
    A_  :       Union[str, Any]										=  timm_model.forward_features(_UpperCAmelCase					)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(_UpperCAmelCase ,					outputs.pooler_output ,					atol=1E-3					)
  else:
    A_  :       Tuple										=  timm_model(_UpperCAmelCase					)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(_UpperCAmelCase ,					outputs.logits ,					atol=1E-3					)
  print('Looks ok!'					)

  if pytorch_dump_folder_path is not None:
    Path(_UpperCAmelCase					).mkdir(exist_ok=_UpperCAmelCase					)
    print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""					)
    model.save_pretrained(_UpperCAmelCase					)
    print(f"""Saving processor to {pytorch_dump_folder_path}"""					)
    processor.save_pretrained(_UpperCAmelCase					)

  if push_to_hub:
    print(f"""Pushing model and processor to the hub {vit_name}"""					)
    model.push_to_hub(f"""ybelkada/{vit_name}"""					)
    processor.push_to_hub(f"""ybelkada/{vit_name}"""					)


if __name__ == "__main__":
   lowerCamelCase_     :						Optional[Any]						=					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--vit_name',
       default='vit_base_r50_s16_384',
       type=str,
       help='Name of the hybrid ViT 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 upload the model to the HuggingFace hub.'
   )

   lowerCamelCase_     :						List[str]						=					parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)