


import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
  from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
  from tensorflow.keras.optimizers import Adam






class        __snake_case	(       tf.keras.optimizers.schedules.LearningRateSchedule):







    """simple docstring"""



    def __init__(       self   :  List[str]   ,						lowerCamelCase   :  float   ,						lowerCamelCase   :  Callable   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  float = 1.0   ,						lowerCamelCase   :  str = None   ,						)       ->  Union[str, Any]:
      super().__init__()
      lowerCAmelCase_      :				Dict      =       initial_learning_rate
      lowerCAmelCase_      :				Tuple      =       warmup_steps
      lowerCAmelCase_      :				Tuple      =       power
      lowerCAmelCase_      :				Any      =       decay_schedule_fn
      lowerCAmelCase_      :				List[str]      =       name

    def __call__(       self   :  Optional[int]   ,						lowerCamelCase   :  Any		)       ->  Union[str, Any]:
      with tf.name_scope(self.name or """WarmUp"""		) as name:
        # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
        # learning rate will be `global_step/num_warmup_steps * init_lr`.
        lowerCAmelCase_      :				Tuple      =       tf.cast(lowerCamelCase   ,						tf.floataa		)
        lowerCAmelCase_      :				int      =       tf.cast(self.warmup_steps   ,						tf.floataa		)
        lowerCAmelCase_      :				Optional[Any]      =       global_step_float / warmup_steps_float
        lowerCAmelCase_      :				Optional[int]      =       self.initial_learning_rate * tf.math.pow(lowerCamelCase   ,						self.power		)
        return tf.cond(
            global_step_float < warmup_steps_float   ,						lambda: warmup_learning_rate   ,						lambda: self.decay_schedule_fn(step - self.warmup_steps		)   ,						name=lowerCamelCase   ,						)





    def __lowercase       (       self   :  List[str]		)       ->  str:
      return {
          "initial_learning_rate": self.initial_learning_rate,
          "decay_schedule_fn": self.decay_schedule_fn,
          "warmup_steps": self.warmup_steps,
          "power": self.power,
          "name": self.name,
      }







def     UpperCamelCase_      (   A__ :       float							,   A__ :       int							,   A__ :       int							,   A__ :       float = 0.0							,   A__ :       float = 0.9							,   A__ :       float = 0.999							,   A__ :       float = 1E-8							,   A__ :       Optional[float] = None							,   A__ :       Optional[float] = None							,   A__ :       float = 0.0							,   A__ :       float = 1.0							,   A__ :       Optional[List[str]] = None							,   ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=A__							,   decay_steps=num_train_steps - num_warmup_steps							,   end_learning_rate=init_lr * min_lr_ratio							,   power=A__							,   )
  if num_warmup_steps:
    lowerCAmelCase_      :				Union[str, Any]      =       WarmUp(
        initial_learning_rate=A__							,   decay_schedule_fn=A__							,   warmup_steps=A__							,   )
  if weight_decay_rate > 0.0:
    lowerCAmelCase_      :				List[str]      =       AdamWeightDecay(
        learning_rate=A__							,   weight_decay_rate=A__							,   beta_a=A__							,   beta_a=A__							,   epsilon=A__							,   clipnorm=A__							,   global_clipnorm=A__							,   exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]							,   include_in_weight_decay=A__							,   )
  else:
    lowerCAmelCase_      :				Optional[int]      =       tf.keras.optimizers.Adam(
        learning_rate=A__							,   beta_a=A__							,   beta_a=A__							,   epsilon=A__							,   clipnorm=A__							,   global_clipnorm=A__							,   )
  # We return the optimizer and the LR scheduler in order to better track the
  # evolution of the LR independently of the optimizer.
  return optimizer, lr_schedule






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(       self   :  str   ,						lowerCamelCase   :  Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001   ,						lowerCamelCase   :  float = 0.9   ,						lowerCamelCase   :  float = 0.999   ,						lowerCamelCase   :  float = 1E-7   ,						lowerCamelCase   :  bool = False   ,						lowerCamelCase   :  float = 0.0   ,						lowerCamelCase   :  Optional[List[str]] = None   ,						lowerCamelCase   :  Optional[List[str]] = None   ,						lowerCamelCase   :  str = "AdamWeightDecay"   ,						**lowerCamelCase   :  Union[str, Any]   ,						)       ->  Optional[Any]:
      super().__init__(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						**lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       weight_decay_rate
      lowerCAmelCase_      :				Union[str, Any]      =       include_in_weight_decay
      lowerCAmelCase_      :				str      =       exclude_from_weight_decay

    @classmethod
    def __lowercase       (       cls   :  Optional[Any]   ,						lowerCamelCase   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				List[Any]      =       {"""WarmUp""": WarmUp}
      return super(lowerCamelCase   ,						cls		).from_config(lowerCamelCase   ,						custom_objects=lowerCamelCase		)

    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  int		)       ->  List[str]:
      super(lowerCamelCase   ,						self		)._prepare_local(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       tf.constant(
          self.weight_decay_rate   ,						name="""adam_weight_decay_rate"""		)

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Tuple:
      lowerCAmelCase_      :				Union[str, Any]      =       self._do_use_weight_decay(var.name		)
      if do_decay:
        return var.assign_sub(
            learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""]   ,						use_locking=self._use_locking   ,						)
      return tf.no_op()

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Tuple=None   ,						**lowerCamelCase   :  List[Any]		)       ->  List[str]:
      lowerCAmelCase_      :				Dict      =       list(zip(*lowerCamelCase		)		)
      return super(lowerCamelCase   ,						self		).apply_gradients(zip(lowerCamelCase   ,						lowerCamelCase		)   ,						name=lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Dict		)       ->  List[Any]:
      if apply_state is None:
        return self._decayed_lr_t[var_dtype], {}

      lowerCAmelCase_      :				int      =       apply_state or {}
      lowerCAmelCase_      :				Any      =       apply_state.get((var_device, var_dtype)		)
      if coefficients is None:
        lowerCAmelCase_      :				Dict      =       self._fallback_apply_state(lowerCamelCase   ,						lowerCamelCase		)
        lowerCAmelCase_      :				Union[str, Any]      =       coefficients

      return coefficients["lr_t"], {"apply_state": apply_state}

    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Optional[int]=None		)       ->  str:
      lowerCAmelCase_      :				Tuple      =       self._get_lr(var.device   ,						var.dtype.base_dtype   ,						lowerCamelCase		)
      lowerCAmelCase_      :				str      =       self._decay_weights_op(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)
      with tf.control_dependencies([decay]		):
        return super(lowerCamelCase   ,						self		)._resource_apply_dense(lowerCamelCase   ,						lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  int   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Any=None		)       ->  List[Any]:
      lowerCAmelCase_      :				Tuple      =       self._get_lr(var.device   ,						var.dtype.base_dtype   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Optional[Any]      =       self._decay_weights_op(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)
      with tf.control_dependencies([decay]		):
        return super(lowerCamelCase   ,						self		)._resource_apply_sparse(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  Tuple		)       ->  int:
      lowerCAmelCase_      :				Any      =       super().get_config()
      config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
      return config





    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  Dict		)       ->  int:
      if self.weight_decay_rate == 0:
        return False

      if self._include_in_weight_decay:
        for r in self._include_in_weight_decay:
          if re.search(lowerCamelCase   ,						lowerCamelCase		) is not None:
            return True

      if self._exclude_from_weight_decay:
        for r in self._exclude_from_weight_decay:
          if re.search(lowerCamelCase   ,						lowerCamelCase		) is not None:
            return False
      return True







class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(       self   :  Any		)       ->  Tuple:
      lowerCAmelCase_      :				List[str]      =       []
      lowerCAmelCase_      :				Tuple      =       None

    @property
    def __lowercase       (       self   :  int		)       ->  List[Any]:
      if self._accum_steps is None:
        lowerCAmelCase_      :				Optional[Any]      =       tf.Variable(
            tf.constant(0   ,						dtype=tf.intaa		)   ,						trainable=lowerCamelCase   ,						synchronization=tf.VariableSynchronization.ON_READ   ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA   ,						)

      return self._accum_steps.value()

    @property
    def __lowercase       (       self   :  List[str]		)       ->  Optional[int]:
      if not self._gradients:
        raise ValueError("""The accumulator should be called first to initialize the gradients"""		)
      return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]

    def __call__(       self   :  Optional[Any]   ,						lowerCamelCase   :  List[Any]		)       ->  str:
      if not self._gradients:
        lowerCAmelCase_      :				Union[str, Any]      =       self.step  # Create the step variable.
        self._gradients.extend(
            [
                tf.Variable(
                    tf.zeros_like(lowerCamelCase		)   ,						trainable=lowerCamelCase   ,						synchronization=tf.VariableSynchronization.ON_READ   ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA   ,						)
                if gradient is not None
                else gradient
                for gradient in gradients
            ]		)
      if len(lowerCamelCase		) != len(self._gradients		):
        raise ValueError(F'Expected {len(self._gradients		)} gradients, but got {len(lowerCamelCase		)}'		)

      for accum_gradient, gradient in zip(self._gradients   ,						lowerCamelCase		):
        if accum_gradient is not None and gradient is not None:
          accum_gradient.assign_add(lowerCamelCase		)

      self._accum_steps.assign_add(1		)





    def __lowercase       (       self   :  Dict		)       ->  List[str]:
      if not self._gradients:
        return
      self._accum_steps.assign(0		)
      for gradient in self._gradients:
        if gradient is not None:
          gradient.assign(tf.zeros_like(lowerCamelCase		)		)







'''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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       Tuple=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       []
  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') )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("""cls_token""", """vit.embeddings.cls_token"""),
          ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
          ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
          ("""pos_embed""", """vit.embeddings.position_embeddings"""),
      ] )

  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"
    lowerCAmelCase_      :				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"""),
        ] )

  return rename_keys







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Any							,   A__ :       Tuple=False ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(A__							,   A__ )







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Optional[Any]							,   A__ :       Dict ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       dct.pop(A__ )
  lowerCAmelCase_      :				Tuple      =       val







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				Optional[int]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       ViTConfig()
  lowerCAmelCase_      :				Any      =       False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       True
    lowerCAmelCase_      :				Tuple      =       int(vit_name[-12:-10] )
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-9:-6] )
  else:
    lowerCAmelCase_      :				Optional[int]      =       10_00
    lowerCAmelCase_      :				Tuple      =       """huggingface/label-files"""
    lowerCAmelCase_      :				Any      =       """imagenet-1k-id2label.json"""
    lowerCAmelCase_      :				Dict      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
    lowerCAmelCase_      :				Union[str, Any]      =       {int(A__ ): v for k, v in idalabel.items()}
    lowerCAmelCase_      :				Union[str, Any]      =       idalabel
    lowerCAmelCase_      :				Union[str, Any]      =       {v: k for k, v in idalabel.items()}
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-6:-4] )
    lowerCAmelCase_      :				Dict      =       int(vit_name[-3:] )
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith("""tiny""" ):
      lowerCAmelCase_      :				int      =       1_92
      lowerCAmelCase_      :				List[str]      =       7_68
      lowerCAmelCase_      :				List[str]      =       12
      lowerCAmelCase_      :				int      =       3
    elif vit_name[9:].startswith("""small""" ):
      lowerCAmelCase_      :				Optional[Any]      =       3_84
      lowerCAmelCase_      :				Optional[int]      =       15_36
      lowerCAmelCase_      :				Dict      =       12
      lowerCAmelCase_      :				str      =       6
    else:
      pass
  else:
    if vit_name[4:].startswith("""small""" ):
      lowerCAmelCase_      :				Tuple      =       7_68
      lowerCAmelCase_      :				Any      =       23_04
      lowerCAmelCase_      :				List[str]      =       8
      lowerCAmelCase_      :				List[str]      =       8
    elif vit_name[4:].startswith("""base""" ):
      pass
    elif vit_name[4:].startswith("""large""" ):
      lowerCAmelCase_      :				Dict      =       10_24
      lowerCAmelCase_      :				List[Any]      =       40_96
      lowerCAmelCase_      :				Any      =       24
      lowerCAmelCase_      :				List[str]      =       16
    elif vit_name[4:].startswith("""huge""" ):
      lowerCAmelCase_      :				Optional[int]      =       12_80
      lowerCAmelCase_      :				Dict      =       51_20
      lowerCAmelCase_      :				Union[str, Any]      =       32
      lowerCAmelCase_      :				Optional[int]      =       16

    # load original model from timm
  lowerCAmelCase_      :				Union[str, Any]      =       timm.create_model(A__							,   pretrained=A__ )
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase_      :				int      =       timm_model.state_dict()
  if base_model:
    remove_classification_head_(A__ )
  lowerCAmelCase_      :				str      =       create_rename_keys(A__							,   A__ )
  for src, dest in rename_keys:
    rename_key(A__							,   A__							,   A__ )
  read_in_q_k_v(A__							,   A__							,   A__ )

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       ViTModel(A__ ).eval()
  else:
    lowerCAmelCase_      :				Optional[int]      =       ViTForImageClassification(A__ ).eval()
  model.load_state_dict(A__ )

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    lowerCAmelCase_      :				Any      =       DeiTImageProcessor(size=config.image_size )
  else:
    lowerCAmelCase_      :				Any      =       ViTImageProcessor(size=config.image_size )
  lowerCAmelCase_      :				Tuple      =       image_processor(images=prepare_img()							,   return_tensors="""pt""" )
  lowerCAmelCase_      :				int      =       encoding["""pixel_values"""]
  lowerCAmelCase_      :				int      =       model(A__ )

  if base_model:
    lowerCAmelCase_      :				Union[str, Any]      =       timm_model.forward_features(A__ )
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(A__							,   outputs.pooler_output							,   atol=1E-3 )
  else:
    lowerCAmelCase_      :				Union[str, Any]      =       timm_model(A__ )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(A__							,   outputs.logits							,   atol=1E-3 )

  Path(A__ ).mkdir(exist_ok=A__ )
  print(f'Saving model {vit_name} to {pytorch_dump_folder_path}' )
  model.save_pretrained(A__ )
  print(f'Saving image processor to {pytorch_dump_folder_path}' )
  image_processor.save_pretrained(A__ )


if __name__ == "__main__":
  __A :  Optional[int]    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--vit_name",
      default="vit_base_patch16_224",
      type=str,
      help="Name of the 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."
  )

  __A :  Union[str, Any]    							=				parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



