


'''simple docstring'''







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 					lowercase_     (    tf.keras.optimizers.schedules.LearningRateSchedule   ):


    def __init__(     self  ,					a  ,					a  ,					a  ,					a = 1.0  ,					a = None  ,					):
          super().__init__()
          UpperCamelCase__										=				initial_learning_rate
          UpperCamelCase__										=				warmup_steps
          UpperCamelCase__										=				power
          UpperCamelCase__										=				decay_schedule_fn
          UpperCamelCase__										=				name


    def __call__(     self  ,					a    ):
          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`.
                UpperCamelCase__										=				tf.cast(A__  ,					tf.floataa    )
                UpperCamelCase__										=				tf.cast(self.warmup_steps  ,					tf.floataa    )
                UpperCamelCase__										=				global_step_float / warmup_steps_float
                UpperCamelCase__										=				self.initial_learning_rate * tf.math.pow(A__  ,					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=A__  ,					)


    def    __a							(     self    ):
          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      ,			__A      ,			__A      ,			__A = 0.0      ,			__A = 0.9      ,			__A = 0.999      ,			__A = 1E-8      ,			__A = None      ,			__A = None      ,			__A = 0.0      ,			__A = 1.0      ,			__A = None      ,			)							->		str:


      '''simple docstring'''



      UpperCamelCase__										=				tf.keras.optimizers.schedules.PolynomialDecay(
          initial_learning_rate=lowerCAmelCase__      ,			decay_steps=num_train_steps - num_warmup_steps      ,			end_learning_rate=init_lr * min_lr_ratio      ,			power=lowerCAmelCase__      ,			)
      if num_warmup_steps:
            UpperCamelCase__										=				WarmUp(
                initial_learning_rate=lowerCAmelCase__      ,			decay_schedule_fn=lowerCAmelCase__      ,			warmup_steps=lowerCAmelCase__      ,			)
      if weight_decay_rate > 0.0:
            UpperCamelCase__										=				AdamWeightDecay(
                learning_rate=lowerCAmelCase__      ,			weight_decay_rate=lowerCAmelCase__      ,			beta_a=lowerCAmelCase__      ,			beta_a=lowerCAmelCase__      ,			epsilon=lowerCAmelCase__      ,			clipnorm=lowerCAmelCase__      ,			global_clipnorm=lowerCAmelCase__      ,			exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]      ,			include_in_weight_decay=lowerCAmelCase__      ,			)
      else:
            UpperCamelCase__										=				tf.keras.optimizers.Adam(
                learning_rate=lowerCAmelCase__      ,			beta_a=lowerCAmelCase__      ,			beta_a=lowerCAmelCase__      ,			epsilon=lowerCAmelCase__      ,			clipnorm=lowerCAmelCase__      ,			global_clipnorm=lowerCAmelCase__      ,			)
      # 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 					lowercase_     (    SCREAMING_SNAKE_CASE__   ):


    def __init__(     self  ,					a = 0.001  ,					a = 0.9  ,					a = 0.999  ,					a = 1e-7  ,					a = False  ,					a = 0.0  ,					a = None  ,					a = None  ,					a = "AdamWeightDecay"  ,					**a  ,					):
          super().__init__(A__  ,					A__  ,					A__  ,					A__  ,					A__  ,					A__  ,					**A__    )
          UpperCamelCase__										=				weight_decay_rate
          UpperCamelCase__										=				include_in_weight_decay
          UpperCamelCase__										=				exclude_from_weight_decay


    @classmethod
    def    __a							(     cls  ,					a    ):
          UpperCamelCase__										=				{"WarmUp": WarmUp}
          return super(A__  ,					cls    ).from_config(A__  ,					custom_objects=A__    )


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          super(A__  ,					self    )._prepare_local(A__  ,					A__  ,					A__    )
          UpperCamelCase__										=				tf.constant(
              self.weight_decay_rate  ,					name="adam_weight_decay_rate"    )


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				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    __a							(     self  ,					a  ,					a=None  ,					**a    ):
          UpperCamelCase__					,	UpperCamelCase__										=				list(zip(*A__    )    )
          return super(A__  ,					self    ).apply_gradients(zip(A__  ,					A__    )  ,					name=A__  ,					**A__    )


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          if apply_state is None:
                return self._decayed_lr_t[var_dtype], {}

          UpperCamelCase__										=				apply_state or {}
          UpperCamelCase__										=				apply_state.get((var_device, var_dtype)    )
          if coefficients is None:
                UpperCamelCase__										=				self._fallback_apply_state(A__  ,					A__    )
                UpperCamelCase__										=				coefficients

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


    def    __a							(     self  ,					a  ,					a  ,					a=None    ):
          UpperCamelCase__					,	UpperCamelCase__										=				self._get_lr(var.device  ,					var.dtype.base_dtype  ,					A__    )
          UpperCamelCase__										=				self._decay_weights_op(A__  ,					A__  ,					A__    )
          with tf.control_dependencies([decay]    ):
                return super(A__  ,					self    )._resource_apply_dense(A__  ,					A__  ,					**A__    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a=None    ):
          UpperCamelCase__					,	UpperCamelCase__										=				self._get_lr(var.device  ,					var.dtype.base_dtype  ,					A__    )
          UpperCamelCase__										=				self._decay_weights_op(A__  ,					A__  ,					A__    )
          with tf.control_dependencies([decay]    ):
                return super(A__  ,					self    )._resource_apply_sparse(A__  ,					A__  ,					A__  ,					**A__    )


    def    __a							(     self    ):
          UpperCamelCase__										=				super().get_config()
          config.update({"weight_decay_rate": self.weight_decay_rate}    )
          return config


    def    __a							(     self  ,					a    ):
          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(A__  ,					A__    ) is not None:
                            return True

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

class 					lowercase_     (    SCREAMING_SNAKE_CASE__   ):


    def __init__(     self    ):
          UpperCamelCase__										=				[]
          UpperCamelCase__										=				None


    @property
    def    __a							(     self    ):
          if self._accum_steps is None:
                UpperCamelCase__										=				tf.Variable(
                    tf.constant(0  ,					dtype=tf.intaa    )  ,					trainable=A__  ,					synchronization=tf.VariableSynchronization.ON_READ  ,					aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA  ,					)

          return self._accum_steps.value()


    @property
    def    __a							(     self    ):
          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  ,					a    ):
          if not self._gradients:
                UpperCamelCase__										=				self.step  # Create the step variable.
                self._gradients.extend(
                    [
                        tf.Variable(
                            tf.zeros_like(A__    )  ,					trainable=A__  ,					synchronization=tf.VariableSynchronization.ON_READ  ,					aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA  ,					)
                        if gradient is not None
                        else gradient
                        for gradient in gradients
                    ]    )
          if len(A__    ) != len(self._gradients    ):
                raise ValueError(f'''Expected {len(self._gradients    )} gradients, but got {len(A__    )}'''    )

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

          self._accum_steps.assign_add(1    )


    def    __a							(     self    ):
          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(A__    )    )







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()
lowercase__		:str   =    logging.get_logger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						'''huggingface/label-files'''
					lowercase							=						'''imagenet-1k-id2label.json'''
					lowercase							=						json.load(open(hf_hub_download(lowerCAmelCase__ ,			lowerCAmelCase__ ,			repo_type='''dataset'''		) ,			'''r'''		)		)
					lowercase							=						{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
					lowercase							=						{v: k for k, v in idalabel.items()}

					lowercase							=						'''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"
					lowercase							=						BitConfig(
					    conv_layer=lowerCAmelCase__ ,			num_labels=1000 ,			idalabel=lowerCAmelCase__ ,			labelaid=lowerCAmelCase__ ,			)

					return config



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if "stem.conv" in name:
										lowercase							=						name.replace('''stem.conv''' ,			'''bit.embedder.convolution'''		)
					if "blocks" in name:
										lowercase							=						name.replace('''blocks''' ,			'''layers'''		)
					if "head.fc" in name:
										lowercase							=						name.replace('''head.fc''' ,			'''classifier.1'''		)
					if name.startswith('''norm'''		):
										lowercase							=						'''bit.''' + name
					if "bit" not in name and "classifier" not in name:
										lowercase							=						'''bit.encoder.''' + name

					return name



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowercase							=						Image.open(requests.get(lowerCAmelCase__ ,			stream=lowerCAmelCase__		).raw		)
					return im



@torch.no_grad()
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					lowercase							=						get_config(lowerCAmelCase__		)

					# load original model from timm
					lowercase							=						create_model(lowerCAmelCase__ ,			pretrained=lowerCAmelCase__		)
					timm_model.eval()

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

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

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

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

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

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

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

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

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

					if pytorch_dump_folder_path is not None:
										Path(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
										print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}'		)
										model.save_pretrained(lowerCAmelCase__		)
										processor.save_pretrained(lowerCAmelCase__		)

					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__":
							lowercase__		:List[Any]   =    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.",
							)

							lowercase__		:List[str]   =    parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




