




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 1_8, 2]
					__magic_name__							:	str    							=							True if 'large' in model_name or 'huge' in model_name else False
					__magic_name__							:	Optional[int]    							=							True if 'large' in model_name or 'huge' in model_name else False
					__magic_name__							:	Optional[Any]    							=							True if 'large' in model_name or 'huge' in model_name else False

					if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
										if "fl3" in model_name:
															__magic_name__							:	Tuple    							=							[3, 3, 3, 3]
															__magic_name__							:	Tuple    							=							[5, 5, 5, 5]
										elif "fl4" in model_name:
															__magic_name__							:	Optional[int]    							=							[4, 4, 4, 4]
															__magic_name__							:	str    							=							[3, 3, 3, 3]

					if "tiny" in model_name or "small" in model_name or "base" in model_name:
										__magic_name__							:	Any    							=							[3, 3, 3, 3]
										if "lrf" in model_name:
															__magic_name__							:	List[Any]    							=							[3, 3, 3, 3]
										else:
															__magic_name__							:	List[str]    							=							[2, 2, 2, 2]

					if "tiny" in model_name:
										__magic_name__							:	str    							=							9_6
					elif "small" in model_name:
										__magic_name__							:	Union[str, Any]    							=							9_6
					elif "base" in model_name:
										__magic_name__							:	Tuple    							=							1_2_8
					elif "large" in model_name:
										__magic_name__							:	Optional[int]    							=							1_9_2
					elif "xlarge" in model_name:
										__magic_name__							:	Any    							=							2_5_6
					elif "huge" in model_name:
										__magic_name__							:	Union[str, Any]    							=							3_5_2

					# set label information
					__magic_name__							:	Tuple    							=							'huggingface/label-files'
					if "large" in model_name or "huge" in model_name:
										__magic_name__							:	List[Any]    							=							'imagenet-22k-id2label.json'
					else:
										__magic_name__							:	Optional[Any]    							=							'imagenet-1k-id2label.json'

					__magic_name__							:	Optional[int]    							=							json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,repo_type='''dataset'''				)							,'''r'''				)				)
					__magic_name__							:	Dict    							=							{int(SCREAMING_SNAKE_CASE_				): v for k, v in idalabel.items()}
					__magic_name__							:	Any    							=							{v: k for k, v in idalabel.items()}

					__magic_name__							:	List[Any]    							=							FocalNetConfig(
					    embed_dim=SCREAMING_SNAKE_CASE_							,depths=SCREAMING_SNAKE_CASE_							,focal_levels=SCREAMING_SNAKE_CASE_							,focal_windows=SCREAMING_SNAKE_CASE_							,use_conv_embed=SCREAMING_SNAKE_CASE_							,idalabel=SCREAMING_SNAKE_CASE_							,labelaid=SCREAMING_SNAKE_CASE_							,use_post_layernorm=SCREAMING_SNAKE_CASE_							,use_layerscale=SCREAMING_SNAKE_CASE_							,)

					return config







def 		lowercase__   (		__A:	Tuple				):





					'''simple docstring'''






					if "patch_embed.proj" in name:
										__magic_name__							:	Optional[Any]    							=							name.replace('''patch_embed.proj'''							,'''embeddings.patch_embeddings.projection'''				)
					if "patch_embed.norm" in name:
										__magic_name__							:	Tuple    							=							name.replace('''patch_embed.norm'''							,'''embeddings.norm'''				)
					if "layers" in name:
										__magic_name__							:	int    							=							'encoder.' + name
					if "encoder.layers" in name:
										__magic_name__							:	Any    							=							name.replace('''encoder.layers'''							,'''encoder.stages'''				)
					if "downsample.proj" in name:
										__magic_name__							:	Optional[int]    							=							name.replace('''downsample.proj'''							,'''downsample.projection'''				)
					if "blocks" in name:
										__magic_name__							:	Union[str, Any]    							=							name.replace('''blocks'''							,'''layers'''				)
					if "modulation.f.weight" in name or "modulation.f.bias" in name:
										__magic_name__							:	Dict    							=							name.replace('''modulation.f'''							,'''modulation.projection_in'''				)
					if "modulation.h.weight" in name or "modulation.h.bias" in name:
										__magic_name__							:	List[Any]    							=							name.replace('''modulation.h'''							,'''modulation.projection_context'''				)
					if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
										__magic_name__							:	Dict    							=							name.replace('''modulation.proj'''							,'''modulation.projection_out'''				)

					if name == "norm.weight":
										__magic_name__							:	List[str]    							=							'layernorm.weight'
					if name == "norm.bias":
										__magic_name__							:	int    							=							'layernorm.bias'

					if "head" in name:
										__magic_name__							:	Tuple    							=							name.replace('''head'''							,'''classifier'''				)
					else:
										__magic_name__							:	Optional[Any]    							=							'focalnet.' + name

					return name







def 		lowercase__   (		__A:	List[Any]							,__A:	List[Any]							,__A:	Union[str, Any]=False				):





					'''simple docstring'''






					__magic_name__							:	str    							=							{
					    'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
					    'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
					    'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
					    'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
					    'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
					    'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
					    'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
					    'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
					    'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
					    'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
					}
					# fmt: on

					__magic_name__							:	Optional[Any]    							=							model_name_to_url[model_name]
					print('''Checkpoint URL: '''							,SCREAMING_SNAKE_CASE_				)
					__magic_name__							:	Dict    							=							torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_							,map_location='''cpu'''				)['model']

					# rename keys
					for key in state_dict.copy().keys():
										__magic_name__							:	List[Any]    							=							state_dict.pop(SCREAMING_SNAKE_CASE_				)
										__magic_name__							:	Optional[int]    							=							val

					__magic_name__							:	Any    							=							get_focalnet_config(SCREAMING_SNAKE_CASE_				)
					__magic_name__							:	Tuple    							=							FocalNetForImageClassification(SCREAMING_SNAKE_CASE_				)
					model.eval()

					# load state dict
					model.load_state_dict(SCREAMING_SNAKE_CASE_				)

					# verify conversion
					__magic_name__							:	Tuple    							=							'http://images.cocodataset.org/val2017/000000039769.jpg'

					__magic_name__							:	Any    							=							BitImageProcessor(
					    do_resize=SCREAMING_SNAKE_CASE_							,size={'''shortest_edge''': 2_5_6}							,resample=PILImageResampling.BILINEAR							,do_center_crop=SCREAMING_SNAKE_CASE_							,crop_size=2_2_4							,do_normalize=SCREAMING_SNAKE_CASE_							,image_mean=SCREAMING_SNAKE_CASE_							,image_std=SCREAMING_SNAKE_CASE_							,)
					__magic_name__							:	Optional[int]    							=							Image.open(requests.get(SCREAMING_SNAKE_CASE_							,stream=SCREAMING_SNAKE_CASE_				).raw				)
					__magic_name__							:	Dict    							=							processor(images=SCREAMING_SNAKE_CASE_							,return_tensors='''pt'''				)

					__magic_name__							:	int    							=							transforms.Compose(
					    [
					        transforms.Resize(2_5_6				),
					        transforms.CenterCrop(2_2_4				),
					        transforms.ToTensor(),
					        transforms.Normalize(mean=[0.485, 0.456, 0.406]							,std=[0.229, 0.224, 0.225]				),
					    ]				)

					__magic_name__							:	str    							=							image_transforms(SCREAMING_SNAKE_CASE_				).unsqueeze(0				)

					# verify pixel_values
					assert torch.allclose(inputs.pixel_values							,SCREAMING_SNAKE_CASE_							,atol=1e-4				)

					__magic_name__							:	Optional[Any]    							=							model(**SCREAMING_SNAKE_CASE_				)

					__magic_name__							:	Dict    							=							outputs.logits.argmax(-1				).item()
					print('''Predicted class:'''							,model.config.idalabel[predicted_class_idx]				)

					print('''First values of logits:'''							,outputs.logits[0, :3]				)

					if model_name == "focalnet-tiny":
										__magic_name__							:	Any    							=							torch.tensor([0.2166, -0.4368, 0.2191]				)
					elif model_name == "focalnet-tiny-lrf":
										__magic_name__							:	Tuple    							=							torch.tensor([1.1669, 0.0125, -0.1695]				)
					elif model_name == "focalnet-small":
										__magic_name__							:	int    							=							torch.tensor([0.4917, -0.0430, 0.1341]				)
					elif model_name == "focalnet-small-lrf":
										__magic_name__							:	Dict    							=							torch.tensor([-0.2588, -0.5342, -0.2331]				)
					elif model_name == "focalnet-base":
										__magic_name__							:	Union[str, Any]    							=							torch.tensor([-0.1655, -0.4090, -0.1730]				)
					elif model_name == "focalnet-base-lrf":
										__magic_name__							:	List[str]    							=							torch.tensor([0.5306, -0.0483, -0.3928]				)
					assert torch.allclose(outputs.logits[0, :3]							,SCREAMING_SNAKE_CASE_							,atol=1e-4				)
					print('''Looks ok!'''				)

					if pytorch_dump_folder_path is not None:
										print(F'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}'''				)
										model.save_pretrained(SCREAMING_SNAKE_CASE_				)
										processor.save_pretrained(SCREAMING_SNAKE_CASE_				)

					if push_to_hub:
										print(F'''Pushing model and processor of {model_name} to the hub...'''				)
										model.push_to_hub(F'''{model_name}'''				)
										processor.push_to_hub(F'''{model_name}'''				)


if __name__ == "__main__":
	__lowerCamelCase   :						str			 =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''focalnet-tiny''',
	    type=str,
	    help='''Name of the FocalNet 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 and processor to the hub.''',
	)

	__lowerCamelCase   :						int			 =			parser.parse_args()
	convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









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






			'''simple docstring'''





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

			def __call__( self			:   Any       ,						lowerCamelCase_			:   Dict  )						->   Optional[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`.
													__magic_name__							:	Union[str, Any]    							=							tf.cast(lowerCamelCase_       ,						tf.floataa  )
													__magic_name__							:	Dict    							=							tf.cast(self.warmup_steps       ,						tf.floataa  )
													__magic_name__							:	Dict    							=							global_step_float / warmup_steps_float
													__magic_name__							:	List[str]    							=							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 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   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 		lowercase__   (		__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'''






					__magic_name__							:	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:
										__magic_name__							:	Tuple    							=							WarmUp(
										    initial_learning_rate=__A							,decay_schedule_fn=__A							,warmup_steps=__A							,)
					if weight_decay_rate > 0.0:
										__magic_name__							:	Union[str, Any]    							=							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:
										__magic_name__							:	str    							=							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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Optional[int]       ,						lowerCamelCase_			:   Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1       ,						lowerCamelCase_			:   float = 0.9       ,						lowerCamelCase_			:   float = 0.9_9_9       ,						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_			:   int       ,						)						->   List[str]:
								super().__init__(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	int    							=							weight_decay_rate
								__magic_name__							:	Tuple    							=							include_in_weight_decay
								__magic_name__							:	Union[str, Any]    							=							exclude_from_weight_decay

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   int       ,						lowerCamelCase_			:   int  )						->   Optional[Any]:
								__magic_name__							:	Tuple    							=							{'''WarmUp''': WarmUp}
								return super(lowerCamelCase_       ,						cls  ).from_config(lowerCamelCase_       ,						custom_objects=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   int:
								super(lowerCamelCase_       ,						self  )._prepare_local(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							:	Any    							=							tf.constant(
								    self.weight_decay_rate       ,						name='''adam_weight_decay_rate'''  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Any  )						->   Optional[Any]:
								__magic_name__							:	Optional[int]    							=							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 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int=None       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							,    __magic_name__							:	Dict    							=							list(zip(*lowerCamelCase_  )  )
								return super(lowerCamelCase_       ,						self  ).apply_gradients(zip(lowerCamelCase_       ,						lowerCamelCase_  )       ,						name=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
								if apply_state is None:
													return self._decayed_lr_t[var_dtype], {}

								__magic_name__							:	str    							=							apply_state or {}
								__magic_name__							:	Optional[Any]    							=							apply_state.get((var_device, var_dtype)  )
								if coefficients is None:
													__magic_name__							:	List[str]    							=							self._fallback_apply_state(lowerCamelCase_       ,						lowerCamelCase_  )
													__magic_name__							:	List[Any]    							=							coefficients

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

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Dict=None  )						->   Optional[Any]:
								__magic_name__							,    __magic_name__							:	Union[str, Any]    							=							self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							self._decay_weights_op(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								with tf.control_dependencies([decay]  ):
													return super(lowerCamelCase_       ,						self  )._resource_apply_dense(lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )

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

			def 							UpperCAmelCase__       ( self			:   int  )						->   Dict:
								__magic_name__							:	List[Any]    							=							super().get_config()
								config.update({'''weight_decay_rate''': self.weight_decay_rate}  )
								return config


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
								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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Optional[Any]  )						->   Any:
								__magic_name__							:	Optional[int]    							=							[]
								__magic_name__							:	Dict    							=							None

			@property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
								if self._accum_steps is None:
													__magic_name__							:	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 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[Any]:
								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[int]       ,						lowerCamelCase_			:   Any  )						->   List[str]:
								if not self._gradients:
													__magic_name__							:	int    							=							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 							UpperCAmelCase__       ( self			:   str  )						->   Optional[int]:
								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_  )  )



