"""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 		__A				(					tf.keras.optimizers.schedules.LearningRateSchedule						):






     def __init__(						self							,	a__							,	a__							,	a__							,	a__ = 1.0							,	a__ = None							,	):
       super().__init__()
       _lowerCAmelCase		:			List[Any]         =	initial_learning_rate
       _lowerCAmelCase		:			List[Any]         =	warmup_steps
       _lowerCAmelCase		:			Tuple         =	power
       _lowerCAmelCase		:			List[str]         =	decay_schedule_fn
       _lowerCAmelCase		:			Union[str, Any]         =	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`.
         _lowerCAmelCase		:			Dict         =	tf.cast(a__							,	tf.floataa    )
         _lowerCAmelCase		:			int         =	tf.cast(self.warmup_steps							,	tf.floataa    )
         _lowerCAmelCase		:			Optional[int]         =	global_step_float / warmup_steps_float
         _lowerCAmelCase		:			List[str]         =	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 				SCREAMING_SNAKE_CASE	(     _lowerCamelCase		:			List[str]     ,_lowerCamelCase		:			str     ,_lowerCamelCase		:			Dict     ,_lowerCamelCase		:			List[Any] = 0.0     ,_lowerCamelCase		:			str = 0.9     ,_lowerCamelCase		:			int = 0.9_99     ,_lowerCamelCase		:			Tuple = 1e-8     ,_lowerCamelCase		:			List[str] = None     ,_lowerCamelCase		:			str = None     ,_lowerCamelCase		:			List[Any] = 0.0     ,_lowerCamelCase		:			List[Any] = 1.0     ,_lowerCamelCase		:			Optional[int] = None     ,)			-> Tuple:
  _lowerCAmelCase		:			Dict         =	tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=lowercase_     ,decay_steps=num_train_steps - num_warmup_steps     ,end_learning_rate=init_lr * min_lr_ratio     ,power=lowercase_     ,)
  if num_warmup_steps:
    _lowerCAmelCase		:			List[str]         =	WarmUp(
        initial_learning_rate=lowercase_     ,decay_schedule_fn=lowercase_     ,warmup_steps=lowercase_     ,)
  if weight_decay_rate > 0.0:
    _lowerCAmelCase		:			Tuple         =	AdamWeightDecay(
        learning_rate=lowercase_     ,weight_decay_rate=lowercase_     ,beta_a=lowercase_     ,beta_a=lowercase_     ,epsilon=lowercase_     ,clipnorm=lowercase_     ,global_clipnorm=lowercase_     ,exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]     ,include_in_weight_decay=lowercase_     ,)
  else:
    _lowerCAmelCase		:			Tuple         =	tf.keras.optimizers.Adam(
        learning_rate=lowercase_     ,beta_a=lowercase_     ,beta_a=lowercase_     ,epsilon=lowercase_     ,clipnorm=lowercase_     ,global_clipnorm=lowercase_     ,)
  # 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 		__A				(					lowerCamelCase__						):






     def __init__(						self							,	a__ = 0.0_0_1							,	a__ = 0.9							,	a__ = 0.9_9_9							,	a__ = 1e-7							,	a__ = False							,	a__ = 0.0							,	a__ = None							,	a__ = None							,	a__ = "AdamWeightDecay"							,	**a__							,	):
       super().__init__(a__							,	a__							,	a__							,	a__							,	a__							,	a__							,	**a__    )
       _lowerCAmelCase		:			Union[str, Any]         =	weight_decay_rate
       _lowerCAmelCase		:			Any         =	include_in_weight_decay
       _lowerCAmelCase		:			Any         =	exclude_from_weight_decay






     @classmethod
     def        __A    (						cls							,	a__    ):
       _lowerCAmelCase		:			Optional[int]         =	{'''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__    )
       _lowerCAmelCase		:			List[Any]         =	tf.constant(
           self.weight_decay_rate							,	name="""adam_weight_decay_rate"""    )






     def        __A    (						self							,	a__							,	a__							,	a__    ):
       _lowerCAmelCase		:			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        __A    (						self							,	a__							,	a__=None							,	**a__    ):
       _lowerCAmelCase		:			List[str]         =	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], {}

       _lowerCAmelCase		:			Union[str, Any]         =	apply_state or {}
       _lowerCAmelCase		:			List[str]         =	apply_state.get((var_device, var_dtype)    )
       if coefficients is None:
         _lowerCAmelCase		:			Tuple         =	self._fallback_apply_state(a__							,	a__    )
         _lowerCAmelCase		:			Union[str, Any]         =	coefficients

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






     def        __A    (						self							,	a__							,	a__							,	a__=None    ):
       _lowerCAmelCase		:			str         =	self._get_lr(var.device							,	var.dtype.base_dtype							,	a__    )
       _lowerCAmelCase		:			Tuple         =	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    ):
       _lowerCAmelCase		:			List[Any]         =	self._get_lr(var.device							,	var.dtype.base_dtype							,	a__    )
       _lowerCAmelCase		:			str         =	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    ):
       _lowerCAmelCase		:			Dict         =	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 		__A				(					lowerCamelCase__						):






     def __init__(						self    ):
       _lowerCAmelCase		:			List[str]         =	[]
       _lowerCAmelCase		:			List[Any]         =	None






     @property
     def        __A    (						self    ):





       if self._accum_steps is None:
         _lowerCAmelCase		:			Tuple         =	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:
         _lowerCAmelCase		:			List[str]         =	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 requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def  __UpperCAmelCase(   lowercase_   ):
       # vision encoder
       if "img_encoder.pos_embed" in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''img_encoder.pos_embed'''			,     '''vision_model.embeddings.position_embeddings'''   )
       if "img_encoder.patch_embed.proj" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''img_encoder.patch_embed.proj'''			,     '''vision_model.embeddings.patch_embeddings.projection'''   )
       if "img_encoder.patch_embed.norm" in name:
              _lowerCamelCase				:  Dict						=		name.replace('''img_encoder.patch_embed.norm'''			,     '''vision_model.embeddings.layernorm'''   )
       if "img_encoder.layers" in name:
              _lowerCamelCase				:  Dict						=		name.replace('''img_encoder.layers'''			,     '''vision_model.encoder.stages'''   )
       if "blocks" in name and "res" not in name:
              _lowerCamelCase				:  Optional[Any]						=		name.replace('''blocks'''			,     '''layers'''   )
       if "attn" in name and "pre_assign" not in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''attn'''			,     '''self_attn'''   )
       if "proj" in name and "self_attn" in name and "text" not in name:
              _lowerCamelCase				:  Optional[int]						=		name.replace('''proj'''			,     '''out_proj'''   )
       if "pre_assign_attn.attn.proj" in name:
              _lowerCamelCase				:  List[Any]						=		name.replace('''pre_assign_attn.attn.proj'''			,     '''pre_assign_attn.attn.out_proj'''   )
       if "norm1" in name:
              _lowerCamelCase				:  int						=		name.replace('''norm1'''			,     '''layer_norm1'''   )
       if "norm2" in name and "pre_assign" not in name:
              _lowerCamelCase				:  Optional[int]						=		name.replace('''norm2'''			,     '''layer_norm2'''   )
       if "img_encoder.norm" in name:
              _lowerCamelCase				:  Dict						=		name.replace('''img_encoder.norm'''			,     '''vision_model.layernorm'''   )
       # text encoder
       if "text_encoder.token_embedding" in name:
              _lowerCamelCase				:  List[Any]						=		name.replace('''text_encoder.token_embedding'''			,     '''text_model.embeddings.token_embedding'''   )
       if "text_encoder.positional_embedding" in name:
              _lowerCamelCase				:  Optional[Any]						=		name.replace('''text_encoder.positional_embedding'''			,     '''text_model.embeddings.position_embedding.weight'''   )
       if "text_encoder.transformer.resblocks." in name:
              _lowerCamelCase				:  Dict						=		name.replace('''text_encoder.transformer.resblocks.'''			,     '''text_model.encoder.layers.'''   )
       if "ln_1" in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''ln_1'''			,     '''layer_norm1'''   )
       if "ln_2" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''ln_2'''			,     '''layer_norm2'''   )
       if "c_fc" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''c_fc'''			,     '''fc1'''   )
       if "c_proj" in name:
              _lowerCamelCase				:  str						=		name.replace('''c_proj'''			,     '''fc2'''   )
       if "text_encoder" in name:
              _lowerCamelCase				:  Tuple						=		name.replace('''text_encoder'''			,     '''text_model'''   )
       if "ln_final" in name:
              _lowerCamelCase				:  str						=		name.replace('''ln_final'''			,     '''final_layer_norm'''   )
       # projection layers
       if "img_projector.linear_hidden." in name:
              _lowerCamelCase				:  str						=		name.replace('''img_projector.linear_hidden.'''			,     '''visual_projection.'''   )
       if "img_projector.linear_out." in name:
              _lowerCamelCase				:  Union[str, Any]						=		name.replace('''img_projector.linear_out.'''			,     '''visual_projection.3.'''   )
       if "text_projector.linear_hidden" in name:
              _lowerCamelCase				:  List[str]						=		name.replace('''text_projector.linear_hidden'''			,     '''text_projection'''   )
       if "text_projector.linear_out" in name:
              _lowerCamelCase				:  Any						=		name.replace('''text_projector.linear_out'''			,     '''text_projection.3'''   )

       return name




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       for key in orig_state_dict.copy().keys():
              _lowerCamelCase				:  Optional[int]						=		orig_state_dict.pop(lowercase_   )

              if "qkv" in key:
                     # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     _lowerCamelCase				:  Tuple						=		key.split('''.'''   )
                     _lowerCamelCase,	_lowerCamelCase				:  Dict						=		int(key_split[2]   ), int(key_split[4]   )
                     _lowerCamelCase				:  Optional[int]						=		config.vision_config.hidden_size
                     if "weight" in key:
                            _lowerCamelCase				:  List[Any]						=		val[:dim, :]
                            _lowerCamelCase				:  List[Any]						=		val[dim : dim * 2, :]
                            _lowerCamelCase				:  Optional[Any]						=		val[-dim:, :]
                     else:
                            _lowerCamelCase				:  Union[str, Any]						=		val[:dim]
                            _lowerCamelCase				:  Optional[Any]						=		val[dim : dim * 2]
                            _lowerCamelCase				:  Any						=		val[-dim:]
              elif "in_proj" in key:
                     # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     _lowerCamelCase				:  Optional[int]						=		key.split('''.'''   )
                     _lowerCamelCase				:  Optional[Any]						=		int(key_split[3]   )
                     _lowerCamelCase				:  Union[str, Any]						=		config.text_config.hidden_size
                     if "weight" in key:
                            _lowerCamelCase				:  Any						=		val[:dim, :]
                            _lowerCamelCase				:  Optional[Any]						=		val[
                                dim : dim * 2, :
                            ]
                            _lowerCamelCase				:  Dict						=		val[-dim:, :]
                     else:
                            _lowerCamelCase				:  List[Any]						=		val[:dim]
                            _lowerCamelCase				:  Tuple						=		val[dim : dim * 2]
                            _lowerCamelCase				:  str						=		val[-dim:]
              else:
                     _lowerCamelCase				:  Optional[Any]						=		rename_key(lowercase_   )
                     # squeeze if necessary
                     if (
                         "text_projection.0" in new_name
                         or "text_projection.3" in new_name
                         or "visual_projection.0" in new_name
                         or "visual_projection.3" in new_name
                     ):
                            _lowerCamelCase				:  List[str]						=		val.squeeze_()
                     else:
                            _lowerCamelCase				:  Tuple						=		val

       return orig_state_dict




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  Union[str, Any]						=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase				:  Any						=		Image.open(requests.get(lowercase_			,     stream=lowercase_   ).raw   )
       return im




@torch.no_grad()
def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_="groupvit-gcc-yfcc"			,     lowercase_=False   ):
       _lowerCamelCase				:  Optional[Any]						=		GroupViTConfig()
       _lowerCamelCase				:  Any						=		GroupViTModel(lowercase_   ).eval()

       _lowerCamelCase				:  Optional[Any]						=		torch.load(lowercase_			,     map_location='''cpu'''   )['''model''']
       _lowerCamelCase				:  List[str]						=		convert_state_dict(lowercase_			,     lowercase_   )
       _lowerCamelCase,	_lowerCamelCase				:  List[str]						=		model.load_state_dict(lowercase_			,     strict=lowercase_   )
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase_   ) == 0)

       # verify result
       _lowerCamelCase				:  Optional[Any]						=		CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''   )
       _lowerCamelCase				:  Dict						=		prepare_img()
       _lowerCamelCase				:  str						=		processor(text=['''a photo of a cat''', '''a photo of a dog''']			,     images=lowercase_			,     padding=lowercase_			,     return_tensors='''pt'''   )

       with torch.no_grad():
              _lowerCamelCase				:  Optional[int]						=		model(**lowercase_   )

       if model_name == "groupvit-gcc-yfcc":
              _lowerCamelCase				:  Dict						=		torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]   )
       elif model_name == "groupvit-gcc-redcaps":
              _lowerCamelCase				:  Tuple						=		torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]   )
       else:
              raise ValueError(F"""Model name {model_name} not supported."""   )
       assert torch.allclose(outputs.logits_per_image			,     lowercase_			,     atol=1e-3   )

       processor.save_pretrained(lowercase_   )
       model.save_pretrained(lowercase_   )
       print('''Successfully saved processor and model to'''			,     lowercase_   )

       if push_to_hub:
              print('''Pushing to the hub...'''   )
              processor.push_to_hub(lowercase_			,     organization='''nielsr'''   )
              model.push_to_hub(lowercase_			,     organization='''nielsr'''   )


if __name__ == "__main__":
       _lowerCamelCase      							=   argparse.ArgumentParser()
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
       )
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
       parser.add_argument(
           '--model_name',
           default='groupvit-gccy-fcc',
           type=str,
           help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
       )
       parser.add_argument(
           '--push_to_hub',
           action='store_true',
           help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
       )
       _lowerCamelCase      							=   parser.parse_args()

       convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

