



'''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   :						Dict      ,						a_   :						float      ,						a_   :						Callable      ,						a_   :						int      ,						a_   :						float = 1.0      ,						a_   :						str = None      ,						)			->					Optional[Any]:
										super().__init__()
										snake_case:    Union[str, Any]      							=initial_learning_rate
										snake_case:    int      							=warmup_steps
										snake_case:    List[str]      							=power
										snake_case:    Tuple      							=decay_schedule_fn
										snake_case:    Optional[Any]      							=name


							def __call__(				self   :						List[str]      ,						a_   :						List[str]			)			->					Dict:
										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`.
													snake_case:    List[Any]      							=tf.cast(a_      ,						tf.floataa			)
													snake_case:    List[Any]      							=tf.cast(self.warmup_steps      ,						tf.floataa			)
													snake_case:    int      							=global_step_float / warmup_steps_float
													snake_case:    Any      							=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    UpperCamelCase     (				self   :						Tuple			)			->					Union[str, Any]:
										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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase = 0.0				,							__UpperCAmelCase = 0.9				,							__UpperCAmelCase = 0.999				,							__UpperCAmelCase = 1e-8				,							__UpperCAmelCase = None				,							__UpperCAmelCase = None				,							__UpperCAmelCase = 0.0				,							__UpperCAmelCase = 1.0				,							__UpperCAmelCase = None				,							)				->					Tuple:





			"""simple docstring"""







			snake_case:    int      							=tf.keras.optimizers.schedules.PolynomialDecay(
			    initial_learning_rate=__UpperCAmelCase				,							decay_steps=num_train_steps - num_warmup_steps				,							end_learning_rate=init_lr * min_lr_ratio				,							power=__UpperCAmelCase				,							)
			if num_warmup_steps:
						snake_case:    str      							=WarmUp(
						    initial_learning_rate=__UpperCAmelCase				,							decay_schedule_fn=__UpperCAmelCase				,							warmup_steps=__UpperCAmelCase				,							)
			if weight_decay_rate > 0.0:
						snake_case:    Tuple      							=AdamWeightDecay(
						    learning_rate=__UpperCAmelCase				,							weight_decay_rate=__UpperCAmelCase				,							beta_a=__UpperCAmelCase				,							beta_a=__UpperCAmelCase				,							epsilon=__UpperCAmelCase				,							clipnorm=__UpperCAmelCase				,							global_clipnorm=__UpperCAmelCase				,							exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']				,							include_in_weight_decay=__UpperCAmelCase				,							)
			else:
						snake_case:    Union[str, Any]      							=tf.keras.optimizers.Adam(
						    learning_rate=__UpperCAmelCase				,							beta_a=__UpperCAmelCase				,							beta_a=__UpperCAmelCase				,							epsilon=__UpperCAmelCase				,							clipnorm=__UpperCAmelCase				,							global_clipnorm=__UpperCAmelCase				,							)
			# 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_		(     snake_case    ):


							def __init__(				self   :						List[str]      ,						a_   :						Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1      ,						a_   :						float = 0.9      ,						a_   :						float = 0.9_9_9      ,						a_   :						float = 1E-7      ,						a_   :						bool = False      ,						a_   :						float = 0.0      ,						a_   :						Optional[List[str]] = None      ,						a_   :						Optional[List[str]] = None      ,						a_   :						str = "AdamWeightDecay"      ,						**a_   :						Tuple      ,						)			->					Dict:
										super().__init__(a_      ,						a_      ,						a_      ,						a_      ,						a_      ,						a_      ,						**a_			)
										snake_case:    str      							=weight_decay_rate
										snake_case:    Tuple      							=include_in_weight_decay
										snake_case:    List[str]      							=exclude_from_weight_decay


							@classmethod
							def    UpperCamelCase     (				cls   :						List[str]      ,						a_   :						str			)			->					List[Any]:
										snake_case:    Optional[int]      							={'WarmUp': WarmUp}
										return super(a_      ,						cls			).from_config(a_      ,						custom_objects=a_			)


							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						Any      ,						a_   :						List[str]      ,						a_   :						Optional[Any]			)			->					Any:
										super(a_      ,						self			)._prepare_local(a_      ,						a_      ,						a_			)
										snake_case:    Union[str, Any]      							=tf.constant(
										    self.weight_decay_rate      ,						name='adam_weight_decay_rate'			)


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						Union[str, Any]      ,						a_   :						Dict      ,						a_   :						Tuple			)			->					Any:
										snake_case:    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    UpperCamelCase     (				self   :						int      ,						a_   :						Optional[int]      ,						a_   :						Tuple=None      ,						**a_   :						Dict			)			->					Optional[int]:
										snake_case							,  snake_case:    Optional[Any]      							=list(zip(*a_			)			)
										return super(a_      ,						self			).apply_gradients(zip(a_      ,						a_			)      ,						name=a_      ,						**a_			)


							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						Any      ,						a_   :						str      ,						a_   :						Tuple			)			->					str:
										if apply_state is None:
													return self._decayed_lr_t[var_dtype], {}

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

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


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


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						str      ,						a_   :						Union[str, Any]      ,						a_   :						Union[str, Any]      ,						a_   :						Tuple=None			)			->					Tuple:
										snake_case							,  snake_case:    Dict      							=self._get_lr(var.device      ,						var.dtype.base_dtype      ,						a_			)
										snake_case:    Any      							=self._decay_weights_op(a_      ,						a_      ,						a_			)
										with tf.control_dependencies([decay]			):
													return super(a_      ,						self			)._resource_apply_sparse(a_      ,						a_      ,						a_      ,						**a_			)


							def    UpperCamelCase     (				self   :						int			)			->					Any:
										snake_case:    int      							=super().get_config()
										config.update({'weight_decay_rate': self.weight_decay_rate}			)
										return config




							def    UpperCamelCase     (				self   :						int      ,						a_   :						str			)			->					Dict:
										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_		(     snake_case    ):


							def __init__(				self   :						List[Any]			)			->					Optional[int]:
										snake_case:    int      							=[]
										snake_case:    Union[str, Any]      							=None


							@property
							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										if self._accum_steps is None:
													snake_case:    Optional[Any]      							=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    UpperCamelCase     (				self   :						Dict			)			->					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   :						List[str]      ,						a_   :						Any			)			->					str:
										if not self._gradients:
													snake_case:    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    UpperCamelCase     (				self   :						List[str]			)			->					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(a_			)			)









'''simple docstring'''

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


a							   =				logging.get_logger(__name__)
logging.set_verbosity_info()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if "xprophetnet" in prophetnet_checkpoint_path:
						snake_case:    Optional[Any]      							=XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    str      							=XLMProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)
			else:
						snake_case:    Optional[int]      							=ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    List[str]      							=ProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)

			snake_case:    str      							=['key_proj', 'value_proj', 'query_proj']

			snake_case:    str      							={
			    'self_attn': 'ngram_self_attn',
			    'cross_attn': 'encoder_attn',
			    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
			    'feed_forward_layer_norm': 'final_layer_norm',
			    'feed_forward': '',
			    'intermediate': 'fc1',
			    'output': 'fc2',
			    'key_proj': 'k_proj',
			    'query_proj': 'q_proj',
			    'value_proj': 'v_proj',
			    'word_embeddings': 'embed_tokens',
			    'embeddings_layer_norm': 'emb_layer_norm',
			    'relative_pos_embeddings': 'relative_linear',
			    'ngram_embeddings': 'ngram_input_embed',
			    'position_embeddings': 'embed_positions',
			}

			for key in loading_info["missing_keys"]:
						snake_case:    List[str]      							=key.split('.'					)

						if attributes[0] == "lm_head":
									snake_case:    Dict      							=prophet
									snake_case:    List[str]      							=prophet_old
						else:
									snake_case:    Any      							=prophet.prophetnet
									snake_case:    int      							=prophet_old.model

						snake_case:    int      							=False
						for attribute in attributes:
									if attribute in mapping:
												snake_case:    Dict      							=mapping[attribute]
												if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					) and len(__UpperCAmelCase					) > 0:
															snake_case:    Optional[int]      							=attribute
									elif hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
												snake_case:    Optional[Any]      							=attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												snake_case:    Optional[Any]      							=old_model.weight
												logger.info(f'''{attribute} is initialized.'''					)
												snake_case:    List[str]      							=True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												snake_case:    List[Any]      							=old_model.bias
												logger.info(f'''{attribute} is initialized'''					)
												snake_case:    List[Any]      							=True
												break
									elif attribute in special_keys and hasattr(__UpperCAmelCase				,							'in_proj_weight'					):
												snake_case:    Optional[int]      							=old_model.in_proj_weight.shape[0] // 3
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)
												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
												if attribute == "query_proj":
															snake_case:    int      							=nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
															snake_case:    Any      							=nn.Parameter(old_model.in_proj_bias[:embed_dim]					)

												elif attribute == "key_proj":
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
												elif attribute == "value_proj":
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
												snake_case:    Any      							=True
												break
									elif attribute == "position_embeddings":
												assert (
												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
												), "Hidden size has to match"
												assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
												snake_case:    Dict      							=nn.Parameter(old_model.embed_positions.weight[:5_12, :]					)
												snake_case:    Dict      							=True
												break

									if attribute.isdigit():
												snake_case:    int      							=model[int(__UpperCAmelCase					)]
												snake_case:    Optional[int]      							=old_model[int(__UpperCAmelCase					)]
									else:
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

												if old_attribute == "":
															snake_case:    Union[str, Any]      							=old_model
												else:
															if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
																		raise ValueError(f'''{old_model} does not have {old_attribute}'''					)
															snake_case:    List[Any]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

						if not is_key_init:
									raise ValueError(f'''{key} was not correctly initialized!'''					)

			print(f'''Saving model to {pytorch_dump_folder_path}'''					)
			prophet.save_pretrained(__UpperCAmelCase					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	a							   =				parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




