




import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase   :						str			 =			logging.get_logger(__name__)

__lowerCamelCase   :						List[Any]			 =			[
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]







def 		lowercase__   (		__A:	Optional[Any]				):





					'''simple docstring'''






					if k == "embeddings.weight":
										return "shared.weight"

					for parlai_name, hf_name in PATTERNS:
										__magic_name__							:	int    							=							k.replace(lowerCAmelCase_							,lowerCAmelCase_				)

					if k.startswith('''encoder'''				):
										__magic_name__							:	Optional[int]    							=							k.replace('''.attn'''							,'''.self_attn'''				)
										__magic_name__							:	str    							=							k.replace('''norm1'''							,'''self_attn_layer_norm'''				)
										__magic_name__							:	Any    							=							k.replace('''norm2'''							,'''final_layer_norm'''				)
					elif k.startswith('''decoder'''				):
										__magic_name__							:	Optional[int]    							=							k.replace('''norm1'''							,'''self_attn_layer_norm'''				)
										__magic_name__							:	Any    							=							k.replace('''norm2'''							,'''encoder_attn_layer_norm'''				)
										__magic_name__							:	Union[str, Any]    							=							k.replace('''norm3'''							,'''final_layer_norm'''				)
					return k







def 		lowercase__   (		__A:	Optional[int]				):





					'''simple docstring'''






					__magic_name__							:	str    							=							[
					    '''model.encoder.layernorm_embedding.weight''',
					    '''model.encoder.layernorm_embedding.bias''',
					    '''model.decoder.layernorm_embedding.weight''',
					    '''model.decoder.layernorm_embedding.bias''',
					]
					for k in keys:
										__magic_name__							:	Optional[int]    							=							sd.pop(lowerCAmelCase_				)
										__magic_name__							:	Tuple    							=							k.replace('''layernorm_embedding'''							,'''layer_norm'''				)
										assert new_k not in sd
										__magic_name__							:	Any    							=							v


__lowerCamelCase   :						Any			 =			['START']







@torch.no_grad()
def 		lowercase__   (		__A:	int							,__A:	Optional[int]							,__A:	Tuple				):





					'''simple docstring'''






					__magic_name__							:	int    							=							torch.load(lowerCAmelCase_							,map_location='''cpu'''				)
					__magic_name__							:	Tuple    							=							model['''model''']
					__magic_name__							:	Dict    							=							BlenderbotConfig.from_json_file(lowerCAmelCase_				)
					__magic_name__							:	Union[str, Any]    							=							BlenderbotForConditionalGeneration(lowerCAmelCase_				)
					__magic_name__							:	Optional[int]    							=							m.model.state_dict().keys()
					__magic_name__							:	Optional[Any]    							=							[]
					__magic_name__							:	Dict    							=							{}
					for k, v in sd.items():
										if k in IGNORE_KEYS:
															continue

										__magic_name__							:	Optional[int]    							=							rename_state_dict_key(lowerCAmelCase_				)
										if new_k not in valid_keys:
															failures.append([k, new_k]				)
										else:
															__magic_name__							:	str    							=							v
					if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
										rename_layernorm_keys(lowerCAmelCase_				)
					m.model.load_state_dict(lowerCAmelCase_							,strict=lowerCAmelCase_				)
					m.half()
					m.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
	__lowerCamelCase   :						Dict			 =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
	parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
	parser.add_argument(
	    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
	)
	__lowerCamelCase   :						str			 =			parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)









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_  )  )



