


"""simple docstring"""



import tensorflow as tf

from ...tf_utils import shape_list







class    __a						( tf.keras.layers.Layer							):







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase=1      ,	UpperCAmelCase=False      ,	**UpperCAmelCase   ):

					'''simple docstring'''






					super().__init__(**a_   )

					lowerCAmelCase_      							=			vocab_size
					lowerCAmelCase_      							=			d_embed
					lowerCAmelCase_      							=			d_proj

					lowerCAmelCase_      							=			cutoffs + [vocab_size]
					lowerCAmelCase_      							=			[0] + self.cutoffs
					lowerCAmelCase_      							=			div_val

					lowerCAmelCase_      							=			self.cutoffs[0]
					lowerCAmelCase_      							=			len(self.cutoffs   ) - 1
					lowerCAmelCase_      							=			self.shortlist_size + self.n_clusters
					lowerCAmelCase_      							=			keep_order

					lowerCAmelCase_      							=			[]
					lowerCAmelCase_      							=			[]







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					if self.n_clusters > 0:
						lowerCAmelCase_      							=			self.add_weight(
						    shape=(self.n_clusters, self.d_embed)      ,	initializer='''zeros'''      ,	trainable=a_      ,	name='''cluster_weight'''   )
						lowerCAmelCase_      							=			self.add_weight(
						    shape=(self.n_clusters,)      ,	initializer='''zeros'''      ,	trainable=a_      ,	name='''cluster_bias'''   )

					if self.div_val == 1:
						for i in range(len(self.cutoffs   )   ):
							if self.d_proj != self.d_embed:
								lowerCAmelCase_      							=			self.add_weight(
								    shape=(self.d_embed, self.d_proj)      ,	initializer='''zeros'''      ,	trainable=a_      ,	name=F"""out_projs_._{i}"""      ,	)
								self.out_projs.append(a_   )
							else:
								self.out_projs.append(a_   )
							lowerCAmelCase_      							=			self.add_weight(
							    shape=(self.vocab_size, self.d_embed)      ,	initializer='''zeros'''      ,	trainable=a_      ,	name=F"""out_layers_._{i}_._weight"""      ,	)
							lowerCAmelCase_      							=			self.add_weight(
							    shape=(self.vocab_size,)      ,	initializer='''zeros'''      ,	trainable=a_      ,	name=F"""out_layers_._{i}_._bias"""      ,	)
							self.out_layers.append((weight, bias)   )
					else:
						for i in range(len(self.cutoffs   )   ):
							lowerCAmelCase_      							=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
							lowerCAmelCase_      							=			self.d_embed // (self.div_val**i)

							lowerCAmelCase_      							=			self.add_weight(
							    shape=(d_emb_i, self.d_proj)      ,	initializer='''zeros'''      ,	trainable=a_      ,	name=F"""out_projs_._{i}"""   )
							self.out_projs.append(a_   )
							lowerCAmelCase_      							=			self.add_weight(
							    shape=(r_idx - l_idx, d_emb_i)      ,	initializer='''zeros'''      ,	trainable=a_      ,	name=F"""out_layers_._{i}_._weight"""      ,	)
							lowerCAmelCase_      							=			self.add_weight(
							    shape=(r_idx - l_idx,)      ,	initializer='''zeros'''      ,	trainable=a_      ,	name=F"""out_layers_._{i}_._bias"""      ,	)
							self.out_layers.append((weight, bias)   )
					super().build(a_   )







				@staticmethod
				def      lowerCamelCase_     ( UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase=None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			x
					if proj is not None:
						lowerCAmelCase_      							=			tf.einsum('''ibd,ed->ibe'''      ,	a_      ,	a_   )
					return tf.einsum('''ibd,nd->ibn'''      ,	a_      ,	a_   ) + b







				@staticmethod
				def      lowerCamelCase_     ( UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			shape_list(a_   )
					lowerCAmelCase_      							=			tf.range(lp_size[0]      ,	dtype=target.dtype   )
					lowerCAmelCase_      							=			tf.stack([r, target]      ,	1   )
					return tf.gather_nd(a_      ,	a_   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase=True      ,	UpperCAmelCase=False   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			0
					if self.n_clusters == 0:
						lowerCAmelCase_      							=			self._logit(a_      ,	self.out_layers[0][0]      ,	self.out_layers[0][1]      ,	self.out_projs[0]   )
						if target is not None:
							lowerCAmelCase_      							=			tf.nn.sparse_softmax_cross_entropy_with_logits(labels=a_      ,	logits=a_   )
						lowerCAmelCase_      							=			tf.nn.log_softmax(a_      ,	axis=-1   )
					else:
						lowerCAmelCase_      							=			shape_list(a_   )
						lowerCAmelCase_      							=			[]
						lowerCAmelCase_      							=			tf.zeros(hidden_sizes[:2]   )
						for i in range(len(self.cutoffs   )   ):
							lowerCAmelCase_      							=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
							if target is not None:
								lowerCAmelCase_      							=			(target >= l_idx) & (target < r_idx)
								lowerCAmelCase_      							=			tf.where(a_   )
								lowerCAmelCase_      							=			tf.boolean_mask(a_      ,	a_   ) - l_idx

							if self.div_val == 1:
								lowerCAmelCase_      							=			self.out_layers[0][0][l_idx:r_idx]
								lowerCAmelCase_      							=			self.out_layers[0][1][l_idx:r_idx]
							else:
								lowerCAmelCase_      							=			self.out_layers[i][0]
								lowerCAmelCase_      							=			self.out_layers[i][1]

							if i == 0:
								lowerCAmelCase_      							=			tf.concat([cur_W, self.cluster_weight]      ,	0   )
								lowerCAmelCase_      							=			tf.concat([cur_b, self.cluster_bias]      ,	0   )

								lowerCAmelCase_      							=			self._logit(a_      ,	a_      ,	a_      ,	self.out_projs[0]   )
								lowerCAmelCase_      							=			tf.nn.log_softmax(a_   )
								out.append(head_logprob[..., : self.cutoffs[0]]   )
								if target is not None:
									lowerCAmelCase_      							=			tf.boolean_mask(a_      ,	a_   )
									lowerCAmelCase_      							=			self._gather_logprob(a_      ,	a_   )
							else:
								lowerCAmelCase_      							=			self._logit(a_      ,	a_      ,	a_      ,	self.out_projs[i]   )
								lowerCAmelCase_      							=			tf.nn.log_softmax(a_   )
								lowerCAmelCase_      							=			self.cutoffs[0] + i - 1  # No probability for the head cluster
								lowerCAmelCase_      							=			head_logprob[..., cluster_prob_idx, None] + tail_logprob
								out.append(a_   )
								if target is not None:
									lowerCAmelCase_      							=			tf.boolean_mask(a_      ,	a_   )
									lowerCAmelCase_      							=			tf.boolean_mask(a_      ,	a_   )
									lowerCAmelCase_      							=			self._gather_logprob(a_      ,	a_   )
									cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
							if target is not None:
								loss += tf.scatter_nd(a_      ,	-cur_logprob      ,	shape_list(a_   )   )
						lowerCAmelCase_      							=			tf.concat(a_      ,	axis=-1   )

					if target is not None:
						if return_mean:
							lowerCAmelCase_      							=			tf.reduce_mean(a_   )
						# Add the training-time loss value to the layer using `self.add_loss()`.
						self.add_loss(a_   )

						# Log the loss as a metric (we could log arbitrary metrics,
						# including different metrics for training and inference.
						self.add_metric(a_      ,	name=self.name      ,	aggregation='''mean''' if return_mean else ''''''   )

					return out




'''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)




