





"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Tuple									=		"""marian"""
				__snake_case :							Any									=		["""past_key_values"""]
				__snake_case :							Optional[Any]									=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self	:List[Any]    ,     __lowercase	:Any=5_8101    ,     __lowercase	:Tuple=None    ,     __lowercase	:Union[str, Any]=1024    ,     __lowercase	:Dict=12    ,     __lowercase	:int=4096    ,     __lowercase	:int=16    ,     __lowercase	:List[Any]=12    ,     __lowercase	:Dict=4096    ,     __lowercase	:Dict=16    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:List[Any]=True    ,     __lowercase	:int=True    ,     __lowercase	:Tuple="gelu"    ,     __lowercase	:str=1024    ,     __lowercase	:Optional[int]=0.1    ,     __lowercase	:List[str]=0.0    ,     __lowercase	:Union[str, Any]=0.0    ,     __lowercase	:Dict=0.02    ,     __lowercase	:Tuple=5_8100    ,     __lowercase	:Optional[Any]=False    ,     __lowercase	:int=5_8100    ,     __lowercase	:Any=0    ,     __lowercase	:str=0    ,     __lowercase	:str=True    ,     **__lowercase	:Any    ,     ):
											__lowerCamelCase   :   List[Any]       =vocab_size
											__lowerCamelCase   :   Optional[int]       =decoder_vocab_size or vocab_size
											__lowerCamelCase   :   Tuple       =max_position_embeddings
											__lowerCamelCase   :   List[Any]       =d_model
											__lowerCamelCase   :   Any       =encoder_ffn_dim
											__lowerCamelCase   :   str       =encoder_layers
											__lowerCamelCase   :   List[str]       =encoder_attention_heads
											__lowerCamelCase   :   str       =decoder_ffn_dim
											__lowerCamelCase   :   Tuple       =decoder_layers
											__lowerCamelCase   :   Any       =decoder_attention_heads
											__lowerCamelCase   :   List[Any]       =dropout
											__lowerCamelCase   :   Any       =attention_dropout
											__lowerCamelCase   :   Union[str, Any]       =activation_dropout
											__lowerCamelCase   :   Optional[int]       =activation_function
											__lowerCamelCase   :   Dict       =init_std
											__lowerCamelCase   :   List[Any]       =encoder_layerdrop
											__lowerCamelCase   :   Optional[Any]       =decoder_layerdrop
											__lowerCamelCase   :   Any       =use_cache
											__lowerCamelCase   :   Any       =encoder_layers
											__lowerCamelCase   :   Optional[int]       =scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCamelCase   :   int       =share_encoder_decoder_embeddings
											super().__init__(
											    pad_token_id=__lowercase    ,     eos_token_id=__lowercase    ,     is_encoder_decoder=__lowercase    ,     decoder_start_token_id=__lowercase    ,     forced_eos_token_id=__lowercase    ,     **__lowercase    ,     )

class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def        __lowercase	(  self	:int      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )

																		if self.use_past:
																									__lowerCamelCase   :   Optional[Any]       ={0: '''batch'''}
																									__lowerCamelCase   :   Any       ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																		else:
																									__lowerCamelCase   :   Optional[Any]       ={0: '''batch''', 1: '''decoder_sequence'''}
																									__lowerCamelCase   :   List[Any]       ={0: '''batch''', 1: '''decoder_sequence'''}

																		if self.use_past:
																									self.fill_with_past_key_values_(__lowercase    ,     direction='''inputs'''      )
											elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		__lowerCamelCase   :   Any       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   List[str]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											else:
																		__lowerCamelCase   :   List[str]       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
																		        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
																		    ]      )

											return common_inputs




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def        __lowercase	(  self	:Optional[int]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =super().outputs
											else:
																		__lowerCamelCase   :   List[str]       =super(__lowercase    ,     self      ).outputs
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   int       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Any       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											return common_outputs




				def        __lowercase	(  self	:str    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

											# Generate decoder inputs
											__lowerCamelCase   :   Optional[Any]       =seq_length if not self.use_past else 1
											__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   Dict       ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
											__lowerCamelCase   :   str       =dict(**__lowercase    ,     **__lowercase      )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
																		else:
																									import torch
																		__lowerCamelCase     ,    __lowerCamelCase   :   int       =common_inputs['''input_ids'''].shape
																		__lowerCamelCase   :   Optional[Any]       =common_inputs['''decoder_input_ids'''].shape[1]
																		__lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =self.num_attention_heads
																		__lowerCamelCase   :   Any       =(
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		__lowerCamelCase   :   Dict       =decoder_seq_length + 3
																		__lowerCamelCase   :   Tuple       =(
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		__lowerCamelCase   :   Optional[int]       =torch.cat(
																		    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowercase    ,     __lowercase      )]    ,     dim=1      )

																		__lowerCamelCase   :   Any       =[]
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.num_layers
																		__lowerCamelCase   :   List[Any]       =min(__lowercase    ,     __lowercase      )
																		__lowerCamelCase   :   int       =max(__lowercase    ,     __lowercase      ) - min_num_layers
																		__lowerCamelCase   :   Any       ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																		for _ in range(__lowercase      ):
																									common_inputs["past_key_values"].append(
																									    (
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									        torch.zeros(__lowercase      ),
																									    )      )
																		# TODO: test this.
																		__lowerCamelCase   :   Dict       =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																		for _ in range(__lowercase    ,     __lowercase      ):
																									common_inputs["past_key_values"].append((torch.zeros(__lowercase      ), torch.zeros(__lowercase      ))      )
											return common_inputs




				def        __lowercase	(  self	:Dict    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

											if self.use_past:
																		if not is_torch_available():
																									raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
																		else:
																									import torch
																		__lowerCamelCase     ,    __lowerCamelCase   :   int       =common_inputs['''input_ids'''].shape
																		# Not using the same length for past_key_values
																		__lowerCamelCase   :   str       =seqlen + 2
																		__lowerCamelCase     ,    __lowerCamelCase   :   List[str]       =self.num_layers
																		__lowerCamelCase     ,    __lowerCamelCase   :   Optional[Any]       =self.num_attention_heads
																		__lowerCamelCase   :   List[str]       =(
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		__lowerCamelCase   :   Any       =common_inputs['''attention_mask'''].dtype
																		__lowerCamelCase   :   Optional[int]       =torch.cat(
																		    [common_inputs['''attention_mask'''], torch.ones(__lowercase    ,     __lowercase    ,     dtype=__lowercase      )]    ,     dim=1      )
																		__lowerCamelCase   :   List[str]       =[
																		    (torch.zeros(__lowercase      ), torch.zeros(__lowercase      )) for _ in range(__lowercase      )
																		]
											return common_inputs




				def        __lowercase	(  self	:int    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											__lowerCamelCase   :   Union[str, Any]       =compute_effective_axis_dimension(
											    __lowercase    ,     fixed_dimension=OnnxConfig.default_fixed_batch    ,     num_token_to_add=0      )

											# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
											__lowerCamelCase   :   Optional[int]       =tokenizer.num_special_tokens_to_add(__lowercase      )
											__lowerCamelCase   :   Optional[int]       =compute_effective_axis_dimension(
											    __lowercase    ,     fixed_dimension=OnnxConfig.default_fixed_sequence    ,     num_token_to_add=__lowercase      )

											# Generate dummy inputs according to compute batch and sequence
											__lowerCamelCase   :   Any       =[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
											__lowerCamelCase   :   Union[str, Any]       =dict(tokenizer(__lowercase    ,     return_tensors=__lowercase      )      )
											return common_inputs




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   int       =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											else:
																		__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_causal_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											return common_inputs




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Tuple    ,     __lowercase	:Tuple    ,     __lowercase	:Dict    ,     __lowercase	:List[str]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   str       =super()._flatten_past_key_values_(__lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											else:
																		__lowerCamelCase   :   Optional[Any]       =super(__lowercase    ,     self      )._flatten_past_key_values_(
																		    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )




				@property
				def        __lowercase	(  self	:List[str]      ):
											return 1e-4











"""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


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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







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

							__lowerCamelCase   :   Optional[Any]       =['''key_proj''', '''value_proj''', '''query_proj''']

							__lowerCamelCase   :   Tuple       ={
							    '''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"]:
														__lowerCamelCase   :   int       =key.split('''.''' )

														if attributes[0] == "lm_head":
																					__lowerCamelCase   :   int       =prophet
																					__lowerCamelCase   :   Optional[int]       =prophet_old
														else:
																					__lowerCamelCase   :   Any       =prophet.prophetnet
																					__lowerCamelCase   :   Union[str, Any]       =prophet_old.model

														__lowerCamelCase   :   Optional[Any]       =False
														for attribute in attributes:
																					if attribute in mapping:
																												__lowerCamelCase   :   Optional[Any]       =mapping[attribute]
																												if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
																																			__lowerCamelCase   :   Any       =attribute
																					elif hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																												__lowerCamelCase   :   Any       =attribute

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

																												elif attribute == "key_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
																																			__lowerCamelCase   :   Tuple       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
																												elif attribute == "value_proj":
																																			__lowerCamelCase   :   Optional[Any]       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
																																			__lowerCamelCase   :   int       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
																												__lowerCamelCase   :   Dict       =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] == 512, "We want 512 position_embeddings."
																												__lowerCamelCase   :   str       =nn.Parameter(old_model.embed_positions.weight[:512, :] )
																												__lowerCamelCase   :   Dict       =True
																												break

																					if attribute.isdigit():
																												__lowerCamelCase   :   List[str]       =model[int(SCREAMING_SNAKE_CASE )]
																												__lowerCamelCase   :   Optional[Any]       =old_model[int(SCREAMING_SNAKE_CASE )]
																					else:
																												__lowerCamelCase   :   int       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

																												if old_attribute == "":
																																			__lowerCamelCase   :   Dict       =old_model
																												else:
																																			if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																																										raise ValueError(F'{old_model} does not have {old_attribute}' )
																																			__lowerCamelCase   :   Tuple       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

														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(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							_UpperCamelCase					      =   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.'
							)
							_UpperCamelCase					      =   parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




