
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


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


					if "xprophetnet" in prophetnet_checkpoint_path:
										SCREAMING_SNAKE_CASE__													= XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= XLMProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase_	,	output_loading_info=UpperCamelCase_		)
					else:
										SCREAMING_SNAKE_CASE__													= ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= ProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase_	,	output_loading_info=UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= ['key_proj', 'value_proj', 'query_proj']

					SCREAMING_SNAKE_CASE__													= {
					    '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"]:
										SCREAMING_SNAKE_CASE__													= key.split('.'		)

										if attributes[0] == "lm_head":
															SCREAMING_SNAKE_CASE__													= prophet
															SCREAMING_SNAKE_CASE__													= prophet_old
										else:
															SCREAMING_SNAKE_CASE__													= prophet.prophetnet
															SCREAMING_SNAKE_CASE__													= prophet_old.model

										SCREAMING_SNAKE_CASE__													= False
										for attribute in attributes:
															if attribute in mapping:
																				SCREAMING_SNAKE_CASE__													= mapping[attribute]
																				if not hasattr(UpperCamelCase_	,	UpperCamelCase_		) and len(UpperCamelCase_		) > 0:
																									SCREAMING_SNAKE_CASE__													= attribute
															elif hasattr(UpperCamelCase_	,	UpperCamelCase_		):
																				SCREAMING_SNAKE_CASE__													= attribute

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

																				elif attribute == "key_proj":
																									SCREAMING_SNAKE_CASE__													= nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
																									SCREAMING_SNAKE_CASE__													= nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
																				elif attribute == "value_proj":
																									SCREAMING_SNAKE_CASE__													= nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
																									SCREAMING_SNAKE_CASE__													= nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
																				SCREAMING_SNAKE_CASE__													= 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."
																				SCREAMING_SNAKE_CASE__													= nn.Parameter(old_model.embed_positions.weight[:512, :]		)
																				SCREAMING_SNAKE_CASE__													= True
																				break

															if attribute.isdigit():
																				SCREAMING_SNAKE_CASE__													= model[int(UpperCamelCase_		)]
																				SCREAMING_SNAKE_CASE__													= old_model[int(UpperCamelCase_		)]
															else:
																				SCREAMING_SNAKE_CASE__													= getattr(UpperCamelCase_	,	UpperCamelCase_		)

																				if old_attribute == "":
																									SCREAMING_SNAKE_CASE__													= old_model
																				else:
																									if not hasattr(UpperCamelCase_	,	UpperCamelCase_		):
																														raise ValueError(F'{old_model} does not have {old_attribute}'		)
																									SCREAMING_SNAKE_CASE__													= 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__":
				__snake_case								=  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."""
				)
				__snake_case								=  parser.parse_args()
				convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {"""vocab_file""": """vocab.txt"""}

__snake_case								=  {
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

__snake_case								=  {
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}






def 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					with open(UpperCamelCase_	,	'r'		) as f:
										SCREAMING_SNAKE_CASE__													= f.read().splitlines()
										return [l.strip() for l in lines]




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Tuple				       =VOCAB_FILES_NAMES
	A__    :   Union[str, Any]				       =PRETRAINED_VOCAB_FILES_MAP
	A__    :   Any				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :   Any				       =["""input_ids""", """attention_mask"""]
	def __init__(			self							:   Optional[int]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Tuple="<unk>"       ,   UpperCAmelCase_							:   Optional[Any]="<cls>"       ,   UpperCAmelCase_							:   List[Any]="<pad>"       ,   UpperCAmelCase_							:   Optional[int]="<mask>"       ,   UpperCAmelCase_							:   Optional[int]="<eos>"       ,   **UpperCAmelCase_							:   Optional[int]       ,   ):
						super().__init__(**UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= load_vocab_file(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= dict(enumerate(self.all_tokens							)							)
						SCREAMING_SNAKE_CASE__													= {tok: ind for ind, tok in enumerate(self.all_tokens							)}
						SCREAMING_SNAKE_CASE__													= unk_token
						SCREAMING_SNAKE_CASE__													= cls_token
						SCREAMING_SNAKE_CASE__													= pad_token
						SCREAMING_SNAKE_CASE__													= mask_token
						SCREAMING_SNAKE_CASE__													= eos_token
						SCREAMING_SNAKE_CASE__													= self.all_tokens
						self._create_trie(self.unique_no_split_tokens							)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Tuple       ,   **UpperCAmelCase_							:   List[Any]							):
						return text.split()
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   Optional[Any]=False							):
						return len(self._id_to_token							)
	def      A_				(			self							:   Union[str, Any]							):
						return {token: i for i, token in enumerate(self.all_tokens							)}
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   str							):
						return self._token_to_id.get(UpperCAmelCase_       ,   self._token_to_id.get(self.unk_token							)							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   int							):
						return self._id_to_token.get(UpperCAmelCase_       ,   self.unk_token							)
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]
						SCREAMING_SNAKE_CASE__													= [self.eos_token_id]  # No sep token in ESM vocabulary
						if token_ids_a is None:
											if self.eos_token_id is None:
																return cls + token_ids_a
											else:
																return cls + token_ids_a + sep
						elif self.eos_token_id is None:
											raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'							)
						return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   List       ,   UpperCAmelCase_							:   Optional[List] = None       ,   UpperCAmelCase_							:   bool = False							):
						if already_has_special_tokens:
											if token_ids_a is not None:
																raise ValueError(
																    'You should not supply a second sequence if the provided sequence of '
																    'ids is already formatted with special tokens for the model.'							)

											return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
						SCREAMING_SNAKE_CASE__													= [1] + ([0] * len(UpperCAmelCase_							)) + [1]
						if token_ids_a is not None:
											mask += [0] * len(UpperCAmelCase_							) + [1]
						return mask
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Any							):
						SCREAMING_SNAKE_CASE__													= os.path.join(UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'							)
						with open(UpperCAmelCase_       ,   'w'							) as f:
											f.write('\n'.join(self.all_tokens							)							)
						return (vocab_file,)
	@property
	def      A_				(			self							:   int							):
						return self.get_vocab_size(with_added_tokens=UpperCAmelCase_							)





	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   Union[List[str], List[AddedToken]]       ,   UpperCAmelCase_							:   bool = False							):
						return super()._add_tokens(UpperCAmelCase_       ,   special_tokens=UpperCAmelCase_							)





