
'''simple docstring'''





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


UpperCAmelCase    					=   logging.getLogger(__name__)

@dataclass
class 						a   :
				_snake_case		    = 42
				_snake_case		    = 42
				_snake_case		    = 42




@dataclass
class 						a   :
				_snake_case		    = 42
				_snake_case		    = 42
				_snake_case		    = None
				_snake_case		    = None




class 						a   (				UpperCAmelCase_    ):
				_snake_case		    = '''train'''
				_snake_case		    = '''dev'''
				_snake_case		    = '''test'''




class 						a   :




				@staticmethod
				def 	__snake_case							(							SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Union[Split, str]      ):
										raise NotImplementedError




				@staticmethod
				def 	__snake_case							(							SCREAMING_SNAKE_CASE_	:				str      ):
										raise NotImplementedError




				@staticmethod
				def 	__snake_case							(							SCREAMING_SNAKE_CASE_	:				List[InputExample],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				PreTrainedTokenizer,    SCREAMING_SNAKE_CASE_	:				List[str]=False,    SCREAMING_SNAKE_CASE_	:				Optional[int]="[CLS]",    SCREAMING_SNAKE_CASE_	:				Any=1,    SCREAMING_SNAKE_CASE_	:				Optional[int]="[SEP]",    SCREAMING_SNAKE_CASE_	:				str=False,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=False,    SCREAMING_SNAKE_CASE_	:				int=0,    SCREAMING_SNAKE_CASE_	:				List[str]=0,    SCREAMING_SNAKE_CASE_	:				Tuple=-1_00,    SCREAMING_SNAKE_CASE_	:				str=0,    SCREAMING_SNAKE_CASE_	:				Any=True,    ):
										snake_case       :     Optional[Any] =    {label: i for i, label in enumerate(_lowercase      )}

										snake_case       :     str =    []
										for ex_index, example in enumerate(_lowercase      ):
																if ex_index % 1_00_00 == 0:
																						logger.info('''Writing example %d of %d''',    _lowercase,    len(_lowercase      )      )

																snake_case       :     int =    []
																snake_case       :     Union[str, Any] =    []
																for word, label in zip(example.words,    example.labels      ):
																						snake_case       :     List[Any] =    tokenizer.tokenize(_lowercase      )

																						# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																						if len(_lowercase      ) > 0:
																												tokens.extend(_lowercase      )
																												# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																												label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_lowercase      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																snake_case       :     List[str] =    tokenizer.num_special_tokens_to_add()
																if len(_lowercase      ) > max_seq_length - special_tokens_count:
																						snake_case       :     List[Any] =    tokens[: (max_seq_length - special_tokens_count)]
																						snake_case       :     Union[str, Any] =    label_ids[: (max_seq_length - special_tokens_count)]

																# The convention in BERT is:
																# (a) For sequence pairs:
																#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
																#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
																# (b) For single sequences:
																#  tokens:   [CLS] the dog is hairy . [SEP]
																#  type_ids:   0   0   0   0  0     0   0
																#
																# Where "type_ids" are used to indicate whether this is the first
																# sequence or the second sequence. The embedding vectors for `type=0` and
																# `type=1` were learned during pre-training and are added to the wordpiece
																# embedding vector (and position vector). This is not *strictly* necessary
																# since the [SEP] token unambiguously separates the sequences, but it makes
																# it easier for the model to learn the concept of sequences.
																#
																# For classification tasks, the first vector (corresponding to [CLS]) is
																# used as the "sentence vector". Note that this only makes sense because
																# the entire model is fine-tuned.
																tokens += [sep_token]
																label_ids += [pad_token_label_id]
																if sep_token_extra:
																						# roberta uses an extra separator b/w pairs of sentences
																						tokens += [sep_token]
																						label_ids += [pad_token_label_id]
																snake_case       :     Dict =    [sequence_a_segment_id] * len(_lowercase      )

																if cls_token_at_end:
																						tokens += [cls_token]
																						label_ids += [pad_token_label_id]
																						segment_ids += [cls_token_segment_id]
																else:
																						snake_case       :     List[str] =    [cls_token] + tokens
																						snake_case       :     str =    [pad_token_label_id] + label_ids
																						snake_case       :     Optional[Any] =    [cls_token_segment_id] + segment_ids

																snake_case       :     str =    tokenizer.convert_tokens_to_ids(_lowercase      )

																# The mask has 1 for real tokens and 0 for padding tokens. Only real
																# tokens are attended to.
																snake_case       :     Any =    [1 if mask_padding_with_zero else 0] * len(_lowercase      )

																# Zero-pad up to the sequence length.
																snake_case       :     List[Any] =    max_seq_length - len(_lowercase      )
																if pad_on_left:
																						snake_case       :     str =    ([pad_token] * padding_length) + input_ids
																						snake_case       :     Union[str, Any] =    ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																						snake_case       :     Optional[Any] =    ([pad_token_segment_id] * padding_length) + segment_ids
																						snake_case       :     List[Any] =    ([pad_token_label_id] * padding_length) + label_ids
																else:
																						input_ids += [pad_token] * padding_length
																						input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																						segment_ids += [pad_token_segment_id] * padding_length
																						label_ids += [pad_token_label_id] * padding_length

																assert len(_lowercase      ) == max_seq_length
																assert len(_lowercase      ) == max_seq_length
																assert len(_lowercase      ) == max_seq_length
																assert len(_lowercase      ) == max_seq_length

																if ex_index < 5:
																						logger.info('''*** Example ***'''      )
																						logger.info('''guid: %s''',    example.guid      )
																						logger.info('''tokens: %s''',    ''' '''.join([str(_lowercase      ) for x in tokens]      )      )
																						logger.info('''input_ids: %s''',    ''' '''.join([str(_lowercase      ) for x in input_ids]      )      )
																						logger.info('''input_mask: %s''',    ''' '''.join([str(_lowercase      ) for x in input_mask]      )      )
																						logger.info('''segment_ids: %s''',    ''' '''.join([str(_lowercase      ) for x in segment_ids]      )      )
																						logger.info('''label_ids: %s''',    ''' '''.join([str(_lowercase      ) for x in label_ids]      )      )

																if "token_type_ids" not in tokenizer.model_input_names:
																						snake_case       :     int =    None

																features.append(
																    InputFeatures(
																        input_ids=_lowercase,    attention_mask=_lowercase,    token_type_ids=_lowercase,    label_ids=_lowercase      )      )
										return features


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset

						class 						a   (				UpperCAmelCase_    ):
										_snake_case		    = 42
										_snake_case		    = nn.CrossEntropyLoss().ignore_index




										def __init__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				TokenClassificationTask,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				PreTrainedTokenizer,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    SCREAMING_SNAKE_CASE_	:				Tuple=False,    SCREAMING_SNAKE_CASE_	:				Split = Split.train,    ):
																# Load data features from cache or dataset file
																snake_case       :     Dict =    os.path.join(
																    _lowercase,    '''cached_{}_{}_{}'''.format(mode.value,    tokenizer.__class__.__name__,    str(_lowercase      )      ),    )

																# Make sure only the first process in distributed training processes the dataset,
																# and the others will use the cache.
																snake_case       :     str =    cached_features_file + '''.lock'''
																with FileLock(_lowercase      ):
																						if os.path.exists(_lowercase      ) and not overwrite_cache:
																												logger.info(F"""Loading features from cached file {cached_features_file}"""      )
																												snake_case       :     Optional[Any] =    torch.load(_lowercase      )
																						else:
																												logger.info(F"""Creating features from dataset file at {data_dir}"""      )
																												snake_case       :     List[Any] =    token_classification_task.read_examples_from_file(_lowercase,    _lowercase      )
																												# TODO clean up all this to leverage built-in features of tokenizers
																												snake_case       :     Optional[Any] =    token_classification_task.convert_examples_to_features(
																												    _lowercase,    _lowercase,    _lowercase,    _lowercase,    cls_token_at_end=bool(model_type in ['''xlnet''']      ),    cls_token=tokenizer.cls_token,    cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0,    sep_token=tokenizer.sep_token,    sep_token_extra=_lowercase,    pad_on_left=bool(tokenizer.padding_side == '''left'''      ),    pad_token=tokenizer.pad_token_id,    pad_token_segment_id=tokenizer.pad_token_type_id,    pad_token_label_id=self.pad_token_label_id,    )
																												logger.info(F"""Saving features into cached file {cached_features_file}"""      )
																												torch.save(self.features,    _lowercase      )




										def __len__(							self	:				Optional[Any]      ):
																return len(self.features      )




										def __getitem__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																return self.features[i]


if is_tf_available():
						import tensorflow as tf

						class 						a   :
										_snake_case		    = 42
										_snake_case		    = -100




										def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				TokenClassificationTask,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				PreTrainedTokenizer,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    SCREAMING_SNAKE_CASE_	:				Optional[int]=False,    SCREAMING_SNAKE_CASE_	:				Split = Split.train,    ):
																snake_case       :     Union[str, Any] =    token_classification_task.read_examples_from_file(_lowercase,    _lowercase      )
																# TODO clean up all this to leverage built-in features of tokenizers
																snake_case       :     List[Any] =    token_classification_task.convert_examples_to_features(
																    _lowercase,    _lowercase,    _lowercase,    _lowercase,    cls_token_at_end=bool(model_type in ['''xlnet''']      ),    cls_token=tokenizer.cls_token,    cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0,    sep_token=tokenizer.sep_token,    sep_token_extra=_lowercase,    pad_on_left=bool(tokenizer.padding_side == '''left'''      ),    pad_token=tokenizer.pad_token_id,    pad_token_segment_id=tokenizer.pad_token_type_id,    pad_token_label_id=self.pad_token_label_id,    )

																def gen():
																						for ex in self.features:
																												if ex.token_type_ids is None:
																																		yield (
																																		    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																																		    ex.label_ids,
																																		)
																												else:
																																		yield (
																																		    {
																																		        "input_ids": ex.input_ids,
																																		        "attention_mask": ex.attention_mask,
																																		        "token_type_ids": ex.token_type_ids,
																																		    },
																																		    ex.label_ids,
																																		)

																if "token_type_ids" not in tokenizer.model_input_names:
																						snake_case       :     int =    tf.data.Dataset.from_generator(
																						    _lowercase,    ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa),    (
																						        {'''input_ids''': tf.TensorShape([None]      ), '''attention_mask''': tf.TensorShape([None]      )},
																						        tf.TensorShape([None]      ),
																						    ),    )
																else:
																						snake_case       :     List[str] =    tf.data.Dataset.from_generator(
																						    _lowercase,    ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa),    (
																						        {
																						            '''input_ids''': tf.TensorShape([None]      ),
																						            '''attention_mask''': tf.TensorShape([None]      ),
																						            '''token_type_ids''': tf.TensorShape([None]      ),
																						        },
																						        tf.TensorShape([None]      ),
																						    ),    )




										def 	__snake_case							(							self	:				Tuple      ):
																snake_case       :     Dict =    self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

																return self.dataset




										def __len__(							self	:				Optional[Any]      ):
																return len(self.features      )




										def __getitem__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																return self.features[i]








'''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      A (       A_							:					str    ,							A_							:					str       ):
						if "xprophetnet" in prophetnet_checkpoint_path:
												snake_case       :     Any =    XLMProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     Tuple =    XLMProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )
						else:
												snake_case       :     Union[str, Any] =    ProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     List[Any] =    ProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )

						snake_case       :     Union[str, Any] =    ['''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       :     Optional[Any] =    key.split('''.'''       )

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

												snake_case       :     Optional[Any] =    False
												for attribute in attributes:
																		if attribute in mapping:
																								snake_case       :     List[str] =    mapping[attribute]
																								if not hasattr(A_    ,							A_       ) and len(A_       ) > 0:
																														snake_case       :     str =    attribute
																		elif hasattr(A_    ,							A_       ):
																								snake_case       :     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       :     Tuple =    True
																								break
																		elif attribute == "bias":
																								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																								snake_case       :     List[str] =    old_model.bias
																								logger.info(F"""{attribute} is initialized"""       )
																								snake_case       :     Tuple =    True
																								break
																		elif attribute in special_keys and hasattr(A_    ,							'''in_proj_weight'''       ):
																								snake_case       :     Union[str, Any] =    old_model.in_proj_weight.shape[0] // 3
																								snake_case       :     Any =    getattr(A_    ,							A_       )
																								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       :     Tuple =    nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
																														snake_case       :     List[Any] =    nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

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

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

																								if old_attribute == "":
																														snake_case       :     Union[str, Any] =    old_model
																								else:
																														if not hasattr(A_    ,							A_       ):
																																				raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																														snake_case       :     Tuple =    getattr(A_    ,							A_       )

												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(A_       )


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)

