
"""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 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


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

		lowerCamelCase					:      Union[str, Any]          =   ['key_proj', 'value_proj', 'query_proj']

		lowerCamelCase					:      Optional[Any]          =   {
		    '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					:      List[str]          =   key.split('.'      )

				if attributes[0] == "lm_head":
						lowerCamelCase					:      str          =   prophet
						lowerCamelCase					:      Union[str, Any]          =   prophet_old
				else:
						lowerCamelCase					:      List[Any]          =   prophet.prophetnet
						lowerCamelCase					:      List[str]          =   prophet_old.model

				lowerCamelCase					:      List[str]          =   False
				for attribute in attributes:
						if attribute in mapping:
								lowerCamelCase					:      Optional[Any]          =   mapping[attribute]
								if not hasattr(a_,						a_      ) and len(a_      ) > 0:
										lowerCamelCase					:      Optional[Any]          =   attribute
						elif hasattr(a_,						a_      ):
								lowerCamelCase					:      Any          =   attribute

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

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

						if attribute.isdigit():
								lowerCamelCase					:      Dict          =   model[int(a_      )]
								lowerCamelCase					:      List[Any]          =   old_model[int(a_      )]
						else:
								lowerCamelCase					:      int          =   getattr(a_,						a_      )

								if old_attribute == "":
										lowerCamelCase					:      List[Any]          =   old_model
								else:
										if not hasattr(a_,						a_      ):
												raise ValueError(F"""{old_model} does not have {old_attribute}"""      )
										lowerCamelCase					:      List[str]          =   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__":
					_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)








"""simple docstring"""

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_A						=			logging.get_logger(__name__)


_A						=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
_A						=			{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_A						=			{
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     VOCAB_FILES_NAMES
	lowercase_				=     PRETRAINED_VOCAB_FILES_MAP
	lowercase_				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_				=     ['input_ids', 'attention_mask']
	lowercase_				=     BartTokenizer




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_="replace"		,			UpperCAmelCase_="<s>"		,			UpperCAmelCase_="</s>"		,			UpperCAmelCase_="</s>"		,			UpperCAmelCase_="<s>"		,			UpperCAmelCase_="<unk>"		,			UpperCAmelCase_="<pad>"		,			UpperCAmelCase_="<mask>"		,			UpperCAmelCase_=False		,			UpperCAmelCase_=True		,			**UpperCAmelCase_		,			)		->							Union[str, Any]:
			super().__init__(
			    UpperCAmelCase_		,			UpperCAmelCase_		,			tokenizer_file=UpperCAmelCase_		,			errors=UpperCAmelCase_		,			bos_token=UpperCAmelCase_		,			eos_token=UpperCAmelCase_		,			sep_token=UpperCAmelCase_		,			cls_token=UpperCAmelCase_		,			unk_token=UpperCAmelCase_		,			pad_token=UpperCAmelCase_		,			mask_token=UpperCAmelCase_		,			add_prefix_space=UpperCAmelCase_		,			trim_offsets=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

			lowerCamelCase					:      List[Any]          =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
			if pre_tok_state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
					lowerCamelCase					:      Tuple          =   getattr(UpperCAmelCase_		,			pre_tok_state.pop('type' ) )
					lowerCamelCase					:      Optional[Any]          =   add_prefix_space
					lowerCamelCase					:      str          =   pre_tok_class(**UpperCAmelCase_ )

			lowerCamelCase					:      Optional[Any]          =   add_prefix_space

			# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
			lowerCamelCase					:      Dict          =   'post_processor'
			lowerCamelCase					:      str          =   getattr(self.backend_tokenizer		,			UpperCAmelCase_		,			UpperCAmelCase_ )
			if tokenizer_component_instance:
					lowerCamelCase					:      Optional[Any]          =   json.loads(tokenizer_component_instance.__getstate__() )

					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
					if "sep" in state:
							lowerCamelCase					:      int          =   tuple(state['sep'] )
					if "cls" in state:
							lowerCamelCase					:      str          =   tuple(state['cls'] )

					lowerCamelCase					:      Optional[Any]          =   False

					if state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
							lowerCamelCase					:      Dict          =   add_prefix_space
							lowerCamelCase					:      Tuple          =   True

					if state.get('trim_offsets'		,			UpperCAmelCase_ ) != trim_offsets:
							lowerCamelCase					:      Tuple          =   trim_offsets
							lowerCamelCase					:      Dict          =   True

					if changes_to_apply:
							lowerCamelCase					:      Optional[int]          =   getattr(UpperCAmelCase_		,			state.pop('type' ) )
							lowerCamelCase					:      Any          =   component_class(**UpperCAmelCase_ )
							setattr(self.backend_tokenizer		,			UpperCAmelCase_		,			UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							str:
			if self._mask_token is None:
					if self.verbose:
							logger.error('Using mask_token, but it is not set yet.' )
					return None
			return str(self._mask_token )




	@mask_token.setter
	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Optional[int]          =   AddedToken(UpperCAmelCase_		,			lstrip=UpperCAmelCase_		,			rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) else value
			lowerCamelCase					:      int          =   value




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      str          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    'to use it with pretokenized inputs.' )

			return super()._batch_encode_plus(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      Optional[Any]          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    'to use it with pretokenized inputs.' )

			return super()._encode_plus(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							Tuple[str]:
			lowerCamelCase					:      Any          =   self._tokenizer.model.save(UpperCAmelCase_		,			name=UpperCAmelCase_ )
			return tuple(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							List[Any]:
			lowerCamelCase					:      Union[str, Any]          =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
			if token_ids_a is None:
					return output

			return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							List[int]:
			lowerCamelCase					:      List[Any]          =   [self.sep_token_id]
			lowerCamelCase					:      str          =   [self.cls_token_id]

			if token_ids_a is None:
					return len(cls + token_ids_a + sep ) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






