




import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_     :    Any      =			logging.get_logger(__name__)

lowercase_     :    Optional[Any]      =			{
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowercase_     :    Any      =			{
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

lowercase_     :    List[str]      =			{'Helsinki-NLP/opus-mt-en-de': 5_12}
lowercase_     :    Optional[int]      =			{}






class    __lowerCAmelCase			(	lowerCamelCase__			):
			snake_case_				:			Optional[int]								=					VOCAB_FILES_NAMES
			snake_case_				:			Optional[int]								=					PRETRAINED_VOCAB_FILES_MAP
			snake_case_				:			str								=					PRETRAINED_INIT_CONFIGURATION
			snake_case_				:			int								=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_				:			Optional[Any]								=					['input_ids', 'attention_mask']
			snake_case_				:			List[Any]								=					re.compile(">>.+<<"			)  # type: re.Pattern







			def __init__(	self   :      Union[str, Any]   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      List[str]   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      List[Any]=None   ,   snake_case__   :      str=None   ,   snake_case__   :      Optional[Any]=None   ,   snake_case__   :      Dict="<unk>"   ,   snake_case__   :      Optional[int]="</s>"   ,   snake_case__   :      Tuple="<pad>"   ,   snake_case__   :      str=512   ,   snake_case__   :      Dict = None   ,   snake_case__   :      str=False   ,   **snake_case__   :      Union[str, Any]   ,   ):




							"""simple docstring"""


							_UpperCAmelCase													=					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
							    source_lang=lowercase_   ,   target_lang=lowercase_   ,   unk_token=lowercase_   ,   eos_token=lowercase_   ,   pad_token=lowercase_   ,   model_max_length=lowercase_   ,   sp_model_kwargs=self.sp_model_kwargs   ,   target_vocab_file=lowercase_   ,   separate_vocabs=lowercase_   ,   **lowercase_   ,   )
							assert Path(lowercase_				).exists(), F"""cannot find spm source {source_spm}"""

							_UpperCAmelCase													=					separate_vocabs
							_UpperCAmelCase													=					load_json(lowercase_				)
							if self.unk_token not in self.encoder:
											raise KeyError("<unk> token must be in vocab"				)
							assert self.pad_token in self.encoder

							if separate_vocabs:
											_UpperCAmelCase													=					load_json(lowercase_				)
											_UpperCAmelCase													=					{v: k for k, v in self.target_encoder.items()}
											_UpperCAmelCase													=					[]
							else:
											_UpperCAmelCase													=					{v: k for k, v in self.encoder.items()}
											_UpperCAmelCase													=					[k for k in self.encoder if k.startswith(">>"				) and k.endswith("<<"				)]

							_UpperCAmelCase													=					source_lang
							_UpperCAmelCase													=					target_lang
							_UpperCAmelCase													=					[source_spm, target_spm]

							# load SentencePiece model for pre-processing
							_UpperCAmelCase													=					load_spm(lowercase_   ,   self.sp_model_kwargs				)
							_UpperCAmelCase													=					load_spm(lowercase_   ,   self.sp_model_kwargs				)
							_UpperCAmelCase													=					self.spm_source
							_UpperCAmelCase													=					self.encoder

							# Multilingual target side: default to using first supported language code.

							self._setup_normalizer()







			def UpperCamelCase      (	self   :      Any				):




							"""simple docstring"""


							try:
											from sacremoses import MosesPunctNormalizer

											_UpperCAmelCase													=					MosesPunctNormalizer(self.source_lang				).normalize
							except (ImportError, FileNotFoundError):
											warnings.warn("Recommended: pip install sacremoses."				)
											_UpperCAmelCase													=					lambda snake_case__				: x







			def UpperCamelCase      (	self   :      List[str]   ,   snake_case__   :      Any				):




							"""simple docstring"""


							return self.punc_normalizer(lowercase_				) if x else ""







			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      int				):




							"""simple docstring"""


							return self.current_encoder.get(lowercase_   ,   self.current_encoder[self.unk_token]				)







			def UpperCamelCase      (	self   :      Any   ,   snake_case__   :      Union[str, Any]				):




							"""simple docstring"""


							_UpperCAmelCase													=					self.language_code_re.match(lowercase_				)
							_UpperCAmelCase													=					[match.group(0				)] if match else []
							return code, self.language_code_re.sub(""   ,   lowercase_				)







			def UpperCamelCase      (	self   :      Dict   ,   snake_case__   :      Any				):




							"""simple docstring"""


							_UpperCAmelCase							,			_UpperCAmelCase													=					self.remove_language_code(lowercase_				)
							_UpperCAmelCase													=					self.current_spm.encode(lowercase_   ,   out_type=lowercase_				)
							return code + pieces







			def UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      Tuple				):




							"""simple docstring"""


							return self.decoder.get(lowercase_   ,   self.unk_token				)







			def UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      str   ,   **snake_case__   :      Optional[int]				):




							"""simple docstring"""


							return super().batch_decode(lowercase_   ,   **lowercase_				)







			def UpperCamelCase      (	self   :      List[str]   ,   snake_case__   :      List[Any]   ,   **snake_case__   :      Dict				):




							"""simple docstring"""


							return super().decode(lowercase_   ,   **lowercase_				)







			def UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      int				):




							"""simple docstring"""


							_UpperCAmelCase													=					self.spm_source if self._decode_use_source_tokenizer else self.spm_target
							_UpperCAmelCase													=					[]
							_UpperCAmelCase													=					""
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += sp_model.decode_pieces(lowercase_				) + token + " "
															_UpperCAmelCase													=					[]
											else:
															current_sub_tokens.append(lowercase_				)
							out_string += sp_model.decode_pieces(lowercase_				)
							return out_string.strip()







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      str=None				):




							"""simple docstring"""


							if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]







			def UpperCamelCase      (	self   :      Optional[int]				):




							"""simple docstring"""


							_UpperCAmelCase													=					self.spm_source
							_UpperCAmelCase													=					self.encoder







			def UpperCamelCase      (	self   :      List[str]				):




							"""simple docstring"""


							_UpperCAmelCase													=					self.spm_target
							if self.separate_vocabs:
											_UpperCAmelCase													=					self.target_encoder







			@property
			def UpperCamelCase      (	self   :      List[str]				):




							"""simple docstring"""


							return len(self.encoder				)







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      List[Any] = None				):




							"""simple docstring"""


							if not os.path.isdir(lowercase_				):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
											return
							_UpperCAmelCase													=					[]

							if self.separate_vocabs:
											_UpperCAmelCase													=					os.path.join(
											    lowercase_   ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]   ,   )
											_UpperCAmelCase													=					os.path.join(
											    lowercase_   ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]   ,   )
											save_json(self.encoder   ,   lowercase_				)
											save_json(self.target_encoder   ,   lowercase_				)
											saved_files.append(lowercase_				)
											saved_files.append(lowercase_				)
							else:
											_UpperCAmelCase													=					os.path.join(
											    lowercase_   ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]				)
											save_json(self.encoder   ,   lowercase_				)
											saved_files.append(lowercase_				)

							for spm_save_filename, spm_orig_path, spm_model in zip(
							    [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]   ,   self.spm_files   ,   [self.spm_source, self.spm_target]   ,   ):
											_UpperCAmelCase													=					os.path.join(
											    lowercase_   ,   (filename_prefix + "-" if filename_prefix else "") + spm_save_filename				)
											if os.path.abspath(lowercase_				) != os.path.abspath(lowercase_				) and os.path.isfile(lowercase_				):
															copyfile(lowercase_   ,   lowercase_				)
															saved_files.append(lowercase_				)
											elif not os.path.isfile(lowercase_				):
															with open(lowercase_   ,   "wb"				) as fi:
																			_UpperCAmelCase													=					spm_model.serialized_model_proto()
																			fi.write(lowercase_				)
															saved_files.append(lowercase_				)

							return tuple(lowercase_				)







			def UpperCamelCase      (	self   :      str				):




							"""simple docstring"""


							return self.get_src_vocab()







			def UpperCamelCase      (	self   :      int				):




							"""simple docstring"""


							return dict(self.encoder   ,   **self.added_tokens_encoder				)







			def UpperCamelCase      (	self   :      Tuple				):




							"""simple docstring"""


							return dict(self.target_encoder   ,   **self.added_tokens_decoder				)







			def __getstate__(	self   :      Tuple				):




							"""simple docstring"""


							_UpperCAmelCase													=					self.__dict__.copy()
							state.update(
							    {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}				)
							return state







			def __setstate__(	self   :      Optional[int]   ,   snake_case__   :      List[str]				):




							"""simple docstring"""


							_UpperCAmelCase													=					d

							# for backward compatibility
							if not hasattr(self   ,   "sp_model_kwargs"				):
											_UpperCAmelCase													=					{}

							_UpperCAmelCase							,			_UpperCAmelCase													=					(load_spm(lowercase_   ,   self.sp_model_kwargs				) for f in self.spm_files)
							_UpperCAmelCase													=					self.spm_source
							self._setup_normalizer()







			def UpperCamelCase      (	self   :      int   ,   *snake_case__   :      Union[str, Any]   ,   **snake_case__   :      Union[str, Any]				):




							"""simple docstring"""


							return 1







			def UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      Optional[Any]				):




							"""simple docstring"""


							_UpperCAmelCase													=					set(self.all_special_ids				)  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id				)  # <unk> is only sometimes special
							return [1 if x in all_special_ids else 0 for x in seq]







			def UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      List[str]   ,   snake_case__   :      Union[str, Any] = None   ,   snake_case__   :      str = False				):




							"""simple docstring"""


							if already_has_special_tokens:
											return self._special_token_mask(lowercase_				)
							elif token_ids_a is None:
											return self._special_token_mask(lowercase_				) + [1]
							else:
											return self._special_token_mask(token_ids_a + token_ids_a				) + [1]




def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

				'''simple docstring'''


				_UpperCAmelCase													=					sentencepiece.SentencePieceProcessor(**_A						)
				spm.Load(_A						)
				return spm


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

				'''simple docstring'''


				with open(_A     ,   "w"						) as f:
								json.dump(_A     ,   _A     ,   indent=2						)





def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

				'''simple docstring'''


				with open(_A     ,   "r"						) as f:
								return json.load(_A						)










import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						if "model" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("model."     ,   ""						)
						if "norm1" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("norm1"     ,   "attention.output.LayerNorm"						)
						if "norm2" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("norm2"     ,   "output.LayerNorm"						)
						if "norm" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("norm"     ,   "LayerNorm"						)
						if "transformer" in orig_key:
												_UpperCAmelCase													=					orig_key.split("."						)[0].split("_"						)[-1]
												_UpperCAmelCase													=					orig_key.replace(f"""transformer_{layer_num}"""     ,   f"""encoder.layer.{layer_num}"""						)
						if "mha.attn" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mha.attn"     ,   "attention.self"						)
						if "mha" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mha"     ,   "attention"						)
						if "W_q" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("W_q"     ,   "self.query"						)
						if "W_k" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("W_k"     ,   "self.key"						)
						if "W_v" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("W_v"     ,   "self.value"						)
						if "ff1" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("ff1"     ,   "intermediate.dense"						)
						if "ff2" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("ff2"     ,   "output.dense"						)
						if "ff" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("ff"     ,   "output.dense"						)
						if "mlm_class" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mlm.mlm_class"     ,   "cls.predictions.decoder"						)
						if "mlm" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mlm"     ,   "cls.predictions.transform"						)
						if "cls" not in orig_key:
												_UpperCAmelCase													=					"nystromformer." + orig_key

						return orig_key


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase													=					orig_state_dict.pop(snake_case_						)

												if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
																		continue
												else:
																		_UpperCAmelCase													=					val

						_UpperCAmelCase													=					orig_state_dict["cls.predictions.decoder.bias"]
						_UpperCAmelCase													=					(
						    torch.arange(config.max_position_embeddings						).expand((1, -1)						) + 2
						)

						return orig_state_dict


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					torch.load(snake_case_     ,   map_location="cpu"						)["model_state_dict"]
						_UpperCAmelCase													=					NystromformerConfig.from_json_file(snake_case_						)
						_UpperCAmelCase													=					NystromformerForMaskedLM(snake_case_						)

						_UpperCAmelCase													=					convert_checkpoint_helper(snake_case_     ,   snake_case_						)

						model.load_state_dict(snake_case_						)
						model.eval()
						model.save_pretrained(snake_case_						)

						print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
				lowercase_     :    List[str]      =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The json file for Nystromformer model config.',
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				lowercase_     :    int      =			parser.parse_args()
				convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




