




import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
				from .tokenization_deberta_va import DebertaVaTokenizer
else:
				lowercase_     :    Union[str, Any]      =			None

lowercase_     :    List[str]      =			logging.get_logger(__name__)

lowercase_     :    Any      =			{'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

lowercase_     :    int      =			{
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

lowercase_     :    Optional[int]      =			{
    'microsoft/deberta-v2-xlarge': 5_12,
    'microsoft/deberta-v2-xxlarge': 5_12,
    'microsoft/deberta-v2-xlarge-mnli': 5_12,
    'microsoft/deberta-v2-xxlarge-mnli': 5_12,
}

lowercase_     :    str      =			{
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}






class    __lowerCAmelCase			(	UpperCAmelCase__			):
			snake_case_				:			Union[str, Any]								=					VOCAB_FILES_NAMES
			snake_case_				:			Tuple								=					PRETRAINED_VOCAB_FILES_MAP
			snake_case_				:			Optional[int]								=					PRETRAINED_INIT_CONFIGURATION
			snake_case_				:			List[str]								=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_				:			int								=					DebertaVaTokenizer







			def __init__(	self   :      Tuple   ,   snake_case__   :      List[Any]=None   ,   snake_case__   :      Union[str, Any]=None   ,   snake_case__   :      Tuple=False   ,   snake_case__   :      Dict=False   ,   snake_case__   :      List[str]="[CLS]"   ,   snake_case__   :      str="[SEP]"   ,   snake_case__   :      Union[str, Any]="[UNK]"   ,   snake_case__   :      str="[SEP]"   ,   snake_case__   :      int="[PAD]"   ,   snake_case__   :      int="[CLS]"   ,   snake_case__   :      List[str]="[MASK]"   ,   **snake_case__   :      List[str]   ,   ):




							"""simple docstring"""


							super().__init__(
							    snake_case__   ,   tokenizer_file=snake_case__   ,   do_lower_case=snake_case__   ,   bos_token=snake_case__   ,   eos_token=snake_case__   ,   unk_token=snake_case__   ,   sep_token=snake_case__   ,   pad_token=snake_case__   ,   cls_token=snake_case__   ,   mask_token=snake_case__   ,   split_by_punct=snake_case__   ,   **snake_case__   ,   )

							_UpperCAmelCase													=					do_lower_case
							_UpperCAmelCase													=					split_by_punct
							_UpperCAmelCase													=					vocab_file
							_UpperCAmelCase													=					False if not self.vocab_file else True







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




							"""simple docstring"""


							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_UpperCAmelCase													=					[self.cls_token_id]
							_UpperCAmelCase													=					[self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Tuple   ,   snake_case__   :      List[str]=None   ,   snake_case__   :      Dict=False				):




							"""simple docstring"""


							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=snake_case__   ,   token_ids_a=snake_case__   ,   already_has_special_tokens=snake_case__				)

							if token_ids_a is not None:
											return [1] + ([0] * len(snake_case__				)) + [1] + ([0] * len(snake_case__				)) + [1]
							return [1] + ([0] * len(snake_case__				)) + [1]







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




							"""simple docstring"""


							_UpperCAmelCase													=					[self.sep_token_id]
							_UpperCAmelCase													=					[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				) * [0] + len(token_ids_a + sep				) * [1]







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




							"""simple docstring"""


							if not self.can_save_slow_tokenizer:
											raise ValueError(
											    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
											    "tokenizer."				)

							if not os.path.isdir(snake_case__				):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
											return
							_UpperCAmelCase													=					os.path.join(
							    snake_case__   ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

							if os.path.abspath(self.vocab_file				) != os.path.abspath(snake_case__				):
											copyfile(self.vocab_file   ,   snake_case__				)

							return (out_vocab_file,)










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)




