




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


lowercase_     :    int      =			logging.get_logger(__name__)

lowercase_     :    Optional[int]      =			{'vocab_file': 'vocab.txt'}

lowercase_     :    List[str]      =			{
    'vocab_file': {
        'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt',
        'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt',
        'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt',
        'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt',
    }
}

lowercase_     :    str      =			{
    'tau/splinter-base': 5_12,
    'tau/splinter-base-qass': 5_12,
    'tau/splinter-large': 5_12,
    'tau/splinter-large-qass': 5_12,
}

lowercase_     :    str      =			{
    'tau/splinter-base': {'do_lower_case': False},
    'tau/splinter-base-qass': {'do_lower_case': False},
    'tau/splinter-large': {'do_lower_case': False},
    'tau/splinter-large-qass': {'do_lower_case': False},
}






class    __lowerCAmelCase			(	UpperCAmelCase__			):
			snake_case_				:			Optional[Any]								=					VOCAB_FILES_NAMES
			snake_case_				:			Optional[Any]								=					PRETRAINED_VOCAB_FILES_MAP
			snake_case_				:			Dict								=					PRETRAINED_INIT_CONFIGURATION
			snake_case_				:			List[Any]								=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_				:			List[str]								=					SplinterTokenizer







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




									"""simple docstring"""


									super().__init__(
									    snake_case__   ,   tokenizer_file=snake_case__   ,   do_lower_case=snake_case__   ,   unk_token=snake_case__   ,   sep_token=snake_case__   ,   pad_token=snake_case__   ,   cls_token=snake_case__   ,   mask_token=snake_case__   ,   tokenize_chinese_chars=snake_case__   ,   strip_accents=snake_case__   ,   additional_special_tokens=(question_token,)   ,   **snake_case__   ,   )

									_UpperCAmelCase													=					json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
									if (
									    pre_tok_state.get("lowercase"   ,   snake_case__				) != do_lower_case
									    or pre_tok_state.get("strip_accents"   ,   snake_case__				) != strip_accents
									):
															_UpperCAmelCase													=					getattr(snake_case__   ,   pre_tok_state.pop("type"				)				)
															_UpperCAmelCase													=					do_lower_case
															_UpperCAmelCase													=					strip_accents
															_UpperCAmelCase													=					pre_tok_class(**snake_case__				)

									_UpperCAmelCase													=					do_lower_case







			@property
			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									return self.convert_tokens_to_ids(self.question_token				)







			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      List[int]   ,   snake_case__   :      Optional[List[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]
									_UpperCAmelCase													=					[self.question_token_id] + [self.convert_tokens_to_ids("."				)]
									if self.padding_side == "right":
															# Input is question-then-context
															return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
									else:
															# Input is context-then-question
															return cls + token_ids_a + sep + token_ids_a + question_suffix + sep







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




									"""simple docstring"""


									_UpperCAmelCase													=					[self.sep_token_id]
									_UpperCAmelCase													=					[self.cls_token_id]
									_UpperCAmelCase													=					[self.question_token_id] + [self.convert_tokens_to_ids("."				)]
									if token_ids_a is None:
															return len(cls + token_ids_a + sep				) * [0]

									if self.padding_side == "right":
															# Input is question-then-context
															return len(cls + token_ids_a + question_suffix + sep				) * [0] + len(token_ids_a + sep				) * [1]
									else:
															# Input is context-then-question
															return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + question_suffix + sep				) * [1]







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




									"""simple docstring"""


									_UpperCAmelCase													=					self._tokenizer.model.save(snake_case__   ,   name=snake_case__				)
									return tuple(snake_case__				)










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)




