




from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

lowercase_     :    Optional[Any]      =			{
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

lowercase_     :    List[str]      =			datasets.load_dataset('xnli', split='test+validation')

lowercase_     :    Union[str, Any]      =			0
lowercase_     :    Dict      =			0
lowercase_     :    str      =			0
lowercase_     :    Tuple      =			0


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_						):

				'''simple docstring'''


				if spm_diff == list(reversed(lowerCamelCase_						)						):
								# AAA -> AA+A vs A+AA case.
								return True
				elif len(lowerCamelCase_						) == len(lowerCamelCase_						) and fast.decode(lowerCamelCase_						) == fast.decode(lowerCamelCase_						):
								# Second order OK
								# Barrich -> Barr + ich vs Bar + rich
								return True
				_UpperCAmelCase													=					slow.encode(slow.decode(lowerCamelCase_						)						)
				_UpperCAmelCase													=					fast.encode(fast.decode(lowerCamelCase_						)						)
				if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
								# Type 3 error.
								# Snehagatha ->
								#       Sne, h, aga, th, a
								#       Sne, ha, gat, ha
								# Encoding the wrong with sp does not even recover what spm gave us
								# It fits tokenizer however...
								return True
				return False


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

				'''simple docstring'''


				_UpperCAmelCase													=					fast.encode_plus(lowerCamelCase_						)[0]
				_UpperCAmelCase													=					enc.offsets
				_UpperCAmelCase							,			_UpperCAmelCase													=					offsets[idx], offsets[idx - 1]
				if curr is not None and line[curr[0] : curr[1]] == "\u200f":
								return True
				if prev is not None and line[prev[0] : prev[1]] == "\u200f":
								return True


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_						):

				'''simple docstring'''


				for i, (spm_id, tok_id) in enumerate(zip(lowerCamelCase_     ,   lowerCamelCase_						)						):
								if spm_id != tok_id:
												break
				_UpperCAmelCase													=					i
				for i, (spm_id, tok_id) in enumerate(zip(reversed(lowerCamelCase_						)     ,   reversed(lowerCamelCase_						)						)						):
								if spm_id != tok_id:
												break
				_UpperCAmelCase													=					len(lowerCamelCase_						) - i

				_UpperCAmelCase													=					spm_ids[first:last]
				_UpperCAmelCase													=					tok_ids[first:last]

				if check_diff(lowerCamelCase_     ,   lowerCamelCase_     ,   lowerCamelCase_     ,   lowerCamelCase_						):
								return True

				if check_LTR_mark(lowerCamelCase_     ,   lowerCamelCase_     ,   lowerCamelCase_						):
								return True

				if last - first > 5:
								# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
								_UpperCAmelCase													=					Counter(spm_ids[first:last]						)
								_UpperCAmelCase													=					Counter(tok_ids[first:last]						)

								_UpperCAmelCase													=					{spm_ for (spm_, si) in spms.items() if toks.get(spm_     ,   0						) == si}
								_UpperCAmelCase													=					3
								for i in range(last - first - min_width						):
												if all(spm_ids[first + i + j] in removable_tokens for j in range(lowerCamelCase_						)						):
																_UpperCAmelCase													=					[
																    k
																    for k in range(last - first - min_width						)
																    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																]
																for j in possible_matches:
																				if check_diff(spm_ids[first : first + i]     ,   tok_ids[first : first + j]     ,   lowerCamelCase_     ,   lowerCamelCase_						) and check_details(
																				    lowerCamelCase_     ,   spm_ids[first + i : last]     ,   tok_ids[first + j : last]     ,   lowerCamelCase_     ,   lowerCamelCase_     ,   ):
																								return True

				print(f"""Spm: {[fast.decode([spm_ids[i]]						) for i in range(lowerCamelCase_     ,   lowerCamelCase_						)]}"""						)
				try:
								print(f"""Tok: {[fast.decode([tok_ids[i]]						) for i in range(lowerCamelCase_     ,   lowerCamelCase_						)]}"""						)
				except Exception:
								pass

				_UpperCAmelCase													=					fast.decode(spm_ids[:first]						)
				_UpperCAmelCase													=					fast.decode(spm_ids[last:]						)
				_UpperCAmelCase													=					fast.decode(spm_ids[first:last]						)
				print()
				print(lowerCamelCase_						)
				return False


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

				'''simple docstring'''


				global perfect
				global imperfect
				global wrong
				global total

				_UpperCAmelCase													=					slow.encode(lowerCamelCase_						)
				_UpperCAmelCase													=					fast.encode(lowerCamelCase_						)

				_UpperCAmelCase													=					False
				total += 1

				if slow_ids != fast_ids:
								if check_details(lowerCamelCase_     ,   lowerCamelCase_     ,   lowerCamelCase_     ,   lowerCamelCase_     ,   lowerCamelCase_						):
												_UpperCAmelCase													=					True
												imperfect += 1
								else:
												wrong += 1
				else:
								perfect += 1

				if total % 1_0000 == 0:
								print(f"""({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})"""						)

				if skip_assert:
								return

				assert (
				    slow_ids == fast_ids
				), f"""line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(lowerCamelCase_						)}\n{fast.tokenize(lowerCamelCase_						)}"""


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

				'''simple docstring'''


				global batch_total
				for i in range(len(lowerCamelCase_						)						):
								# premise, all languages
								for text in dataset[i]["premise"].values():
												test_string(lowerCamelCase_     ,   lowerCamelCase_     ,   lowerCamelCase_						)

								# hypothesis, all languages
								for text in dataset[i]["hypothesis"]["translation"]:
												test_string(lowerCamelCase_     ,   lowerCamelCase_     ,   lowerCamelCase_						)


if __name__ == "__main__":
				for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
								lowercase_     :    Optional[Any]      =			list(slow_class.max_model_input_sizes.keys())
								for checkpoint in checkpoint_names:
												lowercase_     :    Dict      =			0
												lowercase_     :    Dict      =			0
												lowercase_     :    List[str]      =			0
												lowercase_     :    List[str]      =			0

												print(f"""========================== Checking {name}: {checkpoint} ==========================""")
												lowercase_     :    Tuple      =			slow_class.from_pretrained(checkpoint, force_download=True)
												lowercase_     :    Dict      =			fast_class.from_pretrained(checkpoint, force_download=True)
												test_tokenizer(slow, fast)
												print(f"""Accuracy {perfect * 1_00 / total:.2f}""")










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)




