



import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class      UpperCamelCase    (						_UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   List[str]				=	BlenderbotSmallTokenizer
	lowerCAmelCase	:   Dict				=	False



	def 			__A							(       self		):
							super().setUp()

							A__							=       ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
							A__							=       dict(zip(UpperCAmelCase__			,	range(len(UpperCAmelCase__		)		)		)		)

							A__							=       ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
							A__							=       {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

							A__							=       os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES["vocab_file"]		)
							A__							=       os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES["merges_file"]		)
							with open(self.vocab_file			,	"w"			,	encoding="utf-8"		) as fp:
													fp.write(json.dumps(UpperCAmelCase__		) + "\n"		)
							with open(self.merges_file			,	"w"			,	encoding="utf-8"		) as fp:
													fp.write("\n".join(UpperCAmelCase__		)		)



	def 			__A							(       self			,	**UpperCAmelCase__		):
							kwargs.update(self.special_tokens_map		)
							return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       "adapt act apte"
							A__							=       "adapt act apte"
							return input_text, output_text



	def 			__A							(       self		):
							A__							=       BlenderbotSmallTokenizer(self.vocab_file			,	self.merges_file			,	**self.special_tokens_map		)
							A__							=       "adapt act apte"
							A__							=       ["adapt", "act", "ap@@", "te"]
							A__							=       tokenizer.tokenize(UpperCAmelCase__		)
							self.assertListEqual(UpperCAmelCase__			,	UpperCAmelCase__		)

							A__							=       [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

							A__							=       [0, 1, 2, 3, 4, 5]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__		)			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"		)
							assert tok("sam"		).input_ids == [1_384]
							A__							=       "I am a small frog."
							A__							=       tok([src_text]			,	padding=UpperCAmelCase__			,	truncation=UpperCAmelCase__		)["input_ids"]
							A__							=       tok.batch_decode(UpperCAmelCase__			,	skip_special_tokens=UpperCAmelCase__			,	clean_up_tokenization_spaces=UpperCAmelCase__		)[0]
							assert src_text != decoded  # I wish it did!
							assert decoded == "i am a small frog ."



	def 			__A							(       self		):
							A__							=       BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"		)
							A__							=       "I am a small frog ."
							A__							=       "."
							A__							=       tok(UpperCAmelCase__		)["input_ids"]
							A__							=       tok(UpperCAmelCase__		)["input_ids"]

							assert encoded[-1] == encoded_dot[0]










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





