



"""simple docstring"""





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase							:       str          =       logging.get_logger(__name__)


__lowerCAmelCase							:       Optional[int]          =       {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

__lowerCAmelCase							:       Union[str, Any]          =       {
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

__lowerCAmelCase							:       Dict          =       {
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 						__lowerCAmelCase    (		):


						'''simple docstring'''






						snake_case_ :			List[Any]            =      (
						    list(range(ord("""!"""     )       ,			ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     )       ,			ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     )       ,			ord("""ÿ"""     ) + 1     )     )
						)
						snake_case_ :			List[Any]            =      bs[:]
						snake_case_ :			Union[str, Any]            =      0
						for b in range(2**8     ):
												if b not in bs:
																		bs.append(__UpperCamelCase     )
																		cs.append(2**8 + n     )
																		n += 1
						snake_case_ :			Union[str, Any]            =      [chr(__UpperCamelCase     ) for n in cs]
						return dict(zip(__UpperCamelCase       ,			__UpperCamelCase     )     )
def 						__lowerCAmelCase    (		__UpperCamelCase   :      str     ):


						'''simple docstring'''






						snake_case_ :			List[str]            =      set()
						snake_case_ :			Optional[Any]            =      word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)     )
												snake_case_ :			List[str]            =      char
						return pairs






class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							VOCAB_FILES_NAMES
		_lowerCamelCase      	=							PRETRAINED_VOCAB_FILES_MAP
		_lowerCamelCase      	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_lowerCamelCase      	=							['''input_ids''', '''attention_mask''']





		def __init__(       self				,     _lowercase				,     _lowercase				,     _lowercase="replace"				,     _lowercase="<s>"				,     _lowercase="</s>"				,     _lowercase="</s>"				,     _lowercase="<s>"				,     _lowercase="<unk>"				,     _lowercase="<pad>"				,     _lowercase="<mask>"				,     _lowercase=False				,     **_lowercase				,     )			->    Any:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      AddedToken(_lowercase				,     lstrip=_lowercase				,     rstrip=_lowercase ) if isinstance(_lowercase				,     _lowercase ) else bos_token
								snake_case_ :			Union[str, Any]            =      AddedToken(_lowercase				,     lstrip=_lowercase				,     rstrip=_lowercase ) if isinstance(_lowercase				,     _lowercase ) else eos_token
								snake_case_ :			List[Any]            =      AddedToken(_lowercase				,     lstrip=_lowercase				,     rstrip=_lowercase ) if isinstance(_lowercase				,     _lowercase ) else sep_token
								snake_case_ :			Union[str, Any]            =      AddedToken(_lowercase				,     lstrip=_lowercase				,     rstrip=_lowercase ) if isinstance(_lowercase				,     _lowercase ) else cls_token
								snake_case_ :			str            =      AddedToken(_lowercase				,     lstrip=_lowercase				,     rstrip=_lowercase ) if isinstance(_lowercase				,     _lowercase ) else unk_token
								snake_case_ :			Dict            =      AddedToken(_lowercase				,     lstrip=_lowercase				,     rstrip=_lowercase ) if isinstance(_lowercase				,     _lowercase ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								snake_case_ :			str            =      AddedToken(_lowercase				,     lstrip=_lowercase				,     rstrip=_lowercase ) if isinstance(_lowercase				,     _lowercase ) else mask_token

								super().__init__(
								    errors=_lowercase				,     bos_token=_lowercase				,     eos_token=_lowercase				,     unk_token=_lowercase				,     sep_token=_lowercase				,     cls_token=_lowercase				,     pad_token=_lowercase				,     mask_token=_lowercase				,     add_prefix_space=_lowercase				,     **_lowercase				,     )

								with open(_lowercase				,     encoding="""utf-8""" ) as vocab_handle:
														snake_case_ :			Dict            =      json.load(_lowercase )
								snake_case_ :			Optional[int]            =      {v: k for k, v in self.encoder.items()}
								snake_case_ :			Optional[Any]            =      errors  # how to handle errors in decoding
								snake_case_ :			str            =      bytes_to_unicode()
								snake_case_ :			Any            =      {v: k for k, v in self.byte_encoder.items()}
								with open(_lowercase				,     encoding="""utf-8""" ) as merges_handle:
														snake_case_ :			Dict            =      merges_handle.read().split("""\n""" )[1:-1]
								snake_case_ :			List[Any]            =      [tuple(merge.split() ) for merge in bpe_merges]
								snake_case_ :			str            =      dict(zip(_lowercase				,     range(len(_lowercase ) ) ) )
								snake_case_ :			List[str]            =      {}
								snake_case_ :			List[Any]            =      add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								snake_case_ :			Dict            =      re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )





		@property
		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								return len(self.encoder )





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Tuple:







								'''simple docstring'''

								if token in self.cache:
														return self.cache[token]
								snake_case_ :			Union[str, Any]            =      tuple(_lowercase )
								snake_case_ :			Optional[int]            =      get_pairs(_lowercase )

								if not pairs:
														return token

								while True:
														snake_case_ :			Tuple            =      min(_lowercase				,     key=lambda _lowercase : self.bpe_ranks.get(_lowercase				,     float("""inf""" ) ) )
														if bigram not in self.bpe_ranks:
																				break
														snake_case_      ,		snake_case_ :			str            =      bigram
														snake_case_ :			List[Any]            =      []
														snake_case_ :			str            =      0
														while i < len(_lowercase ):
																				try:
																										snake_case_ :			Optional[Any]            =      word.index(_lowercase				,     _lowercase )
																				except ValueError:
																										new_word.extend(word[i:] )
																										break
																				else:
																										new_word.extend(word[i:j] )
																										snake_case_ :			Dict            =      j

																				if word[i] == first and i < len(_lowercase ) - 1 and word[i + 1] == second:
																										new_word.append(first + second )
																										i += 2
																				else:
																										new_word.append(word[i] )
																										i += 1
														snake_case_ :			Optional[int]            =      tuple(_lowercase )
														snake_case_ :			Any            =      new_word
														if len(_lowercase ) == 1:
																				break
														else:
																				snake_case_ :			Any            =      get_pairs(_lowercase )
								snake_case_ :			List[Any]            =      """ """.join(_lowercase )
								snake_case_ :			int            =      word
								return word





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    str:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      []
								for token in re.findall(self.pat				,     _lowercase ):
														snake_case_ :			Any            =      """""".join(
														    self.byte_encoder[b] for b in token.encode("""utf-8""" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
														bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase ).split(""" """ ) )
								return bpe_tokens





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Union[str, Any]:







								'''simple docstring'''

								return self.encoder.get(_lowercase				,     self.encoder.get(self.unk_token ) )





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    List[str]:







								'''simple docstring'''

								return self.decoder.get(_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase )			->    Union[str, Any]:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      """""".join(_lowercase )
								snake_case_ :			Dict            =      bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8"""				,     errors=self.errors )
								return text





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None )			->    Tuple[str]:







								'''simple docstring'''

								if not os.path.isdir(_lowercase ):
														logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
														return
								snake_case_ :			str            =      os.path.join(
								    _lowercase				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
								snake_case_ :			Union[str, Any]            =      os.path.join(
								    _lowercase				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )

								with open(_lowercase				,     """w"""				,     encoding="""utf-8""" ) as f:
														f.write(json.dumps(self.encoder				,     indent=2				,     sort_keys=_lowercase				,     ensure_ascii=_lowercase ) + """\n""" )

								snake_case_ :			str            =      0
								with open(_lowercase				,     """w"""				,     encoding="""utf-8""" ) as writer:
														writer.write("""#version: 0.2\n""" )
														for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,     key=lambda _lowercase : kv[1] ):
																				if index != token_index:
																										logger.warning(
																										    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																										    """ Please check that the tokenizer is not corrupted!""" )
																										snake_case_ :			List[Any]            =      token_index
																				writer.write(""" """.join(_lowercase ) + """\n""" )
																				index += 1

								return vocab_file, merge_file





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None )			->    List[int]:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = False )			->    List[int]:







								'''simple docstring'''

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

								if token_ids_a is None:
														return [1] + ([0] * len(_lowercase )) + [1]
								return [1] + ([0] * len(_lowercase )) + [1, 1] + ([0] * len(_lowercase )) + [1]





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = None )			->    List[int]:







								'''simple docstring'''

								snake_case_ :			Tuple            =      [self.sep_token_id]
								snake_case_ :			Dict            =      [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 + sep + token_ids_a + sep ) * [0]





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=False				,     **_lowercase )			->    List[Any]:







								'''simple docstring'''

								snake_case_ :			List[str]            =      kwargs.pop("""add_prefix_space"""				,     self.add_prefix_space )
								if (is_split_into_words or add_prefix_space) and (len(_lowercase ) > 0 and not text[0].isspace()):
														snake_case_ :			List[str]            =      """ """ + text
								return (text, kwargs)





import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int


def     lowercase	(    a     , a ):



		'''simple docstring'''




		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						flax_key_tuple[:-1] + ("weight",)
				SCREAMING_SNAKE_CASE_				:Any				     =						torch.permute(a     , (0, 2, 1) )
		elif flax_key_tuple[-1] == "kernel" and ".".join(a ):
				# linear layer
				SCREAMING_SNAKE_CASE_				:str				     =						flax_key_tuple[:-1] + ("weight",)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						flax_key_tuple[:-1] + ("weight",)

		return flax_key_tuple, flax_tensor


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		if "metadata" in layer:
				SCREAMING_SNAKE_CASE_				:Dict				     =						layer.split("metadata" )
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"".join(split_layer[0] )[:-1]
				SCREAMING_SNAKE_CASE_				:str				     =						[tuple(("metadata" + split_layer[1]).split("/" ) )]
		elif "kvstore" in layer:
				SCREAMING_SNAKE_CASE_				:str				     =						layer.split("kvstore" )
				SCREAMING_SNAKE_CASE_				:str				     =						"".join(split_layer[0] )[:-1]
				SCREAMING_SNAKE_CASE_				:str				     =						[tuple(("kvstore" + split_layer[1]).split("/" ) )]

		else:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						layer.split("/" )
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"/".join(split_layer[:-1] )
				SCREAMING_SNAKE_CASE_				:int				     =						(split_layer[-1],)

		if "kvstore/path" in layer:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
		elif "kvstore/driver" in layer:
				SCREAMING_SNAKE_CASE_				:Tuple				     =						"file"
		else:
				SCREAMING_SNAKE_CASE_				:str				     =						checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						rename_keys(a )
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{}
		for k, v in current_block.items():
				SCREAMING_SNAKE_CASE_				:List[str]				     =						v
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						new_current_block
		torch.save(a     , a )


def     lowercase	(    a     , a     , a     , a     , a = WEIGHTS_NAME ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						convert_file_size_to_int(a )
		SCREAMING_SNAKE_CASE_				:int				     =						[]
		SCREAMING_SNAKE_CASE_				:str				     =						{}
		SCREAMING_SNAKE_CASE_				:List[str]				     =						0
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						0

		os.makedirs(a     , exist_ok=a )
		with gfile.GFile(switch_checkpoint_path + "/checkpoint"     , "rb" ) as fp:
				SCREAMING_SNAKE_CASE_				:int				     =						serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
				SCREAMING_SNAKE_CASE_				:Any				     =						flatten_dict(a     , sep="/" )

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{}
		for layer in checkpoint_info.keys():
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[Any]				     =						get_key_and_tensorstore_dict(
				    a     , a     , a )
				if curr_real_layer_name in all_layers:
						SCREAMING_SNAKE_CASE_				:str				     =						content
				else:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{split_layer[-1]: content}

		for key in all_layers.keys():
				# open tensorstore file
				SCREAMING_SNAKE_CASE_				:Any				     =						ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.tensor(a )
				SCREAMING_SNAKE_CASE_				:str				     =						raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

				# use the renaming pattern from the small conversion scripts
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						rename_base_flax_keys(tuple(key.split("/" ) )     , a )
				SCREAMING_SNAKE_CASE_				:Any				     =						"/".join(a )

				# If this weight is going to tip up over the maximal size, we split.
				if current_block_size + weight_size > max_shard_size:
						SCREAMING_SNAKE_CASE_				:str				     =						os.path.join(
						    a     , weights_name.replace(".bin"     , F"-{len(a )+1:05d}-of-???.bin" ) )
						rename_and_save_block(a     , a )
						sharded_state_dicts.append(current_block.keys() )
						del current_block
						SCREAMING_SNAKE_CASE_				:Tuple				     =						{}
						SCREAMING_SNAKE_CASE_				:Dict				     =						0

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						raw_weights.to(getattr(a     , a ) )
				current_block_size += weight_size
				total_size += weight_size

		# Add the last block
		SCREAMING_SNAKE_CASE_				:Dict				     =						os.path.join(a     , weights_name.replace(".bin"     , F"-{len(a )+1:05d}-of-???.bin" ) )
		rename_and_save_block(a     , a )
		sharded_state_dicts.append(current_block.keys() )

		# If we only have one shard, we return it
		if len(a ) == 1:
				return {weights_name: sharded_state_dicts[0]}, None

		# Otherwise, let's build the index
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{}
		SCREAMING_SNAKE_CASE_				:int				     =						{}
		for idx, shard in enumerate(a ):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						weights_name.replace(
				    ".bin"     , F"-{idx+1:05d}-of-{len(a ):05d}.bin" )  # len(sharded_state_dicts):05d}
				SCREAMING_SNAKE_CASE_				:Any				     =						os.path.join(a     , weights_name.replace(".bin"     , F"-{idx+1:05d}-of-???.bin" ) )
				os.rename(a     , os.path.join(a     , a ) )
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						shard
				for key in shard:
						SCREAMING_SNAKE_CASE_				:str				     =						shard_file

    # Add the metadata
		SCREAMING_SNAKE_CASE_				:List[str]				     =						{"total_size": total_size}
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{"metadata": metadata, "weight_map": weight_map}

		with open(os.path.join(a     , a )     , "w"     , encoding="utf-8" ) as f:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						json.dumps(a     , indent=2     , sort_keys=a ) + "\n"
				f.write(a )

		return metadata, index


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--switch_t5x_checkpoint_path",
		    default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
		    type=str,
		    required=False,
		    help="Path to a directory containing a folder per layer. Follows the original Google format.",
		)
		parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
		parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
		parser.add_argument(
		    "--pytorch_dump_folder_path",
		    default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
		    type=str,
		    required=False,
		    help="Path to the output pytorch model.",
		)
		SCREAMING_SNAKE_CASE__     					=			parser.parse_args()
		shard_on_the_fly(
		    args.switch_tax_checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.max_shard_size,
		    args.dtype,
		)


def     lowercase	(    ):



		'''simple docstring'''




		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

		SCREAMING_SNAKE_CASE_				:Dict				     =						SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
		config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
		SCREAMING_SNAKE_CASE_				:str				     =						SwitchTransformersForConditionalGeneration.from_pretrained(
		    "/home/arthur_huggingface_co/transformers/switch_converted"     , device_map="auto" )

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						TaTokenizer.from_pretrained("t5-small" )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						tokenizer(a     , return_tensors="pt" ).input_ids
		SCREAMING_SNAKE_CASE_				:List[str]				     =						model.generate(a     , decoder_start_token_id=0 )
		print(tokenizer.decode(out[0] ) )






