





'''simple docstring'''



import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE   =      '''▁'''

_SCREAMING_SNAKE_CASE   =      get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 							__lowercase    ( lowerCAmelCase__	, unittest.TestCase  ):







	'''simple docstring'''

	a      :			Optional[int]     =    BigBirdTokenizer
	a      :			Union[str, Any]     =    BigBirdTokenizerFast
	a      :			Union[str, Any]     =    True
	a      :			Dict     =    True


	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				super().setUp()

				__lowercase			  =							self.tokenizer_class(_lowerCamelCase       ,keep_accents=_lowerCamelCase	)
				tokenizer.save_pretrained(self.tmpdirname	)


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							'''<s>'''
				__lowercase			  =							1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase	)       ,_lowerCamelCase	)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase	)       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				__lowercase			  =							list(self.get_tokenizer().get_vocab().keys()	)

				self.assertEqual(vocab_keys[0]       ,'''<unk>'''	)
				self.assertEqual(vocab_keys[1]       ,'''<s>'''	)
				self.assertEqual(vocab_keys[-1]       ,'''[MASK]'''	)
				self.assertEqual(len(_lowerCamelCase	)       ,1004	)


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				self.assertEqual(self.get_tokenizer().vocab_size       ,1000	)


	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				if not self.test_rust_tokenizer:
							return

				__lowercase			  =							self.get_tokenizer()
				__lowercase			  =							self.get_rust_tokenizer()

				__lowercase			  =							'''I was born in 92000, and this is falsé.'''

				__lowercase			  =							tokenizer.tokenize(_lowerCamelCase	)
				__lowercase			  =							rust_tokenizer.tokenize(_lowerCamelCase	)
				self.assertListEqual(_lowerCamelCase       ,_lowerCamelCase	)

				__lowercase			  =							tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
				__lowercase			  =							rust_tokenizer.encode(_lowerCamelCase       ,add_special_tokens=_lowerCamelCase	)
				self.assertListEqual(_lowerCamelCase       ,_lowerCamelCase	)

				__lowercase			  =							self.get_rust_tokenizer()
				__lowercase			  =							tokenizer.encode(_lowerCamelCase	)
				__lowercase			  =							rust_tokenizer.encode(_lowerCamelCase	)
				self.assertListEqual(_lowerCamelCase       ,_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							BigBirdTokenizer(_lowerCamelCase       ,keep_accents=_lowerCamelCase	)

				__lowercase			  =							tokenizer.tokenize('''This is a test'''	)
				self.assertListEqual(_lowerCamelCase       ,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']	)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(_lowerCamelCase	)       ,[285, 46, 10, 170, 382]       ,)

				__lowercase			  =							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''	)
				self.assertListEqual(
				    _lowerCamelCase       ,[
				        SPIECE_UNDERLINE + '''I''',
				        SPIECE_UNDERLINE + '''was''',
				        SPIECE_UNDERLINE + '''b''',
				        '''or''',
				        '''n''',
				        SPIECE_UNDERLINE + '''in''',
				        SPIECE_UNDERLINE + '''''',
				        '''9''',
				        '''2''',
				        '''0''',
				        '''0''',
				        '''0''',
				        ''',''',
				        SPIECE_UNDERLINE + '''and''',
				        SPIECE_UNDERLINE + '''this''',
				        SPIECE_UNDERLINE + '''is''',
				        SPIECE_UNDERLINE + '''f''',
				        '''al''',
				        '''s''',
				        '''é''',
				        '''.''',
				    ]       ,)
				__lowercase			  =							tokenizer.convert_tokens_to_ids(_lowerCamelCase	)
				self.assertListEqual(
				    _lowerCamelCase       ,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,)

				__lowercase			  =							tokenizer.convert_ids_to_tokens(_lowerCamelCase	)
				self.assertListEqual(
				    _lowerCamelCase       ,[
				        SPIECE_UNDERLINE + '''I''',
				        SPIECE_UNDERLINE + '''was''',
				        SPIECE_UNDERLINE + '''b''',
				        '''or''',
				        '''n''',
				        SPIECE_UNDERLINE + '''in''',
				        SPIECE_UNDERLINE + '''''',
				        '''<unk>''',
				        '''2''',
				        '''0''',
				        '''0''',
				        '''0''',
				        ''',''',
				        SPIECE_UNDERLINE + '''and''',
				        SPIECE_UNDERLINE + '''this''',
				        SPIECE_UNDERLINE + '''is''',
				        SPIECE_UNDERLINE + '''f''',
				        '''al''',
				        '''s''',
				        '''<unk>''',
				        '''.''',
				    ]       ,)


	@cached_property
	def   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





				return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''	)


	@slow
	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							'''Hello World!'''
				__lowercase			  =							[65, 18536, 2260, 101, 66]

				self.assertListEqual(_lowerCamelCase       ,self.big_tokenizer.encode(_lowerCamelCase	)	)


	@slow
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							(
				    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
				    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
				)
				# fmt: off
				__lowercase			  =							[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
				# fmt: on
				self.assertListEqual(_lowerCamelCase       ,self.big_tokenizer.encode(_lowerCamelCase	)	)


	@require_torch
	@slow
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				import torch

				from transformers import BigBirdConfig, BigBirdModel

				# Build sequence
				__lowercase			  =							list(self.big_tokenizer.get_vocab().keys()	)[:10]
				__lowercase			  =							''' '''.join(_lowerCamelCase	)
				__lowercase			  =							self.big_tokenizer.encode_plus(_lowerCamelCase       ,return_tensors='''pt'''       ,return_token_type_ids=_lowerCamelCase	)
				__lowercase			  =							self.big_tokenizer.batch_encode_plus(
				    [sequence + ''' ''' + sequence]       ,return_tensors='''pt'''       ,return_token_type_ids=_lowerCamelCase	)

				__lowercase			  =							BigBirdConfig(attention_type='''original_full'''	)
				__lowercase			  =							BigBirdModel(_lowerCamelCase	)

				assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

				with torch.no_grad():
							model(**_lowerCamelCase	)
							model(**_lowerCamelCase	)


	@slow
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''	)
				__lowercase			  =							tokenizer.decode(tokenizer('''Paris is the [MASK].'''	).input_ids	)

				self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]'''	)


	@slow
	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							{'''input_ids''': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=_lowerCamelCase       ,model_name='''google/bigbird-roberta-base'''       ,revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''       ,)












'''simple docstring'''



import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_SCREAMING_SNAKE_CASE   =      {
    '''169M''': 1_2,
    '''430M''': 2_4,
    '''1B5''': 2_4,
    '''3B''': 3_2,
    '''7B''': 3_2,
    '''14B''': 4_0,
}

_SCREAMING_SNAKE_CASE   =      {
    '''169M''': 7_6_8,
    '''430M''': 1_0_2_4,
    '''1B5''': 2_0_4_8,
    '''3B''': 2_5_6_0,
    '''7B''': 4_0_9_6,
    '''14B''': 5_1_2_0,
}




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Dict			):
			__lowercase			  =							list(state_dict.keys()			)
			for name in state_dict_keys:
						__lowercase			  =							state_dict.pop(lowerCamelCase_			)
						# emb -> embedding
						if name.startswith('''emb.'''			):
									__lowercase			  =							name.replace('''emb.'''						,    '''embeddings.'''			)
						# ln_0 -> pre_ln (only present at block 0)
						if name.startswith('''blocks.0.ln0'''			):
									__lowercase			  =							name.replace('''blocks.0.ln0'''						,    '''blocks.0.pre_ln'''			)
						# att -> attention
						__lowercase			  =							re.sub(r'''blocks\.(\d+)\.att'''						,    r'''blocks.\1.attention'''						,    lowerCamelCase_			)
						# ffn -> feed_forward
						__lowercase			  =							re.sub(r'''blocks\.(\d+)\.ffn'''						,    r'''blocks.\1.feed_forward'''						,    lowerCamelCase_			)
						# time_mix_k -> time_mix_key and reshape
						if name.endswith('''.time_mix_k'''			):
									__lowercase			  =							name.replace('''.time_mix_k'''						,    '''.time_mix_key'''			)
						# time_mix_v -> time_mix_value and reshape
						if name.endswith('''.time_mix_v'''			):
									__lowercase			  =							name.replace('''.time_mix_v'''						,    '''.time_mix_value'''			)
						# time_mix_r -> time_mix_key and reshape
						if name.endswith('''.time_mix_r'''			):
									__lowercase			  =							name.replace('''.time_mix_r'''						,    '''.time_mix_receptance'''			)

						if name != "head.weight":
									__lowercase			  =							'''rwkv.''' + name

						__lowercase			  =							weight
			return state_dict




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]						,    lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					List[Any]=None						,    lowerCamelCase_     :					List[Any]=None						,    lowerCamelCase_     :					Any=False						,    lowerCamelCase_     :					int=None			):
			# 1. If possible, build the tokenizer.
			if tokenizer_file is None:
						print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''			)
						__lowercase			  =							5_0_2_7_7
						__lowercase			  =							AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''			)
			else:
						__lowercase			  =							PreTrainedTokenizerFast(tokenizer_file=lowerCamelCase_			)
						__lowercase			  =							len(lowerCamelCase_			)
			tokenizer.save_pretrained(lowerCamelCase_			)

			# 2. Build the config
			__lowercase			  =							list(NUM_HIDDEN_LAYERS_MAPPING.keys()			)
			if size is None:
						# Try to infer size from the checkpoint name
						for candidate in possible_sizes:
									if candidate in checkpoint_file:
												__lowercase			  =							candidate
												break
						if size is None:
									raise ValueError('''Could not infer the size, please provide it with the `--size` argument.'''			)
			if size not in possible_sizes:
						raise ValueError(f"`size` should be one of {possible_sizes}, got {size}."			)

			__lowercase			  =							RwkvConfig(
			    vocab_size=lowerCamelCase_						,    num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]						,    hidden_size=HIDEN_SIZE_MAPPING[size]						,    )
			config.save_pretrained(lowerCamelCase_			)

			# 3. Download model file then convert state_dict
			__lowercase			  =							hf_hub_download(lowerCamelCase_						,    lowerCamelCase_			)
			__lowercase			  =							torch.load(lowerCamelCase_						,    map_location='''cpu'''			)
			__lowercase			  =							convert_state_dict(lowerCamelCase_			)

			# 4. Split in shards and save
			__lowercase  ,						__lowercase			  =							shard_checkpoint(lowerCamelCase_			)
			for shard_file, shard in shards.items():
						torch.save(lowerCamelCase_						,    os.path.join(lowerCamelCase_						,    lowerCamelCase_			)			)

			if index is not None:
						__lowercase			  =							os.path.join(lowerCamelCase_						,    lowerCamelCase_			)
						# Save the index as well
						with open(lowerCamelCase_						,    '''w'''						,    encoding='''utf-8'''			) as f:
									__lowercase			  =							json.dumps(lowerCamelCase_						,    indent=2						,    sort_keys=lowerCamelCase_			) + '''\n'''
									f.write(lowerCamelCase_			)

						# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
						print(
						    '''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.'''			)
						__lowercase			  =							list(shards.keys()			)

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									__lowercase			  =							torch.load(os.path.join(lowerCamelCase_						,    lowerCamelCase_			)			)
									torch.save({k: v.cpu().clone() for k, v in state_dict.items()}						,    os.path.join(lowerCamelCase_						,    lowerCamelCase_			)			)

			del state_dict
			gc.collect()

			if push_to_hub:
						if model_name is None:
									raise ValueError('''Please provide a `model_name` to push the model to the Hub.'''			)
						__lowercase			  =							AutoModelForCausalLM.from_pretrained(lowerCamelCase_			)
						model.push_to_hub(lowerCamelCase_						,    max_shard_size='''2GB'''			)
						tokenizer.push_to_hub(lowerCamelCase_			)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE   =      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--repo_id''', default=None, type=str, required=True, help='''Repo ID from which to pull the checkpoint.'''
	)
	parser.add_argument(
	    '''--checkpoint_file''', default=None, type=str, required=True, help='''Name of the checkpoint file in the repo.'''
	)
	parser.add_argument(
	    '''--output_dir''', default=None, type=str, required=True, help='''Where to save the converted model.'''
	)
	parser.add_argument(
	    '''--tokenizer_file''',
	    default=None,
	    type=str,
	    help='''Path to the tokenizer file to use (if not provided, only the model is converted).''',
	)
	parser.add_argument(
	    '''--size''',
	    default=None,
	    type=str,
	    help='''Size of the model. Will be inferred from the `checkpoint_file` if not passed.''',
	)
	parser.add_argument(
	    '''--push_to_hub''',
	    action='''store_true''',
	    help='''Push to the Hub the converted model.''',
	)
	parser.add_argument(
	    '''--model_name''',
	    default=None,
	    type=str,
	    help='''Name of the pushed model on the Hub, including the username / organization.''',
	)

	_SCREAMING_SNAKE_CASE   =      parser.parse_args()
	convert_rmkv_checkpoint_to_hf_format(
	    args.repo_id,
	    args.checkpoint_file,
	    args.output_dir,
	    size=args.size,
	    tokenizer_file=args.tokenizer_file,
	    push_to_hub=args.push_to_hub,
	    model_name=args.model_name,
	)





