





'''simple docstring'''



import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






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







	'''simple docstring'''

	a      :			Tuple     =    OpenAIGPTTokenizer
	a      :			Any     =    OpenAIGPTTokenizerFast
	a      :			Union[str, Any]     =    True
	a      :			Optional[Any]     =    False


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				__lowercase			  =							[
				    '''l''',
				    '''o''',
				    '''w''',
				    '''e''',
				    '''r''',
				    '''s''',
				    '''t''',
				    '''i''',
				    '''d''',
				    '''n''',
				    '''w</w>''',
				    '''r</w>''',
				    '''t</w>''',
				    '''lo''',
				    '''low''',
				    '''er</w>''',
				    '''low</w>''',
				    '''lowest</w>''',
				    '''newer</w>''',
				    '''wider</w>''',
				    '''<unk>''',
				]
				__lowercase			  =							dict(zip(_snake_case       ,range(len(_snake_case	)	)	)	)
				__lowercase			  =							['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

				__lowercase			  =							os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['''vocab_file''']	)
				__lowercase			  =							os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['''merges_file''']	)
				with open(self.vocab_file       ,'''w'''	) as fp:
							fp.write(json.dumps(_snake_case	)	)
				with open(self.merges_file       ,'''w'''	) as fp:
							fp.write('''\n'''.join(_snake_case	)	)


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







				'''simple docstring'''





				return "lower newer", "lower newer"


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







				'''simple docstring'''





				__lowercase			  =							OpenAIGPTTokenizer(self.vocab_file       ,self.merges_file	)

				__lowercase			  =							'''lower'''
				__lowercase			  =							['''low''', '''er</w>''']
				__lowercase			  =							tokenizer.tokenize(_snake_case	)
				self.assertListEqual(_snake_case       ,_snake_case	)

				__lowercase			  =							tokens + ['''<unk>''']
				__lowercase			  =							[14, 15, 20]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case	)       ,_snake_case	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase=15	)							->							Optional[int]:







				'''simple docstring'''





				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"	):
										__lowercase			  =							self.rust_tokenizer_class.from_pretrained(_snake_case       ,**_snake_case	)

										# Simple input
										__lowercase			  =							'''This is a simple input'''
										__lowercase			  =							['''This is a simple input 1''', '''This is a simple input 2''']
										__lowercase			  =							('''This is a simple input''', '''This is a pair''')
										__lowercase			  =							[
										    ('''This is a simple input 1''', '''This is a simple input 2'''),
										    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
										]

										# Simple input tests
										self.assertRaises(_snake_case       ,tokenizer_r.encode       ,_snake_case       ,max_length=_snake_case       ,padding='''max_length'''	)

										# Simple input
										self.assertRaises(_snake_case       ,tokenizer_r.encode_plus       ,_snake_case       ,max_length=_snake_case       ,padding='''max_length'''	)

										# Simple input
										self.assertRaises(
										    _snake_case       ,tokenizer_r.batch_encode_plus       ,_snake_case       ,max_length=_snake_case       ,padding='''max_length'''       ,)

										# Pair input
										self.assertRaises(_snake_case       ,tokenizer_r.encode       ,_snake_case       ,max_length=_snake_case       ,padding='''max_length'''	)

										# Pair input
										self.assertRaises(_snake_case       ,tokenizer_r.encode_plus       ,_snake_case       ,max_length=_snake_case       ,padding='''max_length'''	)

										# Pair input
										self.assertRaises(
										    _snake_case       ,tokenizer_r.batch_encode_plus       ,_snake_case       ,max_length=_snake_case       ,padding='''max_length'''       ,)


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







				'''simple docstring'''





				pass






@require_ftfy
@require_spacy
@require_tokenizers
class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	pass












'''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,
	)





