


'''simple docstring'''
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       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					int			):
 if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
  # expert layer
  __A  :		Dict       =			flax_key_tuple[:-1] + ('weight',)
  __A  :		Any       =			torch.permute(__lowerCAmelCase,				(0, 2, 1)			)
 elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase			):
  # linear layer
  __A  :		Union[str, Any]       =			flax_key_tuple[:-1] + ('weight',)
  __A  :		str       =			flax_tensor.T
 elif flax_key_tuple[-1] in ["scale", "embedding"]:
  __A  :		List[Any]       =			flax_key_tuple[:-1] + ('weight',)

 return flax_key_tuple, flax_tensor


def 	_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Union[str, Any]			):
 if "metadata" in layer:
  __A  :		Any       =			layer.split('metadata'			)
  __A  :		int       =			''.join(split_layer[0]			)[:-1]
  __A  :		List[Any]       =			[tuple(('metadata' + split_layer[1]).split('/'			)			)]
 elif "kvstore" in layer:
  __A  :		Union[str, Any]       =			layer.split('kvstore'			)
  __A  :		int       =			''.join(split_layer[0]			)[:-1]
  __A  :		str       =			[tuple(('kvstore' + split_layer[1]).split('/'			)			)]

 else:
  __A  :		str       =			layer.split('/'			)
  __A  :		Dict       =			'/'.join(split_layer[:-1]			)
  __A  :		int       =			(split_layer[-1],)

 if "kvstore/path" in layer:
  __A  :		Optional[int]       =			f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
 elif "kvstore/driver" in layer:
  __A  :		Optional[int]       =			'file'
 else:
  __A  :		Union[str, Any]       =			checkpoint_info[layer]

 return curr_real_layer_name, split_layer, content


def 	_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Tuple			):
 __A  :		Dict       =			rename_keys(__lowerCAmelCase			)
 __A  :		str       =			{}
 for k, v in current_block.items():
  __A  :		Union[str, Any]       =			v
 __A  :		List[Any]       =			new_current_block
 torch.save(__lowerCAmelCase,				__lowerCAmelCase			)


def 	_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					str = WEIGHTS_NAME			):
 __A  :		Optional[int]       =			convert_file_size_to_int(__lowerCAmelCase			)
 __A  :		Optional[Any]       =			[]
 __A  :		str       =			{}
 __A  :		int       =			0
 __A  :		Optional[int]       =			0

 os.makedirs(__lowerCAmelCase,				exist_ok=__lowerCAmelCase			)
 with gfile.GFile(switch_checkpoint_path + '/checkpoint',				'rb'			) as fp:
  __A  :		List[str]       =			serialization.msgpack_restore(fp.read()			)['optimizer']['target']
  __A  :		Optional[int]       =			flatten_dict(__lowerCAmelCase,				sep='/'			)

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

 for key in all_layers.keys():
  # open tensorstore file
  __A  :		Dict       =			ts.open(unflatten_dict(all_layers[key]			)			).result().read().result()
  __A  :		Dict       =			torch.tensor(__lowerCAmelCase			)
  __A  :		Optional[int]       =			raw_weights.numel() * dtype_byte_size(raw_weights.dtype			)

  # use the renaming pattern from the small conversion scripts
  __A     ,__A  :		Optional[int]       =			rename_base_flax_keys(tuple(key.split('/'			)			),				__lowerCAmelCase			)
  __A  :		Dict       =			'/'.join(__lowerCAmelCase			)

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

  __A  :		Optional[Any]       =			raw_weights.to(getattr(__lowerCAmelCase,				__lowerCAmelCase			)			)
  current_block_size += weight_size
  total_size += weight_size

 # Add the last block
 __A  :		Union[str, Any]       =			os.path.join(__lowerCAmelCase,				weights_name.replace('.bin',				f"""-{len(__lowerCAmelCase			)+1:05d}-of-???.bin"""			)			)
 rename_and_save_block(__lowerCAmelCase,				__lowerCAmelCase			)
 sharded_state_dicts.append(current_block.keys()			)

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

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

    # Add the metadata
 __A  :		Dict       =			{'total_size': total_size}
 __A  :		Optional[int]       =			{'metadata': metadata, 'weight_map': weight_map}

 with open(os.path.join(__lowerCAmelCase,				__lowerCAmelCase			),				'w',				encoding='utf-8'			) as f:
  __A  :		Union[str, Any]       =			json.dumps(__lowerCAmelCase,				indent=2,				sort_keys=__lowerCAmelCase			) + '\n'
  f.write(__lowerCAmelCase			)

 return metadata, index


if __name__ == "__main__":
 UpperCAmelCase_ :     List[Any]								=      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.',
 )
 UpperCAmelCase_ :     Dict								=      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       (				):
 from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

 __A  :		int       =			tokenizer(__lowerCAmelCase,				return_tensors='pt'			).input_ids
 __A  :		str       =			model.generate(__lowerCAmelCase,				decoder_start_token_id=0			)
 print(tokenizer.decode(out[0]			)			)










'''simple docstring'''
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCAmelCase_ :     Any								=      logging.getLogger(__name__)


def 			_lowercase       (				):
 __A  :		Tuple       =			argparse.ArgumentParser(
     description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'			)
 parser.add_argument('--file_path',				type=UpperCamelCase__,				default='data/dump.txt',				help='The path to the data.'			)
 parser.add_argument('--tokenizer_type',				type=UpperCamelCase__,				default='bert',				choices=['bert', 'roberta', 'gpt2']			)
 parser.add_argument('--tokenizer_name',				type=UpperCamelCase__,				default='bert-base-uncased',				help='The tokenizer to use.'			)
 parser.add_argument('--dump_file',				type=UpperCamelCase__,				default='data/dump',				help='The dump file prefix.'			)
 __A  :		Tuple       =			parser.parse_args()

 logger.info(f"""Loading Tokenizer ({args.tokenizer_name})"""			)
 if args.tokenizer_type == "bert":
  __A  :		Any       =			BertTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		Any       =			tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  __A  :		List[Any]       =			tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  __A  :		Any       =			RobertaTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		List[Any]       =			tokenizer.special_tokens_map['cls_token']  # `<s>`
  __A  :		Dict       =			tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  __A  :		List[str]       =			GPTaTokenizer.from_pretrained(args.tokenizer_name			)
  __A  :		Any       =			tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  __A  :		Union[str, Any]       =			tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

 logger.info(f"""Loading text from {args.file_path}"""			)
 with open(args.file_path,				'r',				encoding='utf8'			) as fp:
  __A  :		Optional[int]       =			fp.readlines()

 logger.info('Start encoding'			)
 logger.info(f"""{len(UpperCamelCase__			)} examples to process."""			)

 __A  :		int       =			[]
 __A  :		List[str]       =			0
 __A  :		Tuple       =			10000
 __A  :		List[str]       =			time.time()
 for text in data:
  __A  :		int       =			f"""{bos} {text.strip()} {sep}"""
  __A  :		List[Any]       =			tokenizer.encode(UpperCamelCase__,				add_special_tokens=UpperCamelCase__			)
  rslt.append(UpperCamelCase__			)

  iter += 1
  if iter % interval == 0:
   __A  :		Any       =			time.time()
   logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""			)
   __A  :		Any       =			time.time()
 logger.info('Finished binarization'			)
 logger.info(f"""{len(UpperCamelCase__			)} examples processed."""			)

 __A  :		List[str]       =			f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
 __A  :		str       =			tokenizer.vocab_size
 if vocab_size < (1 << 16):
  __A  :		Optional[int]       =			[np.uintaa(UpperCamelCase__			) for d in rslt]
 else:
  __A  :		int       =			[np.intaa(UpperCamelCase__			) for d in rslt]
 random.shuffle(rslt_			)
 logger.info(f"""Dump to {dp_file}"""			)
 with open(UpperCamelCase__,				'wb'			) as handle:
  pickle.dump(rslt_,				UpperCamelCase__,				protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
 main()






