
from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def       UpperCAmelCase__							(    __magic_name__  :       List[str]							,					__magic_name__  :       int							,					__magic_name__  :       Any					):


					'''simple docstring'''

					if not inplace:
										lowerCAmelCase						:   Optional[Any]							=       ma + ma
					else:
										ma += ma

					return ma

def       UpperCAmelCase__							(    __magic_name__  :       Any							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[Any]							=       -1 * len(__magic_name__					)
					lowerCAmelCase						:   List[str]							=       list(range(len(tensor.shape[:zero_index]					)					)					)
					return tensor.permute(first_inds + [zero_index + i for i in inds]					)

def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[str]					):


					'''simple docstring'''

					return t.reshape(t.shape[:-no_dims] + (-1,)					)

def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Tuple							,					__magic_name__  :       Any = 1e-4					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       mask.expand(*value.shape					)
					return torch.sum(mask * value							,					dim=__magic_name__					) / (eps + torch.sum(__magic_name__							,					dim=__magic_name__					))

def       UpperCAmelCase__							(    __magic_name__  :       int							,					__magic_name__  :       str = 2.3125							,					__magic_name__  :       int = 21.6875							,					__magic_name__  :       str = 64					):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       torch.linspace(__magic_name__							,					__magic_name__							,					no_bins - 1							,					device=pts.device					)
					lowerCAmelCase						:   int							=       torch.sqrt(torch.sum((pts.unsqueeze(-2					) - pts.unsqueeze(-3					)) ** 2							,					dim=-1					)					)
					return torch.bucketize(__magic_name__							,					__magic_name__					)

def       UpperCAmelCase__							(    __magic_name__  :       List[str]							,					__magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[int]							=       dicts[0]
					lowerCAmelCase						:   Any							=       {}
					for k, v in first.items():
										lowerCAmelCase						:   str							=       [d[k] for d in dicts]
										if isinstance(__magic_name__							,					__magic_name__					):
															lowerCAmelCase						:   str							=       dict_multimap(__magic_name__							,					__magic_name__					)
										else:
															lowerCAmelCase						:   Optional[Any]							=       fn(__magic_name__					)

					return new_dict

def       UpperCAmelCase__							(    __magic_name__  :       str							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       v_bins.view(((1,) * len(x.shape					)) + (len(__magic_name__					),)					)
					lowerCAmelCase						:   List[Any]							=       x[..., None] - reshaped_bins
					lowerCAmelCase						:   List[str]							=       torch.argmin(torch.abs(__magic_name__					)							,					dim=-1					)
					return nn.functional.one_hot(__magic_name__							,					num_classes=len(__magic_name__					)					).float()

def       UpperCAmelCase__							(    __magic_name__  :       str							,					__magic_name__  :       List[str]							,					__magic_name__  :       List[Any] = 0							,					__magic_name__  :       int = 0					):


					'''simple docstring'''

					lowerCAmelCase						:   Any							=       []
					for i, s in enumerate(data.shape[:no_batch_dims]					):
										lowerCAmelCase						:   int							=       torch.arange(__magic_name__					)
										lowerCAmelCase						:   Any							=       r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape					) - i - 1)))					)
										ranges.append(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       [slice(__magic_name__					) for _ in range(len(data.shape					) - no_batch_dims					)]
					lowerCAmelCase						:   Dict							=       inds
					ranges.extend(__magic_name__					)
					# Matt note: Editing this to get around the behaviour of using a list as an array index changing
					# in recent Numpy versions
					return data[tuple(__magic_name__					)]


__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       TypeVar('T')

def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[str]							,					__magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[int]							=       {}
					for k, v in dic.items():
										if isinstance(__magic_name__							,					__magic_name__					):
															lowerCAmelCase						:   List[str]							=       dict_map(__magic_name__							,					__magic_name__							,					__magic_name__					)
										else:
															lowerCAmelCase						:   int							=       tree_map(__magic_name__							,					__magic_name__							,					__magic_name__					)

					return new_dict

@overload
def       UpperCAmelCase__							(    __magic_name__  :       List[Any]							,					__magic_name__  :       Dict							,					__magic_name__  :       Dict					):


					'''simple docstring'''

					...

@overload
def       UpperCAmelCase__							(    __magic_name__  :       List[str]							,					__magic_name__  :       Optional[int]							,					__magic_name__  :       Optional[int]					):


					'''simple docstring'''

					...

@overload
def       UpperCAmelCase__							(    __magic_name__  :       Tuple							,					__magic_name__  :       Union[str, Any]							,					__magic_name__  :       Optional[Any]					):


					'''simple docstring'''

					...

@overload
def       UpperCAmelCase__							(    __magic_name__  :       Tuple							,					__magic_name__  :       Union[str, Any]							,					__magic_name__  :       List[Any]					):


					'''simple docstring'''

					...

def       UpperCAmelCase__							(    __magic_name__  :       str							,					__magic_name__  :       str							,					__magic_name__  :       Any					):


					'''simple docstring'''

					if isinstance(__magic_name__							,					__magic_name__					):
										return dict_map(__magic_name__							,					__magic_name__							,					__magic_name__					)
					elif isinstance(__magic_name__							,					__magic_name__					):
										return [tree_map(__magic_name__							,					__magic_name__							,					__magic_name__					) for x in tree]
					elif isinstance(__magic_name__							,					__magic_name__					):
										return tuple(tree_map(__magic_name__							,					__magic_name__							,					__magic_name__					) for x in tree					)
					elif isinstance(__magic_name__							,					__magic_name__					):
										return fn(__magic_name__					)
					else:
										print(type(__magic_name__					)					)
										raise ValueError('''Not supported'''					)
__SCREAMING_SNAKE_CASE     :				List[Any]             =       partial(tree_map, leaf_type=torch.Tensor)


from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
