



'''simple docstring'''

import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


_A					:     int      =get_logger(__name__)







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase = None,					)			->      Dict:
	# Model states
	for i, state in enumerate(_lowercase       ):
		_lowercase       :							Optional[int]  	=		f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
		_lowercase       :							Tuple  	=		os.path.join(_lowercase,					_lowercase       )
		save(_lowercase,					_lowercase       )
		logger.info(f'''Model weights saved in {output_model_file}'''       )
	# Optimizer states
	for i, opt in enumerate(_lowercase       ):
		_lowercase       :							Any  	=		opt.state_dict()
		_lowercase       :							Optional[int]  	=		f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
		_lowercase       :							Optional[int]  	=		os.path.join(_lowercase,					_lowercase       )
		save(_lowercase,					_lowercase       )
		logger.info(f'''Optimizer state saved in {output_optimizer_file}'''       )
	# Scheduler states
	for i, scheduler in enumerate(_lowercase       ):
		_lowercase       :							Any  	=		scheduler.state_dict()
		_lowercase       :							Any  	=		f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
		_lowercase       :							Optional[Any]  	=		os.path.join(_lowercase,					_lowercase       )
		save(_lowercase,					_lowercase       )
		logger.info(f'''Scheduler state saved in {output_scheduler_file}'''       )
	# GradScaler state
	if scaler is not None:
		_lowercase       :							List[str]  	=		scaler.state_dict()
		_lowercase       :							Optional[Any]  	=		os.path.join(_lowercase,					_lowercase       )
		torch.save(_lowercase,					_lowercase       )
		logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''       )
	# Random number generator states
	_lowercase       :							Tuple  	=		{}
	_lowercase       :							Any  	=		f'''{RNG_STATE_NAME}_{process_index}.pkl'''
	_lowercase       :							Optional[Any]  	=		random.getstate()
	_lowercase       :							Tuple  	=		np.random.get_state()
	_lowercase       :							str  	=		torch.get_rng_state()
	if is_xpu_available():
		_lowercase       :							Optional[int]  	=		torch.xpu.get_rng_state_all()
	else:
		_lowercase       :							str  	=		torch.cuda.get_rng_state_all()
	if is_tpu_available():
		_lowercase       :							Dict  	=		xm.get_rng_state()
	_lowercase       :							Any  	=		os.path.join(_lowercase,					_lowercase       )
	torch.save(_lowercase,					_lowercase       )
	logger.info(f'''Random states saved in {output_states_file}'''       )
	return output_dir







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase=None,					_lowercase=None,					**_lowercase,					)			->      Dict:
	if map_location not in [None, "cpu", "on_device"]:
		raise TypeError(
		    'Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'       )
	if map_location is None:
		_lowercase       :							Any  	=		'cpu'
	elif map_location == "on_device":
		_lowercase       :							List[Any]  	=		PartialState().device
	# Model states
	for i, model in enumerate(_lowercase       ):
		_lowercase       :							Tuple  	=		f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
		_lowercase       :							Any  	=		os.path.join(_lowercase,					_lowercase       )
		models[i].load_state_dict(torch.load(_lowercase,					map_location=_lowercase       ),					**_lowercase       )
	logger.info('All model weights loaded successfully'       )

	# Optimizer states
	for i, opt in enumerate(_lowercase       ):
		_lowercase       :							Tuple  	=		f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
		_lowercase       :							Union[str, Any]  	=		os.path.join(_lowercase,					_lowercase       )
		_lowercase       :							Optional[Any]  	=		torch.load(_lowercase,					map_location=_lowercase       )
		optimizers[i].load_state_dict(_lowercase       )
	logger.info('All optimizer states loaded successfully'       )

	# Scheduler states
	for i, scheduler in enumerate(_lowercase       ):
		_lowercase       :							Any  	=		f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
		_lowercase       :							Optional[int]  	=		os.path.join(_lowercase,					_lowercase       )
		scheduler.load_state_dict(torch.load(_lowercase       )       )
	logger.info('All scheduler states loaded successfully'       )

	# GradScaler state
	if scaler is not None:
		_lowercase       :							Tuple  	=		os.path.join(_lowercase,					_lowercase       )
		scaler.load_state_dict(torch.load(_lowercase       )       )
		logger.info('GradScaler state loaded successfully'       )

	# Random states
	try:
		_lowercase       :							Tuple  	=		torch.load(os.path.join(_lowercase,					f'''{RNG_STATE_NAME}_{process_index}.pkl'''       )       )
		random.setstate(states['random_state']       )
		np.random.set_state(states['numpy_random_seed']       )
		torch.set_rng_state(states['torch_manual_seed']       )
		if is_xpu_available():
			torch.xpu.set_rng_state_all(states['torch_xpu_manual_seed']       )
		else:
			torch.cuda.set_rng_state_all(states['torch_cuda_manual_seed']       )
		if is_tpu_available():
			xm.set_rng_state(states['xm_seed']       )
		logger.info('All random states loaded successfully'       )
	except Exception:
		logger.info('Could not load random states'       )







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase = 0       )			->      Optional[int]:
	_lowercase       :							Any  	=		Path(_lowercase       ) / f'''custom_checkpoint_{index}.pkl'''
	logger.info(f'''Saving the state of {get_pretty_name(_lowercase       )} to {save_location}'''       )
	torch.save(obj.state_dict(),					_lowercase       )







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase = 0       )			->      Union[str, Any]:
	_lowercase       :							int  	=		f'''{path}/custom_checkpoint_{index}.pkl'''
	logger.info(f'''Loading the state of {get_pretty_name(_lowercase       )} from {load_location}'''       )
	obj.load_state_dict(torch.load(_lowercase,					map_location='cpu'       )       )





'''simple docstring'''

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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A					:     Optional[Any]      =logging.get_logger(__name__)

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

_A					:     str      ={
    '''vocab_file''': {
        '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json''',
    },
    '''merges_file''': {'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'''},
}

_A					:     List[Any]      ={
    '''microsoft/biogpt''': 1_0_2_4,
}







def     __UpperCamelCase					(				_lowercase       )			->      Dict:
	_lowercase       :							List[str]  	=		set()
	_lowercase       :							str  	=		word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)       )
		_lowercase       :							str  	=		char
	return pairs






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    VOCAB_FILES_NAMES
					A_         =    PRETRAINED_VOCAB_FILES_MAP
					A_         =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A_         =    ["""input_ids""", """attention_mask"""]




					def __init__(	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Dict  ,	UpperCamelCase_					:						List[str]  ,	UpperCamelCase_					:						List[Any]="<unk>"  ,	UpperCamelCase_					:						str="<s>"  ,	UpperCamelCase_					:						str="</s>"  ,	UpperCamelCase_					:						int="</s>"  ,	UpperCamelCase_					:						Tuple="<pad>"  ,	**UpperCamelCase_					:						Any  ,	)	->							Tuple:



						'''simple docstring'''





						super().__init__(
						    bos_token=UpperCamelCase_  ,	eos_token=UpperCamelCase_  ,	sep_token=UpperCamelCase_  ,	unk_token=UpperCamelCase_  ,	pad_token=UpperCamelCase_  ,	**UpperCamelCase_  ,	)

						try:
							import sacremoses
						except ImportError:
							raise ImportError(
							    'You need to install sacremoses to use BioGptTokenizer. '
							    'See https://pypi.org/project/sacremoses/ for installation.'       )

						_lowercase       :							Dict  	=		'en'
						_lowercase       :							Any  	=		sacremoses
						# cache of sm.MosesTokenizer instance
						_lowercase       :							Union[str, Any]  	=		{}
						_lowercase       :							List[str]  	=		{}
						with open(UpperCamelCase_  ,	encoding='utf-8'       ) as vocab_handle:
							_lowercase       :							Optional[int]  	=		json.load(UpperCamelCase_       )
						_lowercase       :							str  	=		{v: k for k, v in self.encoder.items()}
						with open(UpperCamelCase_  ,	encoding='utf-8'       ) as merges_handle:
							_lowercase       :							Tuple  	=		merges_handle.read().split('\n'       )[:-1]
						_lowercase       :							List[str]  	=		[tuple(merge.split()[:2]       ) for merge in merges]
						_lowercase       :							str  	=		dict(zip(UpperCamelCase_  ,	range(len(UpperCamelCase_       )       )       )       )
						_lowercase       :							Union[str, Any]  	=		{}




					@property
					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





						return len(self.encoder       )




					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						int  ,	UpperCamelCase_					:						int       )	->							Optional[Any]:



						'''simple docstring'''





						if lang not in self.cache_moses_tokenizer:
							_lowercase       :							Union[str, Any]  	=		self.sm.MosesTokenizer(lang=UpperCamelCase_       )
							_lowercase       :							int  	=		moses_tokenizer
						return self.cache_moses_tokenizer[lang].tokenize(
						    UpperCamelCase_  ,	aggressive_dash_splits=UpperCamelCase_  ,	return_str=UpperCamelCase_  ,	escape=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Any  ,	UpperCamelCase_					:						Dict  ,	UpperCamelCase_					:						str       )	->							List[str]:



						'''simple docstring'''





						if lang not in self.cache_moses_detokenizer:
							_lowercase       :							str  	=		self.sm.MosesDetokenizer(lang=UpperCamelCase_       )
							_lowercase       :							List[Any]  	=		moses_detokenizer
						return self.cache_moses_detokenizer[lang].detokenize(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[int]       )	->							Optional[Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		tuple(token[:-1]       ) + (token[-1] + '</w>',)
						if token in self.cache:
							return self.cache[token]
						_lowercase       :							Dict  	=		get_pairs(UpperCamelCase_       )

						if not pairs:
							return token + "</w>"

						while True:
							_lowercase       :							str  	=		min(UpperCamelCase_  ,	key=lambda UpperCamelCase_       : self.bpe_ranks.get(UpperCamelCase_  ,	float('inf'       )       )       )
							if bigram not in self.bpe_ranks:
								break
							_lowercase	,     _lowercase       :							int  	=		bigram
							_lowercase       :							Any  	=		[]
							_lowercase       :							str  	=		0
							while i < len(UpperCamelCase_       ):
								try:
									_lowercase       :							Union[str, Any]  	=		word.index(UpperCamelCase_  ,	UpperCamelCase_       )
								except ValueError:
									new_word.extend(word[i:]       )
									break
								else:
									new_word.extend(word[i:j]       )
									_lowercase       :							str  	=		j

								if word[i] == first and i < len(UpperCamelCase_       ) - 1 and word[i + 1] == second:
									new_word.append(first + second       )
									i += 2
								else:
									new_word.append(word[i]       )
									i += 1
							_lowercase       :							Optional[int]  	=		tuple(UpperCamelCase_       )
							_lowercase       :							Tuple  	=		new_word
							if len(UpperCamelCase_       ) == 1:
								break
							else:
								_lowercase       :							List[str]  	=		get_pairs(UpperCamelCase_       )
						_lowercase       :							Optional[int]  	=		' '.join(UpperCamelCase_       )
						if word == "\n  </w>":
							_lowercase       :							Union[str, Any]  	=		'\n</w>'
						_lowercase       :							List[Any]  	=		word
						return word




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						int  ,	UpperCamelCase_					:						List[str]=False       )	->							int:



						'''simple docstring'''





						if bypass_tokenizer:
							_lowercase       :							Optional[Any]  	=		text.split()
						else:
							_lowercase       :							List[str]  	=		self.moses_tokenize(UpperCamelCase_  ,	self.lang       )

						_lowercase       :							int  	=		[]
						for token in text:
							if token:
								split_tokens.extend(list(self.bpe(UpperCamelCase_       ).split(' '       )       )       )

						return split_tokens




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Optional[int]       )	->							int:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						Union[str, Any]       )	->							Tuple:



						'''simple docstring'''





						return self.decoder.get(UpperCamelCase_  ,	self.unk_token       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[Any]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		[t.replace(' '  ,	''       ).replace('</w>'  ,	' '       ) for t in tokens]
						_lowercase       :							List[str]  	=		''.join(UpperCamelCase_       ).split()
						# detokenize
						_lowercase       :							Dict  	=		self.moses_detokenize(UpperCamelCase_  ,	self.lang       )
						return text




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None       )	->							List[int]:



						'''simple docstring'''





						if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
						_lowercase       :							List[Any]  	=		[self.sep_token_id]
						return sep + token_ids_a + sep + token_ids_a




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None  ,	UpperCamelCase_					:						bool = False       )	->							List[int]:



						'''simple docstring'''





						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=UpperCamelCase_  ,	token_ids_a=UpperCamelCase_  ,	already_has_special_tokens=UpperCamelCase_       )
						# no bos used in fairseq
						if token_ids_a is not None:
							return [1] + ([0] * len(UpperCamelCase_       )) + [1] + ([0] * len(UpperCamelCase_       ))
						return [1] + ([0] * len(UpperCamelCase_       ))




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None       )	->							List[int]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		[self.sep_token_id]

						# no bos used in fairseq
						if token_ids_a is None:
							return len(token_ids_a + sep       ) * [0]
						return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]




					def     __UpperCAmelCase  (	self					:						Tuple  ,	UpperCamelCase_					:						str  ,	UpperCamelCase_					:						Optional[str] = None       )	->							Tuple[str]:



						'''simple docstring'''





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

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

						_lowercase       :							List[str]  	=		0
						with open(UpperCamelCase_  ,	'w'  ,	encoding='utf-8'       ) as writer:
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,	key=lambda UpperCamelCase_       : 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!'       )
									_lowercase       :							Tuple  	=		token_index
								writer.write(' '.join(UpperCamelCase_       ) + '\n'       )
								index += 1

						return vocab_file, merge_file




					def __getstate__(	self					:						Optional[int]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		self.__dict__.copy()
						_lowercase       :							Dict  	=		None
						return state




					def __setstate__(	self					:						List[str]  ,	UpperCamelCase_					:						Dict       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		d

						try:
							import sacremoses
						except ImportError:
							raise ImportError(
							    'You need to install sacremoses to use XLMTokenizer. '
							    'See https://pypi.org/project/sacremoses/ for installation.'       )

						_lowercase       :							str  	=		sacremoses
