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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation

lowerCAmelCase__	:  str						=  logging.get_logger(__name__)

lowerCAmelCase__	:  List[str]						=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

lowerCAmelCase__	:  Tuple						=  {
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

lowerCAmelCase__	:  str						=  {
    '''microsoft/deberta-base''': 5_12,
    '''microsoft/deberta-large''': 5_12,
    '''microsoft/deberta-xlarge''': 5_12,
    '''microsoft/deberta-base-mnli''': 5_12,
    '''microsoft/deberta-large-mnli''': 5_12,
    '''microsoft/deberta-xlarge-mnli''': 5_12,
}

lowerCAmelCase__	:  Union[str, Any]						=  {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}
def 					UpperCamelCase__		(							)  ->   Optional[Any]:
				snake_case__   :      Dict   				= (
				    list(range(ord('!'       )     ,					ord('~'       ) + 1       )       ) + list(range(ord('¡'       )     ,					ord('¬'       ) + 1       )       ) + list(range(ord('®'       )     ,					ord('ÿ'       ) + 1       )       )
				)
				snake_case__   :      str   				= bs[:]
				snake_case__   :      str   				= 0
				for b in range(2**8       ):
								if b not in bs:
												bs.append(__UpperCAmelCase       )
												cs.append(2**8 + n       )
												n += 1
				snake_case__   :      List[Any]   				= [chr(__UpperCAmelCase       ) for n in cs]
				return dict(zip(__UpperCAmelCase     ,					__UpperCAmelCase       )       )
def 					UpperCamelCase__		(							A__       )  ->   Dict:
				snake_case__   :      Any   				= set()
				snake_case__   :      Dict   				= word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)       )
								snake_case__   :      Optional[int]   				= char
				return pairs





class 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  VOCAB_FILES_NAMES
					__lowerCamelCase			   =  PRETRAINED_VOCAB_FILES_MAP
					__lowerCamelCase			   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__lowerCamelCase			   =  ["input_ids", "attention_mask", "token_type_ids"]




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase="replace"       ,			__UpperCamelCase="[CLS]"       ,			__UpperCamelCase="[SEP]"       ,			__UpperCamelCase="[SEP]"       ,			__UpperCamelCase="[CLS]"       ,			__UpperCamelCase="[UNK]"       ,			__UpperCamelCase="[PAD]"       ,			__UpperCamelCase="[MASK]"       ,			__UpperCamelCase=False       ,			__UpperCamelCase=False       ,			**__UpperCamelCase       ,			)	->    int:



									'''simple docstring'''







									snake_case__   :      Dict   				= AddedToken(__UpperCamelCase       ,			lstrip=__UpperCamelCase       ,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase       ,			__UpperCamelCase						) else bos_token
									snake_case__   :      Union[str, Any]   				= AddedToken(__UpperCamelCase       ,			lstrip=__UpperCamelCase       ,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase       ,			__UpperCamelCase						) else eos_token
									snake_case__   :      Optional[int]   				= AddedToken(__UpperCamelCase       ,			lstrip=__UpperCamelCase       ,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase       ,			__UpperCamelCase						) else sep_token
									snake_case__   :      Any   				= AddedToken(__UpperCamelCase       ,			lstrip=__UpperCamelCase       ,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase       ,			__UpperCamelCase						) else cls_token
									snake_case__   :      str   				= AddedToken(__UpperCamelCase       ,			lstrip=__UpperCamelCase       ,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase       ,			__UpperCamelCase						) else unk_token
									snake_case__   :      Any   				= AddedToken(__UpperCamelCase       ,			lstrip=__UpperCamelCase       ,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase       ,			__UpperCamelCase						) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									snake_case__   :      Union[str, Any]   				= AddedToken(__UpperCamelCase       ,			lstrip=__UpperCamelCase       ,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase       ,			__UpperCamelCase						) else mask_token

									super().__init__(
									    errors=__UpperCamelCase       ,			bos_token=__UpperCamelCase       ,			eos_token=__UpperCamelCase       ,			unk_token=__UpperCamelCase       ,			sep_token=__UpperCamelCase       ,			cls_token=__UpperCamelCase       ,			pad_token=__UpperCamelCase       ,			mask_token=__UpperCamelCase       ,			add_prefix_space=__UpperCamelCase       ,			add_bos_token=__UpperCamelCase       ,			**__UpperCamelCase       ,			)
									snake_case__   :      str   				= add_bos_token

									with open(__UpperCamelCase       ,			encoding='utf-8'						) as vocab_handle:
													snake_case__   :      Optional[int]   				= json.load(__UpperCamelCase						)
									snake_case__   :      Dict   				= {v: k for k, v in self.encoder.items()}
									snake_case__   :      Tuple   				= errors  # how to handle errors in decoding
									snake_case__   :      Optional[int]   				= bytes_to_unicode()
									snake_case__   :      Optional[Any]   				= {v: k for k, v in self.byte_encoder.items()}
									with open(__UpperCamelCase       ,			encoding='utf-8'						) as merges_handle:
													snake_case__   :      Union[str, Any]   				= merges_handle.read().split('\n'						)[1:-1]
									snake_case__   :      Optional[int]   				= [tuple(merge.split()						) for merge in bpe_merges]
									snake_case__   :      List[str]   				= dict(zip(__UpperCamelCase       ,			range(len(__UpperCamelCase						)						)						)						)
									snake_case__   :      Optional[int]   				= {}
									snake_case__   :      List[str]   				= add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									snake_case__   :      Union[str, Any]   				= re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'						)




					@property
					# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									return len(self.encoder						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase						)	->    Union[str, Any]:



									'''simple docstring'''







									if token in self.cache:
													return self.cache[token]
									snake_case__   :      int   				= tuple(__UpperCamelCase						)
									snake_case__   :      List[str]   				= get_pairs(__UpperCamelCase						)

									if not pairs:
													return token

									while True:
													snake_case__   :      Optional[int]   				= min(__UpperCamelCase       ,			key=lambda __UpperCamelCase						: self.bpe_ranks.get(__UpperCamelCase       ,			float('inf'						)						)						)
													if bigram not in self.bpe_ranks:
																	break
													snake_case__   :      Any   				= bigram
													snake_case__   :      int   				= []
													snake_case__   :      Optional[Any]   				= 0
													while i < len(__UpperCamelCase						):
																	try:
																					snake_case__   :      Union[str, Any]   				= word.index(__UpperCamelCase       ,			__UpperCamelCase						)
																	except ValueError:
																					new_word.extend(word[i:]						)
																					break
																	else:
																					new_word.extend(word[i:j]						)
																					snake_case__   :      Optional[Any]   				= 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
													snake_case__   :      List[str]   				= tuple(__UpperCamelCase						)
													snake_case__   :      Dict   				= new_word
													if len(__UpperCamelCase						) == 1:
																	break
													else:
																	snake_case__   :      str   				= get_pairs(__UpperCamelCase						)
									snake_case__   :      List[str]   				= ''' '''.join(__UpperCamelCase						)
									snake_case__   :      str   				= word
									return word




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None						)	->    str:



									'''simple docstring'''







									if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case__   :      int   				= [self.cls_token_id]
									snake_case__   :      str   				= [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None       ,			__UpperCamelCase = False						)	->    List[Any]:



									'''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						)

									if token_ids_a is None:
													return [1] + ([0] * len(__UpperCamelCase						)) + [1]
									return [1] + ([0] * len(__UpperCamelCase						)) + [1] + ([0] * len(__UpperCamelCase						)) + [1]




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Any   				= [self.sep_token_id]
									snake_case__   :      Dict   				= [self.cls_token_id]

									if token_ids_a is None:
													return len(cls + token_ids_a + sep						) * [0]
									return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]




					def      __a     (  self       ,			__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      List[str]   				= []
									for token in re.findall(self.pat       ,			__UpperCamelCase						):
													snake_case__   :      Tuple   				= ''''''.join(
													    self.byte_encoder[b] for b in token.encode('utf-8'						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCamelCase						).split(' '						)						)
									return bpe_tokens




					def      __a     (  self       ,			__UpperCamelCase						)	->    Union[str, Any]:



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase						)	->    int:



									'''simple docstring'''







									return self.decoder.get(__UpperCamelCase						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Tuple   				= ''''''.join(__UpperCamelCase						)
									snake_case__   :      List[Any]   				= bytearray([self.byte_decoder[c] for c in text]						).decode('utf-8'       ,			errors=self.errors						)
									return text




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = None						)	->    Optional[int]:



									'''simple docstring'''







									if not os.path.isdir(__UpperCamelCase						):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
													return
									snake_case__   :      str   				= os.path.join(
									    __UpperCamelCase       ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
									snake_case__   :      List[str]   				= 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'						)

									snake_case__   :      List[Any]   				= 0
									with open(__UpperCamelCase       ,			'w'       ,			encoding='utf-8'						) as writer:
													writer.write('#version: 0.2\n'						)
													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!'						)
																					snake_case__   :      List[Any]   				= token_index
																	writer.write(' '.join(__UpperCamelCase						) + '\n'						)
																	index += 1

									return vocab_file, merge_file




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase=False       ,			**__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Dict   				= kwargs.pop('add_prefix_space'       ,			self.add_prefix_space						)
									if (is_split_into_words or add_prefix_space) and (len(__UpperCamelCase						) > 0 and not text[0].isspace()):
													snake_case__   :      Any   				= ''' ''' + text
									return (text, kwargs)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									snake_case__   :      str   				= []
									for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase						) + [self.eos_token_id]						)
									if len(__UpperCamelCase						) > self.model_max_length:
													snake_case__   :      Optional[Any]   				= input_ids[-self.model_max_length :]
									return input_ids



'''simple docstring'''
import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[Any]=2 ,   __UpperCAmelCase  :					Dict=True ,   __UpperCAmelCase  :					str=False			)							->	List[Any]:
  lowercase				:							Tuple									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[int]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							List[Any]									=   torch.optim.lr_scheduler.OneCycleLR(__UpperCAmelCase ,   max_lr=0.01 ,   steps_per_epoch=2 ,   epochs=1			)
  lowercase				,      lowercase				,      lowercase				:							Optional[Any]									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  scheduler.step()
  if step_scheduler_with_optimizer or (num_processes == 1):
    assert (
        scheduler.scheduler.last_epoch == num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
  else:
    assert (
        scheduler.scheduler.last_epoch != num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					str=2 ,   __UpperCAmelCase  :					str=True ,   __UpperCAmelCase  :					Tuple=False			)							->	str:
  lowercase				:							List[Any]									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[Any]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							List[str]									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							Dict									=   torch.optim.lr_scheduler.LambdaLR(__UpperCAmelCase ,   lr_lambda=lambda __UpperCAmelCase			: 1 - n / 10			)
  lowercase				,      lowercase				,      lowercase				:							int									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  lowercase				:							Any									=   False
  scheduler.step()
  lowercase				:							Optional[Any]									=   1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

  # Optimizer has not stepped
  lowercase				:							List[Any]									=   True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    lowercase				:							List[Any]									=   1 - 2 / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					int = 2			)							->	Dict:
  from transformers import get_linear_schedule_with_warmup

  lowercase				:							Optional[Any]									=   [1, 2, 4]
  for num_steps in steps:
    lowercase				:							Dict									=   GradientAccumulationPlugin(num_steps=__UpperCAmelCase ,   adjust_scheduler=num_steps > 1			)
    lowercase				:							Tuple									=   Accelerator(gradient_accumulation_plugin=__UpperCAmelCase			)
    lowercase				:							str									=   torch.nn.Linear(2 ,   4			)
    lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=10.0			)
    lowercase				:							List[str]									=   get_linear_schedule_with_warmup(optimizer=__UpperCAmelCase ,   num_warmup_steps=0 ,   num_training_steps=20			)

    lowercase				,      lowercase				,      lowercase				:							Dict									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

    for i in range(10 * num_steps			):
      with accelerator.accumulate(__UpperCAmelCase			):
        optimizer.step()
        scheduler.step()

      if i == (10 * num_steps - 2):
        assert (
            scheduler.get_last_lr()[0] != 0
        ), f"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
    assert (
        scheduler.get_last_lr()[0] == 0
    ), f"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
    GradientState._reset_state()






@require_cpu
class _lowerCAmelCase			(	unittest.TestCase					):







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      @require_huggingface_suite
      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1     )     )
        debug_launcher(lowercase     )