




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

UpperCamelCase__  =		logging.get_logger(__name__)

UpperCamelCase__  =		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

UpperCamelCase__  =		{
    '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'
        ),
    },
}

UpperCamelCase__  =		{
    'microsoft/deberta-base': 5_1_2,
    'microsoft/deberta-large': 5_1_2,
    'microsoft/deberta-xlarge': 5_1_2,
    'microsoft/deberta-base-mnli': 5_1_2,
    'microsoft/deberta-large-mnli': 5_1_2,
    'microsoft/deberta-xlarge-mnli': 5_1_2,
}

UpperCamelCase__  =		{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}






def       snake_case      (							)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:	List[str]							     =							(
	    list(range(ord('!'    )		,     ord('~'    ) + 1    )    ) + list(range(ord('¡'    )		,     ord('¬'    ) + 1    )    ) + list(range(ord('®'    )		,     ord('ÿ'    ) + 1    )    )
	)
	lowerCamelCase_							:	Dict							     =							bs[:]
	lowerCamelCase_							:	Optional[int]							     =							0
	for b in range(2**8    ):
		if b not in bs:
			bs.append(_A    )
			cs.append(2**8 + n    )
			n += 1
	lowerCamelCase_							:	Optional[int]							     =							[chr(_A    ) for n in cs]
	return dict(zip(_A		,     _A    )    )






def       snake_case      (							A    )							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:	Tuple							     =							set()
	lowerCamelCase_							:	Optional[Any]							     =							word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)    )
		lowerCamelCase_							:	Any							     =							char
	return pairs



class        __SCREAMING_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				, __lowercase				, __lowercase				, __lowercase="replace"				, __lowercase="[CLS]"				, __lowercase="[SEP]"				, __lowercase="[SEP]"				, __lowercase="[CLS]"				, __lowercase="[UNK]"				, __lowercase="[PAD]"				, __lowercase="[MASK]"				, __lowercase=False				, __lowercase=False				, **__lowercase				, ):







						'''simple docstring'''




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

						# Mask token behave like a normal word, i.e. include the space before it
						lowerCamelCase_							:	int							     =							AddedToken(lowercase_				, lstrip=lowercase_				, rstrip=lowercase_      ) if isinstance(lowercase_				, lowercase_      ) else mask_token

						super().__init__(
						    errors=lowercase_				, bos_token=lowercase_				, eos_token=lowercase_				, unk_token=lowercase_				, sep_token=lowercase_				, cls_token=lowercase_				, pad_token=lowercase_				, mask_token=lowercase_				, add_prefix_space=lowercase_				, add_bos_token=lowercase_				, **lowercase_				, )
						lowerCamelCase_							:	Optional[int]							     =							add_bos_token

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

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						lowerCamelCase_							:	List[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 			lowercase_							(       self      ):







						'''simple docstring'''




						return len(self.encoder      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						if token in self.cache:
							return self.cache[token]
						lowerCamelCase_							:	Any							     =							tuple(lowercase_      )
						lowerCamelCase_							:	Optional[Any]							     =							get_pairs(lowercase_      )

						if not pairs:
							return token

						while True:
							lowerCamelCase_							:	Optional[int]							     =							min(lowercase_				, key=lambda __lowercase      : self.bpe_ranks.get(lowercase_				, float('inf'      )      )      )
							if bigram not in self.bpe_ranks:
								break
							lowerCamelCase_     ,    lowerCamelCase_							:	Any							     =							bigram
							lowerCamelCase_							:	Any							     =							[]
							lowerCamelCase_							:	Dict							     =							0
							while i < len(lowercase_      ):
								try:
									lowerCamelCase_							:	List[Any]							     =							word.index(lowercase_				, lowercase_      )
								except ValueError:
									new_word.extend(word[i:]      )
									break
								else:
									new_word.extend(word[i:j]      )
									lowerCamelCase_							:	Tuple							     =							j

								if word[i] == first and i < len(lowercase_      ) - 1 and word[i + 1] == second:
									new_word.append(first + second      )
									i += 2
								else:
									new_word.append(word[i]      )
									i += 1
							lowerCamelCase_							:	List[Any]							     =							tuple(lowercase_      )
							lowerCamelCase_							:	Union[str, Any]							     =							new_word
							if len(lowercase_      ) == 1:
								break
							else:
								lowerCamelCase_							:	Union[str, Any]							     =							get_pairs(lowercase_      )
						lowerCamelCase_							:	Any							     =							' '.join(lowercase_      )
						lowerCamelCase_							:	Union[str, Any]							     =							word
						return word

					def 			lowercase_							(       self				, __lowercase				, __lowercase = None      ):







						'''simple docstring'''




						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						lowerCamelCase_							:	List[Any]							     =							[self.cls_token_id]
						lowerCamelCase_							:	str							     =							[self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep

					def 			lowercase_							(       self				, __lowercase				, __lowercase = None				, __lowercase = False      ):







						'''simple docstring'''




						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase_				, token_ids_a=lowercase_				, already_has_special_tokens=lowercase_      )

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

					def 			lowercase_							(       self				, __lowercase				, __lowercase = None      ):







						'''simple docstring'''




						lowerCamelCase_							:	Dict							     =							[self.sep_token_id]
						lowerCamelCase_							:	Any							     =							[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 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							[]
						for token in re.findall(self.pat				, lowercase_      ):
							lowerCamelCase_							:	Optional[Any]							     =							''.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(lowercase_      ).split(' '      )      )
						return bpe_tokens

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						return self.decoder.get(lowercase_      )

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self				, __lowercase				, __lowercase = None      ):







						'''simple docstring'''




						if not os.path.isdir(lowercase_      ):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
							return
						lowerCamelCase_							:	str							     =							os.path.join(
						    lowercase_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
						lowerCamelCase_							:	int							     =							os.path.join(
						    lowercase_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

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

						lowerCamelCase_							:	Union[str, Any]							     =							0
						with open(lowercase_				, '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 __lowercase      : 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!'      )
									lowerCamelCase_							:	Optional[int]							     =							token_index
								writer.write(' '.join(lowercase_      ) + '\n'      )
								index += 1

						return vocab_file, merge_file

					def 			lowercase_							(       self				, __lowercase				, __lowercase=False				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							kwargs.pop('add_prefix_space'				, self.add_prefix_space      )
						if (is_split_into_words or add_prefix_space) and (len(lowercase_      ) > 0 and not text[0].isspace()):
							lowerCamelCase_							:	Optional[Any]							     =							' ' + text
						return (text, kwargs)





					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Dict							     =							[]
						for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(lowercase_				, add_special_tokens=lowercase_      ) + [self.eos_token_id]      )
						if len(lowercase_      ) > self.model_max_length:
							lowerCamelCase_							:	Dict							     =							input_ids[-self.model_max_length :]
						return input_ids






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				(DDIMScheduler,)
					_lowerCamelCase							=				(("""eta""", 0.0), ("""num_inference_steps""", 50))

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							{
						    'num_train_timesteps': 1_000,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'clip_sample': True,
						}

						config.update(**__lowercase      )
						return config

					def 			lowercase_							(       self				, **__lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	str							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	List[Any]							     =							self.get_scheduler_config(**__lowercase      )
						lowerCamelCase_							:	Tuple							     =							scheduler_class(**__lowercase      )

						lowerCamelCase_     ,    lowerCamelCase_							:	Dict							     =							10, 0.0

						lowerCamelCase_							:	Optional[int]							     =							self.dummy_model()
						lowerCamelCase_							:	Union[str, Any]							     =							self.dummy_sample_deter

						scheduler.set_timesteps(__lowercase      )

						for t in scheduler.timesteps:
							lowerCamelCase_							:	Any							     =							model(__lowercase				, __lowercase      )
							lowerCamelCase_							:	Optional[Any]							     =							scheduler.step(__lowercase				, __lowercase				, __lowercase				, __lowercase      ).prev_sample

						return sample

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for timesteps in [100, 500, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=__lowercase      )

						lowerCamelCase_							:	Optional[int]							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	List[str]							     =							self.get_scheduler_config(steps_offset=1      )
						lowerCamelCase_							:	int							     =							scheduler_class(**__lowercase      )
						scheduler.set_timesteps(5      )
						assert torch.equal(scheduler.timesteps				, torch.LongTensor([801, 601, 401, 201, 1]      )      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				, [0.0_02, 0.02, 0.2, 2]      ):
							self.check_over_configs(beta_start=__lowercase				, beta_end=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self.check_over_configs(thresholding=__lowercase      )
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(
								    thresholding=__lowercase				, prediction_type=__lowercase				, sample_max_value=__lowercase				, )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t in [1, 10, 49]:
							self.check_over_forward(time_step=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t, num_inference_steps in zip([1, 10, 50]				, [10, 50, 500]      ):
							self.check_over_forward(time_step=__lowercase				, num_inference_steps=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						for t, eta in zip([1, 10, 49]				, [0.0, 0.5, 1.0]      ):
							self.check_over_forward(time_step=__lowercase				, eta=__lowercase      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							self.scheduler_classes[0]
						lowerCamelCase_							:	Dict							     =							self.get_scheduler_config()
						lowerCamelCase_							:	List[str]							     =							scheduler_class(**__lowercase      )

						assert torch.sum(torch.abs(scheduler._get_variance(0				, 0      ) - 0.0      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420				, 400      ) - 0.1_47_71      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980				, 960      ) - 0.3_24_60      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0				, 0      ) - 0.0      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487				, 486      ) - 0.0_09_79      )      ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999				, 998      ) - 0.02      )      ) < 1E-5

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							self.full_loop()

						lowerCamelCase_							:	Tuple							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	str							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_72.00_67      ) < 1E-2
						assert abs(result_mean.item() - 0.22_39_67      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							self.full_loop(prediction_type='v_prediction'      )

						lowerCamelCase_							:	Tuple							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	List[str]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 52.53_02      ) < 1E-2
						assert abs(result_mean.item() - 0.06_84      ) < 1E-3

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							self.full_loop(set_alpha_to_one=__lowercase				, beta_start=0.01      )
						lowerCamelCase_							:	Optional[Any]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Dict							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_49.82_95      ) < 1E-2
						assert abs(result_mean.item() - 0.19_51      ) < 1E-3





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	int							     =							self.full_loop(set_alpha_to_one=__lowercase				, beta_start=0.01      )
						lowerCamelCase_							:	Optional[int]							     =							torch.sum(torch.abs(__lowercase      )      )
						lowerCamelCase_							:	Optional[int]							     =							torch.mean(torch.abs(__lowercase      )      )

						assert abs(result_sum.item() - 1_49.07_84      ) < 1E-2
						assert abs(result_mean.item() - 0.19_41      ) < 1E-3
