



import json
import os
from functools import lru_cache
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__						:	Dict					=logging.get_logger(__name__)


lowerCAmelCase__						:	str					={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase__						:	Optional[Any]					={
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

lowerCAmelCase__						:	Optional[int]					={'''facebook/blenderbot-3B''': 128}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       __lowercase  (					)							->					Union[str, Any]:
							__SCREAMING_SNAKE_CASE										=   (
							    list(range(ord('!'  )				,  ord('~'  ) + 1  )  ) + list(range(ord('¡'  )				,  ord('¬'  ) + 1  )  ) + list(range(ord('®'  )				,  ord('ÿ'  ) + 1  )  )
							)
							__SCREAMING_SNAKE_CASE										=   bs[:]
							__SCREAMING_SNAKE_CASE										=   0
							for b in range(2**8  ):
														if b not in bs:
																					bs.append(__UpperCAmelCase  )
																					cs.append(2**8 + n  )
																					n += 1
							__SCREAMING_SNAKE_CASE										=   [chr(__UpperCAmelCase  ) for n in cs]
							return dict(zip(__UpperCAmelCase				,  __UpperCAmelCase  )  )






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

class   UpperCAmelCase_    (  UpperCamelCase_			):

						'''simple docstring'''




						UpperCamelCase__     :					Dict							    =    VOCAB_FILES_NAMES
						UpperCamelCase__     :					Union[str, Any]							    =    PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase__     :					Dict							    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase__     :					List[Any]							    =    ["""input_ids""", """attention_mask"""]






						def __init__(       self ,				_A ,				_A ,				_A="replace" ,				_A="<s>" ,				_A="</s>" ,				_A="</s>" ,				_A="<s>" ,				_A="<unk>" ,				_A="<pad>" ,				_A="<mask>" ,				_A=False ,				**_A ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   AddedToken(a_ ,				lstrip=a_ ,				rstrip=a_						) if isinstance(a_ ,				a_						) else bos_token
													__SCREAMING_SNAKE_CASE										=   AddedToken(a_ ,				lstrip=a_ ,				rstrip=a_						) if isinstance(a_ ,				a_						) else eos_token
													__SCREAMING_SNAKE_CASE										=   AddedToken(a_ ,				lstrip=a_ ,				rstrip=a_						) if isinstance(a_ ,				a_						) else sep_token
													__SCREAMING_SNAKE_CASE										=   AddedToken(a_ ,				lstrip=a_ ,				rstrip=a_						) if isinstance(a_ ,				a_						) else cls_token
													__SCREAMING_SNAKE_CASE										=   AddedToken(a_ ,				lstrip=a_ ,				rstrip=a_						) if isinstance(a_ ,				a_						) else unk_token
													__SCREAMING_SNAKE_CASE										=   AddedToken(a_ ,				lstrip=a_ ,				rstrip=a_						) if isinstance(a_ ,				a_						) else pad_token

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

													super().__init__(
													    errors=a_ ,				bos_token=a_ ,				eos_token=a_ ,				unk_token=a_ ,				sep_token=a_ ,				cls_token=a_ ,				pad_token=a_ ,				mask_token=a_ ,				add_prefix_space=a_ ,				**a_ ,				)

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

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													__SCREAMING_SNAKE_CASE										=   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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
						def 			_A      (       self						):







													'''simple docstring'''

													return len(self.encoder						)






						def 			_A      (       self						):







													'''simple docstring'''

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






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													if token in self.cache:
																				return self.cache[token]
													__SCREAMING_SNAKE_CASE										=   tuple(a_						)
													__SCREAMING_SNAKE_CASE										=   get_pairs(a_						)

													if not pairs:
																				return token

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

																											if word[i] == first and i < len(a_						) - 1 and word[i + 1] == second:
																																		new_word.append(first + second						)
																																		i += 2
																											else:
																																		new_word.append(word[i]						)
																																		i += 1
																				__SCREAMING_SNAKE_CASE										=   tuple(a_						)
																				__SCREAMING_SNAKE_CASE										=   new_word
																				if len(a_						) == 1:
																											break
																				else:
																											__SCREAMING_SNAKE_CASE										=   get_pairs(a_						)
													__SCREAMING_SNAKE_CASE										=   ' '.join(a_						)
													__SCREAMING_SNAKE_CASE										=   word
													return word






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   []
													for token in re.findall(self.pat ,				a_						):
																				__SCREAMING_SNAKE_CASE										=   ''.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(a_						).split(' '						)						)
													return bpe_tokens






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

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






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													return self.decoder.get(a_						)






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   ''.join(a_						)
													__SCREAMING_SNAKE_CASE										=   bytearray([self.byte_decoder[c] for c in text]						).decode('utf-8' ,				errors=self.errors						)
													return text






						def 			_A      (       self ,				_A ,				_A = None						):







													'''simple docstring'''

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

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

													__SCREAMING_SNAKE_CASE										=   0
													with open(a_ ,				'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 _A						: 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!'						)
																																		__SCREAMING_SNAKE_CASE										=   token_index
																											writer.write(' '.join(a_						) + '\n'						)
																											index += 1

													return vocab_file, merge_file






						def 			_A      (       self ,				_A ,				_A = None ,				_A = False						):







													'''simple docstring'''

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

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






						def 			_A      (       self ,				_A ,				_A = None						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   [self.sep_token_id]
													__SCREAMING_SNAKE_CASE										=   [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 + sep + token_ids_a + sep						) * [0]






						def 			_A      (       self ,				_A ,				_A=False ,				**_A						):







													'''simple docstring'''

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






						def 			_A      (       self ,				_A ,				_A = None						):







													'''simple docstring'''

													return token_ids_a + [self.eos_token_id]






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   []
													for is_user, text in conversation.iter_texts():
																				if is_user:
																											# We need to space prefix as it's being done within blenderbot
																											inputs.append(' ' + text						)
																				else:
																											# Generated responses should contain them already.
																											inputs.append(a_						)

													__SCREAMING_SNAKE_CASE										=   '  '.join(a_						)
													__SCREAMING_SNAKE_CASE										=   self.encode(a_						)
													if len(a_						) > self.model_max_length:
																				__SCREAMING_SNAKE_CASE										=   input_ids[-self.model_max_length :]
																				logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""						)
													return input_ids











'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

										self.assertEqual(output_a.shape      ,						sample.shape			)
										self.assertEqual(output_a.shape      ,						output_a.shape			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Optional[Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
										assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    Any      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Tuple      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
										assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

										with self.assertRaises(a_      ,						msg='`timesteps` must be in descending order.'			):
													scheduler.set_timesteps(timesteps=a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

										with self.assertRaises(a_      ,						msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
													scheduler.set_timesteps(num_inference_steps=a_      ,						timesteps=a_			)




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    a_      ,						msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      ,						):
													scheduler.set_timesteps(timesteps=a_			)




