



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

_lowercase						  =		logging.get_logger(__name__)


_lowercase						  =		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_lowercase						  =		{
    "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"
    },
}

_lowercase						  =		{"facebook/blenderbot-3B": 128}



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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[Any]	)->  str:
    A__		 =					set()
    A__		 =					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)	)
        A__		 =					char
    return pairs



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				['''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__ ,						):
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else bos_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else eos_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else sep_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else cls_token
         A__		 =					AddedToken(a__ ,						lstrip=a__ ,						rstrip=a__) if isinstance(a__ ,						a__) else unk_token
         A__		 =					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
         A__		 =					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:
             A__		 =					json.load(a__)
         A__		 =					{v: k for k, v in self.encoder.items()}
         A__		 =					errors  # how to handle errors in decoding
         A__		 =					bytes_to_unicode()
         A__		 =					{v: k for k, v in self.byte_encoder.items()}
         with open(a__ ,						encoding='''utf-8''') as merges_handle:
             A__		 =					merges_handle.read().split('''\n''')[1:-1]
         A__		 =					[tuple(merge.split()) for merge in bpe_merges]
         A__		 =					dict(zip(a__ ,						range(len(a__))))
         A__		 =					{}
         A__		 =					add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         A__		 =					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 		snake_case_		(				self):
         return len(self.encoder)




     def 		snake_case_		(				self):
         return dict(self.encoder ,						**self.added_tokens_encoder)




     def 		snake_case_		(				self ,						a__):
         if token in self.cache:
             return self.cache[token]
         A__		 =					tuple(a__)
         A__		 =					get_pairs(a__)

         if not pairs:
             return token

         while True:
             A__		 =					min(a__ ,						key=lambda a__: self.bpe_ranks.get(a__ ,						float('''inf''')))
             if bigram not in self.bpe_ranks:
                 break
             A__ ,	A__		 =					bigram
             A__		 =					[]
             A__		 =					0
             while i < len(a__):
                 try:
                     A__		 =					word.index(a__ ,						a__)
                 except ValueError:
                     new_word.extend(word[i:])
                     break
                 else:
                     new_word.extend(word[i:j])
                     A__		 =					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
             A__		 =					tuple(a__)
             A__		 =					new_word
             if len(a__) == 1:
                 break
             else:
                 A__		 =					get_pairs(a__)
         A__		 =					''' '''.join(a__)
         A__		 =					word
         return word




     def 		snake_case_		(				self ,						a__):
         A__		 =					[]
         for token in re.findall(self.pat ,						a__):
             A__		 =					''''''.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 		snake_case_		(				self ,						a__):
         return self.encoder.get(a__ ,						self.encoder.get(self.unk_token))




     def 		snake_case_		(				self ,						a__):
         return self.decoder.get(a__)




     def 		snake_case_		(				self ,						a__):
         A__		 =					''''''.join(a__)
         A__		 =					bytearray([self.byte_decoder[c] for c in text]).decode('''utf-8''' ,						errors=self.errors)
         return text




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         if not os.path.isdir(a__):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory")
             return
         A__		 =					os.path.join(
             a__ ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
         A__		 =					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''')

         A__		 =					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!''')
                     A__		 =					token_index
                 writer.write(''' '''.join(a__) + '''\n''')
                 index += 1

         return vocab_file, merge_file




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = False):
         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 		snake_case_		(				self ,						a__ ,						a__ = None):
         A__		 =					[self.sep_token_id]
         A__		 =					[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 		snake_case_		(				self ,						a__ ,						a__=False ,						**a__):
         A__		 =					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()):
             A__		 =					''' ''' + text
         return (text, kwargs)




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         return token_ids_a + [self.eos_token_id]




     def 		snake_case_		(				self ,						a__):
         A__		 =					[]
         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__)

         A__		 =					'''  '''.join(a__)
         A__		 =					self.encode(a__)
         if len(a__) > self.model_max_length:
             A__		 =					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





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				(DDPMScheduler,)




     def 		snake_case_		(				self ,						**a__):
         A__		 =					{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**a__)
         return config




     def 		snake_case_		(				self):
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=a__)




     def 		snake_case_		(				self):
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,						[0.0_0_2, 0.0_2, 0.2, 2]):
             self.check_over_configs(beta_start=a__ ,						beta_end=a__)




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




     def 		snake_case_		(				self):
         for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=a__)




     def 		snake_case_		(				self):
         for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=a__)




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




     def 		snake_case_		(				self):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=a__)




     def 		snake_case_		(				self):
         for t in [0, 5_0_0, 9_9_9]:
             self.check_over_forward(time_step=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.0_0_9_7_9)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.0_2)) < 1e-5




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter
         A__		 =					torch.manual_seed(0)

         for t in reversed(range(a__)):
             # 1. predict noise residual
             A__		 =					model(a__ ,						a__)

             # 2. predict previous mean of sample x_t-1
             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             A__		 =					pred_prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1e-2
         assert abs(result_mean.item() - 0.3_3_7_2) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config(prediction_type='''v_prediction''')
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter
         A__		 =					torch.manual_seed(0)

         for t in reversed(range(a__)):
             # 1. predict noise residual
             A__		 =					model(a__ ,						a__)

             # 2. predict previous mean of sample x_t-1
             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__).prev_sample

             # if t > 0:
             #     noise = self.dummy_sample_deter
             #     variance = scheduler.get_variance(t) ** (0.5) * noise
             #
             # sample = pred_prev_sample + variance
             A__		 =					pred_prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1e-2
         assert abs(result_mean.item() - 0.2_6_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=a__)

         A__		 =					scheduler.timesteps

         for i, timestep in enumerate(a__):
             if i == len(a__) - 1:
                 A__		 =					-1
             else:
                 A__		 =					timesteps[i + 1]

             A__		 =					scheduler.previous_timestep(a__)
             A__		 =					prev_t.item()

             self.assertEqual(a__ ,						a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 5_1, 0]

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




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]
         A__		 =					len(a__)

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




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[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__)
