

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      42
   lowerCamelCase__				=      42





   def __init__(       self					:			Optional[Any]     , __lowerCamelCase					:			UNetaDModel     , __lowerCamelCase					:			KarrasVeScheduler			):
       super().__init__()
       self.register_modules(unet=__lowerCamelCase     , scheduler=__lowerCamelCase			)





   @torch.no_grad()
   def __call__(       self					:			List[str]     , __lowerCamelCase					:			int = 1     , __lowerCamelCase					:			int = 50     , __lowerCamelCase					:			Optional[Union[torch.Generator, List[torch.Generator]]] = None     , __lowerCamelCase					:			Optional[str] = "pil"     , __lowerCamelCase					:			bool = True     , **__lowerCamelCase					:			List[str]     , ):
       SCREAMING_SNAKE_CASE										=   self.unet.config.sample_size
       SCREAMING_SNAKE_CASE										=   (batch_size, 3, img_size, img_size)

       SCREAMING_SNAKE_CASE										=   self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       SCREAMING_SNAKE_CASE										=   randn_tensor(__lowerCamelCase     , generator=__lowerCamelCase     , device=self.device			) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(__lowerCamelCase			)

       for t in self.progress_bar(self.scheduler.timesteps			):
           # here sigma_t == t_i from the paper
           SCREAMING_SNAKE_CASE										=   self.scheduler.schedule[t]
           SCREAMING_SNAKE_CASE										=   self.scheduler.schedule[t - 1] if t > 0 else 0

           # 1. Select temporarily increased noise level sigma_hat
           # 2. Add new noise to move from sample_i to sample_hat
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.scheduler.add_noise_to_input(__lowerCamelCase     , __lowerCamelCase     , generator=__lowerCamelCase			)

           # 3. Predict the noise residual given the noise magnitude `sigma_hat`
           # The model inputs and output are adjusted by following eq. (213) in [1].
           SCREAMING_SNAKE_CASE										=   (sigma_hat / 2) * model((sample_hat + 1) / 2     , sigma_hat / 2			).sample

           # 4. Evaluate dx/dt at sigma_hat
           # 5. Take Euler step from sigma to sigma_prev
           SCREAMING_SNAKE_CASE										=   self.scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

           if sigma_prev != 0:
               # 6. Apply 2nd order correction
               # The model inputs and output are adjusted by following eq. (213) in [1].
               SCREAMING_SNAKE_CASE										=   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2     , sigma_prev / 2			).sample
               SCREAMING_SNAKE_CASE										=   self.scheduler.step_correct(
                   __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , step_output.prev_sample     , step_output["derivative"]     , )
           SCREAMING_SNAKE_CASE										=   step_output.prev_sample

       SCREAMING_SNAKE_CASE										=   (sample / 2 + 0.5).clamp(0     , 1			)
       SCREAMING_SNAKE_CASE										=   sample.cpu().permute(0     , 2     , 3     , 1			).numpy()
       if output_type == "pil":
           SCREAMING_SNAKE_CASE										=   self.numpy_to_pil(__lowerCamelCase			)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__lowerCamelCase			)


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

__A			:					Any						=			logging.get_logger(__name__)


__A			:					Any						=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__A			:					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'
    },
}

__A			:					Union[str, Any]						=			{'facebook/blenderbot-3B': 1_2_8}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	__a     (      ):
    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(A__       )
            cs.append(2**8 + n       )
            n += 1
    SCREAMING_SNAKE_CASE										=   [chr(A__       ) for n in cs]
    return dict(zip(A__			,  A__       )       )





def 	__a     (      A__			:  List[Any]       ):
    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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




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





   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Any="replace"     , __lowerCamelCase					:			Optional[Any]="<s>"     , __lowerCamelCase					:			Optional[Any]="</s>"     , __lowerCamelCase					:			Any="</s>"     , __lowerCamelCase					:			Union[str, Any]="<s>"     , __lowerCamelCase					:			List[str]="<unk>"     , __lowerCamelCase					:			Optional[Any]="<pad>"     , __lowerCamelCase					:			Dict="<mask>"     , __lowerCamelCase					:			Any=False     , **__lowerCamelCase					:			Optional[Any]     , ):
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else bos_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else eos_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else sep_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else cls_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else unk_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else pad_token

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

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

       with open(__lowerCamelCase     , encoding="utf-8"			) as vocab_handle:
           SCREAMING_SNAKE_CASE										=   json.load(__lowerCamelCase			)
       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(__lowerCamelCase     , 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(__lowerCamelCase     , range(len(__lowerCamelCase			)			)			)			)
       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      _snake_case		(       self					:			str			):
       return len(self.encoder			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       return dict(self.encoder     , **self.added_tokens_encoder			)





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[Any]			):
       if token in self.cache:
           return self.cache[token]
       SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)

       if not pairs:
           return token

       while True:
           SCREAMING_SNAKE_CASE										=   min(__lowerCamelCase     , key=lambda __lowerCamelCase			: self.bpe_ranks.get(__lowerCamelCase     , float("inf"			)			)			)
           if bigram not in self.bpe_ranks:
               break
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   bigram
           SCREAMING_SNAKE_CASE										=   []
           SCREAMING_SNAKE_CASE										=   0
           while i < len(__lowerCamelCase			):
               try:
                   SCREAMING_SNAKE_CASE										=   word.index(__lowerCamelCase     , __lowerCamelCase			)
               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(__lowerCamelCase			) - 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(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   new_word
           if len(__lowerCamelCase			) == 1:
               break
           else:
               SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   " ".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   word
       return word





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   []
       for token in re.findall(self.pat     , __lowerCamelCase			):
           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(__lowerCamelCase			).split(" "			)			)
       return bpe_tokens





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			Dict			):
       return self.encoder.get(__lowerCamelCase     , self.encoder.get(self.unk_token			)			)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			Optional[int]			):
       return self.decoder.get(__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   bytearray([self.byte_decoder[c] for c in text]			).decode("utf-8"     , errors=self.errors			)
       return text





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       if not os.path.isdir(__lowerCamelCase			):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
           return
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]			)

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

       SCREAMING_SNAKE_CASE										=   0
       with open(__lowerCamelCase     , "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 __lowerCamelCase			: 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(__lowerCamelCase			) + "\n"			)
               index += 1

       return vocab_file, merge_file





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None     , __lowerCamelCase					:			bool = False			):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__lowerCamelCase     , token_ids_a=__lowerCamelCase     , already_has_special_tokens=__lowerCamelCase			)

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





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       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      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Any=False     , **__lowerCamelCase					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   kwargs.pop("add_prefix_space"     , self.add_prefix_space			)
       if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase			) > 0 and not text[0].isspace()):
           SCREAMING_SNAKE_CASE										=   " " + text
       return (text, kwargs)





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       return token_ids_a + [self.eos_token_id]





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			"Conversation"			):
       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(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   "  ".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.encode(__lowerCamelCase			)
       if len(__lowerCamelCase			) > 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