
"""simple docstring"""


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''': 1_28}






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






def 					_snake_case (						snake_case__      :   List[Any]   ):
	A        =      set()
	A        =      word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)   )
		A        =      char
	return pairs







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Dict					=			VOCAB_FILES_NAMES
						_lowerCamelCase:			Optional[Any]					=			PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:			Union[str, Any]					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:			Dict					=			['''input_ids''', '''attention_mask''']


						def __init__(       self   :					Any     ,A_   :					List[str]     ,A_   :					int     ,A_   :					int="replace"     ,A_   :					List[str]="<s>"     ,A_   :					List[Any]="</s>"     ,A_   :					Optional[Any]="</s>"     ,A_   :					List[str]="<s>"     ,A_   :					int="<unk>"     ,A_   :					str="<pad>"     ,A_   :					Union[str, Any]="<mask>"     ,A_   :					int=False     ,**A_   :					str     ,)   ->      List[str]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      int:
							return len(self.encoder					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      List[Any]:
							return dict(self.encoder     ,**self.added_tokens_encoder					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					int     ,A_   :					Union[str, Any]					)   ->      Union[str, Any]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					int					)   ->      Tuple:
							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   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					Dict					)   ->      Union[str, Any]:
							return self.encoder.get(A_     ,self.encoder.get(self.unk_token					)					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					List[Any]					)   ->      Dict:
							return self.decoder.get(A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,A_   :					List[Any]					)   ->      int:
							A        =      ''.join(A_					)
							A        =      bytearray([self.byte_decoder[c] for c in text]					).decode('utf-8'     ,errors=self.errors					)
							return text


						def   _SCREAMING_SNAKE_CASE					(       self   :					str     ,A_   :					str     ,A_   :					Optional[str] = None					)   ->      Tuple[str]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					List[int]     ,A_   :					Optional[List[int]] = None     ,A_   :					bool = False					)   ->      List[int]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					List[int]     ,A_   :					Optional[List[int]] = None					)   ->      List[int]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					Optional[int]     ,A_   :					Optional[Any]=False     ,**A_   :					Tuple					)   ->      List[Any]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					List[int]     ,A_   :					Optional[List[int]] = None					)   ->      str:
							return token_ids_a + [self.eos_token_id]




						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					"Conversation"					)   ->      List[int]:
							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






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		42


class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase			):
 @register_to_config
 def __init__(      self  :      List[str]			,     UpperCAmelCase  :      int = 65536			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 2			,     UpperCAmelCase  :      int = 0			,     UpperCAmelCase  :      str = "fourier"			,     UpperCAmelCase  :      bool = True			,     UpperCAmelCase  :      bool = False			,     UpperCAmelCase  :      float = 0.0			,     UpperCAmelCase  :      Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,     UpperCAmelCase  :      Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,     UpperCAmelCase  :      Tuple[str] = "UNetMidBlock1D"			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      Tuple[int] = (32, 32, 64)			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      int = 8			,     UpperCAmelCase  :      int = 1			,     UpperCAmelCase  :      bool = False			,     )			->	List[Any]:
     super().__init__()
     lowerCamelCase__    :      Optional[int]					 =		sample_size

     # time
     if time_embedding_type == "fourier":
         lowerCamelCase__    :      Optional[Any]					 =		GaussianFourierProjection(
             embedding_size=8			,     set_W_to_weight=UpperCAmelCase			,     log=UpperCAmelCase			,     flip_sin_to_cos=UpperCAmelCase	)
         lowerCamelCase__    :      Any					 =		2 * block_out_channels[0]
     elif time_embedding_type == "positional":
         lowerCamelCase__    :      List[Any]					 =		Timesteps(
             block_out_channels[0]			,     flip_sin_to_cos=UpperCAmelCase			,     downscale_freq_shift=UpperCAmelCase	)
         lowerCamelCase__    :      Dict					 =		block_out_channels[0]

     if use_timestep_embedding:
         lowerCamelCase__    :      str					 =		block_out_channels[0] * 4
         lowerCamelCase__    :      List[Any]					 =		TimestepEmbedding(
             in_channels=UpperCAmelCase			,     time_embed_dim=UpperCAmelCase			,     act_fn=UpperCAmelCase			,     out_dim=block_out_channels[0]			,     )

     lowerCamelCase__    :      Any					 =		nn.ModuleList([]	)
     lowerCamelCase__    :      Tuple					 =		None
     lowerCamelCase__    :      List[str]					 =		nn.ModuleList([]	)
     lowerCamelCase__    :      Optional[int]					 =		None

     # down
     lowerCamelCase__    :      Optional[int]					 =		in_channels
     for i, down_block_type in enumerate(UpperCAmelCase	):
         lowerCamelCase__    :      Union[str, Any]					 =		output_channel
         lowerCamelCase__    :      Tuple					 =		block_out_channels[i]

         if i == 0:
             input_channel += extra_in_channels

         lowerCamelCase__    :      Union[str, Any]					 =		i == len(UpperCAmelCase	) - 1

         lowerCamelCase__    :      Optional[int]					 =		get_down_block(
             UpperCAmelCase			,     num_layers=UpperCAmelCase			,     in_channels=UpperCAmelCase			,     out_channels=UpperCAmelCase			,     temb_channels=block_out_channels[0]			,     add_downsample=not is_final_block or downsample_each_block			,     )
         self.down_blocks.append(UpperCAmelCase	)

     # mid
     lowerCamelCase__    :      Optional[int]					 =		get_mid_block(
         UpperCAmelCase			,     in_channels=block_out_channels[-1]			,     mid_channels=block_out_channels[-1]			,     out_channels=block_out_channels[-1]			,     embed_dim=block_out_channels[0]			,     num_layers=UpperCAmelCase			,     add_downsample=UpperCAmelCase			,     )

     # up
     lowerCamelCase__    :      Optional[int]					 =		list(reversed(UpperCAmelCase	)	)
     lowerCamelCase__    :      Optional[int]					 =		reversed_block_out_channels[0]
     if out_block_type is None:
         lowerCamelCase__    :      List[str]					 =		out_channels
     else:
         lowerCamelCase__    :      Any					 =		block_out_channels[0]

     for i, up_block_type in enumerate(UpperCAmelCase	):
         lowerCamelCase__    :      Tuple					 =		output_channel
         lowerCamelCase__    :      Union[str, Any]					 =		(
             reversed_block_out_channels[i + 1] if i < len(UpperCAmelCase	) - 1 else final_upsample_channels
         )

         lowerCamelCase__    :      List[str]					 =		i == len(UpperCAmelCase	) - 1

         lowerCamelCase__    :      Dict					 =		get_up_block(
             UpperCAmelCase			,     num_layers=UpperCAmelCase			,     in_channels=UpperCAmelCase			,     out_channels=UpperCAmelCase			,     temb_channels=block_out_channels[0]			,     add_upsample=not is_final_block			,     )
         self.up_blocks.append(UpperCAmelCase	)
         lowerCamelCase__    :      int					 =		output_channel

     # out
     lowerCamelCase__    :      int					 =		norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,     32	)
     lowerCamelCase__    :      List[Any]					 =		get_out_block(
         out_block_type=UpperCAmelCase			,     num_groups_out=UpperCAmelCase			,     embed_dim=block_out_channels[0]			,     out_channels=UpperCAmelCase			,     act_fn=UpperCAmelCase			,     fc_dim=block_out_channels[-1] // 4			,     )







 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      torch.FloatTensor			,     UpperCAmelCase  :      Union[torch.Tensor, float, int]			,     UpperCAmelCase  :      bool = True			,     )			->	Union[UNetaDOutput, Tuple]:
     lowerCamelCase__    :      Optional[Any]					 =		timestep
     if not torch.is_tensor(UpperCAmelCase	):
         lowerCamelCase__    :      Optional[int]					 =		torch.tensor([timesteps]			,     dtype=torch.long			,     device=sample.device	)
     elif torch.is_tensor(UpperCAmelCase	) and len(timesteps.shape	) == 0:
         lowerCamelCase__    :      List[str]					 =		timesteps[None].to(sample.device	)

     lowerCamelCase__    :      Optional[int]					 =		self.time_proj(UpperCAmelCase	)
     if self.config.use_timestep_embedding:
         lowerCamelCase__    :      str					 =		self.time_mlp(UpperCAmelCase	)
     else:
         lowerCamelCase__    :      List[str]					 =		timestep_embed[..., None]
         lowerCamelCase__    :      str					 =		timestep_embed.repeat([1, 1, sample.shape[2]]	).to(sample.dtype	)
         lowerCamelCase__    :      str					 =		timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])	)

     # 2. down
     lowerCamelCase__    :      str					 =		()
     for downsample_block in self.down_blocks:
         lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		downsample_block(hidden_states=UpperCAmelCase			,     temb=UpperCAmelCase	)
         down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
         lowerCamelCase__    :      Optional[Any]					 =		self.mid_block(UpperCAmelCase			,     UpperCAmelCase	)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks	):
         lowerCamelCase__    :      Dict					 =		down_block_res_samples[-1:]
         lowerCamelCase__    :      Optional[Any]					 =		down_block_res_samples[:-1]
         lowerCamelCase__    :      Any					 =		upsample_block(UpperCAmelCase			,     res_hidden_states_tuple=UpperCAmelCase			,     temb=UpperCAmelCase	)

     # 5. post-process
     if self.out_block:
         lowerCamelCase__    :      Any					 =		self.out_block(UpperCAmelCase			,     UpperCAmelCase	)

     if not return_dict:
         return (sample,)

     return UNetaDOutput(sample=UpperCAmelCase	)





