

'''simple docstring'''


import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowerCAmelCase__										= {
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

lowerCAmelCase__										= {
    "ctrl": 256,
}

lowerCAmelCase__										= {
    "Pregnancy": 168629,
    "Christianity": 7675,
    "Explain": 106423,
    "Fitness": 63440,
    "Saving": 63163,
    "Ask": 27171,
    "Ass": 95985,
    "Joke": 163509,
    "Questions": 45622,
    "Thoughts": 49605,
    "Retail": 52342,
    "Feminism": 164338,
    "Writing": 11992,
    "Atheism": 192263,
    "Netflix": 48616,
    "Computing": 39639,
    "Opinion": 43213,
    "Alone": 44967,
    "Funny": 58917,
    "Gaming": 40358,
    "Human": 4088,
    "India": 1331,
    "Joker": 77138,
    "Diet": 36206,
    "Legal": 11859,
    "Norman": 4939,
    "Tip": 72689,
    "Weight": 52343,
    "Movies": 46273,
    "Running": 23425,
    "Science": 2090,
    "Horror": 37793,
    "Confession": 60572,
    "Finance": 12250,
    "Politics": 16360,
    "Scary": 191985,
    "Support": 12654,
    "Technologies": 32516,
    "Teenage": 66160,
    "Event": 32769,
    "Learned": 67460,
    "Notion": 182770,
    "Wikipedia": 37583,
    "Books": 6665,
    "Extract": 76050,
    "Confessions": 102701,
    "Conspiracy": 75932,
    "Links": 63674,
    "Narcissus": 150425,
    "Relationship": 54766,
    "Relationships": 134796,
    "Reviews": 41671,
    "News": 4256,
    "Translation": 26820,
    "multilingual": 128406,
}







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      Optional[int]       	=					set()
	UpperCAmelCase_		:      str       	=					word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)  )
		UpperCAmelCase_		:      Any       	=					char

	UpperCAmelCase_		:      Optional[int]       	=					set(UpperCamelCase  )
	return pairs







class 					lowercase						(  a_ ):
							_lowerCamelCase		:      str=   VOCAB_FILES_NAMES
							_lowerCamelCase		:      Optional[int]=   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:      Tuple=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:      Optional[Any]=   CONTROL_CODES


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case="<unk>"    ,			**_snake_case)							->				Optional[int]:
								super().__init__(unk_token=_snake_case    ,			**_snake_case)

								with open(_snake_case    ,			encoding='utf-8') as vocab_handle:
									UpperCAmelCase_		:      int       	=					json.load(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					{v: k for k, v in self.encoder.items()}
								with open(_snake_case    ,			encoding='utf-8') as merges_handle:
									UpperCAmelCase_		:      Optional[int]       	=					merges_handle.read().split('\n')[1:-1]
								UpperCAmelCase_		:      List[str]       	=					[tuple(merge.split()) for merge in merges]
								UpperCAmelCase_		:      Optional[int]       	=					dict(zip(_snake_case    ,			range(len(_snake_case))))
								UpperCAmelCase_		:      List[Any]       	=					{}


							@property
							def 						_snake_case							(			self)							->				Optional[Any]:
								return len(self.encoder)


							def 						_snake_case							(			self)							->				Tuple:
								return dict(self.encoder    ,			**self.added_tokens_encoder)


							def 						_snake_case							(			self    ,			_snake_case)							->				str:
								if token in self.cache:
									return self.cache[token]
								UpperCAmelCase_		:      Optional[int]       	=					tuple(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					tuple(list(word[:-1]) + [word[-1] + '</w>'])
								UpperCAmelCase_		:      Optional[Any]       	=					get_pairs(_snake_case)

								if not pairs:
									return token

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

										if word[i] == first and i < len(_snake_case) - 1 and word[i + 1] == second:
											new_word.append(first + second)
											i += 2
										else:
											new_word.append(word[i])
											i += 1
									UpperCAmelCase_		:      Union[str, Any]       	=					tuple(_snake_case)
									UpperCAmelCase_		:      int       	=					new_word
									if len(_snake_case) == 1:
										break
									else:
										UpperCAmelCase_		:      List[Any]       	=					get_pairs(_snake_case)
								UpperCAmelCase_		:      Dict       	=					'@@ '.join(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					word[:-4]
								UpperCAmelCase_		:      int       	=					word
								return word


							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								UpperCAmelCase_		:      Tuple       	=					[]

								UpperCAmelCase_		:      Tuple       	=					re.findall(r'\S+\n?'    ,			_snake_case)

								for token in words:
									split_tokens.extend(list(self.bpe(_snake_case).split(' ')))
								return split_tokens


							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								return self.encoder.get(_snake_case    ,			self.encoder.get(self.unk_token))


							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								return self.decoder.get(_snake_case    ,			self.unk_token)


							def 						_snake_case							(			self    ,			_snake_case)							->				str:
								UpperCAmelCase_		:      int       	=					' '.join(_snake_case).replace('@@ '    ,			'').strip()
								return out_string





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				Tuple[str]:
								if not os.path.isdir(_snake_case):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
									return
								UpperCAmelCase_		:      List[str]       	=					os.path.join(
								    _snake_case    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
								UpperCAmelCase_		:      Optional[int]       	=					os.path.join(
								    _snake_case    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

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

								UpperCAmelCase_		:      Optional[int]       	=					0
								with open(_snake_case    ,			'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 _snake_case: 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!')
											UpperCAmelCase_		:      Dict       	=					token_index
										writer.write(' '.join(_snake_case) + '\n')
										index += 1

								return vocab_file, merge_file

							# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
							#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
							#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
							#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
							#     return ''.join(tokens_generated_so_far)









'''simple docstring'''


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 					lowercase						(  a_ ):
							_lowerCamelCase		:      torch.FloatTensor



class 					lowercase						(  a_,					a_ ):


							@register_to_config
							def __init__(			self    ,			_snake_case = 6_5536    ,			_snake_case = None    ,			_snake_case = 2    ,			_snake_case = 2    ,			_snake_case = 0    ,			_snake_case = "fourier"    ,			_snake_case = True    ,			_snake_case = False    ,			_snake_case = 0.0    ,			_snake_case = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")    ,			_snake_case = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")    ,			_snake_case = "UNetMidBlock1D"    ,			_snake_case = None    ,			_snake_case = (32, 32, 64)    ,			_snake_case = None    ,			_snake_case = 8    ,			_snake_case = 1    ,			_snake_case = False    ,			)							->				List[str]:
								super().__init__()
								UpperCAmelCase_		:      Optional[Any]       	=					sample_size

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

								if use_timestep_embedding:
									UpperCAmelCase_		:      Dict       	=					block_out_channels[0] * 4
									UpperCAmelCase_		:      List[Any]       	=					TimestepEmbedding(
									    in_channels=_snake_case    ,			time_embed_dim=_snake_case    ,			act_fn=_snake_case    ,			out_dim=block_out_channels[0]    ,			)

								UpperCAmelCase_		:      int       	=					nn.ModuleList([])
								UpperCAmelCase_		:      Optional[int]       	=					None
								UpperCAmelCase_		:      Optional[int]       	=					nn.ModuleList([])
								UpperCAmelCase_		:      Any       	=					None

								# down
								UpperCAmelCase_		:      Dict       	=					in_channels
								for i, down_block_type in enumerate(_snake_case):
									UpperCAmelCase_		:      int       	=					output_channel
									UpperCAmelCase_		:      Optional[int]       	=					block_out_channels[i]

									if i == 0:
										input_channel += extra_in_channels

									UpperCAmelCase_		:      int       	=					i == len(_snake_case) - 1

									UpperCAmelCase_		:      Any       	=					get_down_block(
									    _snake_case    ,			num_layers=_snake_case    ,			in_channels=_snake_case    ,			out_channels=_snake_case    ,			temb_channels=block_out_channels[0]    ,			add_downsample=not is_final_block or downsample_each_block    ,			)
									self.down_blocks.append(_snake_case)

								# mid
								UpperCAmelCase_		:      Optional[int]       	=					get_mid_block(
								    _snake_case    ,			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=_snake_case    ,			add_downsample=_snake_case    ,			)

								# up
								UpperCAmelCase_		:      Union[str, Any]       	=					list(reversed(_snake_case))
								UpperCAmelCase_		:      Optional[int]       	=					reversed_block_out_channels[0]
								if out_block_type is None:
									UpperCAmelCase_		:      Tuple       	=					out_channels
								else:
									UpperCAmelCase_		:      int       	=					block_out_channels[0]

								for i, up_block_type in enumerate(_snake_case):
									UpperCAmelCase_		:      Dict       	=					output_channel
									UpperCAmelCase_		:      Optional[Any]       	=					(
									    reversed_block_out_channels[i + 1] if i < len(_snake_case) - 1 else final_upsample_channels
									)

									UpperCAmelCase_		:      str       	=					i == len(_snake_case) - 1

									UpperCAmelCase_		:      Union[str, Any]       	=					get_up_block(
									    _snake_case    ,			num_layers=_snake_case    ,			in_channels=_snake_case    ,			out_channels=_snake_case    ,			temb_channels=block_out_channels[0]    ,			add_upsample=not is_final_block    ,			)
									self.up_blocks.append(_snake_case)
									UpperCAmelCase_		:      Dict       	=					output_channel

								# out
								UpperCAmelCase_		:      Union[str, Any]       	=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,			32)
								UpperCAmelCase_		:      Any       	=					get_out_block(
								    out_block_type=_snake_case    ,			num_groups_out=_snake_case    ,			embed_dim=block_out_channels[0]    ,			out_channels=_snake_case    ,			act_fn=_snake_case    ,			fc_dim=block_out_channels[-1] // 4    ,			)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = True    ,			)							->				Union[UNetaDOutput, Tuple]:
								UpperCAmelCase_		:      Union[str, Any]       	=					timestep
								if not torch.is_tensor(_snake_case):
									UpperCAmelCase_		:      Union[str, Any]       	=					torch.tensor([timesteps]    ,			dtype=torch.long    ,			device=sample.device)
								elif torch.is_tensor(_snake_case) and len(timesteps.shape) == 0:
									UpperCAmelCase_		:      Tuple       	=					timesteps[None].to(sample.device)

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

								# 2. down
								UpperCAmelCase_		:      Optional[Any]       	=					()
								for downsample_block in self.down_blocks:
									UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					downsample_block(hidden_states=_snake_case    ,			temb=_snake_case)
									down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
									UpperCAmelCase_		:      List[Any]       	=					self.mid_block(_snake_case    ,			_snake_case)

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks):
									UpperCAmelCase_		:      int       	=					down_block_res_samples[-1:]
									UpperCAmelCase_		:      Tuple       	=					down_block_res_samples[:-1]
									UpperCAmelCase_		:      List[Any]       	=					upsample_block(_snake_case    ,			res_hidden_states_tuple=_snake_case    ,			temb=_snake_case)

								# 5. post-process
								if self.out_block:
									UpperCAmelCase_		:      Optional[Any]       	=					self.out_block(_snake_case    ,			_snake_case)

								if not return_dict:
									return (sample,)

								return UNetaDOutput(sample=_snake_case)






