

"""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     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      torch.FloatTensor





class     snake_case	(				__snake_case,							__snake_case  ):

				@register_to_config
				def __init__(	self							:				Tuple     ,  UpperCamelCase__							:				int = 6_5_5_3_6     ,  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] = (3_2, 3_2, 6_4)     ,  UpperCamelCase__							:				str = None     ,  UpperCamelCase__							:				int = 8     ,  UpperCamelCase__							:				int = 1     ,  UpperCamelCase__							:				bool = False     ,  )->   Tuple:



						'''simple docstring'''


						super().__init__()
						__lowerCAmelCase:       str          =			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:       Union[str, Any]          =			2 * block_out_channels[0]
						elif time_embedding_type == "positional":
								__lowerCAmelCase:       Tuple          =			Timesteps(
								    block_out_channels[0]     ,  flip_sin_to_cos=UpperCamelCase__     ,  downscale_freq_shift=UpperCamelCase__)
								__lowerCAmelCase:       int          =			block_out_channels[0]

						if use_timestep_embedding:
								__lowerCAmelCase:       Optional[int]          =			block_out_channels[0] * 4
								__lowerCAmelCase:       Optional[Any]          =			TimestepEmbedding(
								    in_channels=UpperCamelCase__     ,  time_embed_dim=UpperCamelCase__     ,  act_fn=UpperCamelCase__     ,  out_dim=block_out_channels[0]     ,  )

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

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

								if i == 0:
										input_channel += extra_in_channels

								__lowerCAmelCase:       Optional[Any]          =			i == len(UpperCamelCase__) - 1

								__lowerCAmelCase:       Union[str, Any]          =			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:       Any          =			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:       Dict          =			list(reversed(UpperCamelCase__))
						__lowerCAmelCase:       str          =			reversed_block_out_channels[0]
						if out_block_type is None:
								__lowerCAmelCase:       List[str]          =			out_channels
						else:
								__lowerCAmelCase:       int          =			block_out_channels[0]

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

								__lowerCAmelCase:       Tuple          =			i == len(UpperCamelCase__) - 1

								__lowerCAmelCase:       List[str]          =			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:       Tuple          =			output_channel

						# out
						__lowerCAmelCase:       str          =			norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,  3_2)
						__lowerCAmelCase:       int          =			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 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				torch.FloatTensor     ,  UpperCamelCase__							:				Union[torch.Tensor, float, int]     ,  UpperCamelCase__							:				bool = True     ,  )->   Union[UNetaDOutput, Tuple]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			timestep
						if not torch.is_tensor(UpperCamelCase__):
								__lowerCAmelCase:       Union[str, Any]          =			torch.tensor([timesteps]     ,  dtype=torch.long     ,  device=sample.device)
						elif torch.is_tensor(UpperCamelCase__) and len(timesteps.shape) == 0:
								__lowerCAmelCase:       Any          =			timesteps[None].to(sample.device)

						__lowerCAmelCase:       Any          =			self.time_proj(UpperCamelCase__)
						if self.config.use_timestep_embedding:
								__lowerCAmelCase:       str          =			self.time_mlp(UpperCamelCase__)
						else:
								__lowerCAmelCase:       Tuple          =			timestep_embed[..., None]
								__lowerCAmelCase:       Optional[int]          =			timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
								__lowerCAmelCase:       Union[str, Any]          =			timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

						# 2. down
						__lowerCAmelCase:       str          =			()
						for downsample_block in self.down_blocks:
								__lowerCAmelCase      , __lowerCAmelCase:       Any          =			downsample_block(hidden_states=UpperCamelCase__     ,  temb=UpperCamelCase__)
								down_block_res_samples += res_samples

						# 3. mid
						if self.mid_block:
								__lowerCAmelCase:       Optional[int]          =			self.mid_block(UpperCamelCase__     ,  UpperCamelCase__)

						# 4. up
						for i, upsample_block in enumerate(self.up_blocks):
								__lowerCAmelCase:       Tuple          =			down_block_res_samples[-1:]
								__lowerCAmelCase:       Tuple          =			down_block_res_samples[:-1]
								__lowerCAmelCase:       Union[str, Any]          =			upsample_block(UpperCamelCase__     ,  res_hidden_states_tuple=UpperCamelCase__     ,  temb=UpperCamelCase__)

						# 5. post-process
						if self.out_block:
								__lowerCAmelCase:       str          =			self.out_block(UpperCamelCase__     ,  UpperCamelCase__)

						if not return_dict:
								return (sample,)

						return UNetaDOutput(sample=UpperCamelCase__)





"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__A						   =			logging.get_logger(__name__)

__A						   =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A						   =			{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__A						   =			{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      str         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_   :      Any         =  RobertaTokenizer

				def __init__(	self							:				Optional[int]     ,  UpperCamelCase__							:				int=None     ,  UpperCamelCase__							:				Union[str, Any]=None     ,  UpperCamelCase__							:				Optional[Any]=None     ,  UpperCamelCase__							:				int="replace"     ,  UpperCamelCase__							:				Union[str, Any]="<s>"     ,  UpperCamelCase__							:				List[Any]="</s>"     ,  UpperCamelCase__							:				Any="</s>"     ,  UpperCamelCase__							:				Union[str, Any]="<s>"     ,  UpperCamelCase__							:				str="<unk>"     ,  UpperCamelCase__							:				Optional[int]="<pad>"     ,  UpperCamelCase__							:				int="<mask>"     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				Optional[Any]=True     ,  **UpperCamelCase__							:				Tuple     ,  )->   Optional[int]:



						'''simple docstring'''


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

						__lowerCAmelCase:       Tuple          =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
								__lowerCAmelCase:       str          =			getattr(UpperCamelCase__     ,  pre_tok_state.pop("type"))
								__lowerCAmelCase:       Optional[int]          =			add_prefix_space
								__lowerCAmelCase:       Dict          =			pre_tok_class(**UpperCamelCase__)

						__lowerCAmelCase:       Any          =			add_prefix_space

						__lowerCAmelCase:       int          =			"post_processor"
						__lowerCAmelCase:       Optional[Any]          =			getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:       Dict          =			json.loads(tokenizer_component_instance.__getstate__())

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
										__lowerCAmelCase:       List[Any]          =			tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:       str          =			tuple(state["cls"])

								__lowerCAmelCase:       str          =			False

								if state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:       Optional[Any]          =			add_prefix_space
										__lowerCAmelCase:       List[str]          =			True

								if state.get("trim_offsets"     ,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:       Any          =			trim_offsets
										__lowerCAmelCase:       List[str]          =			True

								if changes_to_apply:
										__lowerCAmelCase:       str          =			getattr(UpperCamelCase__     ,  state.pop("type"))
										__lowerCAmelCase:       List[str]          =			component_class(**UpperCamelCase__)
										setattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)

				@property
				def 							lowercase_     (	self							:				List[str])->   str:



						'''simple docstring'''


						if self._mask_token is None:
								if self.verbose:
										logger.error("Using mask_token, but it is not set yet.")
								return None
						return str(self._mask_token)

				@mask_token.setter
				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else value
						__lowerCAmelCase:       int          =			value

				def 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				str     ,  **UpperCamelCase__							:				List[Any])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)
						assert self.add_prefix_space or not is_split_into_words, (
						    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				int     ,  *UpperCamelCase__							:				Dict     ,  **UpperCamelCase__							:				List[str])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)

						assert self.add_prefix_space or not is_split_into_words, (
						    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__)
						return tuple(UpperCamelCase__)

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				Union[str, Any]=None)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
								return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			[self.sep_token_id]
						__lowerCAmelCase:       str          =			[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]


