





'''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     (					__magic_name__							):
	__lowerCAmelCase       						=  42






class 				__snake_case     (					__magic_name__       ,       __magic_name__							):





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

		# time
		if time_embedding_type == "fourier":
			snake_case__	       =   GaussianFourierProjection(
			    embedding_size=8      ,       set_W_to_weight=a_      ,       log=a_      ,       flip_sin_to_cos=a_		)
			snake_case__	       =   2 * block_out_channels[0]
		elif time_embedding_type == "positional":
			snake_case__	       =   Timesteps(
			    block_out_channels[0]      ,       flip_sin_to_cos=a_      ,       downscale_freq_shift=a_		)
			snake_case__	       =   block_out_channels[0]

		if use_timestep_embedding:
			snake_case__	       =   block_out_channels[0] * 4
			snake_case__	       =   TimestepEmbedding(
			    in_channels=a_      ,       time_embed_dim=a_      ,       act_fn=a_      ,       out_dim=block_out_channels[0]      ,       )

		snake_case__	       =   nn.ModuleList([]		)
		snake_case__	       =   None
		snake_case__	       =   nn.ModuleList([]		)
		snake_case__	       =   None

		# down
		snake_case__	       =   in_channels
		for i, down_block_type in enumerate(a_		):
			snake_case__	       =   output_channel
			snake_case__	       =   block_out_channels[i]

			if i == 0:
				input_channel += extra_in_channels

			snake_case__	       =   i == len(a_		) - 1

			snake_case__	       =   get_down_block(
			    a_      ,       num_layers=a_      ,       in_channels=a_      ,       out_channels=a_      ,       temb_channels=block_out_channels[0]      ,       add_downsample=not is_final_block or downsample_each_block      ,       )
			self.down_blocks.append(a_		)

		# mid
		snake_case__	       =   get_mid_block(
		    a_      ,       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=a_      ,       add_downsample=a_      ,       )

		# up
		snake_case__	       =   list(reversed(a_		)		)
		snake_case__	       =   reversed_block_out_channels[0]
		if out_block_type is None:
			snake_case__	       =   out_channels
		else:
			snake_case__	       =   block_out_channels[0]

		for i, up_block_type in enumerate(a_		):
			snake_case__	       =   output_channel
			snake_case__	       =   (
			    reversed_block_out_channels[i + 1] if i < len(a_		) - 1 else final_upsample_channels
			)

			snake_case__	       =   i == len(a_		) - 1

			snake_case__	       =   get_up_block(
			    a_      ,       num_layers=a_      ,       in_channels=a_      ,       out_channels=a_      ,       temb_channels=block_out_channels[0]      ,       add_upsample=not is_final_block      ,       )
			self.up_blocks.append(a_		)
			snake_case__	       =   output_channel

		# out
		snake_case__	       =   norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,       32		)
		snake_case__	       =   get_out_block(
		    out_block_type=a_      ,       num_groups_out=a_      ,       embed_dim=block_out_channels[0]      ,       out_channels=a_      ,       act_fn=a_      ,       fc_dim=block_out_channels[-1] // 4      ,       )





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = True      ,       )      ->		Optional[int]:
		snake_case__	       =   timestep
		if not torch.is_tensor(a_		):
			snake_case__	       =   torch.tensor([timesteps]      ,       dtype=torch.long      ,       device=sample.device		)
		elif torch.is_tensor(a_		) and len(timesteps.shape		) == 0:
			snake_case__	       =   timesteps[None].to(sample.device		)

		snake_case__	       =   self.time_proj(a_		)
		if self.config.use_timestep_embedding:
			snake_case__	       =   self.time_mlp(a_		)
		else:
			snake_case__	       =   timestep_embed[..., None]
			snake_case__	       =   timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
			snake_case__	       =   timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

		# 2. down
		snake_case__	       =   ()
		for downsample_block in self.down_blocks:
			snake_case__       ,  snake_case__	       =   downsample_block(hidden_states=a_      ,       temb=a_		)
			down_block_res_samples += res_samples

		# 3. mid
		if self.mid_block:
			snake_case__	       =   self.mid_block(a_      ,       a_		)

		# 4. up
		for i, upsample_block in enumerate(self.up_blocks		):
			snake_case__	       =   down_block_res_samples[-1:]
			snake_case__	       =   down_block_res_samples[:-1]
			snake_case__	       =   upsample_block(a_      ,       res_hidden_states_tuple=a_      ,       temb=a_		)

		# 5. post-process
		if self.out_block:
			snake_case__	       =   self.out_block(a_      ,       a_		)

		if not return_dict:
			return (sample,)

		return UNetaDOutput(sample=a_		)








import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
