"""simple docstring"""




import os
from collections import deque

import torch
from torch.utils.data import Dataset

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  int	,    snake_case__    :  Optional[int]=""	,    snake_case__    :  List[Any]="train"  ):
							assert os.path.isdir(snake_case__  )

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




					def __len__(						self    :  Optional[int]  ):
							return len(self.documents  )




					def __getitem__(						self    :  Any	,    snake_case__    :  Optional[Any]  ):
							lowerCAmelCase__   = self.documents[idx]
							lowerCAmelCase__   = document_path.split("""/"""  )[-1]
							with open(snake_case__	,    encoding="""utf-8"""  ) as source:
									lowerCAmelCase__   = source.read()
									lowerCAmelCase__ ,       lowerCAmelCase__   = process_story(snake_case__  )
							return document_name, story_lines, summary_lines







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = list(filter(lambda lowerCamelCase__  : len(lowerCamelCase__  ) != 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(lowerCamelCase__  ) for line in nonempty_lines]

		# gather article lines
		lowerCAmelCase__   = []
		lowerCAmelCase__   = deque(lowerCamelCase__  )
		while True:
				try:
						lowerCAmelCase__   = lines.popleft()
						if element.startswith("""@highlight"""  ):
								break
						story_lines.append(lowerCamelCase__  )
				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 lowerCamelCase__  : not t.startswith("""@highlight"""  )      ,	lowerCamelCase__  )  )

		return story_lines, summary_lines







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [tokenizer.encode(lowerCamelCase__  ) for line in story_lines]
		lowerCAmelCase__   = [token for sentence in story_lines_token_ids for token in sentence]
		lowerCAmelCase__   = [tokenizer.encode(lowerCamelCase__  ) 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		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""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(lowerCamelCase__  )
		return torch.tensor(lowerCamelCase__  )





"""simple docstring"""




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)  # pylint: disable=invalid-name

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  Optional[int]	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=768  ):
							super().__init__(snake_case__  )
							lowerCAmelCase__   = proj_size

							lowerCAmelCase__   = CLIPVisionModel(snake_case__  )
							lowerCAmelCase__   = PaintByExampleMapper(snake_case__  )
							lowerCAmelCase__   = nn.LayerNorm(config.hidden_size  )
							lowerCAmelCase__   = nn.Linear(config.hidden_size	,    self.proj_size  )

							# uncondition for scaling
							lowerCAmelCase__   = nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Optional[int]	,    snake_case__    :  Optional[Any]=False  ):
							lowerCAmelCase__   = self.model(pixel_values=snake_case__  )
							lowerCAmelCase__   = clip_output.pooler_output
							lowerCAmelCase__   = self.mapper(latent_states[:, None]  )
							lowerCAmelCase__   = self.final_layer_norm(snake_case__  )
							lowerCAmelCase__   = self.proj_out(snake_case__  )
							if return_uncond_vector:
									return latent_states, self.uncond_vector

							return latent_states

class    a_   (  nn.Module      ):




					def __init__(						self    :  int	,    snake_case__    :  Dict  ):
							super().__init__()
							lowerCAmelCase__   = (config.num_hidden_layers + 1) // 5
							lowerCAmelCase__   = config.hidden_size
							lowerCAmelCase__   = 1
							lowerCAmelCase__   = nn.ModuleList(
							    [
							        BasicTransformerBlock(snake_case__	,    snake_case__	,    snake_case__	,    activation_fn="""gelu"""	,    attention_bias=snake_case__  )
							        for _ in range(snake_case__  )
							    ]  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Optional[int]  ):
							for block in self.blocks:
									lowerCAmelCase__   = block(snake_case__  )

							return hidden_states




