



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 					_snake_case      (       lowercase__):




				def __init__(    self       :    int,       __lowercase       :    Optional[int]="",       __lowercase       :    Dict="train"    ):
							assert os.path.isdir(a_    )

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




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




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



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					list(filter(lambda SCREAMING_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
			lowercase__       	=					[_add_missing_period(snake_case__	) for line in nonempty_lines]

			# gather article lines
			lowercase__       	=					[]
			lowercase__       	=					deque(snake_case__	)
			while True:
						try:
									lowercase__       	=					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
			lowercase__       	=					list(filter(lambda SCREAMING_SNAKE_CASE_	: not t.startswith("@highlight"	)    ,	snake_case__	)	)

			return story_lines, summary_lines



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



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):





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



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					torch.ones_like(snake_case__	)
			lowercase__       	=					sequence == pad_token_id
			lowercase__       	=					0
			return mask



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					[tokenizer.encode(snake_case__	) for line in story_lines]
			lowercase__       	=					[token for sentence in story_lines_token_ids for token in sentence]
			lowercase__       	=					[tokenizer.encode(snake_case__	) for line in summary_lines]
			lowercase__       	=					[token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids




def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					[]
			for sequence in batch:
						lowercase__       	=					-1
						lowercase__       	=					[]
						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__	)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase	:   Any    =   {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   str    =   ["ChineseCLIPFeatureExtractor"]
	_lowerCAmelCase	:   Any    =   ["ChineseCLIPImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Optional[int]    =   [
	    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ChineseCLIPModel",
	    "ChineseCLIPPreTrainedModel",
	    "ChineseCLIPTextModel",
	    "ChineseCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_chinese_clip import (
	    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ChineseCLIPConfig,
	    ChineseCLIPOnnxConfig,
	    ChineseCLIPTextConfig,
	    ChineseCLIPVisionConfig,
	)
	from .processing_chinese_clip import ChineseCLIPProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ChineseCLIPModel,
		    ChineseCLIPPreTrainedModel,
		    ChineseCLIPTextModel,
		    ChineseCLIPVisionModel,
		)

else:
	import sys

	_lowerCAmelCase	:   Any    =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
