import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    def __init__(	self , __lowercase="" , __lowercase="train")       ->	Dict:
        assert os.path.isdir(__lowercase)

        __UpperCamelCase     :Any			  =		[]
        __UpperCamelCase     :Optional[Any]			  =		os.listdir(__lowercase)
        for story_filename in story_filenames_list:
            if "summary" in story_filename:
                continue
            __UpperCamelCase     :Dict			  =		os.path.join(__lowercase , __lowercase)
            if not os.path.isfile(__lowercase):
                continue
            self.documents.append(__lowercase)







    def __len__(	self)       ->	List[Any]:
        return len(self.documents)







    def __getitem__(	self , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :List[Any]			  =		self.documents[idx]
        __UpperCamelCase     :List[Any]			  =		document_path.split('''/''')[-1]
        with open(__lowercase , encoding='''utf-8''') as source:
            __UpperCamelCase     :int			  =		source.read()
            __UpperCamelCase  ,       __UpperCamelCase     :int			  =		process_story(__lowercase)
        return document_name, story_lines, summary_lines



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		list(filter(lambda SCREAMING_SNAKE_CASE	: len(SCREAMING_SNAKE_CASE	) != 0  ,			[line.strip() for line in raw_story.split('''\n'''	)]	)	)

    # for some unknown reason some lines miss a period, add it
    __UpperCamelCase     :Tuple			  =		[_add_missing_period(SCREAMING_SNAKE_CASE	) for line in nonempty_lines]

    # gather article lines
    __UpperCamelCase     :Dict			  =		[]
    __UpperCamelCase     :Tuple			  =		deque(SCREAMING_SNAKE_CASE	)
    while True:
        try:
            __UpperCamelCase     :Optional[int]			  =		lines.popleft()
            if element.startswith('''@highlight'''	):
                break
            story_lines.append(SCREAMING_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
    __UpperCamelCase     :Optional[int]			  =		list(filter(lambda SCREAMING_SNAKE_CASE	: not t.startswith('''@highlight'''	)  ,			SCREAMING_SNAKE_CASE	)	)

    return story_lines, summary_lines



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\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	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		torch.ones_like(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :str			  =		sequence == pad_token_id
    __UpperCamelCase     :str			  =		0
    return mask



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		[tokenizer.encode(SCREAMING_SNAKE_CASE	) for line in story_lines]
    __UpperCamelCase     :Tuple			  =		[token for sentence in story_lines_token_ids for token in sentence]
    __UpperCamelCase     :List[str]			  =		[tokenizer.encode(SCREAMING_SNAKE_CASE	) for line in summary_lines]
    __UpperCamelCase     :List[Any]			  =		[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	):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		[]
    for sequence in batch:
        __UpperCamelCase     :Union[str, Any]			  =		-1
        __UpperCamelCase     :List[Any]			  =		[]
        for s in sequence:
            if s == separator_token_id:
                sentence_num += 1
            embeddings.append(sentence_num % 2	)
        batch_embeddings.append(SCREAMING_SNAKE_CASE	)
    return torch.tensor(SCREAMING_SNAKE_CASE	)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						str						=      """ctrl"""
    a__	:						Dict						=      ["""past_key_values"""]
    a__	:						Tuple						=      {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(	self , __lowercase=246_534 , __lowercase=256 , __lowercase=1_280 , __lowercase=8_192 , __lowercase=48 , __lowercase=16 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=1E-6 , __lowercase=0.02 , __lowercase=True , **__lowercase , )       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		vocab_size
        __UpperCamelCase     :Optional[Any]			  =		n_positions
        __UpperCamelCase     :Dict			  =		n_embd
        __UpperCamelCase     :Dict			  =		n_layer
        __UpperCamelCase     :List[Any]			  =		n_head
        __UpperCamelCase     :int			  =		dff
        __UpperCamelCase     :Union[str, Any]			  =		resid_pdrop
        __UpperCamelCase     :Optional[int]			  =		embd_pdrop
        __UpperCamelCase     :List[Any]			  =		layer_norm_epsilon
        __UpperCamelCase     :Dict			  =		initializer_range

        __UpperCamelCase     :Any			  =		use_cache

        super().__init__(**__lowercase)





