

"""simple docstring"""





import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_a				   =							logging.get_logger(__name__)




@dataclass
class       _lowerCAmelCase						:







 """simple docstring"""







 __UpperCAmelCase			:      str
 __UpperCAmelCase			:      str
 __UpperCAmelCase			:      Optional[str]     	=			None
 __UpperCAmelCase			:      Optional[str]     	=			None


 def       _lowercase				(    self							:    Tuple			):
    return json.dumps(dataclasses.asdict(self			),      indent=2			) + "\n"







@dataclass(frozen=lowercase     )
class       _lowerCAmelCase						:







 """simple docstring"""







 __UpperCAmelCase			:      List[int]
 __UpperCAmelCase			:      Optional[List[int]]     	=			None
 __UpperCAmelCase			:      Optional[List[int]]     	=			None
 __UpperCAmelCase			:      Optional[Union[int, float]]     	=			None


 def       _lowercase				(    self							:    int			):
    return json.dumps(dataclasses.asdict(self			)			) + "\n"







class       _lowerCAmelCase						:







 """simple docstring"""







 def       _lowercase				(    self							:    str,      UpperCAmelCase__							:    Tuple			):
    raise NotImplementedError()


 def       _lowercase				(    self							:    Optional[int],      UpperCAmelCase__							:    Optional[Any]			):
    raise NotImplementedError()


 def       _lowercase				(    self							:    Optional[Any],      UpperCAmelCase__							:    Optional[int]			):
    raise NotImplementedError()


 def       _lowercase				(    self							:    Dict,      UpperCAmelCase__							:    str			):
    raise NotImplementedError()


 def       _lowercase				(    self							:    str			):
    raise NotImplementedError()


 def       _lowercase				(    self							:    Optional[int],      UpperCAmelCase__							:    Union[str, Any]			):
    if len(self.get_labels()			) > 1:
       __lowercase			=		self.get_labels()[int(example.label			)]
    return example



 @classmethod
 def       _lowercase				(    cls							:    Dict,      UpperCAmelCase__							:    Optional[Any],      UpperCAmelCase__							:    int=None			):
    with open(UpperCAmelCase__,      "r",      encoding="utf-8-sig"			) as f:
       return list(csv.reader(UpperCAmelCase__,      delimiter="\t",      quotechar=UpperCAmelCase__			)			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 def __init__(    self							:    Tuple,      UpperCAmelCase__							:    str=None,      UpperCAmelCase__							:    Dict=None,      UpperCAmelCase__							:    List[str]="classification",      UpperCAmelCase__							:    str=False			):
    __lowercase			=		[] if labels is None else labels
    __lowercase			=		[] if examples is None else examples
    __lowercase			=		mode
    __lowercase			=		verbose


 def __len__(    self							:    Any			):
    return len(self.examples			)


 def __getitem__(    self							:    Optional[Any],      UpperCAmelCase__							:    int			):
    if isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
       return SingleSentenceClassificationProcessor(labels=self.labels,      examples=self.examples[idx]			)
    return self.examples[idx]


 @classmethod
 def       _lowercase				(    cls							:    Dict,      UpperCAmelCase__							:    Optional[Any],      UpperCAmelCase__							:    Optional[Any]="",      UpperCAmelCase__							:    Tuple=0,      UpperCAmelCase__							:    Optional[int]=1,      UpperCAmelCase__							:    List[str]=None,      UpperCAmelCase__							:    Tuple=False,      **UpperCAmelCase__							:    int			):
    __lowercase			=		cls(**UpperCAmelCase__			)
    processor.add_examples_from_csv(
        UpperCAmelCase__,      split_name=UpperCAmelCase__,      column_label=UpperCAmelCase__,      column_text=UpperCAmelCase__,      column_id=UpperCAmelCase__,      skip_first_row=UpperCAmelCase__,      overwrite_labels=UpperCAmelCase__,      overwrite_examples=UpperCAmelCase__,      )
    return processor


 @classmethod
 def       _lowercase				(    cls							:    Dict,      UpperCAmelCase__							:    Optional[Any],      UpperCAmelCase__							:    List[Any]=None,      **UpperCAmelCase__							:    Any			):
    __lowercase			=		cls(**UpperCAmelCase__			)
    processor.add_examples(UpperCAmelCase__,      labels=UpperCAmelCase__			)
    return processor


 def       _lowercase				(    self							:    int,      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    Dict="",      UpperCAmelCase__							:    Any=0,      UpperCAmelCase__							:    Any=1,      UpperCAmelCase__							:    Union[str, Any]=None,      UpperCAmelCase__							:    Any=False,      UpperCAmelCase__							:    Union[str, Any]=False,      UpperCAmelCase__							:    str=False,      ):
    __lowercase			=		self._read_tsv(UpperCAmelCase__			)
    if skip_first_row:
       __lowercase			=		lines[1:]
    __lowercase			=		[]
    __lowercase			=		[]
    __lowercase			=		[]
    for i, line in enumerate(UpperCAmelCase__			):
       texts.append(line[column_text]			)
       labels.append(line[column_label]			)
       if column_id is not None:
          ids.append(line[column_id]			)
       else:
          __lowercase			=		F"""{split_name}-{i}""" if split_name else str(UpperCAmelCase__			)
          ids.append(UpperCAmelCase__			)

    return self.add_examples(
        UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      overwrite_labels=UpperCAmelCase__,      overwrite_examples=UpperCAmelCase__			)


 def       _lowercase				(    self							:    List[Any],      UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    List[Any]=None,      UpperCAmelCase__							:    Optional[Any]=None,      UpperCAmelCase__							:    Tuple=False,      UpperCAmelCase__							:    Union[str, Any]=False			):
    if labels is not None and len(UpperCAmelCase__			) != len(UpperCAmelCase__			):
       raise ValueError(
           F"""Text and labels have mismatched lengths {len(UpperCAmelCase__			)} and {len(UpperCAmelCase__			)}"""			)
    if ids is not None and len(UpperCAmelCase__			) != len(UpperCAmelCase__			):
       raise ValueError(F"""Text and ids have mismatched lengths {len(UpperCAmelCase__			)} and {len(UpperCAmelCase__			)}"""			)
    if ids is None:
       __lowercase			=		[None] * len(UpperCAmelCase__			)
    if labels is None:
       __lowercase			=		[None] * len(UpperCAmelCase__			)
    __lowercase			=		[]
    __lowercase			=		set()
    for text_or_text_and_label, label, guid in zip(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			):
       if isinstance(UpperCAmelCase__,      (tuple, list)			) and label is None:
          __lowercase	,__lowercase			=		text_or_text_and_label
       else:
          __lowercase			=		text_or_text_and_label
       added_labels.add(UpperCAmelCase__			)
       examples.append(InputExample(guid=UpperCAmelCase__,      text_a=UpperCAmelCase__,      text_b=UpperCAmelCase__,      label=UpperCAmelCase__			)			)

    # Update examples
    if overwrite_examples:
       __lowercase			=		examples
    else:
       self.examples.extend(UpperCAmelCase__			)

    # Update labels
    if overwrite_labels:
       __lowercase			=		list(UpperCAmelCase__			)
    else:
       __lowercase			=		list(set(self.labels			).union(UpperCAmelCase__			)			)

    return self.examples



 def       _lowercase				(    self							:    str,      UpperCAmelCase__							:    Any,      UpperCAmelCase__							:    Optional[Any]=None,      UpperCAmelCase__							:    Tuple=False,      UpperCAmelCase__							:    Dict=0,      UpperCAmelCase__							:    List[Any]=True,      UpperCAmelCase__							:    List[str]=None,      ):
    if max_length is None:
       __lowercase			=		tokenizer.max_len

    __lowercase			=		{label: i for i, label in enumerate(self.labels			)}

    __lowercase			=		[]
    for ex_index, example in enumerate(self.examples			):
       if ex_index % 1_0_0_0_0 == 0:
          logger.info(F"""Tokenizing example {ex_index}"""			)

       __lowercase			=		tokenizer.encode(
           example.text_a,      add_special_tokens=UpperCAmelCase__,      max_length=min(UpperCAmelCase__,      tokenizer.max_len			),      )
       all_input_ids.append(UpperCAmelCase__			)

    __lowercase			=		max(len(UpperCAmelCase__			) for input_ids in all_input_ids			)

    __lowercase			=		[]
    for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase__,      self.examples			)			):
       if ex_index % 1_0_0_0_0 == 0:
          logger.info(F"""Writing example {ex_index}/{len(self.examples			)}"""			)
       # The mask has 1 for real tokens and 0 for padding tokens. Only real
       # tokens are attended to.
       __lowercase			=		[1 if mask_padding_with_zero else 0] * len(UpperCAmelCase__			)

       # Zero-pad up to the sequence length.
       __lowercase			=		batch_length - len(UpperCAmelCase__			)
       if pad_on_left:
          __lowercase			=		([pad_token] * padding_length) + input_ids
          __lowercase			=		([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
       else:
          __lowercase			=		input_ids + ([pad_token] * padding_length)
          __lowercase			=		attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

       if len(UpperCAmelCase__			) != batch_length:
          raise ValueError(F"""Error with input length {len(UpperCAmelCase__			)} vs {batch_length}"""			)
       if len(UpperCAmelCase__			) != batch_length:
          raise ValueError(F"""Error with input length {len(UpperCAmelCase__			)} vs {batch_length}"""			)

       if self.mode == "classification":
          __lowercase			=		label_map[example.label]
       elif self.mode == "regression":
          __lowercase			=		float(example.label			)
       else:
          raise ValueError(self.mode			)

       if ex_index < 5 and self.verbose:
          logger.info("*** Example ***"			)
          logger.info(F"""guid: {example.guid}"""			)
          logger.info(F"""input_ids: {" ".join([str(UpperCAmelCase__			) for x in input_ids]			)}"""			)
          logger.info(F"""attention_mask: {" ".join([str(UpperCAmelCase__			) for x in attention_mask]			)}"""			)
          logger.info(F"""label: {example.label} (id = {label})"""			)

       features.append(InputFeatures(input_ids=UpperCAmelCase__,      attention_mask=UpperCAmelCase__,      label=UpperCAmelCase__			)			)

    if return_tensors is None:
       return features
    elif return_tensors == "tf":
       if not is_tf_available():
          raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"			)
       import tensorflow as tf

       def gen():
          for ex in features:
             yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

       __lowercase			=		tf.data.Dataset.from_generator(
           UpperCAmelCase__,      ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa),      ({"input_ids": tf.TensorShape([None]			), "attention_mask": tf.TensorShape([None]			)}, tf.TensorShape([]			)),      )
       return dataset
    elif return_tensors == "pt":
       if not is_torch_available():
          raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"			)
       import torch
       from torch.utils.data import TensorDataset

       __lowercase			=		torch.tensor([f.input_ids for f in features],      dtype=torch.long			)
       __lowercase			=		torch.tensor([f.attention_mask for f in features],      dtype=torch.long			)
       if self.mode == "classification":
          __lowercase			=		torch.tensor([f.label for f in features],      dtype=torch.long			)
       elif self.mode == "regression":
          __lowercase			=		torch.tensor([f.label for f in features],      dtype=torch.float			)

       __lowercase			=		TensorDataset(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			)
       return dataset
    else:
       raise ValueError("return_tensors should be one of 'tf' or 'pt'"			)







"""simple docstring"""





import shutil

import requests




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : bool = False,    UpperCamelCase_  : str = ".")			->		dict:
   '''simple docstring'''


   __lowercase			=		"https://api.nasa.gov/planetary/apod"
   return requests.get(UpperCamelCase_,    params={"api_key": api_key}).json()




def 						_A						(						UpperCamelCase_  : str,    UpperCamelCase_  : str = ".")			->		dict:
   '''simple docstring'''


   __lowercase			=		get_apod_data(UpperCamelCase_)
   __lowercase			=		apod_data["url"]
   __lowercase			=		img_url.split("/")[-1]
   __lowercase			=		requests.get(UpperCamelCase_,    stream=UpperCamelCase_)

   with open(F"""{path}/{img_name}""",    "wb+") as img_file:
      shutil.copyfileobj(response.raw,    UpperCamelCase_)
   del response
   return apod_data




def 						_A						(						UpperCamelCase_  : str)			->		dict:
   '''simple docstring'''


   __lowercase			=		"https://images-api.nasa.gov/search"
   return requests.get(UpperCamelCase_,    params={"q": query}).json()


if __name__ == "__main__":
   print(save_apod('YOUR API KEY'))
   _a				   =							get_archive_data('apollo 2011')['collection']['items']
   print(apollo_aaaa_items[0]['data'][0]['description'])




