

"""simple docstring"""





import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class       _lowerCAmelCase						(			SCREAMING_SNAKE_CASE__  ,unittest.TestCase     ):







 """simple docstring"""







 __UpperCAmelCase			:      List[str]     	=			BartTokenizer
 __UpperCAmelCase			:      List[Any]     	=			BartTokenizerFast
 __UpperCAmelCase			:      Any     	=			True
 __UpperCAmelCase			:      Dict     	=			filter_roberta_detectors


 def       _lowercase				(    self							:    Dict			):
    super().setUp()
    __lowercase			=		[
        "l",
        "o",
        "w",
        "e",
        "r",
        "s",
        "t",
        "i",
        "d",
        "n",
        "\u0120",
        "\u0120l",
        "\u0120n",
        "\u0120lo",
        "\u0120low",
        "er",
        "\u0120lowest",
        "\u0120newer",
        "\u0120wider",
        "<unk>",
    ]
    __lowercase			=		dict(zip(a_,      range(len(a_			)			)			)			)
    __lowercase			=		["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
    __lowercase			=		{"unk_token": "<unk>"}

    __lowercase			=		os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["vocab_file"]			)
    __lowercase			=		os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["merges_file"]			)
    with open(self.vocab_file,      "w",      encoding="utf-8"			) as fp:
       fp.write(json.dumps(a_			) + "\n"			)
    with open(self.merges_file,      "w",      encoding="utf-8"			) as fp:
       fp.write("\n".join(a_			)			)


 def       _lowercase				(    self							:    int,      **UpperCAmelCase__							:    int			):
    kwargs.update(self.special_tokens_map			)
    return self.tokenizer_class.from_pretrained(self.tmpdirname,      **a_			)


 def       _lowercase				(    self							:    List[Any],      **UpperCAmelCase__							:    List[str]			):
    kwargs.update(self.special_tokens_map			)
    return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,      **a_			)


 def       _lowercase				(    self							:    int,      UpperCAmelCase__							:    int			):
    return "lower newer", "lower newer"


 @cached_property
 def       _lowercase				(    self							:    Any			):
    return BartTokenizer.from_pretrained("facebook/bart-large"			)


 @cached_property
 def       _lowercase				(    self							:    Tuple			):
    return BartTokenizerFast.from_pretrained("facebook/bart-large"			)


 @require_torch
 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		["A long paragraph for summarization.", "Another paragraph for summarization."]
    __lowercase			=		[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __lowercase			=		tokenizer(a_,      max_length=len(a_			),      padding=a_,      return_tensors="pt"			)
       self.assertIsInstance(a_,      a_			)

       self.assertEqual((2, 9),      batch.input_ids.shape			)
       self.assertEqual((2, 9),      batch.attention_mask.shape			)
       __lowercase			=		batch.input_ids.tolist()[0]
       self.assertListEqual(a_,      a_			)
       # Test that special tokens are reset


 @require_torch
 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		["A long paragraph for summarization.", "Another paragraph for summarization."]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __lowercase			=		tokenizer(a_,      padding=a_,      return_tensors="pt"			)
       # check if input_ids are returned and no labels
       self.assertIn("input_ids",      a_			)
       self.assertIn("attention_mask",      a_			)
       self.assertNotIn("labels",      a_			)
       self.assertNotIn("decoder_attention_mask",      a_			)


 @require_torch
 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		[
        "Summary of the text.",
        "Another summary.",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __lowercase			=		tokenizer(text_target=a_,      max_length=3_2,      padding="max_length",      return_tensors="pt"			)
       self.assertEqual(3_2,      targets["input_ids"].shape[1]			)


 @require_torch
 def       _lowercase				(    self							:    Tuple			):



    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __lowercase			=		tokenizer(
           ["I am a small frog" * 1_0_2_4, "I am a small frog"],      padding=a_,      truncation=a_,      return_tensors="pt"			)
       self.assertIsInstance(a_,      a_			)
       self.assertEqual(batch.input_ids.shape,      (2, 1_0_2_4)			)


 @require_torch
 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		["A long paragraph for summarization."]
    __lowercase			=		[
        "Summary of the text.",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       __lowercase			=		tokenizer(a_,      return_tensors="pt"			)
       __lowercase			=		tokenizer(text_target=a_,      return_tensors="pt"			)
       __lowercase			=		inputs["input_ids"]
       __lowercase			=		targets["input_ids"]
       self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()			)
       self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()			)
       self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()			)
       self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()			)


 def       _lowercase				(    self							:    Optional[int]			):
    pass



 def       _lowercase				(    self							:    Optional[int]			):



    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
          __lowercase			=		self.rust_tokenizer_class.from_pretrained(a_,      **a_			)
          __lowercase			=		self.tokenizer_class.from_pretrained(a_,      **a_			)
          __lowercase			=		"A, <mask> AllenNLP sentence."
          __lowercase			=		tokenizer_r.encode_plus(a_,      add_special_tokens=a_,      return_token_type_ids=a_			)
          __lowercase			=		tokenizer_p.encode_plus(a_,      add_special_tokens=a_,      return_token_type_ids=a_			)

          # token_type_ids should put 0 everywhere
          self.assertEqual(sum(tokens_r["token_type_ids"]			),      sum(tokens_p["token_type_ids"]			)			)

          # attention_mask should put 1 everywhere, so sum over length should be 1
          self.assertEqual(
              sum(tokens_r["attention_mask"]			) / len(tokens_r["attention_mask"]			),      sum(tokens_p["attention_mask"]			) / len(tokens_p["attention_mask"]			),      )

          __lowercase			=		tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]			)
          __lowercase			=		tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]			)

          # Rust correctly handles the space before the mask while python doesnt
          self.assertSequenceEqual(tokens_p["input_ids"],      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]			)
          self.assertSequenceEqual(tokens_r["input_ids"],      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]			)

          self.assertSequenceEqual(
              a_,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]			)
          self.assertSequenceEqual(
              a_,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]			)




"""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'])




