





import shutil

import requests




def 						A_			(  lowercase_      ,		lowercase_ = False      ,		lowercase_ = "."  )			->						dict:
       _snake_case      :							Optional[int]     				=						'''https://api.nasa.gov/planetary/apod'''
       return requests.get(lowercase_      ,		params={'''api_key''': api_key}  ).json()




def 						A_			(  lowercase_      ,		lowercase_ = "."  )			->						dict:
       _snake_case      :							Optional[Any]     				=						get_apod_data(lowercase_  )
       _snake_case      :							List[str]     				=						apod_data['''url''']
       _snake_case      :							Tuple     				=						img_url.split('''/'''  )[-1]
       _snake_case      :							List[str]     				=						requests.get(lowercase_      ,		stream=lowercase_  )

       with open(f'''{path}/{img_name}'''      ,		'''wb+'''  ) as img_file:
              shutil.copyfileobj(response.raw      ,		lowercase_  )
       del response
       return apod_data




def 						A_			(  lowercase_  )			->						dict:
       _snake_case      :							List[str]     				=						'''https://images-api.nasa.gov/search'''
       return requests.get(lowercase_      ,		params={'''q''': query}  ).json()


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













import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MarkupLMForQuestionAnswering,
       MarkupLMForSequenceClassification,
       MarkupLMForTokenClassification,
       MarkupLMModel,
   )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer

class 				A    :



      def __init__(					self     ,  lowercase_     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=99     ,  lowercase_=32     ,  lowercase_=5     ,  lowercase_=4     ,  lowercase_=37     ,  lowercase_="gelu"     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=512     ,  lowercase_=16     ,  lowercase_=2     ,  lowercase_=0.02     ,  lowercase_=3     ,  lowercase_=None     ,  lowercase_=20     ,  lowercase_=30     ,  lowercase_=2     ,  lowercase_=2     ,  lowercase_=10     ,  )      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						parent
             _snake_case      :							List[str]     				=						batch_size
             _snake_case      :							str     				=						seq_length
             _snake_case      :							str     				=						is_training
             _snake_case      :							Optional[Any]     				=						use_input_mask
             _snake_case      :							Any     				=						use_token_type_ids
             _snake_case      :							str     				=						use_labels
             _snake_case      :							int     				=						vocab_size
             _snake_case      :							Tuple     				=						hidden_size
             _snake_case      :							str     				=						num_hidden_layers
             _snake_case      :							str     				=						num_attention_heads
             _snake_case      :							Optional[int]     				=						intermediate_size
             _snake_case      :							Tuple     				=						hidden_act
             _snake_case      :							Any     				=						hidden_dropout_prob
             _snake_case      :							Tuple     				=						attention_probs_dropout_prob
             _snake_case      :							str     				=						max_position_embeddings
             _snake_case      :							List[Any]     				=						type_vocab_size
             _snake_case      :							str     				=						type_sequence_label_size
             _snake_case      :							Any     				=						initializer_range
             _snake_case      :							Optional[Any]     				=						num_labels
             _snake_case      :							Union[str, Any]     				=						scope
             _snake_case      :							int     				=						max_xpath_tag_unit_embeddings
             _snake_case      :							Any     				=						max_xpath_subs_unit_embeddings
             _snake_case      :							Union[str, Any]     				=						tag_pad_id
             _snake_case      :							Union[str, Any]     				=						subs_pad_id
             _snake_case      :							int     				=						max_depth



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

             _snake_case      :							Any     				=						ids_tensor(
                 [self.batch_size, self.seq_length, self.max_depth]     ,  self.max_xpath_tag_unit_embeddings					)

             _snake_case      :							Tuple     				=						ids_tensor(
                 [self.batch_size, self.seq_length, self.max_depth]     ,  self.max_xpath_subs_unit_embeddings					)

             _snake_case      :							Optional[int]     				=						None
             if self.use_input_mask:
                    _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  vocab_size=2					)

             _snake_case      :							int     				=						None
             if self.use_token_type_ids:
                    _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)

             _snake_case      :							List[Any]     				=						None
             _snake_case      :							int     				=						None
             if self.use_labels:
                    _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size]     ,  self.type_sequence_label_size					)
                    _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels					)

             _snake_case      :							Tuple     				=						self.get_config()

             return (
                 config,
                 input_ids,
                 xpath_tags_seq,
                 xpath_subs_seq,
                 token_type_ids,
                 input_mask,
                 sequence_labels,
                 token_labels,
             )



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             return MarkupLMConfig(
                 vocab_size=self.vocab_size     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  max_position_embeddings=self.max_position_embeddings     ,  type_vocab_size=self.type_vocab_size     ,  initializer_range=self.initializer_range     ,  max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings     ,  max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings     ,  tag_pad_id=self.tag_pad_id     ,  subs_pad_id=self.subs_pad_id     ,  max_depth=self.max_depth     ,  )



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						MarkupLMModel(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             print('''Configs:'''     ,  model.config.tag_pad_id     ,  model.config.subs_pad_id					)
             _snake_case      :							int     				=						model(lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_					)
             _snake_case      :							Dict     				=						model(lowercase_     ,  token_type_ids=lowercase_					)
             _snake_case      :							str     				=						model(lowercase_					)
             self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)					)
             self.parent.assertEqual(result.pooler_output.shape     ,  (self.batch_size, self.hidden_size)					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.num_labels
             _snake_case      :							List[str]     				=						MarkupLMForSequenceClassification(lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _snake_case      :							Optional[int]     				=						model(
                 lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_     ,  )
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						self.num_labels
             _snake_case      :							Dict     				=						MarkupLMForTokenClassification(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _snake_case      :							List[str]     				=						model(
                 lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_     ,  )
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.num_labels)					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						MarkupLMForQuestionAnswering(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _snake_case      :							Optional[int]     				=						model(
                 lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  start_positions=lowercase_     ,  end_positions=lowercase_     ,  )
             self.parent.assertEqual(result.start_logits.shape     ,  (self.batch_size, self.seq_length)					)
             self.parent.assertEqual(result.end_logits.shape     ,  (self.batch_size, self.seq_length)					)



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.prepare_config_and_inputs()
             (
                 (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    
             )      :							Tuple     				=						config_and_inputs
             _snake_case      :							str     				=						{
                 '''input_ids''': input_ids,
                 '''xpath_tags_seq''': xpath_tags_seq,
                 '''xpath_subs_seq''': xpath_subs_seq,
                 '''token_type_ids''': token_type_ids,
                 '''attention_mask''': input_mask,
             }
             return config, inputs_dict


@require_torch
class 				A    (__UpperCAmelCase       ,__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							(
          (
              MarkupLMModel,
              MarkupLMForSequenceClassification,
              MarkupLMForTokenClassification,
              MarkupLMForQuestionAnswering,
          )
          if is_torch_available()
          else None
      )
      _SCREAMING_SNAKE_CASE					=							(
          {
              """feature-extraction""": MarkupLMModel,
              """question-answering""": MarkupLMForQuestionAnswering,
              """text-classification""": MarkupLMForSequenceClassification,
              """token-classification""": MarkupLMForTokenClassification,
              """zero-shot""": MarkupLMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[str]:



             '''simple docstring'''

             return True



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Any     				=						MarkupLMModelTester(self					)
             _snake_case      :							Dict     				=						ConfigTester(self     ,  config_class=lowercase_     ,  hidden_size=37					)



      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             self.config_tester.run_common_tests()



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase_					)



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*lowercase_					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*lowercase_					)



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*lowercase_					)




def 						A_			(  )			->						Any:
       _snake_case      :							Tuple     				=						'''
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    '''

       return html_string

@require_torch
class 				A    (unittest.TestCase				):



      @cached_property
      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						MarkupLMFeatureExtractor()
             _snake_case      :							Any     				=						MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''					)

             return MarkupLMProcessor(lowercase_     ,  lowercase_					)



      @slow
      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Any     				=						MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''					).to(lowercase_					)

             _snake_case      :							Tuple     				=						self.default_processor

             _snake_case      :							Union[str, Any]     				=						processor(prepare_html_string()     ,  return_tensors='''pt'''					)
             _snake_case      :							List[Any]     				=						inputs.to(lowercase_					)

             # forward pass
             with torch.no_grad():
                    _snake_case      :							List[str]     				=						model(**lowercase_					)

             # verify the last hidden states
             _snake_case      :							Union[str, Any]     				=						torch.Size([1, 14, 768]					)
             self.assertEqual(outputs.last_hidden_state.shape     ,  lowercase_					)

             _snake_case      :							List[str]     				=						torch.tensor(
                 [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]					).to(lowercase_					)

             self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,  lowercase_     ,  atol=1E-4					)					)






