
"""simple docstring"""




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a		:					Optional[int]    	=					logging.get_logger(__name__)







class 						lowercase(_lowercase					):
     __snake_case:  Tuple  				=    "mt5"
     __snake_case:  Optional[Any]  				=    ["past_key_values"]



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE=2_5_0_1_1_2    ,							__SCREAMING_SNAKE_CASE=5_1_2    ,							__SCREAMING_SNAKE_CASE=6_4    ,							__SCREAMING_SNAKE_CASE=1_0_2_4    ,							__SCREAMING_SNAKE_CASE=8    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=6    ,							__SCREAMING_SNAKE_CASE=3_2    ,							__SCREAMING_SNAKE_CASE=1_2_8    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=1e-6    ,							__SCREAMING_SNAKE_CASE=1.0    ,							__SCREAMING_SNAKE_CASE="gated-gelu"    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE="T5Tokenizer"    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=0    ,							__SCREAMING_SNAKE_CASE=1    ,							__SCREAMING_SNAKE_CASE=0    ,							**__SCREAMING_SNAKE_CASE    ,							)		->    str:







         """simple docstring"""



         super().__init__(
             is_encoder_decoder=UpperCAmelCase_    ,							tokenizer_class=UpperCAmelCase_    ,							tie_word_embeddings=UpperCAmelCase_    ,							pad_token_id=UpperCAmelCase_    ,							eos_token_id=UpperCAmelCase_    ,							decoder_start_token_id=UpperCAmelCase_    ,							**UpperCAmelCase_    ,							)
         a__    = vocab_size
         a__    = d_model
         a__    = d_kv
         a__    = d_ff
         a__    = num_layers
         a__    = (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         a__    = num_heads
         a__    = relative_attention_num_buckets
         a__    = relative_attention_max_distance
         a__    = dropout_rate
         a__    = layer_norm_epsilon
         a__    = initializer_factor
         a__    = feed_forward_proj
         a__    = use_cache

         a__    = self.feed_forward_proj.split('-'      )
         a__    = act_info[-1]
         a__    = act_info[0] == 'gated'

         if len(UpperCAmelCase_      ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_      ) > 2:
             raise ValueError(
                 f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                 'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                 '\'gated-gelu\' or \'relu\''      )

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
             a__    = 'gelu_new'



     @property
     def  lowercase__     (       self      )		->    int:







         """simple docstring"""



         return self.d_model



     @property
     def  lowercase__     (       self      )		->    Optional[Any]:







         """simple docstring"""



         return self.num_heads




     @property
     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



         return self.num_layers







class 						lowercase(_lowercase					):



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__    = {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
             a__    = 'past_encoder_sequence + sequence'
             a__    = {0: 'batch'}
             a__    = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
             a__    = {0: 'batch', 1: 'decoder_sequence'}
             a__    = {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
             self.fill_with_past_key_values_(UpperCAmelCase_    ,							direction='inputs'      )

         return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def  lowercase__     (       self      )		->    str:







         """simple docstring"""



         return 1_3




     @property
     def  lowercase__     (       self      )		->    Dict:







         """simple docstring"""



         return 5e-4




"""simple docstring"""




import shutil

import requests



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		bool = False       ,		UpperCamelCase    :		str = "."    )    ->	dict:
    a__    = 'https://api.nasa.gov/planetary/apod'
    return requests.get(UpperCamelCase       ,		params={'api_key': api_key}    ).json()



def 						__magic_name__		(				UpperCamelCase    :		str       ,		UpperCamelCase    :		str = "."    )    ->	dict:
    a__    = get_apod_data(UpperCamelCase    )
    a__    = apod_data['url']
    a__    = img_url.split('/'    )[-1]
    a__    = 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 						__magic_name__		(				UpperCamelCase    :		str    )    ->	dict:
    a__    = 'https://images-api.nasa.gov/search'
    return requests.get(UpperCamelCase       ,		params={'q': query}    ).json()


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


