
from typing import Mapping

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


__SCREAMING_SNAKE_CASE    :   Optional[Any]       =logging.get_logger(__name__)






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Any     ="mt5"
     _snake_case:	List[str]     =["past_key_values"]




     def __init__(    self  :   List[str]	,  A__  :   Tuple=2_5_0_1_1_2	,  A__  :   Union[str, Any]=5_1_2	,  A__  :   Any=6_4	,  A__  :   Any=1_0_2_4	,  A__  :   Any=8	,  A__  :   Union[str, Any]=None	,  A__  :   Union[str, Any]=6	,  A__  :   str=3_2	,  A__  :   Optional[int]=1_2_8	,  A__  :   int=0.1	,  A__  :   Dict=1E-6	,  A__  :   Optional[int]=1.0	,  A__  :   Union[str, Any]="gated-gelu"	,  A__  :   Dict=True	,  A__  :   Any=True	,  A__  :   int="T5Tokenizer"	,  A__  :   str=False	,  A__  :   Union[str, Any]=0	,  A__  :   List[Any]=1	,  A__  :   Tuple=0	,  **A__  :   int	,  )				->       Tuple:
           super().__init__(
               is_encoder_decoder=A__	,  tokenizer_class=A__	,  tie_word_embeddings=A__	,  pad_token_id=A__	,  eos_token_id=A__	,  decoder_start_token_id=A__	,  **A__	,  )
           lowerCamelCase__        =				vocab_size
           lowerCamelCase__        =				d_model
           lowerCamelCase__        =				d_kv
           lowerCamelCase__        =				d_ff
           lowerCamelCase__        =				num_layers
           lowerCamelCase__        =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           lowerCamelCase__        =				num_heads
           lowerCamelCase__        =				relative_attention_num_buckets
           lowerCamelCase__        =				relative_attention_max_distance
           lowerCamelCase__        =				dropout_rate
           lowerCamelCase__        =				layer_norm_epsilon
           lowerCamelCase__        =				initializer_factor
           lowerCamelCase__        =				feed_forward_proj
           lowerCamelCase__        =				use_cache

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

           if len(A__			) > 1 and act_info[0] != "gated" or len(A__			) > 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":
                 lowerCamelCase__        =				'gelu_new'




     @property
     def  __snake_case   (    self  :   Dict			)				->       int:
           return self.d_model




     @property
     def  __snake_case   (    self  :   Tuple			)				->       str:
           return self.num_heads




     @property
     def  __snake_case   (    self  :   Tuple			)				->       Tuple:
           return self.num_layers






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def  __snake_case   (    self  :   str			)				->       Mapping[str, Mapping[int, str]]:
           lowerCamelCase__        =				{
               'input_ids': {0: 'batch', 1: 'encoder_sequence'},
               'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
           }
           if self.use_past:
                 lowerCamelCase__        =				'past_encoder_sequence + sequence'
                 lowerCamelCase__        =				{0: 'batch'}
                 lowerCamelCase__        =				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
                 lowerCamelCase__        =				{0: 'batch', 1: 'decoder_sequence'}
                 lowerCamelCase__        =				{0: 'batch', 1: 'decoder_sequence'}

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

           return common_inputs




     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def  __snake_case   (    self  :   Optional[Any]			)				->       int:
           return 1_3




     @property
     def  __snake_case   (    self  :   Optional[Any]			)				->       float:
           return 5E-4





import shutil

import requests

def 			lowerCAmelCase_			(  lowerCamelCase:       str     ,				lowerCamelCase:       bool = False     ,				lowerCamelCase:       str = "."						)					->			dict:


      '''simple docstring'''




      lowerCamelCase__        =				'https://api.nasa.gov/planetary/apod'
      return requests.get(lowerCamelCase     ,				params={'api_key': api_key}						).json()

def 			lowerCAmelCase_			(  lowerCamelCase:       str     ,				lowerCamelCase:       str = "."						)					->			dict:


      '''simple docstring'''




      lowerCamelCase__        =				get_apod_data(lowerCamelCase						)
      lowerCamelCase__        =				apod_data['url']
      lowerCamelCase__        =				img_url.split('/'						)[-1]
      lowerCamelCase__        =				requests.get(lowerCamelCase     ,				stream=lowerCamelCase						)

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

def 			lowerCAmelCase_			(  lowerCamelCase:       str						)					->			dict:


      '''simple docstring'''




      lowerCamelCase__        =				'https://images-api.nasa.gov/search'
      return requests.get(lowerCamelCase     ,				params={'q': query}						).json()


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