



"""simple docstring"""


from typing import Mapping

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


snake_case_								=			logging.get_logger(__name__)







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""mt5"""
						__UpperCamelCase													=				["""past_key_values"""]






						def __init__(   self					:Any  ,    lowercase_					:Optional[Any]=25_01_12  ,    lowercase_					:List[str]=5_12  ,    lowercase_					:Any=64  ,    lowercase_					:List[str]=10_24  ,    lowercase_					:int=8  ,    lowercase_					:int=None  ,    lowercase_					:int=6  ,    lowercase_					:Any=32  ,    lowercase_					:List[Any]=1_28  ,    lowercase_					:int=0.1  ,    lowercase_					:List[str]=1E-6  ,    lowercase_					:Optional[Any]=1.0  ,    lowercase_					:Union[str, Any]="gated-gelu"  ,    lowercase_					:Optional[int]=True  ,    lowercase_					:Tuple=True  ,    lowercase_					:Union[str, Any]="T5Tokenizer"  ,    lowercase_					:Tuple=False  ,    lowercase_					:str=0  ,    lowercase_					:Optional[Any]=1  ,    lowercase_					:Optional[int]=0  ,    **lowercase_					:Tuple  ,    )							->     List[str]:
												super().__init__(
												    is_encoder_decoder=lowercase_  ,    tokenizer_class=lowercase_  ,    tie_word_embeddings=lowercase_  ,    pad_token_id=lowercase_  ,    eos_token_id=lowercase_  ,    decoder_start_token_id=lowercase_  ,    **lowercase_  ,    )
												UpperCAmelCase			 =					vocab_size
												UpperCAmelCase			 =					d_model
												UpperCAmelCase			 =					d_kv
												UpperCAmelCase			 =					d_ff
												UpperCAmelCase			 =					num_layers
												UpperCAmelCase			 =					(
												    num_decoder_layers if num_decoder_layers is not None else self.num_layers
												)  # default = symmetry
												UpperCAmelCase			 =					num_heads
												UpperCAmelCase			 =					relative_attention_num_buckets
												UpperCAmelCase			 =					relative_attention_max_distance
												UpperCAmelCase			 =					dropout_rate
												UpperCAmelCase			 =					layer_norm_epsilon
												UpperCAmelCase			 =					initializer_factor
												UpperCAmelCase			 =					feed_forward_proj
												UpperCAmelCase			 =					use_cache

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

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






						@property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[int]:
												return self.d_model






						@property
						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Optional[int]:
												return self.num_heads






						@property
						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Dict:
												return self.num_layers







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






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

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

												return common_inputs






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def       UpperCAmelCase__	(   self					:Any   )							->     int:
												return 13






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










"""simple docstring"""


import shutil

import requests

def 					_lowerCAmelCase (  lowercase_						,      lowercase_ = False						,      lowercase_ = "."		):
						UpperCAmelCase			 =					'https://api.nasa.gov/planetary/apod'
						return requests.get(lowercase_						,      params={'api_key': api_key}		).json()

def 					_lowerCAmelCase (  lowercase_						,      lowercase_ = "."		):
						UpperCAmelCase			 =					get_apod_data(lowercase_		)
						UpperCAmelCase			 =					apod_data['url']
						UpperCAmelCase			 =					img_url.split('/'		)[-1]
						UpperCAmelCase			 =					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 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					'https://images-api.nasa.gov/search'
						return requests.get(lowercase_						,      params={'q': query}		).json()


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





