



from typing import Mapping

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


UpperCamelCase_      			=  logging.get_logger(__name__)

UpperCamelCase_      			=  {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}







class 				a_    (						_snake_case							):
						UpperCamelCase__ :		Any										="longt5"
						UpperCamelCase__ :		Dict										=["past_key_values"]
						UpperCamelCase__ :		Optional[int]										={"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

						def __init__(       self      :Optional[int]  ,		_lowercase      :str=32128  ,		_lowercase      :int=512  ,		_lowercase      :Any=64  ,		_lowercase      :List[Any]=2048  ,		_lowercase      :List[str]=6  ,		_lowercase      :Union[str, Any]=None  ,		_lowercase      :str=8  ,		_lowercase      :int=127  ,		_lowercase      :List[Any]=16  ,		_lowercase      :Any=32  ,		_lowercase      :Dict=128  ,		_lowercase      :Optional[Any]=0.1  ,		_lowercase      :int=1E-6  ,		_lowercase      :Dict=1.0  ,		_lowercase      :Optional[int]="relu"  ,		_lowercase      :Dict=True  ,		_lowercase      :str="local"  ,		_lowercase      :List[Any]=True  ,		_lowercase      :Tuple=0  ,		_lowercase      :Optional[Any]=1  ,		**_lowercase      :str  ,		)						->   Optional[Any]:
												UpperCAmelCase_					=	vocab_size
												UpperCAmelCase_					=	d_model
												UpperCAmelCase_					=	d_kv
												UpperCAmelCase_					=	d_ff
												UpperCAmelCase_					=	num_layers
												# default = symmetry
												UpperCAmelCase_					=	num_decoder_layers if num_decoder_layers is not None else self.num_layers
												UpperCAmelCase_					=	num_heads
												UpperCAmelCase_					=	local_radius
												UpperCAmelCase_					=	global_block_size
												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_					=	encoder_attention_type
												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'''

												super().__init__(
												    pad_token_id=_lowercase  ,		eos_token_id=_lowercase  ,		is_encoder_decoder=_lowercase  ,		**_lowercase  ,		)







class 				a_    (						_snake_case							):

						@property
						def       __a			(       self      :Dict)						->   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
						def       __a			(       self      :Any)						->   int:
												return 13










import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info







def 							A     (						__UpperCAmelCase      )			->			Dict:



						'''simple docstring'''






						class 				a_    :

												def __init__(       self      :Optional[int]  ,		_lowercase      :Any)						->   None:
																		assert isinstance(_lowercase  ,		(bytes, str))
																		UpperCAmelCase_					=	content




												def       __a			(       self      :List[str])						->   Tuple:
																		return json.loads(self.content)

						def mock_response(*__UpperCAmelCase				,   **__UpperCAmelCase      ):
												assert args[0] == AUTHENTICATED_USER_ENDPOINT
												assert "Authorization" in kwargs["headers"]
												assert kwargs["headers"]["Authorization"].startswith('''token '''      )
												assert "Accept" in kwargs["headers"]
												return FakeResponse(b'''{"login":"test","id":1}'''      )

						monkeypatch.setattr(__UpperCAmelCase				,   '''get'''				,   __UpperCAmelCase      )
						UpperCAmelCase_					=	fetch_github_info('''token'''      )
						assert result["login"] == "test"
						assert result["id"] == 1





