
'''simple docstring'''



from typing import Mapping

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


UpperCamelCase__ :						List[str]    							=					logging.get_logger(__name__)

UpperCamelCase__ :						int    							=					{
    "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			(A__):
       SCREAMING_SNAKE_CASE =					'longt5'
       SCREAMING_SNAKE_CASE =					['past_key_values']
       SCREAMING_SNAKE_CASE =					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}






       def __init__(							self    ,							A__=3_21_28    ,							A__=5_12    ,							A__=64    ,							A__=20_48    ,							A__=6    ,							A__=None    ,							A__=8    ,							A__=1_27    ,							A__=16    ,							A__=32    ,							A__=1_28    ,							A__=0.1    ,							A__=1E-6    ,							A__=1.0    ,							A__="relu"    ,							A__=True    ,							A__="local"    ,							A__=True    ,							A__=0    ,							A__=1    ,							**A__    ,							)      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			vocab_size
          _SCREAMING_SNAKE_CASE       	=			d_model
          _SCREAMING_SNAKE_CASE       	=			d_kv
          _SCREAMING_SNAKE_CASE       	=			d_ff
          _SCREAMING_SNAKE_CASE       	=			num_layers
          # default = symmetry
          _SCREAMING_SNAKE_CASE       	=			num_decoder_layers if num_decoder_layers is not None else self.num_layers
          _SCREAMING_SNAKE_CASE       	=			num_heads
          _SCREAMING_SNAKE_CASE       	=			local_radius
          _SCREAMING_SNAKE_CASE       	=			global_block_size
          _SCREAMING_SNAKE_CASE       	=			relative_attention_num_buckets
          _SCREAMING_SNAKE_CASE       	=			relative_attention_max_distance
          _SCREAMING_SNAKE_CASE       	=			dropout_rate
          _SCREAMING_SNAKE_CASE       	=			layer_norm_epsilon
          _SCREAMING_SNAKE_CASE       	=			initializer_factor
          _SCREAMING_SNAKE_CASE       	=			feed_forward_proj
          _SCREAMING_SNAKE_CASE       	=			encoder_attention_type
          _SCREAMING_SNAKE_CASE       	=			use_cache

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

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

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






class 				_a			(A__):






       @property
       def     UpperCamelCase			(							self )      ->     Mapping[str, Mapping[int, str]]:
          _SCREAMING_SNAKE_CASE       	=			{
              '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
              '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
          }
          if self.use_past:
             _SCREAMING_SNAKE_CASE       	=			'''past_encoder_sequence + sequence'''
             _SCREAMING_SNAKE_CASE       	=			{0: '''batch'''}
             _SCREAMING_SNAKE_CASE       	=			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
          else:
             _SCREAMING_SNAKE_CASE       	=			{0: '''batch''', 1: '''decoder_sequence'''}
             _SCREAMING_SNAKE_CASE       	=			{0: '''batch''', 1: '''decoder_sequence'''}

          if self.use_past:
             self.fill_with_past_key_values_(__snake_case    ,							direction="""inputs""" )

          return common_inputs






       @property
       def     UpperCamelCase			(							self )      ->     int:
          return 13








'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__ :						int    							=					logging.get_logger(__name__)

UpperCamelCase__ :						Any    							=					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'cpmant'






       def __init__(							self    ,							A__ = 3_07_20    ,							A__ = 40_96    ,							A__ = 32    ,							A__ = 1_28    ,							A__ = 1_02_40    ,							A__ = 48    ,							A__ = 0.0    ,							A__ = 5_12    ,							A__ = 20_48    ,							A__ = 1E-6    ,							A__ = 1.0    ,							A__ = 32    ,							A__ = 32    ,							A__ = 32    ,							A__ = True    ,							**A__    ,							)      ->     List[Any]:
          super().__init__(**A__ )
          _SCREAMING_SNAKE_CASE       	=			prompt_types
          _SCREAMING_SNAKE_CASE       	=			prompt_length
          _SCREAMING_SNAKE_CASE       	=			segment_types
          _SCREAMING_SNAKE_CASE       	=			hidden_size
          _SCREAMING_SNAKE_CASE       	=			num_attention_heads
          _SCREAMING_SNAKE_CASE       	=			dim_head
          _SCREAMING_SNAKE_CASE       	=			dim_ff
          _SCREAMING_SNAKE_CASE       	=			num_hidden_layers
          _SCREAMING_SNAKE_CASE       	=			position_bias_num_buckets
          _SCREAMING_SNAKE_CASE       	=			position_bias_max_distance
          _SCREAMING_SNAKE_CASE       	=			dropout_p
          _SCREAMING_SNAKE_CASE       	=			eps
          _SCREAMING_SNAKE_CASE       	=			use_cache
          _SCREAMING_SNAKE_CASE       	=			vocab_size
          _SCREAMING_SNAKE_CASE       	=			init_std






