


SCREAMING_SNAKE_CASE	:Optional[int]  		=     '''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]








from typing import Mapping

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


SCREAMING_SNAKE_CASE	:int  		=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE	:Optional[Any]  		=     {
    '''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     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _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       :       int    ,				_lowerCAmelCase       :       str=3_2_1_2_8    ,				_lowerCAmelCase       :       Optional[Any]=5_1_2    ,				_lowerCAmelCase       :       List[str]=6_4    ,				_lowerCAmelCase       :       Optional[int]=2_0_4_8    ,				_lowerCAmelCase       :       Any=6    ,				_lowerCAmelCase       :       Any=None    ,				_lowerCAmelCase       :       str=8    ,				_lowerCAmelCase       :       List[str]=1_2_7    ,				_lowerCAmelCase       :       int=1_6    ,				_lowerCAmelCase       :       List[str]=3_2    ,				_lowerCAmelCase       :       Any=1_2_8    ,				_lowerCAmelCase       :       Union[str, Any]=0.1    ,				_lowerCAmelCase       :       Union[str, Any]=1e-6    ,				_lowerCAmelCase       :       str=1.0    ,				_lowerCAmelCase       :       Tuple="relu"    ,				_lowerCAmelCase       :       List[str]=True    ,				_lowerCAmelCase       :       Tuple="local"    ,				_lowerCAmelCase       :       Union[str, Any]=True    ,				_lowerCAmelCase       :       Optional[int]=0    ,				_lowerCAmelCase       :       str=1    ,				**_lowerCAmelCase       :       int    ,				)   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					vocab_size
     snake_case_					    =					d_model
     snake_case_					    =					d_kv
     snake_case_					    =					d_ff
     snake_case_					    =					num_layers
     # default = symmetry
     snake_case_					    =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
     snake_case_					    =					num_heads
     snake_case_					    =					local_radius
     snake_case_					    =					global_block_size
     snake_case_					    =					relative_attention_num_buckets
     snake_case_					    =					relative_attention_max_distance
     snake_case_					    =					dropout_rate
     snake_case_					    =					layer_norm_epsilon
     snake_case_					    =					initializer_factor
     snake_case_					    =					feed_forward_proj
     snake_case_					    =					encoder_attention_type
     snake_case_					    =					use_cache

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

     if len(_lowerCAmelCase  ) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase  ) > 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":
        snake_case_					    =					"gelu_new"

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






class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  @property
  def 					lowerCAmelCase__  (     self       :       str  )   -> Mapping[str, Mapping[int, str]]:




     """simple docstring"""
     snake_case_					    =					{
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
        snake_case_					    =					"past_encoder_sequence + sequence"
        snake_case_					    =					{0: "batch"}
        snake_case_					    =					{0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}

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

     return common_inputs



  @property
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> int:




     """simple docstring"""
     return 1_3




