from typing import Mapping

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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}



class     _A   (    UpperCamelCase_							):
  SCREAMING_SNAKE_CASE_	:	List[Any]						="""t5"""
  SCREAMING_SNAKE_CASE_	:	List[str]						=["""past_key_values"""]
  SCREAMING_SNAKE_CASE_	:	Any						={"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=3_2128   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=64   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=6   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=8   ,				SCREAMING_SNAKE_CASE_=32   ,				SCREAMING_SNAKE_CASE_=128   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=1E-6   ,				SCREAMING_SNAKE_CASE_=1.0   ,				SCREAMING_SNAKE_CASE_="relu"   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=0   ,				SCREAMING_SNAKE_CASE_=1   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[int]:





       '''simple docstring'''





       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(_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":
            UpperCamelCase__										=      """gelu_new"""

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



class     _A   (    UpperCamelCase_							):






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





       '''simple docstring'''





       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_(_a   ,				direction='''inputs'''	)

       return common_inputs






  @property
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       return 13




from datetime import datetime

import matplotlib.pyplot as plt
import torch







def  __UpperCamelCase    (						A							):
     for param in module.parameters():
          UpperCamelCase__										=      False







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
     if torch.backends.mps.is_available() and torch.backends.mps.is_built():
          UpperCamelCase__										=      '''mps'''
     if device == "mps":
          print(
              '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
              ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
              ''' with generations.'''							)
     return device







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      plt.imshow(A							)
     fig.axes.get_xaxis().set_visible(A							)
     fig.axes.get_yaxis().set_visible(A							)
     plt.show()







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      datetime.now()
     UpperCamelCase__										=      current_time.strftime('''%H:%M:%S'''							)
     return timestamp



