


from typing import Mapping

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


snake_case_						:			Any													=							logging.get_logger(__name__)

snake_case_						:			int													=							{
    """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__  ):
      UpperCAmelCase             =						"t5"
      UpperCAmelCase             =						["past_key_values"]
      UpperCAmelCase             =						{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




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


          """simple docstring"""





          _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
          _SCREAMING_SNAKE_CASE			  =(
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _SCREAMING_SNAKE_CASE			  =num_heads
          _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			  =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(UpperCamelCase__  ) > 1 and act_info[0] != "gated" or len(UpperCamelCase__  ) > 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=UpperCamelCase__				,    eos_token_id=UpperCamelCase__				,    is_encoder_decoder=UpperCamelCase__				,    **UpperCamelCase__				,    )

class 						A__							(		UpperCamelCase__  ):




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


          """simple docstring"""





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

          return common_inputs




      @property
      def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     int:


          """simple docstring"""





          return 13



import string
from math import logaa







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =document.translate(
				    str.maketrans(''''''	,''''''	,string.punctuation)).replace('''\n'''	,'''''')
				_SCREAMING_SNAKE_CASE			  =document_without_punctuation.split(''' ''')  # word tokenization
				return len([word for word in tokenize_document if word.lower() == term.lower()])







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =corpus.lower().translate(
				    str.maketrans(''''''	,''''''	,string.punctuation))  # strip all punctuation and replace it with ''
				_SCREAMING_SNAKE_CASE			  =corpus_without_punctuation.split('''\n''')
				_SCREAMING_SNAKE_CASE			  =term.lower()
				return (len([doc for doc in docs if term in doc]), len(a__))







def 						lowerCamelCase(   a__	,a__	,a__=False):
				if smoothing:
								if n == 0:
												raise ValueError('''log10(0) is undefined.''')
								return round(1 + logaa(n / (1 + df))	,3)

				if df == 0:
								raise ZeroDivisionError('''df must be > 0''')
				elif n == 0:
								raise ValueError('''log10(0) is undefined.''')
				return round(logaa(n / df)	,3)







def 						lowerCamelCase(   a__	,a__):
				return round(tf * idf	,3)