

from typing import Mapping

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


_lowerCamelCase    :       str        =							logging.get_logger(__name__)

_lowerCamelCase    :       Union[str, Any]        =							{
    "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  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					"""t5"""
   UpperCamelCase       					=					["""past_key_values"""]
   UpperCamelCase       					=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



   def __init__(							self							:					int,		__A							:					Tuple=3_2_1_2_8,		__A							:					Any=5_1_2,		__A							:					Optional[int]=6_4,		__A							:					Tuple=2_0_4_8,		__A							:					List[str]=6,		__A							:					Any=None,		__A							:					Dict=8,		__A							:					Optional[int]=3_2,		__A							:					List[str]=1_2_8,		__A							:					Dict=0.1,		__A							:					Any=1E-6,		__A							:					Optional[Any]=1.0,		__A							:					Optional[int]="relu",		__A							:					List[str]=True,		__A							:					Optional[Any]=True,		__A							:					Optional[int]=0,		__A							:					Optional[Any]=1,		**__A							:					Tuple,		):
     UpperCAmelCase	:							List[Any]						=			vocab_size
     UpperCAmelCase	:							Optional[Any]						=			d_model
     UpperCAmelCase	:							List[str]						=			d_kv
     UpperCAmelCase	:							Union[str, Any]						=			d_ff
     UpperCAmelCase	:							Optional[int]						=			num_layers
     UpperCAmelCase	:							Optional[int]						=			(
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     UpperCAmelCase	:							Dict						=			num_heads
     UpperCAmelCase	:							Optional[Any]						=			relative_attention_num_buckets
     UpperCAmelCase	:							List[Any]						=			relative_attention_max_distance
     UpperCAmelCase	:							List[Any]						=			dropout_rate
     UpperCAmelCase	:							Any						=			layer_norm_epsilon
     UpperCAmelCase	:							List[str]						=			initializer_factor
     UpperCAmelCase	:							Union[str, Any]						=			feed_forward_proj
     UpperCAmelCase	:							int						=			use_cache

     UpperCAmelCase	:							Tuple						=			self.feed_forward_proj.split('''-'''						)
     UpperCAmelCase	:							Optional[int]						=			act_info[-1]
     UpperCAmelCase	:							Optional[int]						=			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	:							Optional[int]						=			'''gelu_new'''

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






class  __UpperCAmelCase					(	lowerCamelCase__						):



   @property
   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Optional[Any]						=			{
         '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
         '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
     }
     if self.use_past:
       UpperCAmelCase	:							Dict						=			'''past_encoder_sequence + sequence'''
       UpperCAmelCase	:							Any						=			{0: '''batch'''}
       UpperCAmelCase	:							Union[str, Any]						=			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
     else:
       UpperCAmelCase	:							Union[str, Any]						=			{0: '''batch''', 1: '''decoder_sequence'''}
       UpperCAmelCase	:							Tuple						=			{0: '''batch''', 1: '''decoder_sequence'''}

     if self.use_past:
       self.fill_with_past_key_values_(__A,		direction='''inputs'''						)

     return common_inputs





   @property
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     return 1_3








from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean

def       a__					(		UpperCAmelCase							:      int      )  ->						Dict:

  # A local function to see if a dot lands in the circle.
  def is_in_circle(UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float      ) -> bool:
    UpperCAmelCase	:							Dict						=			sqrt((x**2) + (y**2)      )
    # Our circle has a radius of 1, so a distance
    # greater than 1 would land outside the circle.
    return distance_from_centre <= 1

  # The proportion of guesses that landed in the circle
  UpperCAmelCase	:							List[str]						=			mean(
      int(is_in_circle(uniform(-1.0    ,			1.0      )    ,			uniform(-1.0    ,			1.0      )      )      )
      for _ in range(UpperCAmelCase      )      )
  # The ratio of the area for circle to square is pi/4.
  UpperCAmelCase	:							int						=			proportion * 4
  print(f'''The estimated value of pi is {pi_estimate}'''      )
  print(f'''The numpy value of pi is {pi}'''      )
  print(f'''The total error is {abs(pi - pi_estimate      )}'''      )

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      Callable[[float], float]    ,			UpperCAmelCase							:      float = 0.0    ,			UpperCAmelCase							:      float = 1.0    ,			)  ->						float:
  return mean(
      function_to_integrate(uniform(UpperCAmelCase    ,			UpperCAmelCase      )      ) for _ in range(UpperCAmelCase      )      ) * (max_value - min_value)

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      float = 0.0    ,			UpperCAmelCase							:      float = 1.0      )  ->						None:

  def identity_function(UpperCAmelCase							:      float      ) -> float:
    return x

  UpperCAmelCase	:							int						=			area_under_curve_estimator(
      UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			(max_value * max_value - min_value * min_value) / 2

  print('''******************'''      )
  print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''      )
  print(f'''Estimated value is {estimated_value}'''      )
  print(f'''Expected value is {expected_value}'''      )
  print(f'''Total error is {abs(estimated_value - expected_value      )}'''      )
  print('''******************'''      )

def       a__					(		UpperCAmelCase							:      int      )  ->						None:

  def function_to_integrate(UpperCAmelCase							:      float      ) -> float:
    return sqrt(4.0 - x * x      )

  UpperCAmelCase	:							Optional[int]						=			area_under_curve_estimator(
      UpperCAmelCase    ,			UpperCAmelCase    ,			0.0    ,			2.0      )

  print('''******************'''      )
  print('''Estimating pi using area_under_curve_estimator'''      )
  print(f'''Estimated value is {estimated_value}'''      )
  print(f'''Expected value is {pi}'''      )
  print(f'''Total error is {abs(estimated_value - pi      )}'''      )
  print('''******************'''      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





