




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
 from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


lowerCamelCase       :	Tuple									=     logging.get_logger(__name__)

lowerCamelCase       :	List[str]									=     {
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}







class 						A(		UpperCAmelCase__     ):



 '''simple docstring'''


 UpperCamelCase		     =		"bloom"
 UpperCamelCase		     =		["past_key_values"]
 UpperCamelCase		     =		{
     "num_hidden_layers": "n_layer",
     "num_attention_heads": "n_head",
 }

 def __init__(       self      :						Any		,							A_      :						List[Any]=250880		,							A_      :						List[str]=64		,							A_      :						List[str]=2		,							A_      :						List[Any]=8		,							A_      :						Any=1E-5		,							A_      :						str=0.02		,							A_      :						List[Any]=True		,							A_      :						str=1		,							A_      :						Tuple=2		,							A_      :						Any=False		,							A_      :						Union[str, Any]=0.0		,							A_      :						Optional[Any]=0.0		,							A_      :						Optional[Any]=1		,							A_      :						List[str]=False		,							**A_      :						Union[str, Any]		,							)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					vocab_size
       # Backward compatibility with n_embed kwarg
       lowerCamelCase_	  =					kwargs.pop('n_embed'		,							lowerCamelCase__						)
       lowerCamelCase_	  =					hidden_size if n_embed is None else n_embed
       lowerCamelCase_	  =					n_layer
       lowerCamelCase_	  =					n_head
       lowerCamelCase_	  =					layer_norm_epsilon
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					use_cache
       lowerCamelCase_	  =					pretraining_tp
       lowerCamelCase_	  =					apply_residual_connection_post_layernorm
       lowerCamelCase_	  =					hidden_dropout
       lowerCamelCase_	  =					attention_dropout

       lowerCamelCase_	  =					bos_token_id
       lowerCamelCase_	  =					eos_token_id
       lowerCamelCase_	  =					slow_but_exact

       super().__init__(bos_token_id=lowerCamelCase__		,							eos_token_id=lowerCamelCase__		,							**lowerCamelCase__						)







class 						A(		UpperCAmelCase__     ):



 '''simple docstring'''


 UpperCamelCase		     =		version.parse('''1.12'''     )

 def __init__(       self      :						List[Any]		,							A_      :						PretrainedConfig		,							A_      :						str = "default"		,							A_      :						List[PatchingSpec] = None		,							A_      :						bool = False		,							)     ->			int:

       """simple docstring"""






       super().__init__(lowerCamelCase__		,							task=lowerCamelCase__		,							patching_specs=lowerCamelCase__		,							use_past=lowerCamelCase__						)
       if not getattr(self._config		,							'pad_token_id'		,							lowerCamelCase__						):
             # TODO: how to do that better?
             lowerCamelCase_	  =					0

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

       """simple docstring"""






       lowerCamelCase_	  =					OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}						)
       if self.use_past:
             # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
             self.fill_with_past_key_values_(lowerCamelCase__		,							direction='inputs'		,							inverted_values_shape=lowerCamelCase__						)
             lowerCamelCase_	  =					{0: "batch", 1: "past_sequence + sequence"}
       else:
             lowerCamelCase_	  =					{0: "batch", 1: "sequence"}

       return common_inputs

 @property
 def        a__				(       self      :						Dict						)     ->			int:

       """simple docstring"""






       return self._config.n_layer

 @property
 def        a__				(       self      :						Union[str, Any]						)     ->			int:

       """simple docstring"""






       return self._config.n_head

 @property
 def        a__				(       self      :						Any						)     ->			float:

       """simple docstring"""






       return 1E-3

 def        a__				(       self      :						Union[str, Any]		,							A_      :						"PreTrainedTokenizer"		,							A_      :						int = -1		,							A_      :						int = -1		,							A_      :						bool = False		,							A_      :						Optional["TensorType"] = None		,							)     ->			Mapping[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					super(lowerCamelCase__		,							self						).generate_dummy_inputs(
           lowerCamelCase__		,							batch_size=lowerCamelCase__		,							seq_length=lowerCamelCase__		,							is_pair=lowerCamelCase__		,							framework=lowerCamelCase__						)

       # We need to order the input in the way they appears in the forward()
       lowerCamelCase_	  =					OrderedDict({'input_ids': common_inputs['input_ids']}						)

       # Need to add the past_keys
       if self.use_past:
             if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
             else:
                   import torch

                   lowerCamelCase_	  =					common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   lowerCamelCase_	  =					seqlen + 2
                   lowerCamelCase_	  =					self._config.hidden_size // self.num_attention_heads
                   lowerCamelCase_	  =					(
                       batch * self.num_attention_heads,
                       head_dim,
                       past_key_values_length,
                   )
                   lowerCamelCase_	  =					(
                       batch * self.num_attention_heads,
                       past_key_values_length,
                       head_dim,
                   )
                   lowerCamelCase_	  =					[
                       (torch.zeros(lowerCamelCase__						), torch.zeros(lowerCamelCase__						)) for _ in range(self.num_layers						)
                   ]

       lowerCamelCase_	  =					common_inputs["attention_mask"]
       if self.use_past:
             lowerCamelCase_	  =					ordered_inputs["attention_mask"].dtype
             lowerCamelCase_	  =					torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__		,							lowerCamelCase__		,							dtype=lowerCamelCase__						)]		,							dim=1						)

       return ordered_inputs





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

       """simple docstring"""






       return 13





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass







@dataclass
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		None
 UpperCamelCase		     =		None

lowerCamelCase       :	str									=     namedtuple("CoinsDistribResult", "moves excess")

def     _SCREAMING_SNAKE_CASE    (							lowercase			: TreeNode | None  ):


      '''simple docstring'''



      if root is None:
            return 0

      # Validation
      def count_nodes(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

      def count_coins(lowercase			: TreeNode | None  ) -> int:
            if node is None:
                  return 0

            return count_coins(node.left  ) + count_coins(node.right  ) + node.data

      if count_nodes(lowercase  ) != count_coins(lowercase  ):
            raise ValueError('The nodes number should be same as the number of coins'  )

      # Main calculation
      def get_distrib(lowercase			: TreeNode | None  ) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0			,							1  )

            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.left  )
            lowerCamelCase_  ,       lowerCamelCase_	  =					get_distrib(node.right  )

            lowerCamelCase_	  =					1 - left_distrib_excess
            lowerCamelCase_	  =					1 - right_distrib_excess

            lowerCamelCase_	  =					(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowercase  )
                + abs(lowercase  )
            )
            lowerCamelCase_	  =					node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowercase			,							lowercase  )

      return get_distrib(lowercase  )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()



