






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


__a:     Optional[Any]   					=					logging.get_logger(__name__)

__a:     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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  '''bloom'''
  _lowerCamelCase    =  ['''past_key_values''']
  _lowerCamelCase    =  {
      '''num_hidden_layers''': '''n_layer''',
      '''num_attention_heads''': '''n_head''',
  }


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			Any=25_0880       ,				lowerCamelCase		:			List[str]=64       ,				lowerCamelCase		:			Tuple=2       ,				lowerCamelCase		:			Any=8       ,				lowerCamelCase		:			List[Any]=1E-5       ,				lowerCamelCase		:			Any=0.02       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			Any=1       ,				lowerCamelCase		:			Any=2       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			List[Any]=0.0       ,				lowerCamelCase		:			List[str]=0.0       ,				lowerCamelCase		:			Tuple=1       ,				lowerCamelCase		:			Dict=False       ,				**lowerCamelCase		:			Dict       ,				)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    vocab_size
         # Backward compatibility with n_embed kwarg
         _UpperCAmelCase							=    kwargs.pop("""n_embed"""       ,				lowerCamelCase							)
         _UpperCAmelCase							=    hidden_size if n_embed is None else n_embed
         _UpperCAmelCase							=    n_layer
         _UpperCAmelCase							=    n_head
         _UpperCAmelCase							=    layer_norm_epsilon
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    use_cache
         _UpperCAmelCase							=    pretraining_tp
         _UpperCAmelCase							=    apply_residual_connection_post_layernorm
         _UpperCAmelCase							=    hidden_dropout
         _UpperCAmelCase							=    attention_dropout

         _UpperCAmelCase							=    bos_token_id
         _UpperCAmelCase							=    eos_token_id
         _UpperCAmelCase							=    slow_but_exact

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



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







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


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


         """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?
                _UpperCAmelCase							=    0


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


         """simple docstring"""
         _UpperCAmelCase							=    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							)
                _UpperCAmelCase							=    {0: """batch""", 1: """past_sequence + sequence"""}
         else:
                _UpperCAmelCase							=    {0: """batch""", 1: """sequence"""}

         return common_inputs


  @property
  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         return self._config.n_layer


  @property
  def  lowerCamelCase       (      self		:			Tuple							)						->	int:


         """simple docstring"""
         return self._config.n_head


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


         """simple docstring"""
         return 1E-3


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


         """simple docstring"""
         _UpperCAmelCase							=    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()
         _UpperCAmelCase							=    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

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

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

         return ordered_inputs





  @property
  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         return 13


'''simple docstring'''



def       snake_case   (	snake_case      : list			) ->  list:


    """simple docstring"""


    if any(not isinstance(snake_case					,		snake_case			) or x < 0 for x in sequence			):
        raise TypeError('Sequence must be list of non-negative integers'			)
    for _ in range(len(snake_case			)			):
        for i, (rod_upper, rod_lower) in enumerate(zip(snake_case					,		sequence[1:]			)			):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]





