

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


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''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__ (__snake_case					):
    __lowerCamelCase						:	Dict														=					"""bloom"""
    __lowerCamelCase						:	Dict														=					["""past_key_values"""]
    __lowerCamelCase						:	Optional[int]														=					{
        """num_hidden_layers""": """n_layer""",
        """num_attention_heads""": """n_head""",
    }





    def __init__(				self						,       a=25_0880						,       a=64						,       a=2						,       a=8						,       a=1e-5						,       a=0.02						,       a=True						,       a=1						,       a=2						,       a=False						,       a=0.0						,       a=0.0						,       a=1						,       a=False						,       **a						,       ):
         lowercase__   :       Any				=	vocab_size
         # Backward compatibility with n_embed kwarg
         lowercase__   :       Optional[int]				=	kwargs.pop('n_embed'						,       a)
         lowercase__   :       List[str]				=	hidden_size if n_embed is None else n_embed
         lowercase__   :       Optional[int]				=	n_layer
         lowercase__   :       List[Any]				=	n_head
         lowercase__   :       List[Any]				=	layer_norm_epsilon
         lowercase__   :       Optional[Any]				=	initializer_range
         lowercase__   :       List[Any]				=	use_cache
         lowercase__   :       Any				=	pretraining_tp
         lowercase__   :       Dict				=	apply_residual_connection_post_layernorm
         lowercase__   :       List[Any]				=	hidden_dropout
         lowercase__   :       Tuple				=	attention_dropout

         lowercase__   :       Union[str, Any]				=	bos_token_id
         lowercase__   :       Dict				=	eos_token_id
         lowercase__   :       str				=	slow_but_exact

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



class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	int														=					version.parse("""1.12"""					)





    def __init__(				self						,       a						,       a = "default"						,       a = None						,       a = False						,       ):
         super().__init__(a						,       task=a						,       patching_specs=a						,       use_past=a)
         if not getattr(self._config						,       'pad_token_id'						,       a):
              # TODO: how to do that better?
              lowercase__   :       List[Any]				=	0





    @property
    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	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_(a						,       direction='inputs'						,       inverted_values_shape=a)
              lowercase__   :       Optional[Any]				=	{0: 'batch', 1: 'past_sequence + sequence'}
         else:
              lowercase__   :       Union[str, Any]				=	{0: 'batch', 1: 'sequence'}

         return common_inputs





    @property
    def 				snake_case_    (				self):
         return self._config.n_layer





    @property
    def 				snake_case_    (				self):
         return self._config.n_head





    @property
    def 				snake_case_    (				self):
         return 1e-3





    def 				snake_case_    (				self						,       a						,       a = -1						,       a = -1						,       a = False						,       a = None						,       ):
         lowercase__   :       Optional[int]				=	super(a						,       self).generate_dummy_inputs(
             a						,       batch_size=a						,       seq_length=a						,       is_pair=a						,       framework=a)

         # We need to order the input in the way they appears in the forward()
         lowercase__   :       List[Any]				=	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

                   lowercase__   :       int				=	common_inputs['input_ids'].shape
                   # Not using the same length for past_key_values
                   lowercase__   :       Optional[int]				=	seqlen + 2
                   lowercase__   :       List[Any]				=	self._config.hidden_size // self.num_attention_heads
                   lowercase__   :       Any				=	(
                       batch * self.num_attention_heads,
                       head_dim,
                       past_key_values_length,
                   )
                   lowercase__   :       int				=	(
                       batch * self.num_attention_heads,
                       past_key_values_length,
                       head_dim,
                   )
                   lowercase__   :       Tuple				=	[
                       (torch.zeros(a), torch.zeros(a)) for _ in range(self.num_layers)
                   ]

         lowercase__   :       Tuple				=	common_inputs['attention_mask']
         if self.use_past:
              lowercase__   :       Optional[int]				=	ordered_inputs['attention_mask'].dtype
              lowercase__   :       Dict				=	torch.cat(
                  [ordered_inputs['attention_mask'], torch.ones(a						,       a						,       dtype=a)]						,       dim=1)

         return ordered_inputs





    @property
    def 				snake_case_    (				self):
         return 13




import numpy as np

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   List[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	int(np.ceil((x_end - xa) / h      )      )
     lowercase__   :       Dict				=	np.zeros((n + 1,)      )
     lowercase__   :       Dict				=	ya
     lowercase__   :       Union[str, Any]				=	xa

     for k in range(SCREAMING_SNAKE_CASE_      ):
          lowercase__   :       Optional[int]				=	f(SCREAMING_SNAKE_CASE_     ,					y[k]      )
          lowercase__   :       Dict				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       Any				=	f(x + 0.5 * h     ,					y[k] + 0.5 * h * ka      )
          lowercase__   :       List[Any]				=	f(x + h     ,					y[k] + h * ka      )
          lowercase__   :       Tuple				=	y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
          x += h

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()

