



"""simple docstring"""


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      :							Tuple   =      logging.get_logger(__name__)

__A      :							Tuple   =      {
    "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       (    lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__						=		"""bloom"""
			UpperCamelCase__						=		["""past_key_values"""]
			UpperCamelCase__						=		{
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(						self   :			Optional[Any] ,      __UpperCamelCase   :			int=2_5_0_8_8_0 ,      __UpperCamelCase   :			Tuple=6_4 ,      __UpperCamelCase   :			Optional[int]=2 ,      __UpperCamelCase   :			str=8 ,      __UpperCamelCase   :			Union[str, Any]=1e-5 ,      __UpperCamelCase   :			Union[str, Any]=0.0_2 ,      __UpperCamelCase   :			str=True ,      __UpperCamelCase   :			List[str]=1 ,      __UpperCamelCase   :			Any=2 ,      __UpperCamelCase   :			List[Any]=False ,      __UpperCamelCase   :			Union[str, Any]=0.0 ,      __UpperCamelCase   :			int=0.0 ,      __UpperCamelCase   :			str=1 ,      __UpperCamelCase   :			Optional[Any]=False ,      **__UpperCamelCase   :			Any ,      )->Any:
							_UpperCAmelCase							=			vocab_size
							# Backward compatibility with n_embed kwarg
							_UpperCAmelCase							=			kwargs.pop('''n_embed''' ,      __UpperCAmelCase      )
							_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=__UpperCAmelCase ,      eos_token_id=__UpperCAmelCase ,      **__UpperCAmelCase      )



class 	_a       (    lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__						=		version.parse("""1.12""")



			def __init__(						self   :			Optional[int] ,      __UpperCamelCase   :			PretrainedConfig ,      __UpperCamelCase   :			str = "default" ,      __UpperCamelCase   :			List[PatchingSpec] = None ,      __UpperCamelCase   :			bool = False ,      )->List[str]:
							super().__init__(__UpperCAmelCase ,      task=__UpperCAmelCase ,      patching_specs=__UpperCAmelCase ,      use_past=__UpperCAmelCase      )
							if not getattr(self._config ,      '''pad_token_id''' ,      __UpperCAmelCase      ):
											# TODO: how to do that better?
											_UpperCAmelCase							=			0



			@property
			def        lowercase__  (						self   :			Dict      )->Mapping[str, Mapping[int, str]]:
							_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_(__UpperCAmelCase ,      direction='''inputs''' ,      inverted_values_shape=__UpperCAmelCase      )
											_UpperCAmelCase							=			{0: '''batch''', 1: '''past_sequence + sequence'''}
							else:
											_UpperCAmelCase							=			{0: '''batch''', 1: '''sequence'''}

							return common_inputs



			@property
			def        lowercase__  (						self   :			Any      )->int:
							return self._config.n_layer



			@property
			def        lowercase__  (						self   :			int      )->int:
							return self._config.n_head



			@property
			def        lowercase__  (						self   :			Any      )->float:
							return 1e-3



			def        lowercase__  (						self   :			List[str] ,      __UpperCamelCase   :			"PreTrainedTokenizer" ,      __UpperCamelCase   :			int = -1 ,      __UpperCamelCase   :			int = -1 ,      __UpperCamelCase   :			bool = False ,      __UpperCamelCase   :			Optional["TensorType"] = None ,      )->Mapping[str, Any]:
							_UpperCAmelCase							=			super(__UpperCAmelCase ,      self      ).generate_dummy_inputs(
							    __UpperCAmelCase ,      batch_size=__UpperCAmelCase ,      seq_length=__UpperCAmelCase ,      is_pair=__UpperCAmelCase ,      framework=__UpperCAmelCase      )

							# 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(__UpperCAmelCase      ), torch.zeros(__UpperCAmelCase      )) 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(__UpperCAmelCase ,      __UpperCAmelCase ,      dtype=__UpperCAmelCase      )] ,      dim=1      )

							return ordered_inputs



			@property
			def        lowercase__  (						self   :			Tuple      )->int:
							return 1_3











def 						_a    (      a :int )							->			list:

 # bit count represents no. of bits in the gray code
 if bit_count < 0:
  raise ValueError('''The given input must be positive''' )

 # get the generated string sequence
 a		  =      gray_code_sequence_string(a )
 #
 # convert them to integers
 for i in range(len(a ) ):
  a		  =      int(sequence[i]	,							2 )

 return sequence
def 						_a    (      a :int )							->			list:

 # The approach is a recursive one
 # Base case achieved when either n = 0 or n=1
 if bit_count == 0:
  return ["0"]

 if bit_count == 1:
  return ["0", "1"]

 a		  =      1 << bit_count  # defines the length of the sequence
 # 1<< n is equivalent to 2^n

 # recursive answer will generate answer for n-1 bits
 a		  =      gray_code_sequence_string(bit_count - 1 )

 a		  =      []

 # append 0 to first half of the smaller sequence generated
 for i in range(seq_len // 2 ):
  a		  =      '''0''' + smaller_sequence[i]
  sequence.append(a )

 # append 1 to second half ... start from the end of the list
 for i in reversed(range(seq_len // 2 ) ):
  a		  =      '''1''' + smaller_sequence[i]
  sequence.append(a )

 return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()


