

from typing import Any

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list  ,      lowerCAmelCase:  list  ,      lowerCAmelCase:  dict  ,      lowerCAmelCase:  dict  ,      lowerCAmelCase:  dict  ,      )   ->       list:
				_validation(
				    lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      )
				# Creates data structures and fill initial step
				_UpperCAmelCase       :		dict 		=    {}
				_UpperCAmelCase       :		dict 		=    {}
				for state in states_space:
								_UpperCAmelCase       :		List[str] 		=    observations_space[0]
								_UpperCAmelCase       :		int 		=    (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								_UpperCAmelCase       :		Dict 		=    None

				# Fills the data structure with the probabilities of
				# different transitions and pointers to previous states
				for o in range(1  ,      len(lowerCAmelCase ) ):
								_UpperCAmelCase       :		Union[str, Any] 		=    observations_space[o]
								_UpperCAmelCase       :		Tuple 		=    observations_space[o - 1]
								for state in states_space:
												# Calculates the argmax for probability function
												_UpperCAmelCase       :		int 		=    ""
												_UpperCAmelCase       :		Optional[Any] 		=    -1
												for k_state in states_space:
																_UpperCAmelCase       :		Any 		=    (
																    probabilities[(k_state, prior_observation)]
																    * transition_probabilities[k_state][state]
																    * emission_probabilities[state][observation]
																)
																if probability > max_probability:
																				_UpperCAmelCase       :		Optional[Any] 		=    probability
																				_UpperCAmelCase       :		str 		=    k_state

            # Update probabilities and pointers dicts
												_UpperCAmelCase       :		Union[str, Any] 		=    (
												    probabilities[(arg_max, prior_observation)]
												    * transition_probabilities[arg_max][state]
												    * emission_probabilities[state][observation]
												)

												_UpperCAmelCase       :		List[str] 		=    arg_max

    # The final observation
				_UpperCAmelCase       :		Tuple 		=    observations_space[len(lowerCAmelCase ) - 1]

				# argmax for given final observation
				_UpperCAmelCase       :		Optional[Any] 		=    ""
				_UpperCAmelCase       :		Dict 		=    -1
				for k_state in states_space:
								_UpperCAmelCase       :		Optional[int] 		=    probabilities[(k_state, final_observation)]
								if probability > max_probability:
												_UpperCAmelCase       :		Tuple 		=    probability
												_UpperCAmelCase       :		Tuple 		=    k_state
				_UpperCAmelCase       :		Union[str, Any] 		=    arg_max

				# Process pointers backwards
				_UpperCAmelCase       :		Optional[Any] 		=    last_state
				_UpperCAmelCase       :		int 		=    []
				for o in range(len(lowerCAmelCase ) - 1  ,      -1  ,      -1 ):
								result.append(lowerCAmelCase )
								_UpperCAmelCase       :		Optional[int] 		=    pointers[previous, observations_space[o]]
				result.reverse()

				return result

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      )   ->       None:
				_validate_not_empty(
				    lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      )
				_validate_lists(lowerCAmelCase  ,      lowerCAmelCase )
				_validate_dicts(
				    lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      )   ->       None:
				if not all(
				    [
				        observations_space,
				        states_space,
				        initial_probabilities,
				        transition_probabilities,
				        emission_probabilities,
				    ] ):
								raise ValueError("There's an empty parameter" )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any )   ->       None:
				_validate_list(lowerCAmelCase  ,      "observations_space" )
				_validate_list(lowerCAmelCase  ,      "states_space" )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  str )   ->       None:
				if not isinstance(_object  ,      lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    F'{var_name} must be a list'
								raise ValueError(lowerCAmelCase )
				else:
								for x in _object:
												if not isinstance(lowerCAmelCase  ,      lowerCAmelCase ):
																_UpperCAmelCase       :		str 		=    F'{var_name} must be a list of strings'
																raise ValueError(lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any  ,      )   ->       None:
				_validate_dict(lowerCAmelCase  ,      "initial_probabilities"  ,      lowerCAmelCase )
				_validate_nested_dict(lowerCAmelCase  ,      "transition_probabilities" )
				_validate_nested_dict(lowerCAmelCase  ,      "emission_probabilities" )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  str )   ->       None:
				_validate_dict(_object  ,      lowerCAmelCase  ,      lowerCAmelCase )
				for x in _object.values():
								_validate_dict(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  type  ,      lowerCAmelCase:  bool = False )   ->       None:
				if not isinstance(_object  ,      lowerCAmelCase ):
								_UpperCAmelCase       :		Union[str, Any] 		=    F'{var_name} must be a dict'
								raise ValueError(lowerCAmelCase )
				if not all(isinstance(lowerCAmelCase  ,      lowerCAmelCase ) for x in _object ):
								_UpperCAmelCase       :		Tuple 		=    F'{var_name} all keys must be strings'
								raise ValueError(lowerCAmelCase )
				if not all(isinstance(lowerCAmelCase  ,      lowerCAmelCase ) for x in _object.values() ):
								_UpperCAmelCase       :		int 		=    "nested dictionary " if nested else ""
								_UpperCAmelCase       :		Optional[Any] 		=    F'{var_name} {nested_text}all values must be {value_type.__name__}'
								raise ValueError(lowerCAmelCase )


if __name__ == "__main__":
					from doctest import testmod

					testmod()





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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_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 				a  (	UpperCAmelCase				):
						_lowercase    						=			"bloom"
						_lowercase    						=			["past_key_values"]
						_lowercase    						=			{
						    "num_hidden_layers": "n_layer",
						    "num_attention_heads": "n_head",
						}


						def __init__(       self						,					A_=250880						,					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_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    vocab_size
										# Backward compatibility with n_embed kwarg
										_UpperCAmelCase       :		str 		=    kwargs.pop("n_embed"						,					A_	)
										_UpperCAmelCase       :		List[Any] 		=    hidden_size if n_embed is None else n_embed
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Optional[Any] 		=    n_head
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[Any] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    use_cache
										_UpperCAmelCase       :		List[str] 		=    pretraining_tp
										_UpperCAmelCase       :		List[Any] 		=    apply_residual_connection_post_layernorm
										_UpperCAmelCase       :		int 		=    hidden_dropout
										_UpperCAmelCase       :		List[str] 		=    attention_dropout

										_UpperCAmelCase       :		int 		=    bos_token_id
										_UpperCAmelCase       :		str 		=    eos_token_id
										_UpperCAmelCase       :		Optional[Any] 		=    slow_but_exact

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







class 				a  (	UpperCAmelCase				):
						_lowercase    						=			version.parse("1.12"				)


						def __init__(       self						,					A_						,					A_ = "default"						,					A_ = None						,					A_ = False						,					):







										'''simple docstring'''


										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?
														_UpperCAmelCase       :		str 		=    0


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    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_	)
														_UpperCAmelCase       :		Optional[int] 		=    {0: "batch", 1: "past_sequence + sequence"}
										else:
														_UpperCAmelCase       :		Union[str, Any] 		=    {0: "batch", 1: "sequence"}

										return common_inputs


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_layer


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return self._config.n_head


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 1e-3


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = -1						,					A_ = -1						,					A_ = False						,					A_ = None						,					):







										'''simple docstring'''


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

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

										return ordered_inputs





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 13


