



"""simple docstring"""







import numpy

# List of input, output pairs
A		=						(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
A		=						(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A		=						[2, 4, 1, 5]
A		=						len(train_data)
A		=						0.009

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Dict="train" ):
						"""simple docstring"""






						return calculate_hypothesis_value(lowerCamelCase_     ,		lowerCamelCase_ ) - output(
						    lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Dict						    = 0
						for i in range(len(lowerCamelCase_ ) - 1 ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0] )
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0] )
						return None

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							Any=m ):
						"""simple docstring"""






						snake_case   :			Any						    = 0
						for i in range(lowerCamelCase_ ):
												if index == -1:
																		summation_value += _error(lowerCamelCase_ )
												else:
																		summation_value += _error(lowerCamelCase_ ) * train_data[i][0][index]
						return summation_value

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = summation_of_cost_derivative(lowerCamelCase_     ,		lowerCamelCase_ ) / m
						return cost_derivative_value

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						snake_case   :			str						    = 0.0_0_0_0_0_2
						snake_case   :			Union[str, Any]						    = 0
						snake_case   :			Optional[int]						    = 0
						while True:
												j += 1
												snake_case   :			List[Any]						    = [0, 0, 0, 0]
												for i in range(0     ,		len(lowerCamelCase_ ) ):
																		snake_case   :			int						    = get_cost_derivative(i - 1 )
																		snake_case   :			List[Any]						    = (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    lowerCamelCase_     ,		lowerCamelCase_     ,		atol=lowerCamelCase_     ,		rtol=lowerCamelCase_     ,		):
																		break
												snake_case   :			List[str]						    = temp_parameter_vector
						print(("Number of iterations:", j) )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						for i in range(len(lowerCamelCase_ ) ):
												print(("Actual output value:", output(lowerCamelCase_     ,		"test" )) )
												print(("Hypothesis output:", calculate_hypothesis_value(lowerCamelCase_     ,		"test" )) )


if __name__ == "__main__":
			run_gradient_descent()
			print('\nTesting gradient descent for a linear hypothesis function.\n')
			test_gradient_descent()








"""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		=						logging.get_logger(__name__)

A		=						{
    '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  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """bloom"""
			__magic_name__     					=     ["""past_key_values"""]
			__magic_name__     					=     {
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(			self							:				Tuple   ,	_lowercase							:				List[Any]=250880   ,	_lowercase							:				int=64   ,	_lowercase							:				Optional[Any]=2   ,	_lowercase							:				Dict=8   ,	_lowercase							:				List[str]=1E-5   ,	_lowercase							:				List[Any]=0.02   ,	_lowercase							:				Optional[Any]=True   ,	_lowercase							:				Dict=1   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				int=False   ,	**_lowercase							:				int   ,	)							->					List[str]:
									snake_case   :			Any						    = vocab_size
									# Backward compatibility with n_embed kwarg
									snake_case   :			Any						    = kwargs.pop("n_embed"   ,	_lowercase		)
									snake_case   :			Tuple						    = hidden_size if n_embed is None else n_embed
									snake_case   :			Optional[Any]						    = n_layer
									snake_case   :			Optional[Any]						    = n_head
									snake_case   :			Union[str, Any]						    = layer_norm_epsilon
									snake_case   :			int						    = initializer_range
									snake_case   :			int						    = use_cache
									snake_case   :			int						    = pretraining_tp
									snake_case   :			Tuple						    = apply_residual_connection_post_layernorm
									snake_case   :			Union[str, Any]						    = hidden_dropout
									snake_case   :			Optional[Any]						    = attention_dropout

									snake_case   :			List[Any]						    = bos_token_id
									snake_case   :			Any						    = eos_token_id
									snake_case   :			Optional[Any]						    = slow_but_exact

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



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.12""")



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



			@property
			def 				__lowercase					(			self							:				str		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			int						    = 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_(_lowercase   ,	direction="inputs"   ,	inverted_values_shape=_lowercase		)
															snake_case   :			Optional[int]						    = {0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :			Dict						    = {0: "batch", 1: "sequence"}

									return common_inputs



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



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



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					float:
									return 1E-3



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

									# We need to order the input in the way they appears in the forward()
									snake_case   :			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

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

									snake_case   :			Tuple						    = common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :			Any						    = ordered_inputs["attention_mask"].dtype
															snake_case   :			List[str]						    = torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(_lowercase   ,	_lowercase   ,	dtype=_lowercase		)]   ,	dim=1		)

									return ordered_inputs



			@property
			def 				__lowercase					(			self							:				str		)							->					int:
									return 13



