





'''simple docstring'''







from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase_					:    str      	=			logging.get_logger(__name__)


lowerCAmelCase_					:    Any      	=			{
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='codegen'
				__a            ={
				    'max_position_embeddings': 'n_positions',
				    'hidden_size': 'n_embd',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}




				def __init__(  self     :					Dict					,				__a     :					Any=5_04_00					,				__a     :					Union[str, Any]=20_48					,				__a     :					List[Any]=20_48					,				__a     :					Dict=40_96					,				__a     :					List[Any]=28					,				__a     :					List[str]=16					,				__a     :					List[Any]=64					,				__a     :					Any=None					,				__a     :					Tuple="gelu_new"					,				__a     :					List[str]=0.0					,				__a     :					Tuple=0.0					,				__a     :					Optional[int]=0.0					,				__a     :					Tuple=1e-5					,				__a     :					int=0.02					,				__a     :					Union[str, Any]=True					,				__a     :					List[str]=5_02_56					,				__a     :					int=5_02_56					,				__a     :					Optional[int]=False					,				**__a     :					List[Any]					,				):
								_a								= vocab_size
								_a								= n_ctx
								_a								= n_positions
								_a								= n_embd
								_a								= n_layer
								_a								= n_head
								_a								= n_inner
								_a								= rotary_dim
								_a								= activation_function
								_a								= resid_pdrop
								_a								= embd_pdrop
								_a								= attn_pdrop
								_a								= layer_norm_epsilon
								_a								= initializer_range
								_a								= use_cache

								_a								= bos_token_id
								_a								= eos_token_id

								super().__init__(
								    bos_token_id=__a					,				eos_token_id=__a					,				tie_word_embeddings=__a					,				**__a					)


class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Optional[int]					,				__a     :					PretrainedConfig					,				__a     :					str = "default"					,				__a     :					List[PatchingSpec] = None					,				__a     :					bool = 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?
												_a								= 0




				@property
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
								if self.use_past:
												self.fill_with_past_key_values_(__a					,				direction="inputs"					)
												_a								= {0: "batch", 1: "past_sequence + sequence"}
								else:
												_a								= {0: "batch", 1: "sequence"}

								return common_inputs




				@property
				def   UpperCamelCase__   (  self     :					str					):
								return self._config.n_layer




				@property
				def   UpperCamelCase__   (  self     :					int					):
								return self._config.n_head




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					PreTrainedTokenizer					,				__a     :					int = -1					,				__a     :					int = -1					,				__a     :					bool = False					,				__a     :					Optional[TensorType] = None					,				):
								_a								= 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()
								_a								= 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

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

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

								return ordered_inputs




				@property
				def   UpperCamelCase__   (  self     :					int					):
								return 13







'''simple docstring'''







import math
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					list[list[float]]					,				__a     :					list[int]					):
								_a								= 0.0
								_a								= 0.0
								for i in range(len(__a					)					):
												da += math.pow((sample[i] - weights[0][i])					,				2					)
												da += math.pow((sample[i] - weights[1][i])					,				2					)
												return 0 if da > da else 1
								return 0




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					list[list[int | float]]					,				__a     :					list[int]					,				__a     :					int					,				__a     :					float					):
								for i in range(len(__a					)					):
												weights[j][i] += alpha * (sample[i] - weights[j][i])
								return weights






def  _lowerCamelCase				( )	->	None:
				# Training Examples ( m, n )
				_a								= [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

				# weight initialization ( n, C )
				_a								= [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

				# training
				_a								= SelfOrganizingMap()
				_a								= 3
				_a								= 0.5

				for _ in range(lowercase						):
								for j in range(len(lowercase						)						):
												# training sample
												_a								= training_samples[j]

												# Compute the winning vector
												_a								= self_organizing_map.get_winner(lowercase    ,							lowercase						)

												# Update the winning vector
												_a								= self_organizing_map.update(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

    # classify test sample
				_a								= [0, 0, 0, 1]
				_a								= self_organizing_map.get_winner(lowercase    ,							lowercase						)

				# results
				print(F'Clusters that the test sample belongs to : {winner}'						)
				print(F'Weights that have been trained : {weights}'						)


# running the main() function
if __name__ == "__main__":
					main()
