
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


_A     =  logging.get_logger(__name__)


_A     =  {
    "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 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''codegen'''
				_lowercase    	={
				    '''max_position_embeddings''': '''n_positions''',
				    '''hidden_size''': '''n_embd''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(      self       ,       _UpperCamelCase=50_400       ,       _UpperCamelCase=2_048       ,       _UpperCamelCase=2_048       ,       _UpperCamelCase=4_096       ,       _UpperCamelCase=28       ,       _UpperCamelCase=16       ,       _UpperCamelCase=64       ,       _UpperCamelCase=None       ,       _UpperCamelCase="gelu_new"       ,       _UpperCamelCase=0.0       ,       _UpperCamelCase=0.0       ,       _UpperCamelCase=0.0       ,       _UpperCamelCase=1e-5       ,       _UpperCamelCase=0.02       ,       _UpperCamelCase=True       ,       _UpperCamelCase=50_256       ,       _UpperCamelCase=50_256       ,       _UpperCamelCase=False       ,       **_UpperCamelCase       ,       )       ->   Dict:
							lowerCAmelCase_      			=			vocab_size
							lowerCAmelCase_      			=			n_ctx
							lowerCAmelCase_      			=			n_positions
							lowerCAmelCase_      			=			n_embd
							lowerCAmelCase_      			=			n_layer
							lowerCAmelCase_      			=			n_head
							lowerCAmelCase_      			=			n_inner
							lowerCAmelCase_      			=			rotary_dim
							lowerCAmelCase_      			=			activation_function
							lowerCAmelCase_      			=			resid_pdrop
							lowerCAmelCase_      			=			embd_pdrop
							lowerCAmelCase_      			=			attn_pdrop
							lowerCAmelCase_      			=			layer_norm_epsilon
							lowerCAmelCase_      			=			initializer_range
							lowerCAmelCase_      			=			use_cache

							lowerCAmelCase_      			=			bos_token_id
							lowerCAmelCase_      			=			eos_token_id

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





class 			_lowerCAmelCase      (			__a ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = "default"       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False       ,       )       ->   List[Any]:
							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?
										lowerCAmelCase_      			=			0




				@property
				def   __a		(      self  )       ->   Mapping[str, Mapping[int, str]]:
							lowerCAmelCase_      			=			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}  )
							if self.use_past:
										self.fill_with_past_key_values_(_UpperCamelCase       ,       direction="inputs"  )
										lowerCAmelCase_      			=			{0: "batch", 1: "past_sequence + sequence"}
							else:
										lowerCAmelCase_      			=			{0: "batch", 1: "sequence"}

							return common_inputs




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




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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = -1       ,       _UpperCamelCase = -1       ,       _UpperCamelCase = False       ,       _UpperCamelCase = None       ,       )       ->   Mapping[str, Any]:
							lowerCAmelCase_      			=			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()
							lowerCAmelCase_      			=			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

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

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

							return ordered_inputs




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





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_A     =  datasets.load_iris()

_A     =  np.array(data["data"])
_A     =  np.array(data["target"])
_A     =  data["target_names"]

_A,   _A,   _A,   _A     =  train_test_split(X, y)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			return np.linalg.norm(np.array(__lowerCAmelCase       ) - np.array(__lowerCAmelCase       )       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			zip(__lowerCAmelCase					,							__lowerCAmelCase       )
			# List of distances of all points from the point to be classified
			lowerCAmelCase_      			=			[]
			for data_point in data:
						lowerCAmelCase_      			=			euclidean_distance(data_point[0]					,							__lowerCAmelCase       )
						distances.append((distance, data_point[1])       )
			# Choosing 'k' points with the least distances.
			lowerCAmelCase_      			=			[i[1] for i in sorted(__lowerCAmelCase       )[:k]]
			# Most commonly occurring class among them
			# is the class into which the point is classified
			lowerCAmelCase_      			=			Counter(__lowerCAmelCase       ).most_common(1       )[0][0]
			return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



