






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


UpperCamelCase			    =     logging.get_logger(__name__)


UpperCamelCase			    =     {
    '''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 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				List[str]									=							"codegen"
			__snake_case    :				int									=							{
			    "max_position_embeddings": "n_positions",
			    "hidden_size": "n_embd",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}







			def __init__(     self:  Optional[int]				,     UpperCAmelCase_:  str=50_400				,     UpperCAmelCase_:  int=2_048				,     UpperCAmelCase_:  List[str]=2_048				,     UpperCAmelCase_:  Dict=4_096				,     UpperCAmelCase_:  Tuple=28				,     UpperCAmelCase_:  int=16				,     UpperCAmelCase_:  Dict=64				,     UpperCAmelCase_:  Union[str, Any]=None				,     UpperCAmelCase_:  Tuple="gelu_new"				,     UpperCAmelCase_:  Dict=0.0				,     UpperCAmelCase_:  List[str]=0.0				,     UpperCAmelCase_:  Any=0.0				,     UpperCAmelCase_:  Optional[int]=1E-5				,     UpperCAmelCase_:  Any=0.02				,     UpperCAmelCase_:  Optional[int]=True				,     UpperCAmelCase_:  Any=50_256				,     UpperCAmelCase_:  Optional[int]=50_256				,     UpperCAmelCase_:  int=False				,     **UpperCAmelCase_:  str				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  vocab_size
									_SCREAMING_SNAKE_CASE     				=  n_ctx
									_SCREAMING_SNAKE_CASE     				=  n_positions
									_SCREAMING_SNAKE_CASE     				=  n_embd
									_SCREAMING_SNAKE_CASE     				=  n_layer
									_SCREAMING_SNAKE_CASE     				=  n_head
									_SCREAMING_SNAKE_CASE     				=  n_inner
									_SCREAMING_SNAKE_CASE     				=  rotary_dim
									_SCREAMING_SNAKE_CASE     				=  activation_function
									_SCREAMING_SNAKE_CASE     				=  resid_pdrop
									_SCREAMING_SNAKE_CASE     				=  embd_pdrop
									_SCREAMING_SNAKE_CASE     				=  attn_pdrop
									_SCREAMING_SNAKE_CASE     				=  layer_norm_epsilon
									_SCREAMING_SNAKE_CASE     				=  initializer_range
									_SCREAMING_SNAKE_CASE     				=  use_cache

									_SCREAMING_SNAKE_CASE     				=  bos_token_id
									_SCREAMING_SNAKE_CASE     				=  eos_token_id

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



class 				__UpperCAmelCase		(_UpperCAmelCase   ):







			def __init__(     self:  str				,     UpperCAmelCase_:  PretrainedConfig				,     UpperCAmelCase_:  str = "default"				,     UpperCAmelCase_:  List[PatchingSpec] = None				,     UpperCAmelCase_:  bool = False				,     ):







									'''simple docstring'''




									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?
															_SCREAMING_SNAKE_CASE     				=  0







			@property
			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}					)
									if self.use_past:
															self.fill_with_past_key_values_(UpperCAmelCase_				,     direction="""inputs"""					)
															_SCREAMING_SNAKE_CASE     				=  {0: """batch""", 1: """past_sequence + sequence"""}
									else:
															_SCREAMING_SNAKE_CASE     				=  {0: """batch""", 1: """sequence"""}

									return common_inputs







			@property
			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									return self._config.n_layer







			@property
			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									return self._config.n_head







			def 							UpperCamelCase				(     self:  Tuple				,     UpperCAmelCase_:  PreTrainedTokenizer				,     UpperCAmelCase_:  int = -1				,     UpperCAmelCase_:  int = -1				,     UpperCAmelCase_:  bool = False				,     UpperCAmelCase_:  Optional[TensorType] = None				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  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()
									_SCREAMING_SNAKE_CASE     				=  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

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

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

									return ordered_inputs







			@property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									return 13









from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  prime_factors(snake_case__		)
						if is_square_free(snake_case__		):
												return -1 if len(snake_case__		) % 2 else 1
						return 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()

