


'''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


_lowercase    :    int			     =  logging.get_logger(__name__)


_lowercase    :    int			     =  {
    """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__(     lowerCAmelCase ):
				__magic_name__       :   List[Any]							= "codegen"
				__magic_name__       :   Any							= {
				    "max_position_embeddings": "n_positions",
				    "hidden_size": "n_embd",
				    "num_attention_heads": "n_head",
				    "num_hidden_layers": "n_layer",
				}




				def __init__(	self    :				int   ,					lowerCAmelCase    :				Optional[int]=50400   ,					lowerCAmelCase    :				Tuple=2048   ,					lowerCAmelCase    :				List[Any]=2048   ,					lowerCAmelCase    :				Optional[int]=4096   ,					lowerCAmelCase    :				str=28   ,					lowerCAmelCase    :				Tuple=16   ,					lowerCAmelCase    :				List[Any]=64   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				Any="gelu_new"   ,					lowerCAmelCase    :				Tuple=0.0   ,					lowerCAmelCase    :				Union[str, Any]=0.0   ,					lowerCAmelCase    :				Optional[Any]=0.0   ,					lowerCAmelCase    :				List[Any]=1E-5   ,					lowerCAmelCase    :				Optional[int]=0.02   ,					lowerCAmelCase    :				Optional[int]=True   ,					lowerCAmelCase    :				Union[str, Any]=50256   ,					lowerCAmelCase    :				str=50256   ,					lowerCAmelCase    :				List[Any]=False   ,					**lowerCAmelCase    :				Union[str, Any]   ,					)->  Union[str, Any]:

										"""simple docstring"""




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

										UpperCAmelCase         =			bos_token_id
										UpperCAmelCase         =			eos_token_id

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







class  UpperCamelCase__(     lowerCAmelCase ):




				def __init__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				PretrainedConfig   ,					lowerCAmelCase    :				str = "default"   ,					lowerCAmelCase    :				List[PatchingSpec] = None   ,					lowerCAmelCase    :				bool = False   ,					)->  Optional[Any]:

										"""simple docstring"""




										super().__init__(lowerCAmelCase   ,					task=lowerCAmelCase   ,					patching_specs=lowerCAmelCase   ,					use_past=lowerCAmelCase   )
										if not getattr(self._config   ,					'''pad_token_id'''   ,					lowerCAmelCase   ):
																# TODO: how to do that better?
																UpperCAmelCase         =			0




				@property
				def   a__(	self    :				Any   )->  Mapping[str, Mapping[int, str]]:

										"""simple docstring"""




										UpperCAmelCase         =			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
										if self.use_past:
																self.fill_with_past_key_values_(lowerCAmelCase   ,					direction='''inputs'''   )
																UpperCAmelCase         =			{0: '''batch''', 1: '''past_sequence + sequence'''}
										else:
																UpperCAmelCase         =			{0: '''batch''', 1: '''sequence'''}

										return common_inputs




				@property
				def   a__(	self    :				Union[str, Any]   )->  int:

										"""simple docstring"""




										return self._config.n_layer




				@property
				def   a__(	self    :				int   )->  int:

										"""simple docstring"""




										return self._config.n_head




				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				PreTrainedTokenizer   ,					lowerCAmelCase    :				int = -1   ,					lowerCAmelCase    :				int = -1   ,					lowerCAmelCase    :				bool = False   ,					lowerCAmelCase    :				Optional[TensorType] = None   ,					)->  Mapping[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			super(lowerCAmelCase   ,					self   ).generate_dummy_inputs(
										    lowerCAmelCase   ,					batch_size=lowerCAmelCase   ,					seq_length=lowerCAmelCase   ,					is_pair=lowerCAmelCase   ,					framework=lowerCAmelCase   )

										# We need to order the input in the way they appears in the forward()
										UpperCAmelCase         =			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         =			common_inputs['''input_ids'''].shape
																						# Not using the same length for past_key_values
																						UpperCAmelCase         =			seqlen + 2
																						UpperCAmelCase         =			(
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						UpperCAmelCase         =			[
																						    (torch.zeros(lowerCAmelCase   ), torch.zeros(lowerCAmelCase   )) for _ in range(self.num_layers   )
																						]

										UpperCAmelCase         =			common_inputs['''attention_mask''']
										if self.use_past:
																UpperCAmelCase         =			ordered_inputs['''attention_mask'''].dtype
																UpperCAmelCase         =			torch.cat(
																    [ordered_inputs['''attention_mask'''], torch.ones(lowerCAmelCase   ,					lowerCAmelCase   ,					dtype=lowerCAmelCase   )]   ,					dim=1   )

										return ordered_inputs





				@property
				def   a__(	self    :				Dict   )->  int:

										"""simple docstring"""




										return 13





'''simple docstring'''



import functools


def    lowerCamelCase__   ( A     :    list[int]   ,     A     :    list[int]  ):




						'''simple docstring'''






						if not isinstance(A   ,     A  ) or not all(isinstance(A   ,     A  ) for day in days  ):
												raise ValueError('''The parameter days should be a list of integers'''  )

						if len(A  ) != 3 or not all(isinstance(A   ,     A  ) for cost in costs  ):
												raise ValueError('''The parameter costs should be a list of three integers'''  )

						if len(A  ) == 0:
												return 0

						if min(A  ) <= 0:
												raise ValueError('''All days elements should be greater than 0'''  )

						if max(A  ) >= 3_66:
												raise ValueError('''All days elements should be less than 366'''  )

						UpperCAmelCase         =			set(A  )

						@functools.cache
						def dynamic_programming(A     :    int  ) -> int:
												if index > 3_65:
																		return 0

												if index not in days_set:
																		return dynamic_programming(index + 1  )

												return min(
												    costs[0] + dynamic_programming(index + 1  )   ,     costs[1] + dynamic_programming(index + 7  )   ,     costs[2] + dynamic_programming(index + 30  )   ,     )

						return dynamic_programming(1  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





