


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


_snake_case	:						str         =							logging.get_logger(__name__)


_snake_case	:						Tuple         =							{
    "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 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[Any]	    =    "codegen"
							__UpperCAmelCase       :				Dict	    =    {
							    "max_position_embeddings": "n_positions",
							    "hidden_size": "n_embd",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}

							def __init__(				self			:    Optional[int]       ,      lowerCamelCase			:    Dict=50400       ,      lowerCamelCase			:    int=2048       ,      lowerCamelCase			:    List[Any]=2048       ,      lowerCamelCase			:    List[Any]=4096       ,      lowerCamelCase			:    Dict=28       ,      lowerCamelCase			:    List[str]=16       ,      lowerCamelCase			:    str=64       ,      lowerCamelCase			:    Any=None       ,      lowerCamelCase			:    Optional[int]="gelu_new"       ,      lowerCamelCase			:    Any=0.0       ,      lowerCamelCase			:    Union[str, Any]=0.0       ,      lowerCamelCase			:    int=0.0       ,      lowerCamelCase			:    List[str]=1E-5       ,      lowerCamelCase			:    List[Any]=0.02       ,      lowerCamelCase			:    List[str]=True       ,      lowerCamelCase			:    Optional[Any]=50256       ,      lowerCamelCase			:    int=50256       ,      lowerCamelCase			:    str=False       ,      **lowerCamelCase			:    int       ,      )      ->       Union[str, Any]:
														__snake_case						:     str									=  vocab_size
														__snake_case						:     List[Any]									=  n_ctx
														__snake_case						:     Tuple									=  n_positions
														__snake_case						:     int									=  n_embd
														__snake_case						:     List[str]									=  n_layer
														__snake_case						:     int									=  n_head
														__snake_case						:     Union[str, Any]									=  n_inner
														__snake_case						:     List[Any]									=  rotary_dim
														__snake_case						:     Tuple									=  activation_function
														__snake_case						:     str									=  resid_pdrop
														__snake_case						:     Dict									=  embd_pdrop
														__snake_case						:     Optional[int]									=  attn_pdrop
														__snake_case						:     Dict									=  layer_norm_epsilon
														__snake_case						:     Tuple									=  initializer_range
														__snake_case						:     Union[str, Any]									=  use_cache

														__snake_case						:     Dict									=  bos_token_id
														__snake_case						:     Any									=  eos_token_id

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

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							def __init__(				self			:    str       ,      lowerCamelCase			:    PretrainedConfig       ,      lowerCamelCase			:    str = "default"       ,      lowerCamelCase			:    List[PatchingSpec] = None       ,      lowerCamelCase			:    bool = False       ,      )      ->       Any:
														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?
																					__snake_case						:     Any									=  0

							@property
							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Mapping[str, Mapping[int, str]]:
														__snake_case						:     Dict									=  OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}							)
														if self.use_past:
																					self.fill_with_past_key_values_(lowerCamelCase       ,      direction="inputs"							)
																					__snake_case						:     Optional[Any]									=  {0: "batch", 1: "past_sequence + sequence"}
														else:
																					__snake_case						:     int									=  {0: "batch", 1: "sequence"}

														return common_inputs

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

							@property
							def        __snake_case   (				self			:    List[str]							)      ->       int:
														return self._config.n_head

							def        __snake_case   (				self			:    Tuple       ,      lowerCamelCase			:    PreTrainedTokenizer       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    int = -1       ,      lowerCamelCase			:    bool = False       ,      lowerCamelCase			:    Optional[TensorType] = None       ,      )      ->       Mapping[str, Any]:
														__snake_case						:     Union[str, Any]									=  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()
														__snake_case						:     List[str]									=  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						:     Optional[Any]									=  common_inputs["input_ids"].shape
																												# Not using the same length for past_key_values
																												__snake_case						:     Optional[Any]									=  seqlen + 2
																												__snake_case						:     str									=  (
																												    batch,
																												    self.num_attention_heads,
																												    past_key_values_length,
																												    self._config.hidden_size // self.num_attention_heads,
																												)
																												__snake_case						:     str									=  [
																												    (torch.zeros(lowerCamelCase							), torch.zeros(lowerCamelCase							)) for _ in range(self.num_layers							)
																												]

														__snake_case						:     List[Any]									=  common_inputs["attention_mask"]
														if self.use_past:
																					__snake_case						:     List[Any]									=  ordered_inputs["attention_mask"].dtype
																					__snake_case						:     Any									=  torch.cat(
																					    [ordered_inputs["attention_mask"], torch.ones(lowerCamelCase       ,      lowerCamelCase       ,      dtype=lowerCamelCase							)]       ,      dim=1							)

														return ordered_inputs






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








def   __lowerCamelCase   (				__a  :int       )						->							Dict:






		"""simple docstring"""

		A__     =  len(__a       )
		A__     =  sum(__a       )

		A__     =  [[False for x in range(s + 1       )] for y in range(n + 1       )]

		for i in range(1       ,	n + 1       ):
				A__     =  True

		for i in range(1       ,	s + 1       ):
				A__     =  False

		for i in range(1       ,	n + 1       ):
				for j in range(1       ,	s + 1       ):
						A__     =  dp[i][j - 1]

						if arr[i - 1] <= j:
								A__     =  dp[i][j] or dp[i - 1][j - arr[i - 1]]

		for j in range(int(s / 2       )       ,	-1       ,	-1       ):
				if dp[n][j] is True:
						A__     =  s - 2 * j
						break

		return diff



