


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        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[Any]    =		'codegen'
			_UpperCamelCase		:					List[str]    =		{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}




			def __init__(							self      ,						snake_case=50400      ,						snake_case=2048      ,						snake_case=2048      ,						snake_case=4096      ,						snake_case=28      ,						snake_case=16      ,						snake_case=64      ,						snake_case=None      ,						snake_case="gelu_new"      ,						snake_case=0.0      ,						snake_case=0.0      ,						snake_case=0.0      ,						snake_case=1E-5      ,						snake_case=0.02      ,						snake_case=True      ,						snake_case=50256      ,						snake_case=50256      ,						snake_case=False      ,						**snake_case      ,						):

							'''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=snake_case      ,						eos_token_id=snake_case      ,						tie_word_embeddings=snake_case      ,						**snake_case     )







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case = "default"      ,						snake_case = None      ,						snake_case = False      ,						):

							'''simple docstring'''


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




			@property
			def 				snake_case__				(							self     ):

							'''simple docstring'''


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

							return common_inputs




			@property
			def 				snake_case__				(							self     ):

							'''simple docstring'''


							return self._config.n_layer




			@property
			def 				snake_case__				(							self     ):

							'''simple docstring'''


							return self._config.n_head




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = -1      ,						snake_case = -1      ,						snake_case = False      ,						snake_case = None      ,						):

							'''simple docstring'''


							UpperCamelCase__					=       super(snake_case      ,						self     ).generate_dummy_inputs(
							    snake_case      ,						batch_size=snake_case      ,						seq_length=snake_case      ,						is_pair=snake_case      ,						framework=snake_case     )

							# 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(snake_case     ), torch.zeros(snake_case     )) 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(snake_case      ,						snake_case      ,						dtype=snake_case     )]      ,						dim=1     )

							return ordered_inputs







			@property
			def 				snake_case__				(							self     ):

							'''simple docstring'''


							return 13






from __future__ import annotations

import numpy as np
def        UpperCamelCase_(				_A   :list[float]  )->		Union[str, Any]:
			return np.maximum(0				,     _A  )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


