





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class    __snake_case	(							__UpperCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__									=					0
       lowerCAmelCase__									=					False
       lowerCAmelCase__									=					3.0







class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       def 							UpperCAmelCase__			(  self			:   List[str]		):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()							,    {}		)
         self.assertDictEqual(MockClass(a=2		).to_kwargs()							,    {"""a""": 2}		)
         self.assertDictEqual(MockClass(a=2							,    b=A		).to_kwargs()							,    {"""a""": 2, """b""": True}		)
         self.assertDictEqual(MockClass(a=2							,    c=2.25		).to_kwargs()							,    {"""a""": 2, """c""": 2.25}		)




       @require_cuda
       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __snake_case:						str			  =						GradScalerKwargs(init_scale=1_024							,    growth_factor=2		)
         AcceleratorState._reset_state()
         __snake_case:						Tuple			  =						Accelerator(mixed_precision="""fp16"""							,    kwargs_handlers=[scaler_handler]		)
         print(accelerator.use_fpaa		)
         __snake_case:						List[Any]			  =						accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale							,    1_024.0		)
         self.assertEqual(scaler._growth_factor							,    2.0		)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor							,    0.5		)
         self.assertEqual(scaler._growth_interval							,    2_000		)
         self.assertEqual(scaler._enabled							,    A		)






       @require_multi_gpu
       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Any			  =						['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__		)]
         execute_subprocess_async(A							,    env=os.environ.copy()		)


if __name__ == "__main__":
      __UpperCAmelCase	:       Tuple    	=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
      __UpperCAmelCase	:       Dict    	=							Accelerator(kwargs_handlers=[ddp_scaler])
      __UpperCAmelCase	:       Optional[int]    	=							torch.nn.Linear(100, 200)
      __UpperCAmelCase	:       Any    	=							accelerator.prepare(model)

      # Check the values changed in kwargs
      __UpperCAmelCase	:       List[Any]    	=							""""""
      __UpperCAmelCase	:       Union[str, Any]    	=							model.bucket_bytes_cap // (1_024 * 1_024)
      if observed_bucket_cap_map != 15:
            error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
      if model.find_unused_parameters is not True:
            error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

      # Check the values of the defaults
      if model.dim != 0:
            error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
      if model.broadcast_buffers is not True:
            error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
      if model.gradient_as_bucket_view is not False:
            error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)













from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase	:       str    	=							logging.get_logger(__name__)

__UpperCAmelCase	:       int    	=							{
    "RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
    "RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
    "RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
    "RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
    "RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
    "RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					"""rwkv"""
       lowerCAmelCase__						=					{"""max_position_embeddings""": """context_length"""}




       def __init__(  self			:   Dict							,    A			:   List[Any]=50_277							,    A			:   List[Any]=1_024							,    A			:   Union[str, Any]=4_096							,    A			:   Tuple=32							,    A			:   List[Any]=None							,    A			:   Tuple=None							,    A			:   Tuple=1E-5							,    A			:   int=0							,    A			:   Optional[int]=0							,    A			:   Dict=6							,    A			:   Dict=False							,    A			:   int=True							,    **A			:   List[Any]							,    ):
         __snake_case:						Tuple			  =						vocab_size
         __snake_case:						Any			  =						context_length
         __snake_case:						Dict			  =						hidden_size
         __snake_case:						Dict			  =						num_hidden_layers
         __snake_case:						Union[str, Any]			  =						attention_hidden_size if attention_hidden_size is not None else hidden_size
         __snake_case:						str			  =						intermediate_size if intermediate_size is not None else 4 * hidden_size
         __snake_case:						Any			  =						layer_norm_epsilon
         __snake_case:						int			  =						rescale_every
         __snake_case:						str			  =						use_cache

         __snake_case:						Dict			  =						bos_token_id
         __snake_case:						Union[str, Any]			  =						eos_token_id

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






