

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 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       0
			__lowerCamelCase        =       False
			__lowerCamelCase        =       3.0

class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							self.assertDictEqual(MockClass().to_kwargs()      ,      {}       )
							self.assertDictEqual(MockClass(a=2       ).to_kwargs()      ,      {"a": 2}       )
							self.assertDictEqual(MockClass(a=2      ,      b=lowercase       ).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       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			GradScalerKwargs(init_scale=1024      ,      growth_factor=2       )
							AcceleratorState._reset_state()
							A__        =			Accelerator(mixed_precision="fp16"      ,      kwargs_handlers=[scaler_handler]       )
							print(accelerator.use_fpaa       )
							A__        =			accelerator.scaler

							# Check the kwargs have been applied
							self.assertEqual(scaler._init_scale      ,      1024.0       )
							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      ,      2000       )
							self.assertEqual(scaler._enabled      ,      lowercase       )


			@require_multi_gpu
			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__       )]
							execute_subprocess_async(lowercase      ,      env=os.environ.copy()       )


if __name__ == "__main__":
	lowerCAmelCase__	   = DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
	lowerCAmelCase__	   = Accelerator(kwargs_handlers=[ddp_scaler])
	lowerCAmelCase__	   = torch.nn.Linear(1_0_0, 2_0_0)
	lowerCAmelCase__	   = accelerator.prepare(model)

	# Check the values changed in kwargs
	lowerCAmelCase__	   = """"""
	lowerCAmelCase__	   = model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
	if observed_bucket_cap_map != 1_5:
		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)




import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__	   = """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase__	   = direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCAmelCase__	   = transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCAmelCase__	   = re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


lowerCAmelCase__	   = {
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      Dict:




				'''simple docstring'''





				A__        =			None

				# source code of `config_class`
				A__        =			inspect.getsource(SCREAMING_SNAKE_CASE_      )
				A__        =			_re_checkpoint.findall(SCREAMING_SNAKE_CASE_      )

				# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
				# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
				for ckpt_name, ckpt_link in checkpoints:
								# allow the link to end with `/`
								if ckpt_link.endswith("/"      ):
												A__        =			ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								A__        =			F'https://huggingface.co/{ckpt_name}'
								if ckpt_link == ckpt_link_from_name:
												A__        =			ckpt_name
												break

				return checkpoint



def 					lowerCAmelCase__							(	)			->      List[str]:




				'''simple docstring'''





				A__        =			[]

				for config_class in list(CONFIG_MAPPING.values()      ):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								A__        =			get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE_      )

								A__        =			config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(SCREAMING_SNAKE_CASE_      )

				if len(SCREAMING_SNAKE_CASE_      ) > 0:
								A__        =			"\n".join(sorted(SCREAMING_SNAKE_CASE_      )      )
								raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'      )


if __name__ == "__main__":
	check_config_docstrings_have_checkpoints()

