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 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	int    		= 0
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	float    		= 3.0






class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    self.assertDictEqual(MockClass().to_kwargs()				,				{}   )
    self.assertDictEqual(MockClass(a=2   ).to_kwargs()				,				{"a": 2}   )
    self.assertDictEqual(MockClass(a=2				,				b=__lowerCAmelCase   ).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:			str   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       GradScalerKwargs(init_scale=1_024				,				growth_factor=2   )
    AcceleratorState._reset_state()
    __UpperCAmelCase						    =       Accelerator(mixed_precision="fp16"				,				kwargs_handlers=[scaler_handler]   )
    print(accelerator.use_fpaa   )
    __UpperCAmelCase						    =       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				,				2_000   )
    self.assertEqual(scaler._enabled				,				__lowerCAmelCase   )






  @require_multi_gpu
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[str]:




    '''simple docstring'''




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


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

       # Check the values changed in kwargs
       a_							=      """"""
       a_							=      model.bucket_bytes_cap // (1024 * 1024)
       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)




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= ['image_processor', 'tokenizer']
  lowerCAmelCase__		:	Any    		= 'CLIPImageProcessor'
  lowerCAmelCase__		:	Any    		= ('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(				self:			str				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Any=None				,				**__lowerCAmelCase:			str   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       None
    if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."				,				__lowerCAmelCase				,				)
      __UpperCAmelCase						    =       kwargs.pop("feature_extractor"   )

    __UpperCAmelCase						    =       image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."   )
    if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."   )

    super().__init__(__lowerCAmelCase				,				__lowerCAmelCase   )






  def __call__(				self:			Optional[int]				,				__lowerCAmelCase:			Union[str, Any]=None				,				__lowerCAmelCase:			Dict=None				,				__lowerCAmelCase:			Optional[int]=None				,				**__lowerCAmelCase:			Optional[int]   ) -> Dict:




    '''simple docstring'''




    if text is None and images is None:
      raise ValueError("You have to specify either text or images. Both cannot be none."   )

    if text is not None:
      __UpperCAmelCase						    =       self.tokenizer(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    if images is not None:
      __UpperCAmelCase						    =       self.image_processor(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    if text is not None and images is not None:
      __UpperCAmelCase						    =       image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**__lowerCAmelCase   )				,				tensor_type=__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			str				,				*__lowerCAmelCase:			str				,				**__lowerCAmelCase:			List[Any]   ) -> Optional[Any]:




    '''simple docstring'''




    return self.tokenizer.batch_decode(*__lowerCAmelCase				,				**__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Any				,				*__lowerCAmelCase:			List[Any]				,				**__lowerCAmelCase:			Dict   ) -> Union[str, Any]:




    '''simple docstring'''




    return self.tokenizer.decode(*__lowerCAmelCase				,				**__lowerCAmelCase   )






  @property
  def 	_UpperCAmelCase				(				self:			Tuple   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer.model_input_names
    __UpperCAmelCase						    =       self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






  @property
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    warnings.warn(
        "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,				__lowerCAmelCase				,				)
    return self.image_processor_class






  @property
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[str]:




    '''simple docstring'''




    warnings.warn(
        "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,				__lowerCAmelCase				,				)
    return self.image_processor



