



'''simple docstring'''

import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 	a__     (				_lowercase	):
	__magic_name__		:    Dict	    =     ["image_processor", "tokenizer"]
	__magic_name__		:    Union[str, Any]	    =     "FlavaImageProcessor"
	__magic_name__		:    Dict	    =     ("BertTokenizer", "BertTokenizerFast")


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

					"""simple docstring"""

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

					SCREAMING_SNAKE_CASE  :						Dict      					= 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__(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= self.image_processor


	def __call__(self : Optional[int],					__UpperCAmelCase : Optional[ImageInput] = None,					__UpperCAmelCase : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,					__UpperCAmelCase : bool = True,					__UpperCAmelCase : Union[bool, str, PaddingStrategy] = False,					__UpperCAmelCase : Union[bool, str, TruncationStrategy] = False,					__UpperCAmelCase : Optional[int] = None,					__UpperCAmelCase : int = 0,					__UpperCAmelCase : Optional[int] = None,					__UpperCAmelCase : Optional[bool] = None,					__UpperCAmelCase : Optional[bool] = None,					__UpperCAmelCase : Optional[bool] = None,					__UpperCAmelCase : Optional[bool] = None,					__UpperCAmelCase : bool = False,					__UpperCAmelCase : bool = False,					__UpperCAmelCase : bool = False,					__UpperCAmelCase : bool = False,					__UpperCAmelCase : bool = True,					__UpperCAmelCase : Optional[Union[str, TensorType]] = None,					**__UpperCAmelCase : List[str],					)     ->			Tuple:

					"""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:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.tokenizer(
									    text=__UpperCAmelCase,					add_special_tokens=__UpperCAmelCase,					padding=__UpperCAmelCase,					truncation=__UpperCAmelCase,					max_length=__UpperCAmelCase,					stride=__UpperCAmelCase,					pad_to_multiple_of=__UpperCAmelCase,					return_token_type_ids=__UpperCAmelCase,					return_attention_mask=__UpperCAmelCase,					return_overflowing_tokens=__UpperCAmelCase,					return_special_tokens_mask=__UpperCAmelCase,					return_offsets_mapping=__UpperCAmelCase,					return_length=__UpperCAmelCase,					verbose=__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase,					)
					if images is not None:
									SCREAMING_SNAKE_CASE  :						Any      					= self.image_processor(
									    __UpperCAmelCase,					return_image_mask=__UpperCAmelCase,					return_codebook_pixels=__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase,					)

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


	def 				lowercase__  (self : Dict,					*__UpperCAmelCase : Optional[int],					**__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Tuple,					*__UpperCAmelCase : int,					**__UpperCAmelCase : int      )     ->			Union[str, Any]:

					"""simple docstring"""

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


	@property
	def 				lowercase__  (self : Union[str, Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.tokenizer.model_input_names
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )


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

					"""simple docstring"""

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






	@property
	def 				lowercase__  (self : Optional[Any]      )     ->			Any:

					"""simple docstring"""

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









'''simple docstring'''

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__     (				_lowercase	):
	__magic_name__		:    int	    =     0
	__magic_name__		:    bool	    =     False
	__magic_name__		:    float	    =     3.0



class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : str      )     ->			Union[str, Any]:

					"""simple docstring"""

					self.assertDictEqual(MockClass().to_kwargs(),					{}      )
					self.assertDictEqual(MockClass(a=2      ).to_kwargs(),					{'''a''': 2}      )
					self.assertDictEqual(MockClass(a=2,					b=__UpperCAmelCase      ).to_kwargs(),					{'''a''': 2, '''b''': True}      )
					self.assertDictEqual(MockClass(a=2,					c=2.25      ).to_kwargs(),					{'''a''': 2, '''c''': 2.25}      )


	@require_cuda
	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= GradScalerKwargs(init_scale=1024,					growth_factor=2      )
					AcceleratorState._reset_state()
					SCREAMING_SNAKE_CASE  :						Tuple      					= Accelerator(mixed_precision='''fp16''',					kwargs_handlers=[scaler_handler]      )
					print(accelerator.use_fpaa      )
					SCREAMING_SNAKE_CASE  :						str      					= 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,					__UpperCAmelCase      )






	@require_multi_gpu
	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

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


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

	# Check the values changed in kwargs
	snake_case_			=     """"""
	snake_case_			=     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)




