



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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






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







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






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



					@require_cuda
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    GradScalerKwargs(init_scale=10_24				,					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				,					20_00					)
								self.assertEqual(scaler._enabled				,					_snake_case					)



					@require_multi_gpu
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


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


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

							# Check the values changed in kwargs
							SCREAMING_SNAKE_CASE__       =      ''''''
							SCREAMING_SNAKE_CASE__       =      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)









from typing import List, Optional, Union

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


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



					def __init__(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_snake_case					:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,					_snake_case					:       bool = True				,					_snake_case					:       Union[bool, str, PaddingStrategy] = False				,					_snake_case					:       Union[bool, str, TruncationStrategy] = None				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       int = 0				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = True				,					_snake_case					:       Optional[Union[str, TensorType]] = None				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    self.tokenizer(
								    text=_snake_case				,					add_special_tokens=_snake_case				,					padding=_snake_case				,					truncation=_snake_case				,					max_length=_snake_case				,					stride=_snake_case				,					pad_to_multiple_of=_snake_case				,					return_token_type_ids=_snake_case				,					return_attention_mask=_snake_case				,					return_overflowing_tokens=_snake_case				,					return_special_tokens_mask=_snake_case				,					return_offsets_mapping=_snake_case				,					return_length=_snake_case				,					verbose=_snake_case				,					return_tensors=_snake_case				,					**_snake_case				,					)
								# add pixel_values + pixel_mask
								A__       =    self.image_processor(
								    _snake_case				,					return_tensors=_snake_case				,					do_normalize=_snake_case				,					do_center_crop=_snake_case				,					**_snake_case					)
								encoding.update(_snake_case					)

								return encoding



					def     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


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



					@property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


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




