



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   ):
			UpperCamelCase       :int	 =      0
			UpperCamelCase       :bool	 =      False
			UpperCamelCase       :float	 =      3.0







class 					__A       (			unittest.TestCase   ):





			def 							_snake_case     (self					):
						# 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 							_snake_case     (self					):
						# If no defaults are changed, `to_kwargs` returns an empty dict.
						lowerCamelCase__			:   Tuple										=		GradScalerKwargs(init_scale=1024				,     growth_factor=2					)
						AcceleratorState._reset_state()
						lowerCamelCase__			:   List[Any]										=		Accelerator(mixed_precision="""fp16"""				,     kwargs_handlers=[scaler_handler]					)
						print(accelerator.use_fpaa					)
						lowerCamelCase__			:   Union[str, Any]										=		accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale				,     10_24.0					)
						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				,     _a					)





			@require_multi_gpu
			def 							_snake_case     (self					):
						lowerCamelCase__			:   int										=		['''torchrun''', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__					)]
						execute_subprocess_async(_a				,     env=os.environ.copy()					)


if __name__ == "__main__":
				_lowercase  				=    DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
				_lowercase  				=    Accelerator(kwargs_handlers=[ddp_scaler])
				_lowercase  				=    torch.nn.Linear(1_00, 2_00)
				_lowercase  				=    accelerator.prepare(model)

				# Check the values changed in kwargs
				_lowercase  				=    ''''''
				_lowercase  				=    model.bucket_bytes_cap // (10_24 * 10_24)
				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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
			from transformers import TvltImageProcessor

if is_speech_available():
			from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       int:
												_a      :       Dict   =						'''ZinengTang/tvlt-base'''
												_a      :       List[str]   =						tempfile.mkdtemp()


							def 							__lowercase							(     self			,				**_a			)       ->       int:
												return TvltImageProcessor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			,				**_a			)       ->       List[Any]:
												return TvltFeatureExtractor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												shutil.rmtree(self.tmpdirname			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Union[str, Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Optional[int]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)
												processor.save_pretrained(self.tmpdirname			)
												_a      :       Any   =						TvltProcessor.from_pretrained(self.tmpdirname			)

												self.assertIsInstance(processor.feature_extractor			,				_a			)
												self.assertIsInstance(processor.image_processor			,				_a			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       Union[str, Any]   =						np.ones([1_2_0_0_0]			)

												_a      :       Dict   =						feature_extractor(_a			,				return_tensors='''np'''			)
												_a      :       Tuple   =						processor(audio=_a			,				return_tensors='''np'''			)

												for key in audio_dict.keys():
																	self.assertAlmostEqual(audio_dict[key].sum()			,				input_processor[key].sum()			,				delta=1e-2			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Optional[Any]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[Any]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						image_processor(_a			,				return_tensors='''np'''			)
												_a      :       Optional[int]   =						processor(images=_a			,				return_tensors='''np'''			)

												for key in image_dict.keys():
																	self.assertAlmostEqual(image_dict[key].sum()			,				input_processor[key].sum()			,				delta=1e-2			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Any   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[str]   =						np.ones([1_2_0_0_0]			)
												_a      :       Optional[int]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						processor(audio=_a			,				images=_a			)

												self.assertListEqual(list(inputs.keys()			)			,				['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']			)

												# test if it raises when no input is passed
												with pytest.raises(_a			):
																	processor()




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       str   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												self.assertListEqual(
												    processor.model_input_names			,				image_processor.model_input_names + feature_extractor.model_input_names			,				msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''			,				)



