






import warnings

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



class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   ['''image_processor''', '''tokenizer''']
	A						       =   '''AutoImageProcessor'''
	A						       =   '''AutoTokenizer'''





	def __init__(       self			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    **SCREAMING_SNAKE_CASE			)     ->				Dict:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_			=	image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
							raise ValueError('You have to specify an image_processor.'			)
				if tokenizer is None:
							raise ValueError('You have to specify a tokenizer.'			)

				super().__init__(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	self.image_processor





	def __call__(       self			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    **SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:




				"""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_			=	self.tokenizer(SCREAMING_SNAKE_CASE			,    return_tensors=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)

				if images is not None:
							SCREAMING_SNAKE_CASE_			=	self.image_processor(SCREAMING_SNAKE_CASE			,    return_tensors=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)

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





	def 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				str:




				"""simple docstring"""



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





	def 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				List[str]:




				"""simple docstring"""



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





	@property
	def 	A_(       self			)     ->				str:




				"""simple docstring"""



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





	@property
	def 	A_(       self			)     ->				str:




				"""simple docstring"""



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





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




				"""simple docstring"""



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






import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					namedtuple("ExampleNamedTuple", "a b c")



class 			a_					(    unittest.TestCase    ):





	def 	A_(       self			)     ->				List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch.randn(5			,    2			)
				SCREAMING_SNAKE_CASE_			=	torch.device('cuda'			) if torch.cuda.is_available() else torch.device('cpu'			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.cpu()			,    SCREAMING_SNAKE_CASE			)			)

				SCREAMING_SNAKE_CASE_			=	send_to_device((tensor, [tensor, tensor], 1)			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta[1]			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta[1][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta[1][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta[2]			,    1			)

				SCREAMING_SNAKE_CASE_			=	send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta['c']			,    1			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(ExampleNamedTuple(a=SCREAMING_SNAKE_CASE			,    b=[tensor, tensor]			,    c=1			)			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.a.cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta.b			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.b[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta.b[1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta.c			,    1			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}			)			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta['c']			,    1			)





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				with self.assertRaises(SCREAMING_SNAKE_CASE			) as cm:
							SCREAMING_SNAKE_CASE_			=	recursively_apply(torch.tensor			,    (torch.tensor(1			), 1)			,    error_on_other_type=SCREAMING_SNAKE_CASE			)
				self.assertEqual(
				    str(cm.exception			)			,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'			,    )





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch.tensor([1, 2, 3, 4, 5]			)
				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [1, 2, 3, 4, 5]			)

				SCREAMING_SNAKE_CASE_			=	torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)

				SCREAMING_SNAKE_CASE_			=	torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)
				self.assertEqual(
				    listify(SCREAMING_SNAKE_CASE			)			,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)





	def 	A_(       self			)     ->				Tuple:




				"""simple docstring"""



				with patch_environment(aa=1			,    BB=2			):
							self.assertEqual(os.environ.get('AA'			)			,    '1'			)
							self.assertEqual(os.environ.get('BB'			)			,    '2'			)

				self.assertNotIn('AA'			,    os.environ			)
				self.assertNotIn('BB'			,    os.environ			)





	def 	A_(       self			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)





	@require_cuda
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)





	@require_cuda
	@require_torch_min_version(version='2.0'			)
	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	torch.compile(model.forward			,    backend='inductor'			)
				SCREAMING_SNAKE_CASE_			=	torch.randn(4			,    10			).cuda()
				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)

				self.assertEqual(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)





	@require_torch_min_version(version='2.0'			)
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)

				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)

				self.assertEqual(compiled_model._orig_mod			,    compiled_model_unwrapped._orig_mod			)





	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]			)]			)			,    torch.device('cpu'			)			)
				self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]			)}			)			,    torch.device('cpu'			)			)
				self.assertIsNone(find_device([1, 'a']			)			)
