



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,
)


lowerCamelCase__     =	namedtuple("""ExampleNamedTuple""", """a b c""")






class 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''
											__a					       =						torch.randn(5 ,			2		)
											__a					       =						torch.device("""cuda"""		) if torch.cuda.is_available() else torch.device("""cpu"""		)

											__a					       =						send_to_device(__lowercase ,			__lowercase		)
											self.assertTrue(torch.equal(resulta.cpu() ,			__lowercase		)		)

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

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

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

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





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											with self.assertRaises(__lowercase		) as cm:
															__a					       =						recursively_apply(torch.tensor ,			(torch.tensor(1		), 1) ,			error_on_other_type=__lowercase		)
											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   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											__a					       =						torch.tensor([1, 2, 3, 4, 5]		)
											self.assertEqual(listify(__lowercase		) ,			[1, 2, 3, 4, 5]		)

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

											__a					       =						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(__lowercase		) ,			[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]		)





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''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   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''
											__a					       =						RegressionModel()
											__a					       =						model.forward
											__a					       =						convert_outputs_to_fpaa(model.forward		)
											__a					       =						extract_model_from_parallel(__lowercase ,			keep_fpaa_wrapper=__lowercase		)
											__a					       =						pickle.dumps(__lowercase		)





							@require_cuda
							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						RegressionModel()
											__a					       =						model.forward
											__a					       =						torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
											__a					       =						convert_outputs_to_fpaa(model.forward		)
											__a					       =						extract_model_from_parallel(__lowercase ,			keep_fpaa_wrapper=__lowercase		)
											__a					       =						pickle.dumps(__lowercase		)





							@require_cuda
							@require_torch_min_version(version="""2.0"""		)
							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											__a					       =						RegressionModel()
											__a					       =						model.forward
											__a					       =						torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
											__a					       =						convert_outputs_to_fpaa(model.forward		)
											__a					       =						torch.compile(model.forward ,			backend="""inductor"""		)
											__a					       =						torch.randn(4 ,			10		).cuda()
											__a					       =						model(__lowercase		)





							def   UpperCamelCase_   (				self						:						str		):

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

											self.assertEqual(__lowercase ,			__lowercase		)





							@require_torch_min_version(version="""2.0"""		)
							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											__a					       =						RegressionModel()
											__a					       =						torch.compile(__lowercase		)

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

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







							def   UpperCamelCase_   (				self						:						List[str]		):

											'''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"""]		)		)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__     =	{
    """configuration_oneformer""": ["""ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OneFormerConfig"""],
    """processing_oneformer""": ["""OneFormerProcessor"""],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	["""OneFormerImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """OneFormerForUniversalSegmentation""",
						    """OneFormerModel""",
						    """OneFormerPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
						from .processing_oneformer import OneFormerProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_oneformer import OneFormerImageProcessor
						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_oneformer import (
												    ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    OneFormerForUniversalSegmentation,
												    OneFormerModel,
												    OneFormerPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
