




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE     : str      ={"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =[
          """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """YosoForMaskedLM""",
          """YosoForMultipleChoice""",
          """YosoForQuestionAnswering""",
          """YosoForSequenceClassification""",
          """YosoForTokenClassification""",
          """YosoLayer""",
          """YosoModel""",
          """YosoPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_yoso import (
                YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                YosoForMaskedLM,
                YosoForMultipleChoice,
                YosoForQuestionAnswering,
                YosoForSequenceClassification,
                YosoForTokenClassification,
                YosoLayer,
                YosoModel,
                YosoPreTrainedModel,
            )


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Dict      =_LazyModule(__name__, globals()["""__file__"""], _import_structure)




"""simple docstring"""







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,
)


UpperCAmelCase:  Union[str, Any]			     =				namedtuple("""ExampleNamedTuple""", """a b c""")





class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   torch.randn(5				,2   )
			_lowercase				: List[Any]     =   torch.device("""cuda"""   ) if torch.cuda.is_available() else torch.device("""cpu"""   )

			_lowercase				: int     =   send_to_device(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta.cpu()				,UpperCAmelCase_   )   )

			_lowercase				: int     =   send_to_device((tensor, [tensor, tensor], 1)				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta[0].cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta[1]				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta[1][0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta[1][1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta[2]				,1   )

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

			_lowercase				: Optional[int]     =   send_to_device(ExampleNamedTuple(a=UpperCAmelCase_				,b=[tensor, tensor]				,c=1   )				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta.a.cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta.b				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta.b[0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta.b[1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta.c				,1   )

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






	def    lowerCamelCase__					(     self   ):
			with self.assertRaises(UpperCAmelCase_   ) as cm:
					_lowercase				: str     =   recursively_apply(torch.tensor				,(torch.tensor(1   ), 1)				,error_on_other_type=UpperCAmelCase_   )
			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    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   torch.tensor([1, 2, 3, 4, 5]   )
			self.assertEqual(listify(UpperCAmelCase_   )				,[1, 2, 3, 4, 5]   )

			_lowercase				: Optional[int]     =   torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]   )
			self.assertEqual(listify(UpperCAmelCase_   )				,[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]   )

			_lowercase				: Optional[Any]     =   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(UpperCAmelCase_   )				,[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]   )






	def    lowerCamelCase__					(     self   ):
			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    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   RegressionModel()
			_lowercase				: List[Any]     =   model.forward
			_lowercase				: Optional[Any]     =   convert_outputs_to_fpaa(model.forward   )
			_lowercase				: Any     =   extract_model_from_parallel(UpperCAmelCase_				,keep_fpaa_wrapper=UpperCAmelCase_   )
			_lowercase				: Tuple     =   pickle.dumps(UpperCAmelCase_   )






	@require_cuda
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   RegressionModel()
			_lowercase				: Any     =   model.forward
			_lowercase				: Any     =   torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
			_lowercase				: Optional[Any]     =   convert_outputs_to_fpaa(model.forward   )
			_lowercase				: Any     =   extract_model_from_parallel(UpperCAmelCase_				,keep_fpaa_wrapper=UpperCAmelCase_   )
			_lowercase				: Tuple     =   pickle.dumps(UpperCAmelCase_   )






	@require_cuda
	@require_torch_min_version(version="""2.0"""   )
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   RegressionModel()
			_lowercase				: Any     =   model.forward
			_lowercase				: Union[str, Any]     =   torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
			_lowercase				: Optional[Any]     =   convert_outputs_to_fpaa(model.forward   )
			_lowercase				: Union[str, Any]     =   torch.compile(model.forward				,backend="""inductor"""   )
			_lowercase				: Any     =   torch.randn(4				,10   ).cuda()
			_lowercase				: int     =   model(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   RegressionModel()
			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase				: Any     =   torch.nn.parallel.DataParallel(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   extract_model_from_parallel(UpperCAmelCase_   )

			self.assertEqual(UpperCAmelCase_				,UpperCAmelCase_   )






	@require_torch_min_version(version="""2.0"""   )
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   RegressionModel()
			_lowercase				: List[str]     =   torch.compile(UpperCAmelCase_   )

			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase				: Optional[int]     =   torch.nn.parallel.DataParallel(UpperCAmelCase_   )
			_lowercase				: int     =   torch.compile(UpperCAmelCase_   )
			_lowercase				: Dict     =   extract_model_from_parallel(UpperCAmelCase_   )

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






	def    lowerCamelCase__					(     self   ):
			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"""]   )   )



