



"""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,
)


__a           =							namedtuple("ExampleNamedTuple", "a b c")
class 							lowerCamelCase    (     unittest.TestCase							):






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				Dict		)  ->      Any:
											snake_case_					:Tuple		=     torch.randn(5		,	2		)
											snake_case_					:Dict		=     torch.device("""cuda"""		) if torch.cuda.is_available() else torch.device("""cpu"""		)

											snake_case_					:Tuple		=     send_to_device(snake_case		,	snake_case		)
											self.assertTrue(torch.equal(resulta.cpu()		,	snake_case		)		)

											snake_case_					:List[str]		=     send_to_device((tensor, [tensor, tensor], 1)		,	snake_case		)
											self.assertIsInstance(snake_case		,	snake_case		)
											self.assertTrue(torch.equal(resulta[0].cpu()		,	snake_case		)		)
											self.assertIsInstance(resulta[1]		,	snake_case		)
											self.assertTrue(torch.equal(resulta[1][0].cpu()		,	snake_case		)		)
											self.assertTrue(torch.equal(resulta[1][1].cpu()		,	snake_case		)		)
											self.assertEqual(resulta[2]		,	1		)

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

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

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






					def   lowerCAmelCase_							(			self:				Any		)  ->      Optional[int]:
											with self.assertRaises(snake_case		) as cm:
																	snake_case_					:int		=     recursively_apply(torch.tensor		,	(torch.tensor(1		), 1)		,	error_on_other_type=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   lowerCAmelCase_							(			self:				str		)  ->      int:
											snake_case_					:Tuple		=     torch.tensor([1, 2, 3, 4, 5]		)
											self.assertEqual(listify(snake_case		)		,	[1, 2, 3, 4, 5]		)

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

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






					def   lowerCAmelCase_							(			self:				Dict		)  ->      List[Any]:
											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:				Optional[Any]		)  ->      List[Any]:
											snake_case_					:Any		=     RegressionModel()
											snake_case_					:Union[str, Any]		=     model.forward
											snake_case_					:int		=     convert_outputs_to_fpaa(model.forward		)
											snake_case_					:Optional[int]		=     extract_model_from_parallel(snake_case		,	keep_fpaa_wrapper=snake_case		)
											snake_case_					:str		=     pickle.dumps(snake_case		)






					@require_cuda
					def   lowerCAmelCase_							(			self:				Tuple		)  ->      str:
											snake_case_					:Union[str, Any]		=     RegressionModel()
											snake_case_					:Optional[Any]		=     model.forward
											snake_case_					:Tuple		=     torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
											snake_case_					:Any		=     convert_outputs_to_fpaa(model.forward		)
											snake_case_					:List[str]		=     extract_model_from_parallel(snake_case		,	keep_fpaa_wrapper=snake_case		)
											snake_case_					:int		=     pickle.dumps(snake_case		)






					@require_cuda
					@require_torch_min_version(version="""2.0"""		)
					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      List[str]:
											snake_case_					:Tuple		=     RegressionModel()
											snake_case_					:Any		=     model.forward
											snake_case_					:Optional[Any]		=     torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
											snake_case_					:str		=     convert_outputs_to_fpaa(model.forward		)
											snake_case_					:str		=     torch.compile(model.forward		,	backend="""inductor"""		)
											snake_case_					:Tuple		=     torch.randn(4		,	10		).cuda()
											snake_case_					:List[Any]		=     model(snake_case		)






					def   lowerCAmelCase_							(			self:				Any		)  ->      List[str]:
											snake_case_					:Optional[int]		=     RegressionModel()
											# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
											snake_case_					:int		=     torch.nn.parallel.DataParallel(snake_case		)
											snake_case_					:str		=     extract_model_from_parallel(snake_case		)

											self.assertEqual(snake_case		,	snake_case		)






					@require_torch_min_version(version="""2.0"""		)
					def   lowerCAmelCase_							(			self:				Dict		)  ->      Tuple:
											snake_case_					:Tuple		=     RegressionModel()
											snake_case_					:Optional[Any]		=     torch.compile(snake_case		)

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

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






					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      List[Any]:
											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"""]		)		)


'''simple docstring'''


from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE						=      {
    "configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
    "feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
    "processing_markuplm": ["MarkupLMProcessor"],
    "tokenization_markuplm": ["MarkupLMTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      ["MarkupLMTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      [
	    "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "MarkupLMForQuestionAnswering",
	    "MarkupLMForSequenceClassification",
	    "MarkupLMForTokenClassification",
	    "MarkupLMModel",
	    "MarkupLMPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
	from .feature_extraction_markuplm import MarkupLMFeatureExtractor
	from .processing_markuplm import MarkupLMProcessor
	from .tokenization_markuplm import MarkupLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_markuplm_fast import MarkupLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_markuplm import (
		    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MarkupLMForQuestionAnswering,
		    MarkupLMForSequenceClassification,
		    MarkupLMForTokenClassification,
		    MarkupLMModel,
		    MarkupLMPreTrainedModel,
		)


else:
	import sys

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




