



"""simple docstring"""







import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
		import numpy as np
		import torch

if is_vision_available():
		from PIL import Image


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase=False   ,				__UpperCAmelCase=False   ,				__UpperCAmelCase=False      ):
		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		_lowercase				: List[str]     =   []
		for i in range(image_processor_tester.batch_size      ):
				if equal_resolution:
						_lowercase				: Any     =   image_processor_tester.max_resolution
				else:
						# To avoid getting image width/height 0
						_lowercase				: Optional[Any]     =   image_processor_tester.min_resolution
						if getattr(__UpperCAmelCase   ,				"""size_divisor"""   ,				__UpperCAmelCase      ):
								# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
								_lowercase				: List[Any]     =   max(image_processor_tester.size_divisor   ,				__UpperCAmelCase      )
						_lowercase     ,  _lowercase				: Dict     =   np.random.choice(np.arange(__UpperCAmelCase   ,				image_processor_tester.max_resolution      )   ,				2      )
				image_inputs.append(
				    np.random.randint(255   ,				size=(image_processor_tester.num_channels, width, height)   ,				dtype=np.uinta      )      )

		if not numpify and not torchify:
				# PIL expects the channel dimension as last dimension
				_lowercase				: str     =   [Image.fromarray(np.moveaxis(__UpperCAmelCase   ,				0   ,				-1      )      ) for image in image_inputs]

		if torchify:
				_lowercase				: Union[str, Any]     =   [torch.from_numpy(__UpperCAmelCase      ) for image in image_inputs]

		return image_inputs


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase=10   ,				__UpperCAmelCase=10   ,				__UpperCAmelCase=False   ,				__UpperCAmelCase=False      ):
		_lowercase				: int     =   []
		for i in range(image_processor_tester.num_frames      ):
				video.append(np.random.randint(255   ,				size=(image_processor_tester.num_channels, width, height)   ,				dtype=np.uinta      )      )

		if not numpify and not torchify:
				# PIL expects the channel dimension as last dimension
				_lowercase				: Union[str, Any]     =   [Image.fromarray(np.moveaxis(__UpperCAmelCase   ,				0   ,				-1      )      ) for frame in video]

		if torchify:
				_lowercase				: List[str]     =   [torch.from_numpy(__UpperCAmelCase      ) for frame in video]

		return video


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase=False   ,				__UpperCAmelCase=False   ,				__UpperCAmelCase=False      ):
		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		_lowercase				: List[str]     =   []
		for i in range(image_processor_tester.batch_size      ):
				if equal_resolution:
						_lowercase				: Optional[int]     =   image_processor_tester.max_resolution
				else:
						_lowercase     ,  _lowercase				: Tuple     =   np.random.choice(
						    np.arange(image_processor_tester.min_resolution   ,				image_processor_tester.max_resolution      )   ,				2      )
						_lowercase				: Optional[Any]     =   prepare_video(
						    image_processor_tester=__UpperCAmelCase   ,				width=__UpperCAmelCase   ,				height=__UpperCAmelCase   ,				numpify=__UpperCAmelCase   ,				torchify=__UpperCAmelCase   ,				)
				video_inputs.append(__UpperCAmelCase      )

		return video_inputs





class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  None






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   self.image_processing_class(**self.image_processor_dict   )
			_lowercase				: str     =   json.loads(image_processor.to_json_string()   )
			for key, value in self.image_processor_dict.items():
					self.assertEqual(obj[key]				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   self.image_processing_class(**self.image_processor_dict   )

			with tempfile.TemporaryDirectory() as tmpdirname:
					_lowercase				: Dict     =   os.path.join(UpperCAmelCase_				,"""image_processor.json"""   )
					image_processor_first.to_json_file(UpperCAmelCase_   )
					_lowercase				: str     =   self.image_processing_class.from_json_file(UpperCAmelCase_   )

			self.assertEqual(image_processor_second.to_dict()				,image_processor_first.to_dict()   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   self.image_processing_class(**self.image_processor_dict   )

			with tempfile.TemporaryDirectory() as tmpdirname:
					_lowercase				: Optional[Any]     =   image_processor_first.save_pretrained(UpperCAmelCase_   )[0]
					check_json_file_has_correct_format(UpperCAmelCase_   )
					_lowercase				: Tuple     =   self.image_processing_class.from_pretrained(UpperCAmelCase_   )

			self.assertEqual(image_processor_second.to_dict()				,image_processor_first.to_dict()   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   self.image_processing_class()
			self.assertIsNotNone(UpperCAmelCase_   )






	@require_torch
	@require_vision
	def    lowerCamelCase__					(     self   ):
			if self.test_cast_dtype is not None:
					# Initialize image_processor
					_lowercase				: Tuple     =   self.image_processing_class(**self.image_processor_dict   )

					# create random PyTorch tensors
					_lowercase				: List[Any]     =   prepare_image_inputs(self.image_processor_tester				,equal_resolution=UpperCAmelCase_				,torchify=UpperCAmelCase_   )

					_lowercase				: Optional[int]     =   image_processor(UpperCAmelCase_				,return_tensors="""pt"""   )
					# for layoutLM compatiblity
					self.assertEqual(encoding.pixel_values.device				,torch.device("""cpu"""   )   )
					self.assertEqual(encoding.pixel_values.dtype				,torch.floataa   )

					_lowercase				: int     =   image_processor(UpperCAmelCase_				,return_tensors="""pt"""   ).to(torch.floataa   )
					self.assertEqual(encoding.pixel_values.device				,torch.device("""cpu"""   )   )
					self.assertEqual(encoding.pixel_values.dtype				,torch.floataa   )

					_lowercase				: Optional[int]     =   image_processor(UpperCAmelCase_				,return_tensors="""pt"""   ).to("""cpu"""				,torch.bfloataa   )
					self.assertEqual(encoding.pixel_values.device				,torch.device("""cpu"""   )   )
					self.assertEqual(encoding.pixel_values.dtype				,torch.bfloataa   )

					with self.assertRaises(UpperCAmelCase_   ):
							_lowercase				: Dict     =   image_processor(UpperCAmelCase_				,return_tensors="""pt"""   ).to(torch.bfloataa				,"""cpu"""   )

					# Try with text + image feature
					_lowercase				: Union[str, Any]     =   image_processor(UpperCAmelCase_				,return_tensors="""pt"""   )
					encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]   )}   )
					_lowercase				: Optional[Any]     =   encoding.to(torch.floataa   )

					self.assertEqual(encoding.pixel_values.device				,torch.device("""cpu"""   )   )
					self.assertEqual(encoding.pixel_values.dtype				,torch.floataa   )
					self.assertEqual(encoding.input_ids.dtype				,torch.long   )








"""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"""]   )   )



