




'''simple docstring'''
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor






class 	SCREAMING_SNAKE_CASE(    unittest.TestCase					):

							def __init__(   self ,		lowerCamelCase__ ,		lowerCamelCase__=7 ,		lowerCamelCase__=3 ,		lowerCamelCase__=18 ,		lowerCamelCase__=30 ,		lowerCamelCase__=400 ,		lowerCamelCase__=True ,		lowerCamelCase__=None ,		lowerCamelCase__=True ,		lowerCamelCase__=None ,		lowerCamelCase__=True ,		lowerCamelCase__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] ,		lowerCamelCase__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] ,		lowerCamelCase__=True ,		)   -> List[Any]:

											"""simple docstring"""



											__lowercase          =						parent
											__lowercase          =						batch_size
											__lowercase          =						num_channels
											__lowercase          =						image_size
											__lowercase          =						min_resolution
											__lowercase          =						max_resolution
											__lowercase          =						do_resize
											__lowercase          =						size if size is not None else {"""height""": 18, """width""": 18}
											__lowercase          =						do_center_crop
											__lowercase          =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											__lowercase          =						do_normalize
											__lowercase          =						image_mean
											__lowercase          =						image_std
											__lowercase          =						do_convert_rgb




							def    snake_case__      (   self					)   -> Union[str, Any]:

											"""simple docstring"""



											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_convert_rgb": self.do_convert_rgb,
											}






@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE(    _lowerCAmelCase       ,				unittest.TestCase					):
							snake_case_			:					Optional[int]   = OwlViTImageProcessor if is_vision_available() else None

							def    snake_case__      (   self					)   -> int:

											"""simple docstring"""



											__lowercase          =						OwlViTImageProcessingTester(self					)

							@property
							def    snake_case__      (   self					)   -> Dict:

											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()

							def    snake_case__      (   self					)   -> str:

											"""simple docstring"""



											__lowercase          =						self.image_processing_class(**self.image_processor_dict					)
											self.assertTrue(hasattr(_lowerCamelCase ,		"""do_resize"""					)					)
											self.assertTrue(hasattr(_lowerCamelCase ,		"""size"""					)					)
											self.assertTrue(hasattr(_lowerCamelCase ,		"""do_center_crop"""					)					)
											self.assertTrue(hasattr(_lowerCamelCase ,		"""center_crop"""					)					)
											self.assertTrue(hasattr(_lowerCamelCase ,		"""do_normalize"""					)					)
											self.assertTrue(hasattr(_lowerCamelCase ,		"""image_mean"""					)					)
											self.assertTrue(hasattr(_lowerCamelCase ,		"""image_std"""					)					)
											self.assertTrue(hasattr(_lowerCamelCase ,		"""do_convert_rgb"""					)					)

							def    snake_case__      (   self					)   -> List[Any]:

											"""simple docstring"""



											__lowercase          =						self.image_processing_class.from_dict(self.image_processor_dict					)
											self.assertEqual(image_processor.size ,		{"""height""": 18, """width""": 18}					)
											self.assertEqual(image_processor.crop_size ,		{"""height""": 18, """width""": 18}					)

											__lowercase          =						self.image_processing_class.from_dict(self.image_processor_dict ,		size=42 ,		crop_size=84					)
											self.assertEqual(image_processor.size ,		{"""height""": 42, """width""": 42}					)
											self.assertEqual(image_processor.crop_size ,		{"""height""": 84, """width""": 84}					)

							def    snake_case__      (   self					)   -> Optional[int]:

											"""simple docstring"""



											__lowercase          =						self.image_processing_class(**self.image_processor_dict					)
											# create random PIL images
											__lowercase          =						prepare_image_inputs(self.image_processor_tester ,		equal_resolution=_lowerCamelCase					)

											for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase ,		Image.Image					)

											# Test not batched input
											__lowercase          =						image_processing(image_inputs[0] ,		return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    ) ,		)

											# Test batched
											__lowercase          =						image_processing(_lowerCamelCase ,		return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    ) ,		)

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

											"""simple docstring"""



											__lowercase          =						self.image_processing_class(**self.image_processor_dict					)
											# create random numpy tensors
											__lowercase          =						prepare_image_inputs(self.image_processor_tester ,		equal_resolution=_lowerCamelCase ,		numpify=_lowerCamelCase					)
											for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase ,		np.ndarray					)

											# Test not batched input
											__lowercase          =						image_processing(image_inputs[0] ,		return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    ) ,		)

											# Test batched
											__lowercase          =						image_processing(_lowerCamelCase ,		return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    ) ,		)




							def    snake_case__      (   self					)   -> Tuple:

											"""simple docstring"""



											__lowercase          =						self.image_processing_class(**self.image_processor_dict					)
											# create random PyTorch tensors
											__lowercase          =						prepare_image_inputs(self.image_processor_tester ,		equal_resolution=_lowerCamelCase ,		torchify=_lowerCamelCase					)
											for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase ,		torch.Tensor					)

											# Test not batched input
											__lowercase          =						image_processing(image_inputs[0] ,		return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,		(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    ) ,		)

											# Test batched
											__lowercase          =						image_processing(_lowerCamelCase ,		return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape ,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    ) ,		)










'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
def     snake_case_  (       a__		:     int   ,a__		:     bool = False      ):



    """simple docstring"""




    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(a__      )
    np.random.seed(a__      )
    torch.manual_seed(a__      )
    if is_xpu_available():
        torch.xpu.manual_seed_all(a__      )
    elif is_npu_available():
        torch.npu.manual_seed_all(a__      )
    else:
        torch.cuda.manual_seed_all(a__      )
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(a__      )
def     snake_case_  (       a__		:     Optional[RNGType] = None   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




    if rng_type == RNGType.TORCH:
        __lowercase          =						torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __lowercase          =						torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        __lowercase          =						torch.tensor(xm.get_rng_state()      )
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        __lowercase          =						torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        __lowercase          =						torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __lowercase          =						generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __lowercase          =						AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __lowercase          =						rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __lowercase          =						rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        __lowercase          =						rng_state.to(state.device      )
        torch.distributed.broadcast(a__   ,0      )
        __lowercase          =						rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(a__   ,0      )

    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(a__      )
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(a__      )
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(a__      )
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(a__      )
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item()      )
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(a__      )



def     snake_case_  (       a__		:     List[Union[str, RNGType]]   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




    for rng_type in rng_types:
        synchronize_rng_state(RNGType(a__      )   ,generator=a__      )




