import unittest

import numpy as np

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

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 DPTImageProcessor
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def __init__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]=7   ,   lowerCamelCase_						:						Dict=3   ,   lowerCamelCase_						:						List[str]=18   ,   lowerCamelCase_						:						Tuple=30   ,   lowerCamelCase_						:						int=400   ,   lowerCamelCase_						:						List[Any]=True   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Dict=True   ,   lowerCamelCase_						:						int=[0.5, 0.5, 0.5]   ,   lowerCamelCase_						:						Dict=[0.5, 0.5, 0.5]   ,   ):





    """simple docstring"""


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







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


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





@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_		(      _UpperCamelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				DPTImageProcessor if is_vision_available() else None







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      DPTImageProcessingTester(self				)







   @property
   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    return self.image_processor_tester.prepare_image_processor_dict()







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """image_mean"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """image_std"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """do_normalize"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """do_resize"""				)				)
    self.assertTrue(hasattr(lowerCamelCase_   ,   """size"""				)				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


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

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







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    # create random PIL images
    UpperCamelCase			  =      prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=lowerCamelCase_				)
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_   ,   Image.Image				)

    # Test not batched input
    UpperCamelCase			  =      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.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )

    # Test batched
    UpperCamelCase			  =      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.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    # create random numpy tensors
    UpperCamelCase			  =      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
    UpperCamelCase			  =      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.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )

    # Test batched
    UpperCamelCase			  =      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.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.image_processing_class(**self.image_processor_dict				)
    # create random PyTorch tensors
    UpperCamelCase			  =      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
    UpperCamelCase			  =      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.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )

    # Test batched
    UpperCamelCase			  =      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.size["""height"""],
            self.image_processor_tester.size["""width"""],
        )   ,   )





"""simple docstring"""




# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")






class       UpperCAmelCase_    :



      def __init__(	self	:      Optional[Any]	,      A	:      List[str]	,      A	:      List[str]	,      A	:      bool = True	,      A	:      bool = False		):
           _UpperCAmelCase     :      Dict														=       scheduler
           _UpperCAmelCase     :      str														=       optimizers if isinstance(A	,      (list, tuple)		) else [optimizers]
           _UpperCAmelCase     :      List[str]														=       split_batches
           _UpperCAmelCase     :      Dict														=       step_with_optimizer
           _UpperCAmelCase     :      Tuple														=       GradientState()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[Any]	,      **A	:      Optional[int]		):
           if not self.step_with_optimizer:
                # No link between scheduler and optimizer -> just step
                self.scheduler.step(*A	,      **A		)
                return

           # Otherwise, first make sure the optimizer was stepped.
           if not self.gradient_state.sync_gradients:
                if self.gradient_state.adjust_scheduler:
                     self.scheduler._step_count += 1
                return

           for opt in self.optimizers:
                if opt.step_was_skipped:
                     return
           if self.split_batches:
                # Split batches -> the training dataloader batch size is not changed so one step per training step
                self.scheduler.step(*A	,      **A		)
           else:
                # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
                # num_processes steps per training step
                _UpperCAmelCase     :      int														=       AcceleratorState().num_processes
                for _ in range(A		):
                     # Special case when using OneCycle and `drop_last` was not used
                     if hasattr(self.scheduler	,      "total_steps"		):
                          if self.scheduler._step_count <= self.scheduler.total_steps:
                               self.scheduler.step(*A	,      **A		)
                     else:
                          self.scheduler.step(*A	,      **A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           return self.scheduler.get_last_lr()



      def 		snake_case_   (	self	:      str		):
           return self.scheduler.state_dict()



      def 		snake_case_   (	self	:      str	,      A	:      Optional[Any]		):
           self.scheduler.load_state_dict(A		)



      def 		snake_case_   (	self	:      Tuple		):
           return self.scheduler.get_lr()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[str]	,      **A	:      Optional[Any]		):
           return self.scheduler.print_lr(*A	,      **A		)
