

'''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 EfficientNetImageProcessor







class 		__magic_name__    (							unittest.TestCase							):

   '''simple docstring'''




   def __init__(       self,					lowercase_,					lowercase_=13,					lowercase_=3,					lowercase_=18,					lowercase_=30,					lowercase_=400,					lowercase_=True,					lowercase_=None,					lowercase_=True,					lowercase_=[0.5, 0.5, 0.5],					lowercase_=[0.5, 0.5, 0.5],					)							->					Dict:






     """simple docstring"""






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



   def 				_UpperCAmelCase				(       self )							->					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 		__magic_name__    (							snake_case__	, unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__				:			List[str]												=     EfficientNetImageProcessor if is_vision_available() else None



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






     """simple docstring"""






     a__						=EfficientNetImageProcessorTester(self )



   @property
   def 				_UpperCAmelCase				(       self )							->					List[str]:






     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()



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






     """simple docstring"""






     a__						=self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowercase_,					'''image_mean''' ) )
     self.assertTrue(hasattr(lowercase_,					'''image_std''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_normalize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''do_resize''' ) )
     self.assertTrue(hasattr(lowercase_,					'''size''' ) )



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






     """simple docstring"""






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

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



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






     """simple docstring"""






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

     # Test not batched input
     a__						=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
     a__						=image_processing(lowercase_,					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 				_UpperCAmelCase				(       self )							->					List[str]:






     """simple docstring"""






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

     # Test not batched input
     a__						=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
     a__						=image_processing(lowercase_,					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 				_UpperCAmelCase				(       self )							->					Dict:






     """simple docstring"""






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

     # Test not batched input
     a__						=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
     a__						=image_processing(lowercase_,					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'''],
         ),					)



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowerCamelCase     =      get_logger(__name__)




def     UpperCAmelCase__       (    _A   :				str			,   _A   :				List[dict]			,   _A   :				list			,   _A   :				list			,   _A   :				int			,   _A   :				GradScaler = None			,   ):





  '''simple docstring'''







  for i, state in enumerate(_A							):
    a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Model weights saved in {output_model_file}"""							)
  # Optimizer states
  for i, opt in enumerate(_A							):
    a__						=opt.state_dict()
    a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__						=scheduler.state_dict()
    a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    save(_A			,   _A							)
    logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
  # GradScaler state
  if scaler is not None:
    a__						=scaler.state_dict()
    a__						=os.path.join(_A			,   _A							)
    torch.save(_A			,   _A							)
    logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
  # Random number generator states
  a__						={}
  a__						=F"""{RNG_STATE_NAME}_{process_index}.pkl"""
  a__						=random.getstate()
  a__						=np.random.get_state()
  a__						=torch.get_rng_state()
  if is_xpu_available():
    a__						=torch.xpu.get_rng_state_all()
  else:
    a__						=torch.cuda.get_rng_state_all()
  if is_tpu_available():
    a__						=xm.get_rng_state()
  a__						=os.path.join(_A			,   _A							)
  torch.save(_A			,   _A							)
  logger.info(F"""Random states saved in {output_states_file}"""							)
  return output_dir




def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				int			,   _A   :				int			,   _A   :				Union[str, Any]			,   _A   :				Tuple			,   _A   :				Optional[int]=None			,   _A   :				List[Any]=None			,   **_A   :				Dict			,   ):





  '''simple docstring'''







  if map_location not in [None, "cpu", "on_device"]:
    raise TypeError(
        '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
  if map_location is None:
    a__						='''cpu'''
  elif map_location == "on_device":
    a__						=PartialState().device
  # Model states
  for i, model in enumerate(_A							):
    a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    models[i].load_state_dict(torch.load(_A			,   map_location=_A							)			,   **_A							)
  logger.info('''All model weights loaded successfully'''							)

  # Optimizer states
  for i, opt in enumerate(_A							):
    a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    a__						=torch.load(_A			,   map_location=_A							)
    optimizers[i].load_state_dict(_A							)
  logger.info('''All optimizer states loaded successfully'''							)

  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__						=os.path.join(_A			,   _A							)
    scheduler.load_state_dict(torch.load(_A							)							)
  logger.info('''All scheduler states loaded successfully'''							)

  # GradScaler state
  if scaler is not None:
    a__						=os.path.join(_A			,   _A							)
    scaler.load_state_dict(torch.load(_A							)							)
    logger.info('''GradScaler state loaded successfully'''							)

  # Random states
  try:
    a__						=torch.load(os.path.join(_A			,   F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
    random.setstate(states['''random_state''']							)
    np.random.set_state(states['''numpy_random_seed''']							)
    torch.set_rng_state(states['''torch_manual_seed''']							)
    if is_xpu_available():
      torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
    else:
      torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
    if is_tpu_available():
      xm.set_rng_state(states['''xm_seed''']							)
    logger.info('''All random states loaded successfully'''							)
  except Exception:
    logger.info('''Could not load random states'''							)




def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				Any			,   _A   :				int = 0							):





  '''simple docstring'''







  a__						=Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
  logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
  torch.save(obj.state_dict()			,   _A							)




def     UpperCAmelCase__       (    _A   :				int			,   _A   :				Any			,   _A   :				int = 0							):





  '''simple docstring'''







  a__						=F"""{path}/custom_checkpoint_{index}.pkl"""
  logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
  obj.load_state_dict(torch.load(_A			,   map_location='''cpu'''							)							)
