

'''simple docstring'''

import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					str					,      lowercase_			:					List[str]	)						->     Any:
      if isinstance(lowercase_					,      lowercase_	):
       # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
       # modified it, it will not be accepted here again, since `auto` values would have been overridden
       UpperCAmelCase	:     int						 =			deepcopy(lowercase_	)
      elif os.path.exists(lowercase_	):
       with io.open(lowercase_					,      'r'					,      encoding='utf-8'	) as f:
        UpperCAmelCase	:     Dict						 =			json.load(lowercase_	)
      else:
       try:
        UpperCAmelCase	:     int						 =			baseaa.urlsafe_baadecode(lowercase_	).decode('utf-8'	)
        UpperCAmelCase	:     Dict						 =			json.loads(lowercase_	)
       except (UnicodeDecodeError, AttributeError, ValueError):
        raise ValueError(
            f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""	)

      UpperCAmelCase	:     Optional[int]						 =			config

      self.set_stage_and_offload()


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Dict:
      # zero stage - this is done as early as possible, before model is created, to allow
      # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
      # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
      UpperCAmelCase	:     Any						 =			self.get_value('zero_optimization.stage'					,      -1	)

      # offload
      UpperCAmelCase	:     List[Any]						 =			False
      if self.is_zeroa() or self.is_zeroa():
       UpperCAmelCase	:     Tuple						 =			set(['cpu', 'nvme']	)
       UpperCAmelCase	:     Dict						 =			set(
           [
               self.get_value('zero_optimization.offload_optimizer.device'	),
               self.get_value('zero_optimization.offload_param.device'	),
           ]	)
       if len(offload_devices & offload_devices_valid	) > 0:
        UpperCAmelCase	:     Optional[int]						 =			True


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					List[Any]	)						->     Any:
      UpperCAmelCase	:     str						 =			self.config

      # find the config node of interest if it exists
      UpperCAmelCase	:     int						 =			ds_key_long.split('.'	)
      UpperCAmelCase	:     Optional[int]						 =			nodes.pop()
      for node in nodes:
       UpperCAmelCase	:     Dict						 =			config.get(lowercase_	)
       if config is None:
        return None, ds_key

      return config, ds_key


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Tuple=None	)						->     Union[str, Any]:
      UpperCAmelCase ,			UpperCAmelCase	:     Optional[int]						 =			self.find_config_node(lowercase_	)
      if config is None:
       return default
      return config.get(lowercase_					,      lowercase_	)


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]=False	)						->     Optional[Any]:
      UpperCAmelCase	:     Union[str, Any]						 =			self.config

      # find the config node of interest if it exists
      UpperCAmelCase	:     int						 =			ds_key_long.split('.'	)
      for node in nodes:
       UpperCAmelCase	:     str						 =			config
       UpperCAmelCase	:     Union[str, Any]						 =			config.get(lowercase_	)
       if config is None:
        if must_exist:
         raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}"""	)
        else:
         return

        # if found remove it
      if parent_config is not None:
       parent_config.pop(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					str	)						->     Optional[int]:
      UpperCAmelCase	:     Dict						 =			self.get_value(lowercase_	)
      return False if value is None else bool(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					str	)						->     Optional[Any]:
      UpperCAmelCase	:     List[Any]						 =			self.get_value(lowercase_	)
      return False if value is None else not bool(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     int:
      return self._stage == 2


     def        UpperCAmelCase_	(		self			:					str	)						->     Dict:
      return self._stage == 3




     def        UpperCAmelCase_	(		self			:					Any	)						->     Optional[Any]:
      return self._offload




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					str					,      lowercase_			:					List[str]	)						->     List[Any]:
      UpperCAmelCase	:     List[str]						 =			engine




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Dict					,      **lowercase_			:					int	)						->     Optional[Any]:
      # runs backpropagation and handles mixed precision
      self.engine.backward(lowercase_					,      **lowercase_	)

      # Deepspeed's `engine.step` performs the following operations:
      # - gradient accumulation check
      # - gradient clipping
      # - optimizer step
      # - zero grad
      # - checking overflow
      # - lr_scheduler step (only if engine.lr_scheduler is not None)
      self.engine.step()
      # and this plugin overrides the above calls with no-ops when Accelerate runs under
      # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
      # training loop that works transparently under many training regimes.




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					Tuple					,      lowercase_			:					int	)						->     List[str]:
      super().__init__(lowercase_					,      device_placement=lowercase_					,      scaler=lowercase_	)
      UpperCAmelCase	:     str						 =			hasattr(self.optimizer					,      'overflow'	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					List[Any]=None	)						->     Any:
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


     def        UpperCAmelCase_	(		self			:					Any	)						->     Any:
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




     @property
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
      if self.__has_overflow__:
       return self.optimizer.overflow
      return False




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[str]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Optional[Any]	)						->     Dict:
      super().__init__(lowercase_					,      lowercase_	)




     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[int]:
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Optional[int]					,      lowercase_			:					Optional[int]					,      lowercase_			:					List[str]=0.001					,      lowercase_			:					str=0					,      **lowercase_			:					Dict	)						->     Optional[Any]:
      UpperCAmelCase	:     List[str]						 =			params
      UpperCAmelCase	:     Dict						 =			lr
      UpperCAmelCase	:     List[str]						 =			weight_decay
      UpperCAmelCase	:     List[str]						 =			kwargs




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					int					,      lowercase_			:					Any					,      lowercase_			:					Union[str, Any]=None					,      lowercase_			:					Any=0					,      **lowercase_			:					Optional[Any]	)						->     Any:
      UpperCAmelCase	:     str						 =			optimizer
      UpperCAmelCase	:     Tuple						 =			total_num_steps
      UpperCAmelCase	:     int						 =			warmup_num_steps
      UpperCAmelCase	:     int						 =			kwargs







'''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 PoolFormerImageProcessor




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self			:					Any					,      lowercase_			:					str					,      lowercase_			:					Union[str, Any]=7					,      lowercase_			:					Union[str, Any]=3					,      lowercase_			:					int=30					,      lowercase_			:					Tuple=400					,      lowercase_			:					Tuple=True					,      lowercase_			:					Optional[int]=None					,      lowercase_			:					List[str]=0.9					,      lowercase_			:					Tuple=None					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					int=[0.5, 0.5, 0.5]					,      lowercase_			:					List[str]=[0.5, 0.5, 0.5]					,      )						->     Tuple:
      UpperCAmelCase	:     Optional[int]						 =			size if size is not None else {'shortest_edge': 30}
      UpperCAmelCase	:     int						 =			crop_size if crop_size is not None else {'height': 30, 'width': 30}
      UpperCAmelCase	:     Tuple						 =			parent
      UpperCAmelCase	:     Optional[Any]						 =			batch_size
      UpperCAmelCase	:     int						 =			num_channels
      UpperCAmelCase	:     int						 =			min_resolution
      UpperCAmelCase	:     Optional[int]						 =			max_resolution
      UpperCAmelCase	:     str						 =			do_resize_and_center_crop
      UpperCAmelCase	:     int						 =			size
      UpperCAmelCase	:     Dict						 =			crop_pct
      UpperCAmelCase	:     Union[str, Any]						 =			crop_size
      UpperCAmelCase	:     Optional[int]						 =			do_normalize
      UpperCAmelCase	:     Optional[Any]						 =			image_mean
      UpperCAmelCase	:     Optional[Any]						 =			image_std




     def        UpperCAmelCase_	(		self			:					str	)						->     int:
      return {
          "size": self.size,
          "do_resize_and_center_crop": self.do_resize_and_center_crop,
          "crop_pct": self.crop_pct,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }




@require_torch
@require_vision
class 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  PoolFormerImageProcessor if is_vision_available() else None


     def        UpperCAmelCase_	(		self			:					Dict	)						->     str:
      UpperCAmelCase	:     Any						 =			PoolFormerImageProcessingTester(self	)


     @property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      UpperCAmelCase	:     Any						 =			self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(lowercase_					,      'do_resize_and_center_crop'	)	)
      self.assertTrue(hasattr(lowercase_					,      'size'	)	)
      self.assertTrue(hasattr(lowercase_					,      'crop_pct'	)	)
      self.assertTrue(hasattr(lowercase_					,      'do_normalize'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_mean'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_std'	)	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Tuple:
      UpperCAmelCase	:     List[str]						 =			self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 30}	)
      self.assertEqual(image_processor.crop_size					,      {'height': 30, 'width': 30}	)

      UpperCAmelCase	:     Any						 =			self.image_processing_class.from_dict(self.image_processor_dict					,      size=42					,      crop_size=84	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 42}	)
      self.assertEqual(image_processor.crop_size					,      {'height': 84, 'width': 84}	)


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


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     List[str]:
      # Initialize image_processing
      UpperCAmelCase	:     int						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      UpperCAmelCase	:     Any						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      Image.Image	)

      # Test not batched input
      UpperCAmelCase	:     Dict						 =			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
      UpperCAmelCase	:     str						 =			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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,      )


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     int						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      UpperCAmelCase	:     Dict						 =			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
      UpperCAmelCase	:     Optional[Any]						 =			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
      UpperCAmelCase	:     Optional[Any]						 =			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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,      )




     def        UpperCAmelCase_	(		self			:					str	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     Optional[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      UpperCAmelCase	:     Tuple						 =			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
      UpperCAmelCase	:     int						 =			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
      UpperCAmelCase	:     Optional[int]						 =			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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )					,      )




