






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

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


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	Optional[Any]					, UpperCAmelCase_       :	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
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    deepcopy(lowercase__      )
       elif os.path.exists(lowercase__      ):
         with io.open(lowercase__					, "r"					, encoding="utf-8"      ) as f:
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    json.load(lowercase__      )
       else:
         try:
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    baseaa.urlsafe_baadecode(lowercase__      ).decode("utf-8"      )
           SCREAMING_SNAKE_CASE       :      Tuple							=    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}'''      )

       SCREAMING_SNAKE_CASE       :      int							=    config

       self.set_stage_and_offload()


     def 				_A       (				self       :	Optional[Any]      ):
       # 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.
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.get_value("zero_optimization.stage"					, -1      )

       # offload
       SCREAMING_SNAKE_CASE       :      List[Any]							=    False
       if self.is_zeroa() or self.is_zeroa():
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    set(["cpu", "nvme"]      )
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    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:
           SCREAMING_SNAKE_CASE       :      Optional[Any]							=    True


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	int      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.config

       # find the config node of interest if it exists
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ds_key_long.split("."      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    nodes.pop()
       for node in nodes:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    config.get(lowercase__      )
         if config is None:
           return None, ds_key

       return config, ds_key


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	List[Any]=None      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.find_config_node(lowercase__      )
       if config is None:
         return default
       return config.get(lowercase__					, lowercase__      )


     def 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	int=False      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.config

       # find the config node of interest if it exists
       SCREAMING_SNAKE_CASE       :      int							=    ds_key_long.split("."      )
       for node in nodes:
         SCREAMING_SNAKE_CASE       :      int							=    config
         SCREAMING_SNAKE_CASE       :      int							=    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 				_A       (				self       :	str					, UpperCAmelCase_       :	Dict      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.get_value(lowercase__      )
       return False if value is None else bool(lowercase__      )


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.get_value(lowercase__      )
       return False if value is None else not bool(lowercase__      )


     def 				_A       (				self       :	Optional[int]      ):
       return self._stage == 2


     def 				_A       (				self       :	List[str]      ):
       return self._stage == 3






     def 				_A       (				self       :	str      ):
       return self._offload





class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	Any					, UpperCAmelCase_       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    engine






     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	List[str]					, **UpperCAmelCase_       :	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  SCREAMING_SNAKE_CASE			(			_UpperCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	Optional[Any]					, UpperCAmelCase_       :	Union[str, Any]      ):
       super().__init__(lowercase__					, device_placement=lowercase__					, scaler=lowercase__      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    hasattr(self.optimizer					, "overflow"      )


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	str=None      ):
       pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


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






     @property
     def 				_A       (				self       :	int      ):
       if self.__has_overflow__:
         return self.optimizer.overflow
       return False





class  SCREAMING_SNAKE_CASE			(			_UpperCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	Optional[int]					, UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	List[Any]      ):
       super().__init__(lowercase__					, lowercase__      )






     def 				_A       (				self       :	Tuple      ):
       pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Tuple=0.001					, UpperCAmelCase_       :	str=0					, **UpperCAmelCase_       :	int      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    params
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    lr
       SCREAMING_SNAKE_CASE       :      Dict							=    weight_decay
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    kwargs





class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	List[str]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	str=None					, UpperCAmelCase_       :	Tuple=0					, **UpperCAmelCase_       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    optimizer
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    total_num_steps
       SCREAMING_SNAKE_CASE       :      List[str]							=    warmup_num_steps
       SCREAMING_SNAKE_CASE       :      Any							=    kwargs














import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Tuple=7					, UpperCAmelCase_       :	Dict=3					, UpperCAmelCase_       :	Any=18					, UpperCAmelCase_       :	Optional[int]=30					, UpperCAmelCase_       :	Any=400					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Union[str, Any]=None					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	Optional[int]=False					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	Optional[Any]=True					, UpperCAmelCase_       :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase_       :	Dict=[0.5, 0.5, 0.5]					, ):
       SCREAMING_SNAKE_CASE       :      Any							=    parent
       SCREAMING_SNAKE_CASE       :      int							=    batch_size
       SCREAMING_SNAKE_CASE       :      Dict							=    num_channels
       SCREAMING_SNAKE_CASE       :      str							=    image_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    min_resolution
       SCREAMING_SNAKE_CASE       :      Any							=    max_resolution
       SCREAMING_SNAKE_CASE       :      int							=    do_resize
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    size if size is not None else {"height": 18, "width": 20}
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_thumbnail
       SCREAMING_SNAKE_CASE       :      Tuple							=    do_align_axis
       SCREAMING_SNAKE_CASE       :      List[str]							=    do_pad
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_normalize
       SCREAMING_SNAKE_CASE       :      Any							=    image_mean
       SCREAMING_SNAKE_CASE       :      Any							=    image_std






     def 				_A       (				self       :	str      ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }





@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		str			=	DonutImageProcessor if is_vision_available() else None


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    DonutImageProcessingTester(self      )


     @property
     def 				_A       (				self       :	Tuple      ):
       return self.image_processor_tester.prepare_image_processor_dict()


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_resize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "size"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_thumbnail"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_align_long_axis"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_pad"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_normalize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_mean"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_std"      )      )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size					, {"height": 18, "width": 20}      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=42      )
       self.assertEqual(image_processor.size					, {"height": 42, "width": 42}      )

       # Previous config had dimensions in (width, height) order
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=(42, 84)      )
       self.assertEqual(image_processor.size					, {"height": 84, "width": 42}      )


     def 				_A       (				self       :	Dict      ):
       pass


     @is_flaky()
     def 				_A       (				self       :	Optional[Any]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, Image.Image      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      List[str]							=    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
       SCREAMING_SNAKE_CASE       :      Dict							=    image_processing(UpperCAmelCase_					, 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"],
           )					, )


     @is_flaky()
     def 				_A       (				self       :	List[str]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Dict							=    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE       :      List[Any]							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, numpify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, np.ndarray      )

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

       # Test batched
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    image_processing(UpperCAmelCase_					, 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"],
           )					, )






     @is_flaky()
     def 				_A       (				self       :	Dict      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, torchify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, torch.Tensor      )

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

       # Test batched
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_processing(UpperCAmelCase_					, 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"],
           )					, )






