


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

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


class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        if isinstance(__A  ,       __A    ):
           # 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
           __a					=  deepcopy(__A    )
        elif os.path.exists(__A    ):
           with io.open(__A  ,       """r"""  ,       encoding="""utf-8"""    ) as f:
              __a					=  json.load(__A    )
        else:
           try:
              __a					=  baseaa.urlsafe_baadecode(__A    ).decode("""utf-8"""    )
              __a					=  json.loads(__A    )
           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}'''    )

        __a					=  config

        self.set_stage_and_offload()


     def 	snake_case_  (	self    ):
        # 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.
        __a					=  self.get_value("""zero_optimization.stage"""  ,       -1    )

        # offload
        __a					=  False
        if self.is_zeroa() or self.is_zeroa():
           __a					=  set(["""cpu""", """nvme"""]    )
           __a					=  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:
              __a					=  True


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  self.config

        # find the config node of interest if it exists
        __a					=  ds_key_long.split("""."""    )
        __a					=  nodes.pop()
        for node in nodes:
           __a					=  config.get(__A    )
           if config is None:
              return None, ds_key

        return config, ds_key


     def 	snake_case_  (	self  ,       __A  ,       __A=None    ):
        __a	,    __a					=  self.find_config_node(__A    )
        if config is None:
           return default
        return config.get(__A  ,       __A    )


     def 	snake_case_  (	self  ,       __A  ,       __A=False    ):
        __a					=  self.config

        # find the config node of interest if it exists
        __a					=  ds_key_long.split("""."""    )
        for node in nodes:
           __a					=  config
           __a					=  config.get(__A    )
           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(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  self.get_value(__A    )
        return False if value is None else bool(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  self.get_value(__A    )
        return False if value is None else not bool(__A    )


     def 	snake_case_  (	self    ):
        return self._stage == 2


     def 	snake_case_  (	self    ):
        return self._stage == 3


     def 	snake_case_  (	self    ):
        return self._offload







class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        __a					=  engine


     def 	snake_case_  (	self  ,       __A  ,       **__A    ):
        # runs backpropagation and handles mixed precision
        self.engine.backward(__A  ,       **__A    )

        # 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     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__(__A  ,       device_placement=__A  ,       scaler=__A    )
        __a					=  hasattr(self.optimizer  ,       """overflow"""    )


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


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


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







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A    ):
        super().__init__(__A  ,       __A    )


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







class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=0.001  ,       __A=0  ,       **__A    ):
        __a					=  params
        __a					=  lr
        __a					=  weight_decay
        __a					=  kwargs







class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=None  ,       __A=0  ,       **__A    ):
        __a					=  optimizer
        __a					=  total_num_steps
        __a					=  warmup_num_steps
        __a					=  kwargs







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 ViTImageProcessor


class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=13  ,       __A=3  ,       __A=224  ,       __A=30  ,       __A=400  ,       __A=True  ,       __A=None  ,       __A=True  ,       __A=[0.5, 0.5, 0.5]  ,       __A=[0.5, 0.5, 0.5]  ,       ):
        __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 	snake_case_  (	self    ):
        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     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      ViTImageProcessor if is_vision_available() else None


     def 	snake_case_  (	self    ):
        __a					=  EfficientFormerImageProcessorTester(self    )


     @property
     def 	snake_case_  (	self    ):
        return self.image_proc_tester.prepare_image_processor_dict()


     def 	snake_case_  (	self    ):
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(__A  ,       """image_mean"""    )    )
        self.assertTrue(hasattr(__A  ,       """image_std"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_normalize"""    )    )
        self.assertTrue(hasattr(__A  ,       """do_resize"""    )    )
        self.assertTrue(hasattr(__A  ,       """size"""    )    )


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       Image.Image    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       numpify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       np.ndarray    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )


     def 	snake_case_  (	self    ):
        # Initialize image_processor
        __a					=  self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        __a					=  prepare_image_inputs(self.image_proc_tester  ,       equal_resolution=__A  ,       torchify=__A    )
        for image in image_inputs:
           self.assertIsInstance(__A  ,       torch.Tensor    )

        # Test not batched input
        __a					=  image_processor(image_inputs[0]  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                1,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )

        # Test batched
        __a					=  image_processor(__A  ,       return_tensors="""pt"""    ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,       (
                self.image_proc_tester.batch_size,
                self.image_proc_tester.num_channels,
                self.image_proc_tester.size["""height"""],
                self.image_proc_tester.size["""width"""],
            )  ,       )



