
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__ :  Tuple):







        '''simple docstring'''



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

        lowerCAmelCase__					     = config

        self.set_stage_and_offload()




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_value('zero_optimization.stage'      ,   -1)

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




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = self.config

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

        return config, ds_key




 def 		__snake_case						(      self :  int      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  str=None):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = self.find_config_node(__lowerCAmelCase)
        if config is None:
               return default
        return config.get(__lowerCAmelCase      ,   __lowerCAmelCase)




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Union[str, Any]=False):







        '''simple docstring'''



        lowerCAmelCase__					     = self.config

        # find the config node of interest if it exists
        lowerCAmelCase__					     = ds_key_long.split('.')
        for node in nodes:
               lowerCAmelCase__					     = config
               lowerCAmelCase__					     = config.get(__lowerCAmelCase)
               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(__lowerCAmelCase)




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_value(__lowerCAmelCase)
        return False if value is None else bool(__lowerCAmelCase)




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_value(__lowerCAmelCase)
        return False if value is None else not bool(__lowerCAmelCase)




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        return self._stage == 2




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        return self._stage == 3




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        return self._offload






class     a_			:






 '''simple docstring'''






 def __init__(      self :  Any      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = engine




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  List[str]      ,   **lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        self.engine.backward(__lowerCAmelCase      ,   **__lowerCAmelCase)

        # 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_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  Union[str, Any]      ,   lowercase__ :  int):







        '''simple docstring'''



        super().__init__(__lowerCAmelCase      ,   device_placement=__lowerCAmelCase      ,   scaler=__lowerCAmelCase)
        lowerCAmelCase__					     = hasattr(self.optimizer      ,   'overflow')




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  str=None):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



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




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







        '''simple docstring'''



        if self.__has_overflow__:
               return self.optimizer.overflow
        return False






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  str      ,   lowercase__ :  List[Any]      ,   lowercase__ :  Dict):







        '''simple docstring'''



        super().__init__(__lowerCAmelCase      ,   __lowerCAmelCase)




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



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






class     a_			:






 '''simple docstring'''






 def __init__(      self :  Union[str, Any]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  str=0.001      ,   lowercase__ :  Any=0      ,   **lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = params
        lowerCAmelCase__					     = lr
        lowerCAmelCase__					     = weight_decay
        lowerCAmelCase__					     = kwargs






class     a_			:






 '''simple docstring'''






 def __init__(      self :  Union[str, Any]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  str=0      ,   **lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = optimizer
        lowerCAmelCase__					     = total_num_steps
        lowerCAmelCase__					     = warmup_num_steps
        lowerCAmelCase__					     = kwargs

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCAmelCase__     						=			logging.get_logger(__name__)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['pixel_values']




 def __init__(      self :  Tuple      ,   lowercase__ :  bool = True      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  bool = True      ,   lowercase__ :  Union[int, float] = 1 / 255      ,   lowercase__ :  bool = True      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  bool = True      ,   **lowercase__ :  List[Any]      ,   ):







        '''simple docstring'''



        super().__init__(**lowercase__)
        lowerCAmelCase__					     = size if size is not None else {'height': 384, 'width': 384}
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

        lowerCAmelCase__					     = do_resize
        lowerCAmelCase__					     = size
        lowerCAmelCase__					     = resample
        lowerCAmelCase__					     = do_rescale
        lowerCAmelCase__					     = rescale_factor
        lowerCAmelCase__					     = do_normalize
        lowerCAmelCase__					     = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowerCAmelCase__					     = image_std if image_std is not None else OPENAI_CLIP_STD
        lowerCAmelCase__					     = do_convert_rgb




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
        if "height" not in size or "width" not in size:
               raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""")
        lowerCAmelCase__					     = (size['height'], size['width'])
        return resize(lowercase__      ,   size=lowercase__      ,   resample=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[int, float]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        return rescale(lowercase__      ,   scale=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Any      ,   ):







        '''simple docstring'''



        return normalize(lowercase__      ,   mean=lowercase__      ,   std=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  ImageInput      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Dict[str, int]] = None      ,   lowercase__ :  PILImageResampling = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[float] = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  ChannelDimension = ChannelDimension.FIRST      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__					     = resample if resample is not None else self.resample
        lowerCAmelCase__					     = do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__					     = rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__					     = do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__					     = image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__					     = image_std if image_std is not None else self.image_std
        lowerCAmelCase__					     = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowerCAmelCase__					     = size if size is not None else self.size
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

        lowerCAmelCase__					     = make_list_of_images(lowercase__)

        if not valid_images(lowercase__):
               raise ValueError(
                   'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                   'torch.Tensor, tf.Tensor or jax.ndarray.')

        if do_resize and size is None or resample is None:
               raise ValueError('Size and resample must be specified if do_resize is True.')

        if do_rescale and rescale_factor is None:
               raise ValueError('Rescale factor must be specified if do_rescale is True.')

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('Image mean and std must be specified if do_normalize is True.')

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
               lowerCAmelCase__					     = [convert_to_rgb(lowercase__) for image in images]

        # All transformations expect numpy arrays.
        lowerCAmelCase__					     = [to_numpy_array(lowercase__) for image in images]

        if do_resize:
               lowerCAmelCase__					     = [self.resize(image=lowercase__      ,   size=lowercase__      ,   resample=lowercase__) for image in images]

        if do_rescale:
               lowerCAmelCase__					     = [self.rescale(image=lowercase__      ,   scale=lowercase__) for image in images]

        if do_normalize:
               lowerCAmelCase__					     = [self.normalize(image=lowercase__      ,   mean=lowercase__      ,   std=lowercase__) for image in images]

        lowerCAmelCase__					     = [to_channel_dimension_format(lowercase__      ,   lowercase__) for image in images]

        lowerCAmelCase__					     = BatchFeature(data={'pixel_values': images}      ,   tensor_type=lowercase__)

        return encoded_outputs
