

'''simple docstring'''



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

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



class 				_snake_case					:

  def __init__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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





      '''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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''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(_SCREAMING_SNAKE_CASE	)
          if config is None:
              return None, ds_key

      return config, ds_key

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=None	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							self.find_config_node(_SCREAMING_SNAKE_CASE	)
      if config is None:
          return default
      return config.get(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=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(_SCREAMING_SNAKE_CASE	)
          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(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_value(_SCREAMING_SNAKE_CASE	)
      return False if value is None else bool(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_value(_SCREAMING_SNAKE_CASE	)
      return False if value is None else not bool(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return self._stage == 2

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return self._stage == 3


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return self._offload



class 				_snake_case					:

  def __init__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							engine


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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

      # 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 				_snake_case					(   a_			):

  def __init__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__(_SCREAMING_SNAKE_CASE      , device_placement=_SCREAMING_SNAKE_CASE      , scaler=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							hasattr(self.optimizer      , 'overflow'	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=None	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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


  @property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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



class 				_snake_case					(   a_			):

  def __init__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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



class 				_snake_case					:

  def __init__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=0.001      , _SCREAMING_SNAKE_CASE=0      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							params
      lowerCAmelCase 						=							lr
      lowerCAmelCase 						=							weight_decay
      lowerCAmelCase 						=							kwargs



class 				_snake_case					:

  def __init__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=None      , _SCREAMING_SNAKE_CASE=0      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							optimizer
      lowerCAmelCase 						=							total_num_steps
      lowerCAmelCase 						=							warmup_num_steps
      lowerCAmelCase 						=							kwargs








'''simple docstring'''



import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard



@slow
@require_flax
class 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().tearDown()
      gc.collect()

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-canny'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'bird'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

      lowerCAmelCase 						=							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'	)
      lowerCAmelCase 						=							pipe.prepare_image_inputs([canny_image] * num_samples	)

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-openpose'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'Chef in the kitchen'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

      lowerCAmelCase 						=							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'	)
      lowerCAmelCase 						=							pipe.prepare_image_inputs([pose_image] * num_samples	)

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





