



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     unittest.TestCase			):
     lowerCAmelCase       =      UnCLIPPipeline
     lowerCAmelCase       =      TEXT_TO_IMAGE_PARAMS - {
         """negative_prompt""",
         """height""",
         """width""",
         """negative_prompt_embeds""",
         """guidance_scale""",
         """prompt_embeds""",
         """cross_attention_kwargs""",
     }
     lowerCAmelCase       =      TEXT_TO_IMAGE_BATCH_PARAMS
     lowerCAmelCase       =      [
         """generator""",
         """return_dict""",
         """prior_num_inference_steps""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     lowerCAmelCase       =      False





     @property
     def 						__snake_case     (							self			:				List[str]					)->Optional[Any]:
      return 3_2





     @property
     def 						__snake_case     (							self			:				List[Any]					)->List[Any]:
      return 3_2





     @property
     def 						__snake_case     (							self			:				List[str]					)->Dict:
      return self.time_input_dim





     @property
     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      return self.time_input_dim * 4





     @property
     def 						__snake_case     (							self			:				Optional[Any]					)->Any:
      return 1_0_0





     @property
     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
      return tokenizer





     @property
     def 						__snake_case     (							self			:				List[Any]					)->Any:
      torch.manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     Any							=						CLIPTextConfig(
          bos_token_id=0  ,	eos_token_id=2  ,	hidden_size=self.text_embedder_hidden_size  ,	projection_dim=self.text_embedder_hidden_size  ,	intermediate_size=3_7  ,	layer_norm_eps=1E-05  ,	num_attention_heads=4  ,	num_hidden_layers=5  ,	pad_token_id=1  ,	vocab_size=1_0_0_0  ,	)
      return CLIPTextModelWithProjection(UpperCamelCase					)





     @property
     def 						__snake_case     (							self			:				Tuple					)->List[str]:
      torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						{
          "num_attention_heads": 2,
          "attention_head_dim": 1_2,
          "embedding_dim": self.text_embedder_hidden_size,
          "num_layers": 1,
      }

      __SCREAMING_SNAKE_CASE							:     Tuple							=						PriorTransformer(**UpperCamelCase					)
      return model





     @property
     def 						__snake_case     (							self			:				Optional[int]					)->List[Any]:
      torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     Dict							=						{
          "clip_embeddings_dim": self.text_embedder_hidden_size,
          "time_embed_dim": self.time_embed_dim,
          "cross_attention_dim": self.cross_attention_dim,
      }

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						UnCLIPTextProjModel(**UpperCamelCase					)
      return model





     @property
     def 						__snake_case     (							self			:				Tuple					)->List[Any]:
      torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						{
          "sample_size": 3_2,
          # RGB in channels
          "in_channels": 3,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 6,
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": "identity",
      }

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						UNetaDConditionModel(**UpperCamelCase					)
      return model





     @property
     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      return {
          "sample_size": 6_4,
          "layers_per_block": 1,
          "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
          "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "in_channels": 6,
          "out_channels": 3,
      }





     @property
     def 						__snake_case     (							self			:				Any					)->Dict:
      torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						UNetaDModel(**self.dummy_super_res_kwargs					)
      return model





     @property
     def 						__snake_case     (							self			:				Dict					)->int:
      # seeded differently to get different unet than `self.dummy_super_res_first`
      torch.manual_seed(1					)

      __SCREAMING_SNAKE_CASE							:     Any							=						UNetaDModel(**self.dummy_super_res_kwargs					)
      return model





     def 						__snake_case     (							self			:				Union[str, Any]					)->str:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.dummy_prior
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.dummy_decoder
      __SCREAMING_SNAKE_CASE							:     int							=						self.dummy_text_proj
      __SCREAMING_SNAKE_CASE							:     str							=						self.dummy_text_encoder
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.dummy_tokenizer
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.dummy_super_res_first
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.dummy_super_res_last

      __SCREAMING_SNAKE_CASE							:     Dict							=						UnCLIPScheduler(
          variance_type="fixed_small_log"  ,	prediction_type="sample"  ,	num_train_timesteps=1_0_0_0  ,	clip_sample_range=5.0  ,	)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						UnCLIPScheduler(
          variance_type="learned_range"  ,	prediction_type="epsilon"  ,	num_train_timesteps=1_0_0_0  ,	)

      __SCREAMING_SNAKE_CASE							:     str							=						UnCLIPScheduler(
          variance_type="fixed_small_log"  ,	prediction_type="epsilon"  ,	num_train_timesteps=1_0_0_0  ,	)

      __SCREAMING_SNAKE_CASE							:     str							=						{
          "prior": prior,
          "decoder": decoder,
          "text_proj": text_proj,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "super_res_first": super_res_first,
          "super_res_last": super_res_last,
          "prior_scheduler": prior_scheduler,
          "decoder_scheduler": decoder_scheduler,
          "super_res_scheduler": super_res_scheduler,
      }

      return components





     def 						__snake_case     (							self			:				Tuple  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Tuple=0					)->List[str]:
      if str(UpperCamelCase					).startswith("mps"					):
       __SCREAMING_SNAKE_CASE							:     Any							=						torch.manual_seed(UpperCamelCase					)
      else:
       __SCREAMING_SNAKE_CASE							:     Any							=						torch.Generator(device=UpperCamelCase					).manual_seed(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						{
          "prompt": "horse",
          "generator": generator,
          "prior_num_inference_steps": 2,
          "decoder_num_inference_steps": 2,
          "super_res_num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs





     def 						__snake_case     (							self			:				Dict					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     int							=						"cpu"

      __SCREAMING_SNAKE_CASE							:     str							=						self.get_dummy_components()

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.pipeline_class(**UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						pipe.to(UpperCamelCase					)

      pipe.set_progress_bar_config(disable=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						pipe(**self.get_dummy_inputs(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.images

      __SCREAMING_SNAKE_CASE							:     str							=						pipe(
          **self.get_dummy_inputs(UpperCamelCase					)  ,	return_dict=UpperCamelCase  ,	)[0]

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE							:     Tuple							=						image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						np.array(
          [
              0.9_9_9_7,
              0.9_9_8_8,
              0.0_0_2_8,
              0.9_9_9_7,
              0.9_9_8_4,
              0.9_9_6_5,
              0.0_0_2_9,
              0.9_9_8_6,
              0.0_0_2_5,
          ]					)

      assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2





     def 						__snake_case     (							self			:				Tuple					)->str:
      __SCREAMING_SNAKE_CASE							:     str							=						torch.device("cpu"					)

      class    _SCREAMING_SNAKE_CASE						:
           lowerCAmelCase       =      1






      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.get_dummy_components()

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.pipeline_class(**UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     int							=						pipe.to(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						components["prior"]
      __SCREAMING_SNAKE_CASE							:     str							=						components["decoder"]
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						components["super_res_first"]
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						components["tokenizer"]
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						components["text_encoder"]

      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.Generator(device=UpperCamelCase					).manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     int							=						prior.dtype
      __SCREAMING_SNAKE_CASE							:     Dict							=						1

      __SCREAMING_SNAKE_CASE							:     Tuple							=						(batch_size, prior.config.embedding_dim)
      __SCREAMING_SNAKE_CASE							:     Dict							=						pipe.prepare_latents(
          UpperCamelCase  ,	dtype=UpperCamelCase  ,	device=UpperCamelCase  ,	generator=UpperCamelCase  ,	latents=UpperCamelCase  ,	scheduler=DummyScheduler()					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						pipe.prepare_latents(
          UpperCamelCase  ,	dtype=UpperCamelCase  ,	device=UpperCamelCase  ,	generator=UpperCamelCase  ,	latents=UpperCamelCase  ,	scheduler=DummyScheduler()					)

      __SCREAMING_SNAKE_CASE							:     int							=						(
          batch_size,
          super_res_first.config.in_channels // 2,
          super_res_first.config.sample_size,
          super_res_first.config.sample_size,
      )
      __SCREAMING_SNAKE_CASE							:     Dict							=						pipe.prepare_latents(
          UpperCamelCase  ,	dtype=UpperCamelCase  ,	device=UpperCamelCase  ,	generator=UpperCamelCase  ,	latents=UpperCamelCase  ,	scheduler=DummyScheduler()					)

      pipe.set_progress_bar_config(disable=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						"this is a prompt example"

      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.Generator(device=UpperCamelCase					).manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						pipe(
          [prompt]  ,	generator=UpperCamelCase  ,	prior_num_inference_steps=2  ,	decoder_num_inference_steps=2  ,	super_res_num_inference_steps=2  ,	prior_latents=UpperCamelCase  ,	decoder_latents=UpperCamelCase  ,	super_res_latents=UpperCamelCase  ,	output_type="np"  ,	)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						output.images

      __SCREAMING_SNAKE_CASE							:     List[str]							=						tokenizer(
          UpperCamelCase  ,	padding="max_length"  ,	max_length=tokenizer.model_max_length  ,	return_tensors="pt"  ,	)
      __SCREAMING_SNAKE_CASE							:     Dict							=						text_encoder(text_inputs.input_ids					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						text_inputs.attention_mask

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.Generator(device=UpperCamelCase					).manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						pipe(
          generator=UpperCamelCase  ,	prior_num_inference_steps=2  ,	decoder_num_inference_steps=2  ,	super_res_num_inference_steps=2  ,	prior_latents=UpperCamelCase  ,	decoder_latents=UpperCamelCase  ,	super_res_latents=UpperCamelCase  ,	text_model_output=UpperCamelCase  ,	text_attention_mask=UpperCamelCase  ,	output_type="np"  ,	)[0]

      # make sure passing text embeddings manually is identical
      assert np.abs(image - image_from_text					).max() < 1E-4





     @skip_mps
     def 						__snake_case     (							self			:				Dict					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch_device == "cpu"

      self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase  ,	expected_max_diff=0.0_1					)





     @skip_mps
     def 						__snake_case     (							self			:				Union[str, Any]					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch_device == "cpu"
      __SCREAMING_SNAKE_CASE							:     Dict							=						True
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						[
          "prior_num_inference_steps",
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

      self._test_inference_batch_single_identical(
          test_max_difference=UpperCamelCase  ,	relax_max_difference=UpperCamelCase  ,	additional_params_copy_to_batched_inputs=UpperCamelCase  ,	)





     def 						__snake_case     (							self			:				List[str]					)->Any:
      __SCREAMING_SNAKE_CASE							:     Dict							=						[
          "prior_num_inference_steps",
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

      if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       __SCREAMING_SNAKE_CASE							:     int							=						[2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=UpperCamelCase  ,	additional_params_copy_to_batched_inputs=UpperCamelCase  ,	)
      else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=UpperCamelCase					)





     @skip_mps
     def 						__snake_case     (							self			:				str					)->List[Any]:
      return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def 						__snake_case     (							self			:				Tuple					)->str:
      return super().test_save_load_local()






     @skip_mps
     def 						__snake_case     (							self			:				Dict					)->List[Any]:
      return super().test_save_load_optional_components()






@nightly
class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def 						__snake_case     (							self			:				Dict					)->Any:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






     def 						__snake_case     (							self			:				Optional[int]					)->Any:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/unclip/karlo_v1_alpha_horse_cpu.npy"					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"					)
      pipeline.set_progress_bar_config(disable=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Any							=						torch.manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     int							=						pipeline(
          "horse"  ,	num_images_per_prompt=1  ,	generator=UpperCamelCase  ,	output_type="np"  ,	)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.images[0]

      assert image.shape == (2_5_6, 2_5_6, 3)
      assert np.abs(expected_image - image					).max() < 1E-1






@slow
@require_torch_gpu
class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def 						__snake_case     (							self			:				Optional[Any]					)->int:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





     def 						__snake_case     (							self			:				Tuple					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Any							=						load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/unclip/karlo_v1_alpha_horse_fp16.npy"					)

      __SCREAMING_SNAKE_CASE							:     int							=						UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"  ,	torch_dtype=torch.floataa					)
      __SCREAMING_SNAKE_CASE							:     int							=						pipeline.to(UpperCamelCase					)
      pipeline.set_progress_bar_config(disable=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						torch.Generator(device="cpu"					).manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						pipeline(
          "horse"  ,	generator=UpperCamelCase  ,	output_type="np"  ,	)

      __SCREAMING_SNAKE_CASE							:     Any							=						output.images[0]

      assert image.shape == (2_5_6, 2_5_6, 3)

      assert_mean_pixel_difference(UpperCamelCase  ,	UpperCamelCase					)






     def 						__snake_case     (							self			:				Tuple					)->List[Any]:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"  ,	torch_dtype=torch.floataa					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						pipe.to(UpperCamelCase					)
      pipe.set_progress_bar_config(disable=UpperCamelCase					)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE							:     Any							=						pipe(
          "horse"  ,	num_images_per_prompt=1  ,	prior_num_inference_steps=2  ,	decoder_num_inference_steps=2  ,	super_res_num_inference_steps=2  ,	output_type="np"  ,	)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 1_0**9











import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  str  ):



 """simple docstring"""





 if isinstance(__lowerCamelCase		,						(list, tuple)  ):
  return honor_type(__lowerCamelCase		,						(move_to_device(__lowerCamelCase		,						__lowerCamelCase  ) for t in state)  )
 elif isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  return type(__lowerCamelCase  )({k: move_to_device(__lowerCamelCase		,						__lowerCamelCase  ) for k, v in state.items()}  )
 elif isinstance(__lowerCamelCase		,						torch.Tensor  ):
  return state.to(__lowerCamelCase  )
 return state






class    _SCREAMING_SNAKE_CASE						(torch.optim.Optimizer			):





     def __init__(							self			:				int  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				List[Any]=None					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     str							=						optimizer
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						scaler
      __SCREAMING_SNAKE_CASE							:     Any							=						AcceleratorState()
      __SCREAMING_SNAKE_CASE							:     int							=						GradientState()
      __SCREAMING_SNAKE_CASE							:     str							=						device_placement
      __SCREAMING_SNAKE_CASE							:     int							=						False
      __SCREAMING_SNAKE_CASE							:     Dict							=						None

      # Handle device placement
      if device_placement:
       __SCREAMING_SNAKE_CASE							:     List[str]							=						self.optimizer.state_dict()
       if self.accelerator_state.distributed_type == DistributedType.TPU:
        xm.send_cpu_data_to_device(UpperCamelCase  ,	self.accelerator_state.device					)
       else:
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						move_to_device(UpperCamelCase  ,	self.accelerator_state.device					)
       self.optimizer.load_state_dict(UpperCamelCase					)





     @property
     def 						__snake_case     (							self			:				Optional[Any]					)->List[str]:
      return self.optimizer.state





     @state.setter
     def 						__snake_case     (							self			:				Optional[int]  ,	UpperCamelCase			:				List[str]					)->Dict:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						state





     @property
     def 						__snake_case     (							self			:				List[Any]					)->str:
      return self.optimizer.param_groups





     @param_groups.setter
     def 						__snake_case     (							self			:				List[str]  ,	UpperCamelCase			:				Any					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						param_groups





     @property
     def 						__snake_case     (							self			:				str					)->str:
      return self.optimizer.defaults





     @defaults.setter
     def 						__snake_case     (							self			:				int  ,	UpperCamelCase			:				Tuple					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						defaults





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				Tuple					)->Optional[int]:
      self.optimizer.add_param_group(UpperCamelCase					)





     def 						__snake_case     (							self			:				Union[str, Any]  ,	UpperCamelCase			:				Union[str, Any]					)->Dict:
      if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
       xm.send_cpu_data_to_device(UpperCamelCase  ,	self.accelerator_state.device					)
      self.optimizer.load_state_dict(UpperCamelCase					)





     def 						__snake_case     (							self			:				List[str]					)->List[Any]:
      return self.optimizer.state_dict()





     def 						__snake_case     (							self			:				Optional[int]  ,	UpperCamelCase			:				Any=None					)->Any:
      if self.gradient_state.sync_gradients:
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"set_to_none" in inspect.signature(self.optimizer.zero_grad					).parameters
       if accept_arg:
        if set_to_none is None:
         __SCREAMING_SNAKE_CASE							:     Optional[int]							=						False
        self.optimizer.zero_grad(set_to_none=UpperCamelCase					)
       else:
        if set_to_none is not None:
         raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."					)
        self.optimizer.zero_grad()





     def 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				Tuple=None					)->List[Any]:
      if self.gradient_state.sync_gradients:
       if self.accelerator_state.distributed_type == DistributedType.TPU:
        __SCREAMING_SNAKE_CASE							:     List[str]							=						{"closure": closure} if closure is not None else {}
        xm.optimizer_step(self.optimizer  ,	optimizer_args=UpperCamelCase					)
       elif self.scaler is not None:
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						False
        if self._last_scale is None:
         # `get_scale` is an async operation requiring full synchronization
         # on CPU and GPUs before finishing. As a result, we store away
         # the prior one to reduce the call overhead
         __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.scaler.get_scale()
         __SCREAMING_SNAKE_CASE							:     List[Any]							=						True
        self.scaler.step(self.optimizer  ,	UpperCamelCase					)
        self.scaler.update()
        __SCREAMING_SNAKE_CASE							:     Dict							=						self.scaler.get_scale()
        if not new_scale:
         # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
         __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						scale_after < self._last_scale
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						scale_after

       else:
        self.optimizer.step(UpperCamelCase					)





     def 						__snake_case     (							self			:				Optional[int]  ,	UpperCamelCase			:				Optional[Any]					)->Any:
      for param_group in self.optimizer.param_groups:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						[parameters_map.get(UpperCamelCase  ,	UpperCamelCase					) for p in param_group["params"]]





     @property
     def 						__snake_case     (							self			:				List[Any]					)->int:
      warnings.warn(
          "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
          "`optimizer.step_was_skipped` instead."  ,	UpperCamelCase  ,	)
      return self._is_overflow





     @property
     def 						__snake_case     (							self			:				Optional[int]					)->List[Any]:
      return self._is_overflow





     def __getstate__(							self			:				List[str]					)->List[str]:
      return self.__dict__.copy()






     def __setstate__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				str					)->int:
      self.__dict__.update(UpperCamelCase					)






