





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class 				a			:






				def 			snake_case_    (  self					):

										"""simple docstring"""




										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   UNetaDConditionModel(
										    sample_size=32			, layers_per_block=1			, block_out_channels=[32, 64]			, down_block_types=[
										        '''ResnetDownsampleBlock2D''',
										        '''SimpleCrossAttnDownBlock2D''',
										    ]			, mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''			, up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']			, in_channels=3			, out_channels=6			, cross_attention_dim=32			, encoder_hid_dim=32			, attention_head_dim=8			, addition_embed_type='''text'''			, addition_embed_type_num_heads=2			, cross_attention_norm='''group_norm'''			, resnet_time_scale_shift='''scale_shift'''			, act_fn='''gelu'''			, )
										unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   DDPMScheduler(
										    num_train_timesteps=1000			, beta_schedule='''squaredcos_cap_v2'''			, beta_start=0.0001			, beta_end=0.02			, thresholding=_lowerCAmelCase			, dynamic_thresholding_ratio=0.95			, sample_max_value=1.0			, prediction_type='''epsilon'''			, variance_type='''learned_range'''			, )

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   IFWatermarker()

										return {
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "unet": unet,
										    "scheduler": scheduler,
										    "watermarker": watermarker,
										    "safety_checker": None,
										    "feature_extractor": None,
										}






				def 			snake_case_    (  self					):

										"""simple docstring"""




										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   UNetaDConditionModel(
										    sample_size=32			, layers_per_block=[1, 2]			, block_out_channels=[32, 64]			, down_block_types=[
										        '''ResnetDownsampleBlock2D''',
										        '''SimpleCrossAttnDownBlock2D''',
										    ]			, mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''			, up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']			, in_channels=6			, out_channels=6			, cross_attention_dim=32			, encoder_hid_dim=32			, attention_head_dim=8			, addition_embed_type='''text'''			, addition_embed_type_num_heads=2			, cross_attention_norm='''group_norm'''			, resnet_time_scale_shift='''scale_shift'''			, act_fn='''gelu'''			, class_embed_type='''timestep'''			, mid_block_scale_factor=1.414			, time_embedding_act_fn='''gelu'''			, time_embedding_dim=32			, )
										unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				str           =   DDPMScheduler(
										    num_train_timesteps=1000			, beta_schedule='''squaredcos_cap_v2'''			, beta_start=0.0001			, beta_end=0.02			, thresholding=_lowerCAmelCase			, dynamic_thresholding_ratio=0.95			, sample_max_value=1.0			, prediction_type='''epsilon'''			, variance_type='''learned_range'''			, )

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   DDPMScheduler(
										    num_train_timesteps=1000			, beta_schedule='''squaredcos_cap_v2'''			, beta_start=0.0001			, beta_end=0.02			, )

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   IFWatermarker()

										return {
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "unet": unet,
										    "scheduler": scheduler,
										    "image_noising_scheduler": image_noising_scheduler,
										    "watermarker": watermarker,
										    "safety_checker": None,
										    "feature_extractor": None,
										}






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   self.get_dummy_components()
										__SCREAMING_SNAKE_CASE:				Dict           =   self.pipeline_class(**_lowerCAmelCase					)
										pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_dummy_inputs(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Any           =   inputs['''prompt''']
										__SCREAMING_SNAKE_CASE:				int           =   inputs['''generator''']
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   inputs['''num_inference_steps''']
										__SCREAMING_SNAKE_CASE:				Tuple           =   inputs['''output_type''']

										if "image" in inputs:
																__SCREAMING_SNAKE_CASE:				Tuple           =   inputs['''image''']
										else:
																__SCREAMING_SNAKE_CASE:				Any           =   None

										if "mask_image" in inputs:
																__SCREAMING_SNAKE_CASE:				str           =   inputs['''mask_image''']
										else:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   None

										if "original_image" in inputs:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   inputs['''original_image''']
										else:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   None

										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   pipe.encode_prompt(_lowerCAmelCase					)

										# inputs with prompt converted to embeddings
										__SCREAMING_SNAKE_CASE:				Any           =   {
										    '''prompt_embeds''': prompt_embeds,
										    '''negative_prompt_embeds''': negative_prompt_embeds,
										    '''generator''': generator,
										    '''num_inference_steps''': num_inference_steps,
										    '''output_type''': output_type,
										}

										if image is not None:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   image

										if mask_image is not None:
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   mask_image

										if original_image is not None:
																__SCREAMING_SNAKE_CASE:				Tuple           =   original_image

										# set all optional components to None
										for optional_component in pipe._optional_components:
																setattr(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   pipe(**_lowerCAmelCase					)[0]

										with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Tuple           =   self.pipeline_class.from_pretrained(_lowerCAmelCase					)
																pipe_loaded.to(_lowerCAmelCase					)
																pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase					)

										pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

										for optional_component in pipe._optional_components:
																self.assertTrue(
																    getattr(_lowerCAmelCase			, _lowerCAmelCase					) is None			, f"""`{optional_component}` did not stay set to None after loading."""			, )

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_dummy_inputs(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   inputs['''generator''']
										__SCREAMING_SNAKE_CASE:				Dict           =   inputs['''num_inference_steps''']
										__SCREAMING_SNAKE_CASE:				str           =   inputs['''output_type''']

										# inputs with prompt converted to embeddings
										__SCREAMING_SNAKE_CASE:				List[Any]           =   {
										    '''prompt_embeds''': prompt_embeds,
										    '''negative_prompt_embeds''': negative_prompt_embeds,
										    '''generator''': generator,
										    '''num_inference_steps''': num_inference_steps,
										    '''output_type''': output_type,
										}

										if image is not None:
																__SCREAMING_SNAKE_CASE:				str           =   image

										if mask_image is not None:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   mask_image

										if original_image is not None:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   original_image

										__SCREAMING_SNAKE_CASE:				List[Any]           =   pipe_loaded(**_lowerCAmelCase					)[0]

										__SCREAMING_SNAKE_CASE:				List[str]           =   np.abs(to_np(_lowerCAmelCase					) - to_np(_lowerCAmelCase					)					).max()
										self.assertLess(_lowerCAmelCase			, 1e-4					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_dummy_components()
										__SCREAMING_SNAKE_CASE:				Dict           =   self.pipeline_class(**_lowerCAmelCase					)
										pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   pipe(**_lowerCAmelCase					)[0]

										with tempfile.TemporaryDirectory() as tmpdir:
																pipe.save_pretrained(_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.pipeline_class.from_pretrained(_lowerCAmelCase					)
																pipe_loaded.to(_lowerCAmelCase					)
																pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase					)

										pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

										__SCREAMING_SNAKE_CASE:				str           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   pipe_loaded(**_lowerCAmelCase					)[0]

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   np.abs(to_np(_lowerCAmelCase					) - to_np(_lowerCAmelCase					)					).max()
										self.assertLess(_lowerCAmelCase			, 1e-4					)










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

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





class 				a			:






				def __init__(  self			, _lowerCAmelCase					):

										"""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
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   deepcopy(_lowerCAmelCase					)
										elif os.path.exists(_lowerCAmelCase					):
																with io.open(_lowerCAmelCase			, '''r'''			, encoding='''utf-8'''					) as f:
																						__SCREAMING_SNAKE_CASE:				int           =   json.load(_lowerCAmelCase					)
										else:
																try:
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   baseaa.urlsafe_baadecode(_lowerCAmelCase					).decode('''utf-8'''					)
																						__SCREAMING_SNAKE_CASE:				List[Any]           =   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}"""					)

										__SCREAMING_SNAKE_CASE:				str           =   config

										self.set_stage_and_offload()






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.get_value('''zero_optimization.stage'''			, -1					)

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






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   self.config

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

										return config, ds_key






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   self.find_config_node(_lowerCAmelCase					)
										if config is None:
																return default
										return config.get(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=False					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.config

										# find the config node of interest if it exists
										__SCREAMING_SNAKE_CASE:				str           =   ds_key_long.split('''.'''					)
										for node in nodes:
																__SCREAMING_SNAKE_CASE:				Tuple           =   config
																__SCREAMING_SNAKE_CASE:				List[str]           =   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			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_value(_lowerCAmelCase					)
										return False if value is None else bool(_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.get_value(_lowerCAmelCase					)
										return False if value is None else not bool(_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._stage == 2






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._stage == 3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._offload





class 				a			:






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   engine






				def 			snake_case_    (  self			, _lowerCAmelCase			, **_lowerCAmelCase					):

										"""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			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, device_placement=_lowerCAmelCase			, scaler=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				str           =   hasattr(self.optimizer			, '''overflow'''					)






				def 			snake_case_    (  self			, _lowerCAmelCase=None					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




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





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




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





class 				a			:






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

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   params
										__SCREAMING_SNAKE_CASE:				int           =   lr
										__SCREAMING_SNAKE_CASE:				List[str]           =   weight_decay
										__SCREAMING_SNAKE_CASE:				List[Any]           =   kwargs





class 				a			:






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

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   optimizer
										__SCREAMING_SNAKE_CASE:				int           =   total_num_steps
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   warmup_num_steps
										__SCREAMING_SNAKE_CASE:				str           =   kwargs



