


import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
   import torch

UpperCAmelCase     =       logging.get_logger(__name__)






def 			__lowerCAmelCase       ()->    Tuple:




      """simple docstring"""

      return importlib.util.find_spec('''deepspeed'''   ) is not None


if is_accelerate_available() and is_deepspeed_available():
   from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
   # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
   # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
   from builtins import object as DeepSpeedConfig







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 def __init__(      self						, _UpperCAmelCase   ):
       # set global weakref object
       set_hf_deepspeed_config(self   )
       dep_version_check('''accelerate'''   )
       dep_version_check('''deepspeed'''   )
       super().__init__(_UpperCAmelCase   )







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 def __init__(      self						, _UpperCAmelCase   ):
       super().__init__(_UpperCAmelCase   )
       snake_case_			= None
       snake_case_			= []



 def 		UpperCamelCase__	(      self   ):
       if self._dtype is None:
             raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''   )
       return self._dtype



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= self.get_value(_UpperCAmelCase   )
       if val is None:
             return False
       else:
             return val == "auto"



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase=None						, _UpperCAmelCase=True   ):
       snake_case_			,       snake_case_			= self.find_config_node(_UpperCAmelCase   )
       if config is None:
             return

       if config.get(_UpperCAmelCase   ) == "auto":
             snake_case_			= hf_val
             return

       if not must_match:
             return

       snake_case_			= config.get(_UpperCAmelCase   )
       if ds_val is not None and ds_val != hf_val:
             self.mismatches.append(F'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''   )

 __snake_case				=    partialmethod(lowerCamelCase__   ,		must_match=lowerCamelCase__			)



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
       self.fill_match(
           '''train_micro_batch_size_per_gpu'''						, args.per_device_train_batch_size						, '''per_device_train_batch_size'''   )
       self.fill_match('''gradient_accumulation_steps'''						, args.gradient_accumulation_steps						, '''gradient_accumulation_steps'''   )
       self.fill_match('''train_batch_size'''						, _UpperCAmelCase						, '''train_batch_size (calculated)'''   )
       self.fill_match('''gradient_clipping'''						, args.max_grad_norm						, '''max_grad_norm'''   )

       self.fill_match('''optimizer.params.lr'''						, args.learning_rate						, '''learning_rate'''   )
       self.fill_match('''optimizer.params.betas'''						, [args.adam_betaa, args.adam_betaa]						, '''adam_beta1+adam_beta2'''   )
       self.fill_match('''optimizer.params.eps'''						, args.adam_epsilon						, '''adam_epsilon'''   )
       self.fill_match('''optimizer.params.weight_decay'''						, args.weight_decay						, '''weight_decay'''   )

       self.fill_only('''scheduler.params.warmup_min_lr'''						, 0   )  # not a trainer arg
       self.fill_match('''scheduler.params.warmup_max_lr'''						, args.learning_rate						, '''learning_rate'''   )
       # total_num_steps - will get set in trainer_config_finalize

       # fp16
       if args.fpaa or args.fpaa_full_eval:
             snake_case_			= '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
       else:
             snake_case_			= None

       if args.save_on_each_node:
             # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
             snake_case_			= self.config.get('''checkpoint'''						, {}   )
             snake_case_			= args.save_on_each_node

       # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
       # any here unless the user did the work
       self.fill_match(
           '''fp16.enabled'''						, ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')						, '''fp16|fp16_full_eval+fp16_backend(amp)'''						, )

       # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
       # ZeRO features
       self.fill_match('''amp.enabled'''						, fpaa_backend == '''apex'''						, '''fp16+fp16_backend(apex)'''   )
       self.fill_match('''amp.opt_level'''						, args.fpaa_opt_level						, '''fp16_opt_level'''   )

       self.fill_match('''bf16.enabled'''						, (args.bfaa or args.bfaa_full_eval)						, '''bf16|bf16_full_eval'''   )

       # deepspeed's default mode is fp16 unless there is a config that says differently
       if self.is_true('''bf16.enabled'''   ):
             snake_case_			= torch.bfloataa
       elif self.is_false('''fp16.enabled'''   ):
             snake_case_			= torch.floataa
       else:
             snake_case_			= torch.floataa





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   ):
       snake_case_			= [
           '''zero_optimization.reduce_bucket_size''',
           '''zero_optimization.stage3_prefetch_bucket_size''',
           '''zero_optimization.stage3_param_persistence_threshold''',
       ]
       snake_case_			= [x for x in hidden_size_based_keys if self.is_auto(_UpperCAmelCase   )]

       if len(_UpperCAmelCase   ) > 0:
             if hasattr(model.config						, '''hidden_size'''   ):
                   snake_case_			= model.config.hidden_size
             elif hasattr(model.config						, '''hidden_sizes'''   ):
                   # if there are many hidden sizes pick the largest one
                   snake_case_			= max(model.config.hidden_sizes   )
             else:
                   raise ValueError(
                       '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
                       '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
                       F'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
                       '''`auto` values for these keys with an integer value of your choice.'''   )

             self.fill_only('''zero_optimization.reduce_bucket_size'''						, hidden_size * hidden_size   )
             if self.is_zeroa():
                   # automatically assign the optimal config values based on model config
                   self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''						, 0.9 * hidden_size * hidden_size   )
                   self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''						, 10 * hidden_size   )

        # scheduler
       self.fill_match('''scheduler.params.total_num_steps'''						, _UpperCAmelCase						, '''num_training_steps (calculated)'''   )
       self.fill_match('''scheduler.params.warmup_num_steps'''						, args.get_warmup_steps(_UpperCAmelCase   )						, '''warmup_steps'''   )

       if len(self.mismatches   ) > 0:
             snake_case_			= '''\n'''.join(self.mismatches   )
             raise ValueError(
                 '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
                 F''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'''   )





UpperCAmelCase     =       None






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    str:




      """simple docstring"""

      global _hf_deepspeed_config_weak_ref
      # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
      snake_case_			= weakref.ref(SCREAMING_SNAKE_CASE   )






def 			__lowerCAmelCase       ()->    str:




      """simple docstring"""

      global _hf_deepspeed_config_weak_ref
      snake_case_			= None






def 			__lowerCAmelCase       ()->    List[str]:




      """simple docstring"""

      if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
            return _hf_deepspeed_config_weak_ref().is_zeroa()
      else:
            return False






def 			__lowerCAmelCase       ()->    Dict:




      """simple docstring"""

      if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
            return _hf_deepspeed_config_weak_ref().config
      else:
            return None






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Any:




      """simple docstring"""

      from accelerate.utils import DummyOptim, DummyScheduler

      snake_case_			= hf_deepspeed_config.config

      # Optimizer + Scheduler
      # Currently supported combos:
      # 1. DS scheduler + DS optimizer: Yes
      # 2. HF scheduler + HF optimizer: Yes
      # 3. DS scheduler + HF optimizer: Yes
      # 4. HF scheduler + DS optimizer: No
      #
      # Unless Offload is enabled in which case it's:
      # 1. DS scheduler + DS optimizer: Yes
      # 2. HF scheduler + HF optimizer: Mostly*
      # 3. DS scheduler + HF optimizer: Mostly*
      # 4. HF scheduler + DS optimizer: No
      #
      # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

      snake_case_			= None
      if "optimizer" in config:
            if args.adafactor:
                  raise ValueError(
                      '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
                      '''Only one optimizer can be configured.'''   )
            snake_case_			= DummyOptim(params=SCREAMING_SNAKE_CASE   )
      else:
            if hf_deepspeed_config.is_offload():
                  logger.info(
                      '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
                      ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''   )

            # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
            # But trainer uses AdamW by default.
            snake_case_			= trainer.create_optimizer()
            # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
            snake_case_			= True

      snake_case_			= None
      if "scheduler" in config:
            snake_case_			= DummyScheduler(SCREAMING_SNAKE_CASE   )
      else:
            if isinstance(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
                  raise ValueError(
                      '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
                      '''Please configure a scheduler in the DeepSpeed config.'''   )
            snake_case_			= trainer.create_scheduler(num_training_steps=SCREAMING_SNAKE_CASE   ,   optimizer=SCREAMING_SNAKE_CASE   )

      return optimizer, lr_scheduler






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE=False   )->    Optional[Any]:




      """simple docstring"""

      from deepspeed.utils import logger as ds_logger

      snake_case_			= trainer.model
      snake_case_			= trainer.args

      snake_case_			= trainer.accelerator.state.deepspeed_plugin.hf_ds_config

      # resume config update - some bits like `model` and `num_training_steps` only become available during train
      hf_deepspeed_config.trainer_config_finalize(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

      # set the Deepspeed log level consistent with the Trainer
      ds_logger.setLevel(args.get_process_log_level()   )

      if inference:
            # only Z3 makes sense for the inference
            if not hf_deepspeed_config.is_zeroa():
                  raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''   )

            # in case the training config is re-used for inference
            hf_deepspeed_config.del_config_sub_tree('''optimizer'''   )
            hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''   )
            snake_case_			,       snake_case_			= None, None
            snake_case_			= None
      else:
            snake_case_			= None  # important for when deepspeed_init is used as re-init
            snake_case_			= list(filter(lambda SCREAMING_SNAKE_CASE   : p.requires_grad   ,   model.parameters()   )   )
            snake_case_			,       snake_case_			= deepspeed_optim_sched(
                SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

      # keep for quick debug:
      # from pprint import pprint; pprint(config)

      return optimizer, lr_scheduler






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Union[str, Any]:




      """simple docstring"""

      import glob

      snake_case_			= sorted(glob.glob(f'''{checkpoint_path}/global_step*'''   )   )

      if len(SCREAMING_SNAKE_CASE   ) > 0:
            logger.info(f'''Attempting to resume from {checkpoint_path}'''   )
            # this magically updates self.optimizer and self.lr_scheduler
            snake_case_			,       snake_case_			= deepspeed_engine.load_checkpoint(
                SCREAMING_SNAKE_CASE   ,   load_optimizer_states=SCREAMING_SNAKE_CASE   ,   load_lr_scheduler_states=SCREAMING_SNAKE_CASE   )
            if load_path is None:
                  raise ValueError(f'''[deepspeed] failed to resume from checkpoint {checkpoint_path}'''   )
      else:
            raise ValueError(f'''Can\'t find a valid checkpoint at {checkpoint_path}'''   )



import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

       snake_case_			= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )