




'''simple docstring'''
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        _UpperCAmelCase						:




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Union[str, Any]           =     TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"			)

       torch.manual_seed(0			)
       lowercase_	:    Union[str, Any]           =     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"			)

       torch.manual_seed(0			)
       lowercase_	:    Union[str, Any]           =     UNetaDConditionModel(
           sample_size=3_2  ,     layers_per_block=1  ,     block_out_channels=[3_2, 6_4]  ,     down_block_types=[
               "ResnetDownsampleBlock2D",
               "SimpleCrossAttnDownBlock2D",
           ]  ,     mid_block_type="UNetMidBlock2DSimpleCrossAttn"  ,     up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]  ,     in_channels=3  ,     out_channels=6  ,     cross_attention_dim=3_2  ,     encoder_hid_dim=3_2  ,     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			)
       lowercase_	:    List[str]           =     DDPMScheduler(
           num_train_timesteps=1_0_0_0  ,     beta_schedule="squaredcos_cap_v2"  ,     beta_start=0.0001  ,     beta_end=0.02  ,     thresholding=a  ,     dynamic_thresholding_ratio=0.95  ,     sample_max_value=1.0  ,     prediction_type="epsilon"  ,     variance_type="learned_range"  ,     )

       torch.manual_seed(0			)
       lowercase_	:    str           =     IFWatermarker()

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




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    List[str]           =     TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"			)

       torch.manual_seed(0			)
       lowercase_	:    Optional[int]           =     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"			)

       torch.manual_seed(0			)
       lowercase_	:    Optional[int]           =     UNetaDConditionModel(
           sample_size=3_2  ,     layers_per_block=[1, 2]  ,     block_out_channels=[3_2, 6_4]  ,     down_block_types=[
               "ResnetDownsampleBlock2D",
               "SimpleCrossAttnDownBlock2D",
           ]  ,     mid_block_type="UNetMidBlock2DSimpleCrossAttn"  ,     up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]  ,     in_channels=6  ,     out_channels=6  ,     cross_attention_dim=3_2  ,     encoder_hid_dim=3_2  ,     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=3_2  ,     )
       unet.set_attn_processor(AttnAddedKVProcessor()			)  # For reproducibility tests

       torch.manual_seed(0			)
       lowercase_	:    Optional[int]           =     DDPMScheduler(
           num_train_timesteps=1_0_0_0  ,     beta_schedule="squaredcos_cap_v2"  ,     beta_start=0.0001  ,     beta_end=0.02  ,     thresholding=a  ,     dynamic_thresholding_ratio=0.95  ,     sample_max_value=1.0  ,     prediction_type="epsilon"  ,     variance_type="learned_range"  ,     )

       torch.manual_seed(0			)
       lowercase_	:    Optional[Any]           =     DDPMScheduler(
           num_train_timesteps=1_0_0_0  ,     beta_schedule="squaredcos_cap_v2"  ,     beta_start=0.0001  ,     beta_end=0.02  ,     )

       torch.manual_seed(0			)
       lowercase_	:    Optional[Any]           =     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 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self.get_dummy_components()
       lowercase_	:    Any           =     self.pipeline_class(**a			)
       pipe.to(a			)
       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    Dict           =     self.get_dummy_inputs(a			)

       lowercase_	:    List[str]           =     inputs["prompt"]
       lowercase_	:    List[str]           =     inputs["generator"]
       lowercase_	:    Optional[int]           =     inputs["num_inference_steps"]
       lowercase_	:    Tuple           =     inputs["output_type"]

       if "image" in inputs:
          lowercase_	:    Optional[Any]           =     inputs["image"]
       else:
          lowercase_	:    int           =     None

       if "mask_image" in inputs:
          lowercase_	:    str           =     inputs["mask_image"]
       else:
          lowercase_	:    Dict           =     None

       if "original_image" in inputs:
          lowercase_	:    Any           =     inputs["original_image"]
       else:
          lowercase_	:    Optional[int]           =     None

       lowercase_	:    Tuple           =     pipe.encode_prompt(a			)

       # inputs with prompt converted to embeddings
       lowercase_	:    Tuple           =     {
           "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:
          lowercase_	:    List[Any]           =     image

       if mask_image is not None:
          lowercase_	:    Any           =     mask_image

       if original_image is not None:
          lowercase_	:    Optional[int]           =     original_image

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

       lowercase_	:    Union[str, Any]           =     pipe(**a			)[0]

       with tempfile.TemporaryDirectory() as tmpdir:
          pipe.save_pretrained(a			)
          lowercase_	:    List[str]           =     self.pipeline_class.from_pretrained(a			)
          pipe_loaded.to(a			)
          pipe_loaded.set_progress_bar_config(disable=a			)

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

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

       lowercase_	:    str           =     self.get_dummy_inputs(a			)

       lowercase_	:    Any           =     inputs["generator"]
       lowercase_	:    int           =     inputs["num_inference_steps"]
       lowercase_	:    int           =     inputs["output_type"]

       # inputs with prompt converted to embeddings
       lowercase_	:    Optional[int]           =     {
           "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:
          lowercase_	:    Optional[int]           =     image

       if mask_image is not None:
          lowercase_	:    Dict           =     mask_image

       if original_image is not None:
          lowercase_	:    Union[str, Any]           =     original_image

       lowercase_	:    Tuple           =     pipe_loaded(**a			)[0]

       lowercase_	:    List[Any]           =     np.abs(to_np(a			) - to_np(a			)			).max()
       self.assertLess(a  ,     1e-4			)





    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     self.get_dummy_components()
       lowercase_	:    str           =     self.pipeline_class(**a			)
       pipe.to(a			)
       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    Optional[int]           =     self.get_dummy_inputs(a			)
       lowercase_	:    int           =     pipe(**a			)[0]

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

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

       lowercase_	:    Tuple           =     self.get_dummy_inputs(a			)
       lowercase_	:    Tuple           =     pipe_loaded(**a			)[0]

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












'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Tuple		       =    'linear'
    __lowerCamelCase:   Any		       =    'cosine'
    __lowerCamelCase:   Optional[Any]		       =    'cosine_with_restarts'
    __lowerCamelCase:   Tuple		       =    'polynomial'
    __lowerCamelCase:   int		       =    'constant'
    __lowerCamelCase:   Optional[Any]		       =    'constant_with_warmup'
    __lowerCamelCase:   List[str]		       =    'piecewise_constant'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   return LambdaLR(_UpperCamelCase						,		lambda _UpperCamelCase							: 1						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1.0						,		_UpperCamelCase							)							)
      return 1.0

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     {}
   lowercase_	:    Dict           =     step_rules.split(","							)
   for rule_str in rule_list[:-1]:
      lowercase_    ,		lowercase_	:    Any           =     rule_str.split(":"							)
      lowercase_	:    List[Any]           =     int(_UpperCamelCase							)
      lowercase_	:    int           =     float(_UpperCamelCase							)
      lowercase_	:    Optional[int]           =     value
   lowercase_	:    Union[str, Any]           =     float(rule_list[-1]							)

   def create_rules_function(_UpperCamelCase						,		_UpperCamelCase							):
      def rule_func(_UpperCamelCase							) -> float:
         lowercase_	:    Optional[Any]           =     sorted(rules_dict.keys()							)
         for i, sorted_step in enumerate(_UpperCamelCase							):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   lowercase_	:    Optional[int]           =     create_rules_function(_UpperCamelCase						,		_UpperCamelCase							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=-1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      return max(
          0.0						,		float(num_training_steps - current_step							) / float(max(1						,		num_training_steps - num_warmup_steps							)							)							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 0.5						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    List[str]           =     float(current_step - num_warmup_steps							) / float(max(1						,		num_training_steps - num_warmup_steps							)							)
      return max(0.0						,		0.5 * (1.0 + math.cos(math.pi * float(_UpperCamelCase							) * 2.0 * progress							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 1						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    Dict           =     float(current_step - num_warmup_steps							) / float(max(1						,		num_training_steps - num_warmup_steps							)							)
      if progress >= 1.0:
         return 0.0
      return max(0.0						,		0.5 * (1.0 + math.cos(math.pi * ((float(_UpperCamelCase							) * progress) % 1.0)							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=1e-7						,		_UpperCamelCase=1.0						,		_UpperCamelCase=-1							):


   """simple docstring"""




   lowercase_	:    Dict           =     optimizer.defaults["lr"]
   if not (lr_init > lr_end):
      raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""							)

   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         lowercase_	:    int           =     lr_init - lr_end
         lowercase_	:    Optional[int]           =     num_training_steps - num_warmup_steps
         lowercase_	:    Optional[Any]           =     1 - (current_step - num_warmup_steps) / decay_steps
         lowercase_	:    List[Any]           =     lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)


UpperCamelCase__ =			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = 1						,		_UpperCamelCase = 1.0						,		_UpperCamelCase = -1						,		):


   """simple docstring"""




   lowercase_	:    Any           =     SchedulerType(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(_UpperCamelCase						,		step_rules=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""							)

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(_UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""							)

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		num_cycles=_UpperCamelCase						,		last_epoch=_UpperCamelCase						,		)

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		power=_UpperCamelCase						,		last_epoch=_UpperCamelCase						,		)

   return schedule_func(
       _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




