



"""simple docstring"""

import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class  _UpperCAmelCase :

 @property
 def 		_snake_case      (  self	: Tuple       ):
     return self.get_dummy_input()

 @property
 def 		_snake_case      (  self	: Union[str, Any]       ):
     if self.block_type == "down":
         return (4, 32, 16, 16)
     elif self.block_type == "mid":
         return (4, 32, 32, 32)
     elif self.block_type == "up":
         return (4, 32, 64, 64)

     raise ValueError(f"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."       )

 def 		_snake_case      (  self	: int						,  lowercase_	: Union[str, Any]=True						,  lowercase_	: Any=False						,  lowercase_	: Dict=False						,  lowercase_	: Union[str, Any]=False						,  ):
     snake_case_				:  Optional[Any]       =			4
     snake_case_				:  Optional[int]       =			32
     snake_case_				:  Any       =			(32, 32)

     snake_case_				:  int       =			torch.manual_seed(0       )
     snake_case_				:  int       =			torch.device(lowercase_       )
     snake_case_				:  str       =			(batch_size, num_channels) + sizes
     snake_case_				:  Optional[int]       =			randn_tensor(lowercase_						,  generator=lowercase_						,  device=lowercase_       )
     snake_case_				:  Any       =			{'''hidden_states''': hidden_states}

     if include_temb:
         snake_case_				:  Any       =			128
         snake_case_				:  Optional[int]       =			randn_tensor((batch_size, temb_channels)						,  generator=lowercase_						,  device=lowercase_       )

     if include_res_hidden_states_tuple:
         snake_case_				:  Union[str, Any]       =			torch.manual_seed(1       )
         snake_case_				:  List[Any]       =			(randn_tensor(lowercase_						,  generator=lowercase_						,  device=lowercase_       ),)

     if include_encoder_hidden_states:
         snake_case_				:  Optional[int]       =			floats_tensor((batch_size, 32, 32)       ).to(lowercase_       )

     if include_skip_sample:
         snake_case_				:  Dict       =			randn_tensor(((batch_size, 3) + sizes)						,  generator=lowercase_						,  device=lowercase_       )

     return dummy_input

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Any       =			{
         '''in_channels''': 32,
         '''out_channels''': 32,
         '''temb_channels''': 128,
     }
     if self.block_type == "up":
         snake_case_				:  str       =			32

     if self.block_type == "mid":
         init_dict.pop('''out_channels'''       )

     snake_case_				:  Any       =			self.dummy_input
     return init_dict, inputs_dict

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: Union[str, Any]       ):
     snake_case_,			snake_case_				:  int       =			self.prepare_init_args_and_inputs_for_common()
     snake_case_				:  Tuple       =			self.block_class(**lowercase_       )
     unet_block.to(lowercase_       )
     unet_block.eval()

     with torch.no_grad():
         snake_case_				:  Any       =			unet_block(**lowercase_       )

     if isinstance(lowercase_						,  lowercase_       ):
         snake_case_				:  int       =			output[0]

     self.assertEqual(output.shape						,  self.output_shape       )

     snake_case_				:  Union[str, Any]       =			output[0, -1, -3:, -3:]
     snake_case_				:  Union[str, Any]       =			torch.tensor(lowercase_       ).to(lowercase_       )
     assert torch_all_close(output_slice.flatten()						,  lowercase_						,  atol=5E-3       )






 @unittest.skipIf(torch_device == '''mps'''						,  '''Training is not supported in mps'''       )
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_,			snake_case_				:  List[Any]       =			self.prepare_init_args_and_inputs_for_common()
     snake_case_				:  Optional[Any]       =			self.block_class(**lowercase_       )
     model.to(lowercase_       )
     model.train()
     snake_case_				:  Union[str, Any]       =			model(**lowercase_       )

     if isinstance(lowercase_						,  lowercase_       ):
         snake_case_				:  Optional[int]       =			output[0]

     snake_case_				:  List[Any]       =			torch.device(lowercase_       )
     snake_case_				:  Dict       =			randn_tensor(output.shape						,  device=lowercase_       )
     snake_case_				:  str       =			torch.nn.functional.mse_loss(lowercase_						,  lowercase_       )
     loss.backward()










"""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


lowercase__					:						List[Any]												=			logging.get_logger(__name__)




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Tuple	      =	"""linear"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine_with_restarts"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""polynomial"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant_with_warmup"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""piecewise_constant"""





def __lowercase    (							_a	,    _a = -1					):
    return LambdaLR(_a	,    lambda _a					: 1	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):

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

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):
    snake_case_				:  Dict       =			{}
    snake_case_				:  Optional[Any]       =			step_rules.split(''','''					)
    for rule_str in rule_list[:-1]:
        snake_case_,			snake_case_				:  Union[str, Any]       =			rule_str.split(''':'''					)
        snake_case_				:  Dict       =			int(_a					)
        snake_case_				:  int       =			float(_a					)
        snake_case_				:  Any       =			value
    snake_case_				:  Any       =			float(rule_list[-1]					)

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

        return rule_func

    snake_case_				:  List[str]       =			create_rules_function(_a	,    _a					)

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=-1					):

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

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = 0.5	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  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(_a					) * 2.0 * progress					))					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = 1	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  int       =			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(_a					) * progress) % 1.0)					))					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=1E-7	,    _a=1.0	,    _a=-1					):
    snake_case_				:  List[str]       =			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(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            snake_case_				:  Tuple       =			lr_init - lr_end
            snake_case_				:  List[str]       =			num_training_steps - num_warmup_steps
            snake_case_				:  str       =			1 - (current_step - num_warmup_steps) / decay_steps
            snake_case_				:  Optional[int]       =			lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_a	,    _a	,    _a					)


lowercase__					:						Dict												=			{
    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 __lowercase    (							_a	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = 1	,    _a = 1.0	,    _a = -1	,    ):
    snake_case_				:  Union[str, Any]       =			SchedulerType(_a					)
    snake_case_				:  Any       =			TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(_a	,    last_epoch=_a					)

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

    # 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(_a	,    num_warmup_steps=_a	,    last_epoch=_a					)

    # 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(
            _a	,    num_warmup_steps=_a	,    num_training_steps=_a	,    num_cycles=_a	,    last_epoch=_a	,    )

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

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





