
'''simple docstring'''



import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)


def A				(__lowerCamelCase				:str			,     __lowerCamelCase				:List[str]=10		):
						_lowerCAmelCase  						=			[]
						for _ in range(__lowerCamelCase		):
												lrs.append(scheduler.get_lr()[0]		)
												scheduler.step()
						return lrs


def A				(__lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Optional[Any]=10		):
						_lowerCAmelCase  						=			[]
						for step in range(__lowerCamelCase		):
												lrs.append(scheduler.get_lr()[0]		)
												scheduler.step()
												if step == num_steps // 2:
																		with tempfile.TemporaryDirectory() as tmpdirname:
																								_lowerCAmelCase  						=			os.path.join(__lowerCamelCase			,     """schedule.bin"""		)
																								torch.save(scheduler.state_dict()			,     __lowerCamelCase		)

																								_lowerCAmelCase  						=			torch.load(__lowerCamelCase		)
																								scheduler.load_state_dict(__lowerCamelCase		)
						return lrs




@require_torch
class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												self.assertEqual(len(_lowercase				)   ,       len(_lowercase				)				)
												for a, b in zip(_lowercase   ,       _lowercase				):
																		self.assertAlmostEqual(_lowercase   ,       _lowercase   ,       delta=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			torch.tensor([0.1, -0.2, -0.1]   ,       requires_grad=_lowercase				)
												_lowerCAmelCase  						=			torch.tensor([0.4, 0.2, -0.5]				)
												_lowerCAmelCase  						=			nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												_lowerCAmelCase  						=			AdamW(params=[w]   ,       lr=2e-1   ,       weight_decay=0.0				)
												for _ in range(100				):
																		_lowerCAmelCase  						=			criterion(_lowercase   ,       _lowercase				)
																		loss.backward()
																		optimizer.step()
																		w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																		w.grad.zero_()
												self.assertListAlmostEqual(w.tolist()   ,       [0.4, 0.2, -0.5]   ,       tol=1e-2				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			torch.tensor([0.1, -0.2, -0.1]   ,       requires_grad=_lowercase				)
												_lowerCAmelCase  						=			torch.tensor([0.4, 0.2, -0.5]				)
												_lowerCAmelCase  						=			nn.MSELoss()
												# No warmup, constant schedule, no gradient clipping
												_lowerCAmelCase  						=			Adafactor(
												    params=[w]   ,       lr=1e-2   ,       eps=(1e-30, 1e-3)   ,       clip_threshold=1.0   ,       decay_rate=-0.8   ,       betaa=_lowercase   ,       weight_decay=0.0   ,       relative_step=_lowercase   ,       scale_parameter=_lowercase   ,       warmup_init=_lowercase   ,       )
												for _ in range(1_000				):
																		_lowerCAmelCase  						=			criterion(_lowercase   ,       _lowercase				)
																		loss.backward()
																		optimizer.step()
																		w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																		w.grad.zero_()
												self.assertListAlmostEqual(w.tolist()   ,       [0.4, 0.2, -0.5]   ,       tol=1e-2				)






@require_torch
class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Union[str, Any]									=       nn.Linear(5_0	,							5_0			) if is_torch_available() else None
						_lowercase		:				Tuple									=       AdamW(m.parameters()	,							lr=1_0.0			) if is_torch_available() else None
						_lowercase		:				Optional[int]									=       1_0




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase=None				):

												"""simple docstring"""



												self.assertEqual(len(_lowercase				)   ,       len(_lowercase				)				)
												for a, b in zip(_lowercase   ,       _lowercase				):
																		self.assertAlmostEqual(_lowercase   ,       _lowercase   ,       delta=_lowercase   ,       msg=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			{"""num_warmup_steps""": 2, """num_training_steps""": 10}
												# schedulers doct format
												# function: (sched_args_dict, expected_learning_rates)
												_lowerCAmelCase  						=			{
												    get_constant_schedule: ({}, [10.0] * self.num_steps),
												    get_constant_schedule_with_warmup: (
												        {"""num_warmup_steps""": 4},
												        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
												    ),
												    get_linear_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
												    ),
												    get_cosine_schedule_with_warmup: (
												        {**common_kwargs},
												        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
												    ),
												    get_cosine_with_hard_restarts_schedule_with_warmup: (
												        {**common_kwargs, """num_cycles""": 2},
												        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
												    ),
												    get_polynomial_decay_schedule_with_warmup: (
												        {**common_kwargs, """power""": 2.0, """lr_end""": 1e-7},
												        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
												    ),
												    get_inverse_sqrt_schedule: (
												        {"""num_warmup_steps""": 2},
												        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
												    ),
												}

												for scheduler_func, data in scheds.items():
																		_lowerCAmelCase						,   _lowerCAmelCase  						=			data

																		_lowerCAmelCase  						=			scheduler_func(self.optimizer   ,       **_lowercase				)
																		self.assertEqual(len([scheduler.get_lr()[0]]				)   ,       1				)
																		_lowerCAmelCase  						=			unwrap_schedule(_lowercase   ,       self.num_steps				)
																		self.assertListAlmostEqual(
																		    _lowercase   ,       _lowercase   ,       tol=1e-2   ,       msg=F'failed for {scheduler_func} in normal scheduler'   ,       )

																		_lowerCAmelCase  						=			scheduler_func(self.optimizer   ,       **_lowercase				)
																		if scheduler_func.__name__ != "get_constant_schedule":
																								LambdaScheduleWrapper.wrap_scheduler(_lowercase				)  # wrap to test picklability of the schedule
																		_lowerCAmelCase  						=			unwrap_and_save_reload_schedule(_lowercase   ,       self.num_steps				)
																		self.assertListEqual(_lowercase   ,       _lowercase   ,       msg=F'failed for {scheduler_func} in save and reload'				)






class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			fn




						def __call__( self   ,       *_lowercase   ,       **_lowercase				):

												"""simple docstring"""



												return self.fn(*_lowercase   ,       **_lowercase				)




						@classmethod
						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			list(map(self   ,       scheduler.lr_lambdas				)				)


'''simple docstring'''



from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)




@flax.struct.dataclass
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				jnp.ndarray






@flax_register_to_config
class   UpperCAmelCase_					(		nn.Module	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				int									=       3_2
						_lowercase		:				int									=       4
						_lowercase		:				int									=       4
						_lowercase		:				Tuple[str]									=       (
						    "CrossAttnDownBlock2D",
						    "CrossAttnDownBlock2D",
						    "CrossAttnDownBlock2D",
						    "DownBlock2D",
						)
						_lowercase		:				Tuple[str]									=       ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
						_lowercase		:				Union[bool, Tuple[bool]]									=       False
						_lowercase		:				Tuple[int]									=       (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
						_lowercase		:				int									=       2
						_lowercase		:				Union[int, Tuple[int]]									=       8
						_lowercase		:				Optional[Union[int, Tuple[int]]]									=       None
						_lowercase		:				int									=       1_2_8_0
						_lowercase		:				float									=       0.0
						_lowercase		:				bool									=       False
						_lowercase		:				jnp.dtype									=       jnp.floataa
						_lowercase		:				bool									=       True
						_lowercase		:				int									=       0
						_lowercase		:				bool									=       False




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			(1, self.in_channels, self.sample_size, self.sample_size)
												_lowerCAmelCase  						=			jnp.zeros(_lowercase   ,       dtype=jnp.floataa				)
												_lowerCAmelCase  						=			jnp.ones((1,)   ,       dtype=jnp.intaa				)
												_lowerCAmelCase  						=			jnp.zeros((1, 1, self.cross_attention_dim)   ,       dtype=jnp.floataa				)

												_lowerCAmelCase						,   _lowerCAmelCase  						=			jax.random.split(_lowercase				)
												_lowerCAmelCase  						=			{"""params""": params_rng, """dropout""": dropout_rng}

												return self.init(_lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase				)["params"]




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.block_out_channels
												_lowerCAmelCase  						=			block_out_channels[0] * 4

												if self.num_attention_heads is not None:
																		raise ValueError(
																		    """At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."""				)

												# If `num_attention_heads` is not defined (which is the case for most models)
												# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
												# The reason for this behavior is to correct for incorrectly named variables that were introduced
												# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
												# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
												# which is why we correct for the naming here.
												_lowerCAmelCase  						=			self.num_attention_heads or self.attention_head_dim

												# input
												_lowerCAmelCase  						=			nn.Conv(
												    block_out_channels[0]   ,       kernel_size=(3, 3)   ,       strides=(1, 1)   ,       padding=((1, 1), (1, 1))   ,       dtype=self.dtype   ,       )

												# time
												_lowerCAmelCase  						=			FlaxTimesteps(
												    block_out_channels[0]   ,       flip_sin_to_cos=self.flip_sin_to_cos   ,       freq_shift=self.config.freq_shift				)
												_lowerCAmelCase  						=			FlaxTimestepEmbedding(_lowercase   ,       dtype=self.dtype				)

												_lowerCAmelCase  						=			self.only_cross_attention
												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			(only_cross_attention,) * len(self.down_block_types				)

												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			(num_attention_heads,) * len(self.down_block_types				)

												# down
												_lowerCAmelCase  						=			[]
												_lowerCAmelCase  						=			block_out_channels[0]
												for i, down_block_type in enumerate(self.down_block_types				):
																		_lowerCAmelCase  						=			output_channel
																		_lowerCAmelCase  						=			block_out_channels[i]
																		_lowerCAmelCase  						=			i == len(_lowercase				) - 1

																		if down_block_type == "CrossAttnDownBlock2D":
																								_lowerCAmelCase  						=			FlaxCrossAttnDownBlockaD(
																								    in_channels=_lowercase   ,       out_channels=_lowercase   ,       dropout=self.dropout   ,       num_layers=self.layers_per_block   ,       num_attention_heads=num_attention_heads[i]   ,       add_downsample=not is_final_block   ,       use_linear_projection=self.use_linear_projection   ,       only_cross_attention=only_cross_attention[i]   ,       use_memory_efficient_attention=self.use_memory_efficient_attention   ,       dtype=self.dtype   ,       )
																		else:
																								_lowerCAmelCase  						=			FlaxDownBlockaD(
																								    in_channels=_lowercase   ,       out_channels=_lowercase   ,       dropout=self.dropout   ,       num_layers=self.layers_per_block   ,       add_downsample=not is_final_block   ,       dtype=self.dtype   ,       )

																		down_blocks.append(_lowercase				)
												_lowerCAmelCase  						=			down_blocks

												# mid
												_lowerCAmelCase  						=			FlaxUNetMidBlockaDCrossAttn(
												    in_channels=block_out_channels[-1]   ,       dropout=self.dropout   ,       num_attention_heads=num_attention_heads[-1]   ,       use_linear_projection=self.use_linear_projection   ,       use_memory_efficient_attention=self.use_memory_efficient_attention   ,       dtype=self.dtype   ,       )

												# up
												_lowerCAmelCase  						=			[]
												_lowerCAmelCase  						=			list(reversed(_lowercase				)				)
												_lowerCAmelCase  						=			list(reversed(_lowercase				)				)
												_lowerCAmelCase  						=			list(reversed(_lowercase				)				)
												_lowerCAmelCase  						=			reversed_block_out_channels[0]
												for i, up_block_type in enumerate(self.up_block_types				):
																		_lowerCAmelCase  						=			output_channel
																		_lowerCAmelCase  						=			reversed_block_out_channels[i]
																		_lowerCAmelCase  						=			reversed_block_out_channels[min(i + 1   ,       len(_lowercase				) - 1				)]

																		_lowerCAmelCase  						=			i == len(_lowercase				) - 1

																		if up_block_type == "CrossAttnUpBlock2D":
																								_lowerCAmelCase  						=			FlaxCrossAttnUpBlockaD(
																								    in_channels=_lowercase   ,       out_channels=_lowercase   ,       prev_output_channel=_lowercase   ,       num_layers=self.layers_per_block + 1   ,       num_attention_heads=reversed_num_attention_heads[i]   ,       add_upsample=not is_final_block   ,       dropout=self.dropout   ,       use_linear_projection=self.use_linear_projection   ,       only_cross_attention=only_cross_attention[i]   ,       use_memory_efficient_attention=self.use_memory_efficient_attention   ,       dtype=self.dtype   ,       )
																		else:
																								_lowerCAmelCase  						=			FlaxUpBlockaD(
																								    in_channels=_lowercase   ,       out_channels=_lowercase   ,       prev_output_channel=_lowercase   ,       num_layers=self.layers_per_block + 1   ,       add_upsample=not is_final_block   ,       dropout=self.dropout   ,       dtype=self.dtype   ,       )

																		up_blocks.append(_lowercase				)
																		_lowerCAmelCase  						=			output_channel
												_lowerCAmelCase  						=			up_blocks

												# out
												_lowerCAmelCase  						=			nn.GroupNorm(num_groups=32   ,       epsilon=1e-5				)
												_lowerCAmelCase  						=			nn.Conv(
												    self.out_channels   ,       kernel_size=(3, 3)   ,       strides=(1, 1)   ,       padding=((1, 1), (1, 1))   ,       dtype=self.dtype   ,       )




						def __call__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase=None   ,       _lowercase=None   ,       _lowercase = True   ,       _lowercase = False   ,       ):

												"""simple docstring"""



												if not isinstance(_lowercase   ,       jnp.ndarray				):
																		_lowerCAmelCase  						=			jnp.array([timesteps]   ,       dtype=jnp.intaa				)
												elif isinstance(_lowercase   ,       jnp.ndarray				) and len(timesteps.shape				) == 0:
																		_lowerCAmelCase  						=			timesteps.astype(dtype=jnp.floataa				)
																		_lowerCAmelCase  						=			jnp.expand_dims(_lowercase   ,       0				)

												_lowerCAmelCase  						=			self.time_proj(_lowercase				)
												_lowerCAmelCase  						=			self.time_embedding(_lowercase				)

												# 2. pre-process
												_lowerCAmelCase  						=			jnp.transpose(_lowercase   ,       (0, 2, 3, 1)				)
												_lowerCAmelCase  						=			self.conv_in(_lowercase				)

												# 3. down
												_lowerCAmelCase  						=			(sample,)
												for down_block in self.down_blocks:
																		if isinstance(_lowercase   ,       _lowercase				):
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			down_block(_lowercase   ,       _lowercase   ,       _lowercase   ,       deterministic=not train				)
																		else:
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			down_block(_lowercase   ,       _lowercase   ,       deterministic=not train				)
																		down_block_res_samples += res_samples

												if down_block_additional_residuals is not None:
																		_lowerCAmelCase  						=			()

																		for down_block_res_sample, down_block_additional_residual in zip(
																		    _lowercase   ,       _lowercase				):
																								down_block_res_sample += down_block_additional_residual
																								new_down_block_res_samples += (down_block_res_sample,)

																		_lowerCAmelCase  						=			new_down_block_res_samples

												# 4. mid
												_lowerCAmelCase  						=			self.mid_block(_lowercase   ,       _lowercase   ,       _lowercase   ,       deterministic=not train				)

												if mid_block_additional_residual is not None:
																		sample += mid_block_additional_residual

												# 5. up
												for up_block in self.up_blocks:
																		_lowerCAmelCase  						=			down_block_res_samples[-(self.layers_per_block + 1) :]
																		_lowerCAmelCase  						=			down_block_res_samples[: -(self.layers_per_block + 1)]
																		if isinstance(_lowercase   ,       _lowercase				):
																								_lowerCAmelCase  						=			up_block(
																								    _lowercase   ,       temb=_lowercase   ,       encoder_hidden_states=_lowercase   ,       res_hidden_states_tuple=_lowercase   ,       deterministic=not train   ,       )
																		else:
																								_lowerCAmelCase  						=			up_block(_lowercase   ,       temb=_lowercase   ,       res_hidden_states_tuple=_lowercase   ,       deterministic=not train				)

        # 6. post-process
												_lowerCAmelCase  						=			self.conv_norm_out(_lowercase				)
												_lowerCAmelCase  						=			nn.silu(_lowercase				)
												_lowerCAmelCase  						=			self.conv_out(_lowercase				)
												_lowerCAmelCase  						=			jnp.transpose(_lowercase   ,       (0, 3, 1, 2)				)

												if not return_dict:
																		return (sample,)

												return FlaxUNetaDConditionOutput(sample=_lowercase				)
