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   snake_case_       (		__A       ):
   __A	:		Tuple         =				"linear"
   __A	:		Union[str, Any]         =				"cosine"
   __A	:		Any         =				"cosine_with_restarts"
   __A	:		int         =				"polynomial"
   __A	:		Union[str, Any]         =				"constant"
   __A	:		Tuple         =				"constant_with_warmup"
   __A	:		str         =				"piecewise_constant"



def 			lowercase_				(			_lowerCamelCase							:       Optimizer		,	_lowerCamelCase							:       int = -1):
    return LambdaLR(_lowerCamelCase		,	lambda _lowerCamelCase: 1		,	last_epoch=_lowerCamelCase)

def 			lowercase_				(			_lowerCamelCase							:       Optimizer		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       int = -1):

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

    return LambdaLR(_lowerCamelCase		,	_lowerCamelCase		,	last_epoch=_lowerCamelCase)

def 			lowercase_				(			_lowerCamelCase							:       Optimizer		,	_lowerCamelCase							:       str		,	_lowerCamelCase							:       int = -1):
    lowercase__ :   Optional[Any]    					=				{}
    lowercase__ :   Any    					=				step_rules.split(",")
    for rule_str in rule_list[:-1]:
        lowercase__					,   lowercase__ :   str    					=				rule_str.split(":")
        lowercase__ :   Optional[Any]    					=				int(_lowerCamelCase)
        lowercase__ :   Optional[Any]    					=				float(_lowerCamelCase)
        lowercase__ :   Union[str, Any]    					=				value
    lowercase__ :   Optional[int]    					=				float(rule_list[-1])

    def create_rules_function(_lowerCamelCase							:       str		,	_lowerCamelCase							:       Tuple):
        def rule_func(_lowerCamelCase							:       int) -> float:
            lowercase__ :   str    					=				sorted(rules_dict.keys())
            for i, sorted_step in enumerate(_lowerCamelCase):
                if steps < sorted_step:
                    return rules_dict[sorted_steps[i]]
            return last_lr_multiple

        return rule_func

    lowercase__ :   Optional[int]    					=				create_rules_function(_lowerCamelCase		,	_lowerCamelCase)

    return LambdaLR(_lowerCamelCase		,	_lowerCamelCase		,	last_epoch=_lowerCamelCase)

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Optional[Any]		,	_lowerCamelCase							:       Dict		,	_lowerCamelCase							:       Any=-1):

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

    return LambdaLR(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)

def 			lowercase_				(			_lowerCamelCase							:       Optimizer		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       float = 0.5		,	_lowerCamelCase							:       int = -1):

    def lr_lambda(_lowerCamelCase							:       List[str]):
        if current_step < num_warmup_steps:
            return float(_lowerCamelCase) / float(max(1		,	_lowerCamelCase))
        lowercase__ :   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(_lowerCamelCase) * 2.0 * progress)))

    return LambdaLR(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)

def 			lowercase_				(			_lowerCamelCase							:       Optimizer		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       int		,	_lowerCamelCase							:       int = 1		,	_lowerCamelCase							:       int = -1):

    def lr_lambda(_lowerCamelCase							:       Optional[Any]):
        if current_step < num_warmup_steps:
            return float(_lowerCamelCase) / float(max(1		,	_lowerCamelCase))
        lowercase__ :   Tuple    					=				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(_lowerCamelCase) * progress) % 1.0))))

    return LambdaLR(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)

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

    return LambdaLR(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)


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 			lowercase_				(			_lowerCamelCase							:       Union[str, SchedulerType]		,	_lowerCamelCase							:       Optimizer		,	_lowerCamelCase							:       Optional[str] = None		,	_lowerCamelCase							:       Optional[int] = None		,	_lowerCamelCase							:       Optional[int] = None		,	_lowerCamelCase							:       int = 1		,	_lowerCamelCase							:       float = 1.0		,	_lowerCamelCase							:       int = -1		,	):
    lowercase__ :   List[str]    					=				SchedulerType(_lowerCamelCase)
    lowercase__ :   int    					=				TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(_lowerCamelCase		,	last_epoch=_lowerCamelCase)

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

    # 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(_lowerCamelCase		,	num_warmup_steps=_lowerCamelCase		,	last_epoch=_lowerCamelCase)

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

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

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





import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Optional[Any]       					=IFPipeline
							lowercase_       :       List[str]       					=TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
							lowercase_       :       List[str]       					=TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase_       :       int       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




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




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

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

												return inputs




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				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)




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




							def  A__      (				self):
												self._test_save_load_local()




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





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







@slow
@require_torch_gpu
class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def  A__      (				self):
												# if

												lowercase							=						IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa)

												lowercase							=						IFSuperResolutionPipeline.from_pretrained(
												    '''DeepFloyd/IF-II-L-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa   ,text_encoder=A__   ,tokenizer=A__)

												# pre compute text embeddings and remove T5 to save memory

												pipe_a.text_encoder.to('''cuda''')

												lowercase	,						lowercase							=						pipe_a.encode_prompt('''anime turtle'''   ,device='''cuda''')

												del pipe_a.tokenizer
												del pipe_a.text_encoder
												gc.collect()

												lowercase							=						None
												lowercase							=						None

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if(A__   ,A__   ,A__   ,A__)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# img2img

												lowercase							=						IFImgaImgPipeline(**pipe_a.components)
												lowercase							=						IFImgaImgSuperResolutionPipeline(**pipe_a.components)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if_imgaimg(A__   ,A__   ,A__   ,A__)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# inpainting

												lowercase							=						IFInpaintingPipeline(**pipe_a.components)
												lowercase							=						IFInpaintingSuperResolutionPipeline(**pipe_a.components)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if_inpainting(A__   ,A__   ,A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_3 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_0 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,original_image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)





							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(1)).to(A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,mask_image=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_0 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(1)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,mask_image=A__   ,original_image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)



def        UpperCamelCase    (							):
					'''simple docstring'''
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()




