



'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					UnCLIPImageVariationPipeline
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					IMAGE_VARIATION_BATCH_PARAMS

	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					[
	    'generator',
	    'return_dict',
	    'decoder_num_inference_steps',
	    'super_res_num_inference_steps',
	]
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					False


	@property
	def     _a	(self							):
				return 32


	@property
	def     _a	(self							):
				return 32


	@property
	def     _a	(self							):
				return self.time_input_dim


	@property
	def     _a	(self							):
				return self.time_input_dim * 4


	@property
	def     _a	(self							):
				return 100


	@property
	def     _a	(self							):
				A_	:						List[Any]	=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
				return tokenizer


	@property
	def     _a	(self							):
				torch.manual_seed(0							)
				A_	:						Any	=     CLIPTextConfig(
				    bos_token_id=0       ,      eos_token_id=2       ,      hidden_size=self.text_embedder_hidden_size       ,      projection_dim=self.text_embedder_hidden_size       ,      intermediate_size=37       ,      layer_norm_eps=1E-05       ,      num_attention_heads=4       ,      num_hidden_layers=5       ,      pad_token_id=1       ,      vocab_size=1000       ,      )
				return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE							)


	@property
	def     _a	(self							):
				torch.manual_seed(0							)
				A_	:						Any	=     CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size       ,      projection_dim=self.text_embedder_hidden_size       ,      num_hidden_layers=5       ,      num_attention_heads=4       ,      image_size=32       ,      intermediate_size=37       ,      patch_size=1       ,      )
				return CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE							)


	@property
	def     _a	(self							):
				torch.manual_seed(0							)

				A_	:						Optional[int]	=     {
				    """clip_embeddings_dim""": self.text_embedder_hidden_size,
				    """time_embed_dim""": self.time_embed_dim,
				    """cross_attention_dim""": self.cross_attention_dim,
				}

				A_	:						Any	=     UnCLIPTextProjModel(**__SCREAMING_SNAKE_CASE							)
				return model


	@property
	def     _a	(self							):
				torch.manual_seed(0							)

				A_	:						str	=     {
				    """sample_size""": 32,
				    # RGB in channels
				    """in_channels""": 3,
				    # Out channels is double in channels because predicts mean and variance
				    """out_channels""": 6,
				    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
				    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
				    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
				    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    """layers_per_block""": 1,
				    """cross_attention_dim""": self.cross_attention_dim,
				    """attention_head_dim""": 4,
				    """resnet_time_scale_shift""": """scale_shift""",
				    """class_embed_type""": """identity""",
				}

				A_	:						Tuple	=     UNetaDConditionModel(**__SCREAMING_SNAKE_CASE							)
				return model


	@property
	def     _a	(self							):
				return {
				    "sample_size": 64,
				    "layers_per_block": 1,
				    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
				    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
				    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    "in_channels": 6,
				    "out_channels": 3,
				}


	@property
	def     _a	(self							):
				torch.manual_seed(0							)

				A_	:						int	=     UNetaDModel(**self.dummy_super_res_kwargs							)
				return model


	@property
	def     _a	(self							):
				torch.manual_seed(1							)

				A_	:						Tuple	=     UNetaDModel(**self.dummy_super_res_kwargs							)
				return model


	def     _a	(self							):
				A_	:						List[Any]	=     self.dummy_decoder
				A_	:						Optional[Any]	=     self.dummy_text_proj
				A_	:						int	=     self.dummy_text_encoder
				A_	:						List[str]	=     self.dummy_tokenizer
				A_	:						int	=     self.dummy_super_res_first
				A_	:						List[str]	=     self.dummy_super_res_last

				A_	:						int	=     UnCLIPScheduler(
				    variance_type="""learned_range"""       ,      prediction_type="""epsilon"""       ,      num_train_timesteps=1000       ,      )

				A_	:						Dict	=     UnCLIPScheduler(
				    variance_type="""fixed_small_log"""       ,      prediction_type="""epsilon"""       ,      num_train_timesteps=1000       ,      )

				A_	:						Tuple	=     CLIPImageProcessor(crop_size=32       ,      size=32							)

				A_	:						List[Any]	=     self.dummy_image_encoder

				return {
				    "decoder": decoder,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "text_proj": text_proj,
				    "feature_extractor": feature_extractor,
				    "image_encoder": image_encoder,
				    "super_res_first": super_res_first,
				    "super_res_last": super_res_last,
				    "decoder_scheduler": decoder_scheduler,
				    "super_res_scheduler": super_res_scheduler,
				}


	def     _a	(self       ,      lowercase       ,      lowercase=0       ,      lowercase=True							):
				A_	:						Optional[int]	=     floats_tensor((1, 3, 32, 32)       ,      rng=random.Random(__SCREAMING_SNAKE_CASE							)							).to(__SCREAMING_SNAKE_CASE							)
				if str(__SCREAMING_SNAKE_CASE							).startswith("""mps"""							):
							A_	:						List[Any]	=     torch.manual_seed(__SCREAMING_SNAKE_CASE							)
				else:
							A_	:						int	=     torch.Generator(device=__SCREAMING_SNAKE_CASE							).manual_seed(__SCREAMING_SNAKE_CASE							)

				if pil_image:
							A_	:						List[str]	=     input_image * 0.5 + 0.5
							A_	:						Optional[Any]	=     input_image.clamp(0       ,      1							)
							A_	:						str	=     input_image.cpu().permute(0       ,      2       ,      3       ,      1							).float().numpy()
							A_	:						Optional[Any]	=     DiffusionPipeline.numpy_to_pil(__SCREAMING_SNAKE_CASE							)[0]

				return {
				    "image": input_image,
				    "generator": generator,
				    "decoder_num_inference_steps": 2,
				    "super_res_num_inference_steps": 2,
				    "output_type": "np",
				}


	def     _a	(self							):
				A_	:						str	=     """cpu"""

				A_	:						Any	=     self.get_dummy_components()

				A_	:						Union[str, Any]	=     self.pipeline_class(**__SCREAMING_SNAKE_CASE							)
				A_	:						Optional[Any]	=     pipe.to(__SCREAMING_SNAKE_CASE							)

				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

				A_	:						str	=     self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       ,      pil_image=__SCREAMING_SNAKE_CASE							)

				A_	:						Union[str, Any]	=     pipe(**__SCREAMING_SNAKE_CASE							)
				A_	:						Union[str, Any]	=     output.images

				A_	:						Optional[int]	=     self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       ,      pil_image=__SCREAMING_SNAKE_CASE							)

				A_	:						Dict	=     pipe(
				    **__SCREAMING_SNAKE_CASE       ,      return_dict=__SCREAMING_SNAKE_CASE       ,      )[0]

				A_	:						int	=     image[0, -3:, -3:, -1]
				A_	:						str	=     image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				A_	:						int	=     np.array(
				    [
				        0.99_97,
				        0.00_02,
				        0.99_97,
				        0.99_97,
				        0.99_69,
				        0.00_23,
				        0.99_97,
				        0.99_69,
				        0.99_70,
				    ]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2


	def     _a	(self							):
				A_	:						str	=     """cpu"""

				A_	:						Optional[Any]	=     self.get_dummy_components()

				A_	:						str	=     self.pipeline_class(**__SCREAMING_SNAKE_CASE							)
				A_	:						Dict	=     pipe.to(__SCREAMING_SNAKE_CASE							)

				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

				A_	:						Union[str, Any]	=     self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       ,      pil_image=__SCREAMING_SNAKE_CASE							)

				A_	:						Any	=     pipe(**__SCREAMING_SNAKE_CASE							)
				A_	:						List[str]	=     output.images

				A_	:						List[str]	=     self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       ,      pil_image=__SCREAMING_SNAKE_CASE							)

				A_	:						Dict	=     pipe(
				    **__SCREAMING_SNAKE_CASE       ,      return_dict=__SCREAMING_SNAKE_CASE       ,      )[0]

				A_	:						Union[str, Any]	=     image[0, -3:, -3:, -1]
				A_	:						List[Any]	=     image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				A_	:						Union[str, Any]	=     np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2


	def     _a	(self							):
				A_	:						List[Any]	=     """cpu"""

				A_	:						Optional[Any]	=     self.get_dummy_components()

				A_	:						List[str]	=     self.pipeline_class(**__SCREAMING_SNAKE_CASE							)
				A_	:						int	=     pipe.to(__SCREAMING_SNAKE_CASE							)

				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

				A_	:						Union[str, Any]	=     self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       ,      pil_image=__SCREAMING_SNAKE_CASE							)
				A_	:						int	=     [
				    pipeline_inputs["""image"""],
				    pipeline_inputs["""image"""],
				]

				A_	:						List[str]	=     pipe(**__SCREAMING_SNAKE_CASE							)
				A_	:						Union[str, Any]	=     output.images

				A_	:						Optional[int]	=     self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       ,      pil_image=__SCREAMING_SNAKE_CASE							)
				A_	:						int	=     [
				    tuple_pipeline_inputs["""image"""],
				    tuple_pipeline_inputs["""image"""],
				]

				A_	:						List[Any]	=     pipe(
				    **__SCREAMING_SNAKE_CASE       ,      return_dict=__SCREAMING_SNAKE_CASE       ,      )[0]

				A_	:						List[Any]	=     image[0, -3:, -3:, -1]
				A_	:						int	=     image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (2, 64, 64, 3)

				A_	:						str	=     np.array(
				    [
				        0.99_97,
				        0.99_89,
				        0.00_08,
				        0.00_21,
				        0.99_60,
				        0.00_18,
				        0.00_14,
				        0.00_02,
				        0.99_33,
				    ]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2


	def     _a	(self							):
				A_	:						Dict	=     torch.device("""cpu"""							)







				class 	_lowerCAmelCase							:
					__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					1






				A_	:						Tuple	=     self.get_dummy_components()

				A_	:						Optional[Any]	=     self.pipeline_class(**__SCREAMING_SNAKE_CASE							)
				A_	:						Union[str, Any]	=     pipe.to(__SCREAMING_SNAKE_CASE							)

				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

				A_	:						Optional[Any]	=     torch.Generator(device=__SCREAMING_SNAKE_CASE							).manual_seed(0							)
				A_	:						Tuple	=     pipe.decoder.dtype
				A_	:						Any	=     1

				A_	:						List[str]	=     (
				    batch_size,
				    pipe.decoder.config.in_channels,
				    pipe.decoder.config.sample_size,
				    pipe.decoder.config.sample_size,
				)
				A_	:						List[Any]	=     pipe.prepare_latents(
				    __SCREAMING_SNAKE_CASE       ,      dtype=__SCREAMING_SNAKE_CASE       ,      device=__SCREAMING_SNAKE_CASE       ,      generator=__SCREAMING_SNAKE_CASE       ,      latents=__SCREAMING_SNAKE_CASE       ,      scheduler=DummyScheduler()							)

				A_	:						Tuple	=     (
				    batch_size,
				    pipe.super_res_first.config.in_channels // 2,
				    pipe.super_res_first.config.sample_size,
				    pipe.super_res_first.config.sample_size,
				)
				A_	:						str	=     pipe.prepare_latents(
				    __SCREAMING_SNAKE_CASE       ,      dtype=__SCREAMING_SNAKE_CASE       ,      device=__SCREAMING_SNAKE_CASE       ,      generator=__SCREAMING_SNAKE_CASE       ,      latents=__SCREAMING_SNAKE_CASE       ,      scheduler=DummyScheduler()							)

				A_	:						Optional[Any]	=     self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       ,      pil_image=__SCREAMING_SNAKE_CASE							)

				A_	:						Tuple	=     pipe(
				    **__SCREAMING_SNAKE_CASE       ,      decoder_latents=__SCREAMING_SNAKE_CASE       ,      super_res_latents=__SCREAMING_SNAKE_CASE							).images

				A_	:						Optional[int]	=     self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       ,      pil_image=__SCREAMING_SNAKE_CASE							)
				# Don't pass image, instead pass embedding
				A_	:						List[str]	=     pipeline_inputs.pop("""image"""							)
				A_	:						Tuple	=     pipe.image_encoder(__SCREAMING_SNAKE_CASE							).image_embeds

				A_	:						Optional[Any]	=     pipe(
				    **__SCREAMING_SNAKE_CASE       ,      decoder_latents=__SCREAMING_SNAKE_CASE       ,      super_res_latents=__SCREAMING_SNAKE_CASE       ,      image_embeddings=__SCREAMING_SNAKE_CASE       ,      ).images

				# make sure passing text embeddings manually is identical
				assert np.abs(img_out_a - img_out_a							).max() < 1E-4


	@skip_mps
	def     _a	(self							):
				A_	:						Tuple	=     torch_device == """cpu"""

				# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
				A_	:						str	=     1E-2

				self._test_attention_slicing_forward_pass(
				    test_max_difference=__SCREAMING_SNAKE_CASE       ,      expected_max_diff=__SCREAMING_SNAKE_CASE							)


	@skip_mps
	def     _a	(self							):
				A_	:						Union[str, Any]	=     torch_device == """cpu"""
				A_	:						List[str]	=     True
				A_	:						Optional[Any]	=     [
				    """decoder_num_inference_steps""",
				    """super_res_num_inference_steps""",
				]

				self._test_inference_batch_single_identical(
				    test_max_difference=__SCREAMING_SNAKE_CASE       ,      relax_max_difference=__SCREAMING_SNAKE_CASE       ,      additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE       ,      )


	def     _a	(self							):
				A_	:						str	=     [
				    """decoder_num_inference_steps""",
				    """super_res_num_inference_steps""",
				]

				if torch_device == "mps":
							# TODO: MPS errors with larger batch sizes
							A_	:						List[Any]	=     [2, 3]
							self._test_inference_batch_consistent(
							    batch_sizes=__SCREAMING_SNAKE_CASE       ,      additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE       ,      )
				else:
							self._test_inference_batch_consistent(
							    additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE							)


	@skip_mps
	def     _a	(self							):
				return super().test_dict_tuple_outputs_equivalent()


	@skip_mps
	def     _a	(self							):
				return super().test_save_load_local()


	@skip_mps
	def     _a	(self							):
				return super().test_save_load_optional_components()







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


	def     _a	(self							):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	def     _a	(self							):
				A_	:						Optional[Any]	=     load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""							)
				A_	:						Any	=     load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""							)

				A_	:						int	=     UnCLIPImageVariationPipeline.from_pretrained(
				    """kakaobrain/karlo-v1-alpha-image-variations"""       ,      torch_dtype=torch.floataa							)
				A_	:						Any	=     pipeline.to(__SCREAMING_SNAKE_CASE							)
				pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

				A_	:						Tuple	=     torch.Generator(device="""cpu"""							).manual_seed(0							)
				A_	:						Any	=     pipeline(
				    __SCREAMING_SNAKE_CASE       ,      generator=__SCREAMING_SNAKE_CASE       ,      output_type="""np"""       ,      )

				A_	:						Optional[int]	=     output.images[0]

				assert image.shape == (256, 256, 3)

				assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE       ,      15							)


'''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 :       str			   =     logging.get_logger(__name__)







class 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "linear"
 lowerCAmelCase__  					=      "cosine"
 lowerCAmelCase__  					=      "cosine_with_restarts"
 lowerCAmelCase__  					=      "polynomial"
 lowerCAmelCase__  					=      "constant"
 lowerCAmelCase__  					=      "constant_with_warmup"
 lowerCAmelCase__  					=      "piecewise_constant"







def    a__       (  a__      ,    a__ = -1						):




     """simple docstring"""


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







def    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     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    a__       (  a__      ,    a__      ,    a__ = -1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      {}
     __SCREAMING_SNAKE_CASE     =      step_rules.split(""","""						)
     for rule_str in rule_list[:-1]:
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      rule_str.split(""":"""						)
          __SCREAMING_SNAKE_CASE     =      int(a__						)
          __SCREAMING_SNAKE_CASE     =      float(a__						)
          __SCREAMING_SNAKE_CASE     =      value
     __SCREAMING_SNAKE_CASE     =      float(rule_list[-1]						)

     def create_rules_function(a__      ,    a__						):
          def rule_func(a__						) -> float:
               __SCREAMING_SNAKE_CASE     =      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

     __SCREAMING_SNAKE_CASE     =      create_rules_function(a__      ,    a__						)

     return LambdaLR(a__      ,    a__      ,    last_epoch=a__						)







def    a__       (  a__      ,    a__      ,    a__      ,    a__=-1						):




     """simple docstring"""


     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    a__       (  a__      ,    a__      ,    a__      ,    a__ = 0.5      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      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    a__       (  a__      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = -1						):




     """simple docstring"""


     def lr_lambda(a__						):
          if current_step < num_warmup_steps:
               return float(a__						) / float(max(1      ,    a__						)						)
          __SCREAMING_SNAKE_CASE     =      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    a__       (  a__      ,    a__      ,    a__      ,    a__=1E-7      ,    a__=1.0      ,    a__=-1						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      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:
               __SCREAMING_SNAKE_CASE     =      lr_init - lr_end
               __SCREAMING_SNAKE_CASE     =      num_training_steps - num_warmup_steps
               __SCREAMING_SNAKE_CASE     =      1 - (current_step - num_warmup_steps) / decay_steps
               __SCREAMING_SNAKE_CASE     =      lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(a__      ,    a__      ,    a__						)


UpperCAmelCase :       Optional[Any]			   =     {
    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    a__       (  a__      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = 1      ,    a__ = 1.0      ,    a__ = -1      ,    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      SchedulerType(a__						)
     __SCREAMING_SNAKE_CASE     =      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__						)






