
"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__							=				logging.get_logger(__name__)



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = SwinConfig(
		    embed_dim=192,							depths=(2, 2, 18, 2),							num_heads=(6, 12, 24, 48),							window_size=12,							out_features=['''stage2''', '''stage3''', '''stage4'''],							)

		SCREAMING_SNAKE_CASE_						 = DetaConfig(
		    backbone_config=__a,							num_queries=900,							encoder_ffn_dim=2_048,							decoder_ffn_dim=2_048,							num_feature_levels=5,							assign_first_stage=__a,							with_box_refine=__a,							two_stage=__a,							)

		# set labels
		SCREAMING_SNAKE_CASE_						 = '''huggingface/label-files'''
		if "o365" in model_name:
				SCREAMING_SNAKE_CASE_						 = 366
				SCREAMING_SNAKE_CASE_						 = '''object365-id2label.json'''
		else:
				SCREAMING_SNAKE_CASE_						 = 91
				SCREAMING_SNAKE_CASE_						 = '''coco-detection-id2label.json'''

		SCREAMING_SNAKE_CASE_						 = num_labels
		SCREAMING_SNAKE_CASE_						 = json.load(open(cached_download(hf_hub_url(__a,							__a,							repo_type='''dataset'''    )    ),							'''r'''    )    )
		SCREAMING_SNAKE_CASE_						 = {int(__a    ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_						 = idalabel
		SCREAMING_SNAKE_CASE_						 = {v: k for k, v in idalabel.items()}

		return config



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = []

		# stem
		# fmt: off
		rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')    )
		rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')    )
		rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')    )
		rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')    )
		# stages
		for i in range(len(config.backbone_config.depths    )    ):
				for j in range(config.backbone_config.depths[i]    ):
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')    )

				if i < 3:
						rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')    )
						rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')    )

		rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')    )
		rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')    )
		rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')    )
		rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')    )
		rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')    )
		rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')    )

		# transformer encoder
		for i in range(config.encoder_layers    ):
				rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight')    )
				rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias')    )

		# transformer decoder
		for i in range(config.decoder_layers    ):
				rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight')    )
				rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias')    )

		# fmt: on

		return rename_keys



def        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = dct.pop(__a    )
		SCREAMING_SNAKE_CASE_						 = val



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
		for i in range(len(backbone_config.depths    )    ):
				SCREAMING_SNAKE_CASE_						 = num_features[i]
				for j in range(backbone_config.depths[i]    ):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						SCREAMING_SNAKE_CASE_						 = state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'    )
						SCREAMING_SNAKE_CASE_						 = state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'    )
						# next, add query, keys and values (in that order) to the state dict
						SCREAMING_SNAKE_CASE_						 = in_proj_weight[:dim, :]
						SCREAMING_SNAKE_CASE_						 = in_proj_bias[: dim]
						SCREAMING_SNAKE_CASE_						 = in_proj_weight[
						    dim : dim * 2, :
						]
						SCREAMING_SNAKE_CASE_						 = in_proj_bias[
						    dim : dim * 2
						]
						SCREAMING_SNAKE_CASE_						 = in_proj_weight[
						    -dim :, :
						]
						SCREAMING_SNAKE_CASE_						 = in_proj_bias[-dim :]
						# fmt: on



def        _lowerCamelCase ( __a,							__a    ):
		# transformer decoder self-attention layers
		SCREAMING_SNAKE_CASE_						 = config.d_model
		for i in range(config.decoder_layers    ):
				# read in weights + bias of input projection layer of self-attention
				SCREAMING_SNAKE_CASE_						 = state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'    )
				SCREAMING_SNAKE_CASE_						 = state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_bias'    )
				# next, add query, keys and values (in that order) to the state dict
				SCREAMING_SNAKE_CASE_						 = in_proj_weight[:hidden_size, :]
				SCREAMING_SNAKE_CASE_						 = in_proj_bias[:hidden_size]
				SCREAMING_SNAKE_CASE_						 = in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE_						 = in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE_						 = in_proj_weight[-hidden_size:, :]
				SCREAMING_SNAKE_CASE_						 = in_proj_bias[-hidden_size:]



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_						 = Image.open(requests.get(__a,							stream=__a    ).raw    )

		return im



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = get_deta_config(__a    )

		# load original state dict
		if model_name == "deta-swin-large":
				SCREAMING_SNAKE_CASE_						 = hf_hub_download(repo_id='''nielsr/deta-checkpoints''',							filename='''adet_swin_ft.pth'''    )
		elif model_name == "deta-swin-large-o365":
				SCREAMING_SNAKE_CASE_						 = hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365''',							filename='''deta_swin_pt_o365.pth'''    )
		else:
				raise ValueError(F'Model name {model_name} not supported'    )

		SCREAMING_SNAKE_CASE_						 = torch.load(__a,							map_location='''cpu'''    )['''model''']

		# original state dict
		for name, param in state_dict.items():
				print(__a,							param.shape    )

		# rename keys
		SCREAMING_SNAKE_CASE_						 = create_rename_keys(__a    )
		for src, dest in rename_keys:
				rename_key(__a,							__a,							__a    )
		read_in_swin_q_k_v(__a,							config.backbone_config    )
		read_in_decoder_q_k_v(__a,							__a    )

		# fix some prefixes
		for key in state_dict.copy().keys():
				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
						SCREAMING_SNAKE_CASE_						 = state_dict.pop(__a    )
						SCREAMING_SNAKE_CASE_						 = val
				if "input_proj" in key:
						SCREAMING_SNAKE_CASE_						 = state_dict.pop(__a    )
						SCREAMING_SNAKE_CASE_						 = val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						SCREAMING_SNAKE_CASE_						 = state_dict.pop(__a    )
						SCREAMING_SNAKE_CASE_						 = val

    # finally, create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE_						 = DetaForObjectDetection(__a    )
		model.load_state_dict(__a    )
		model.eval()

		SCREAMING_SNAKE_CASE_						 = '''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(__a    )

		# load image processor
		SCREAMING_SNAKE_CASE_						 = DetaImageProcessor(format='''coco_detection'''    )

		# verify our conversion on image
		SCREAMING_SNAKE_CASE_						 = prepare_img()
		SCREAMING_SNAKE_CASE_						 = processor(images=__a,							return_tensors='''pt'''    )
		SCREAMING_SNAKE_CASE_						 = encoding['''pixel_values''']
		SCREAMING_SNAKE_CASE_						 = model(pixel_values.to(__a    )    )

		# verify logits
		print('''Logits:''',							outputs.logits[0, :3, :3]    )
		print('''Boxes:''',							outputs.pred_boxes[0, :3, :3]    )
		if model_name == "deta-swin-large":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]    )
				SCREAMING_SNAKE_CASE_						 = torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]    )
		elif model_name == "deta-swin-large-o365":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]    )
				SCREAMING_SNAKE_CASE_						 = torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]    )
		assert torch.allclose(outputs.logits[0, :3, :3],							expected_logits.to(__a    ),							atol=1E-4    )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3],							expected_boxes.to(__a    ),							atol=1E-4    )
		print('''Everything ok!'''    )

		if pytorch_dump_folder_path:
				# Save model and processor
				logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'    )
				Path(__a    ).mkdir(exist_ok=__a    )
				model.save_pretrained(__a    )
				processor.save_pretrained(__a    )

		# Push to hub
		if push_to_hub:
				print('''Pushing model and processor to hub...'''    )
				model.push_to_hub(F'jozhang97/{model_name}'    )
				processor.push_to_hub(F'jozhang97/{model_name}'    )


if __name__ == "__main__":
						lowerCAmelCase__							=				argparse.ArgumentParser()

						parser.add_argument(
						    '--model_name',
						    type=str,
						    default='deta-swin-large',
						    choices=['deta-swin-large', 'deta-swin-large-o365'],
						    help='Name of the model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default=None,
						    type=str,
						    help='Path to the folder to output PyTorch model.',
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)
						lowerCAmelCase__							=				parser.parse_args()
						convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      (DDIMParallelScheduler,)
							UpperCAmelCase__						     =      (('''eta''', 0.0), ('''num_inference_steps''', 50))

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = {
									    '''num_train_timesteps''': 10_00,
									    '''beta_start''': 0.00_01,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''clip_sample''': True,
									}

									config.update(**SCREAMING_SNAKE_CASE_    )
									return config

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter

									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE_						 = model(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

									return sample

							def _lowercase					(self    ):

									"""simple docstring"""







									for timesteps in [1_00, 5_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config(steps_offset=1    )
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )
									scheduler.set_timesteps(5    )
									assert torch.equal(scheduler.timesteps       ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,       [0.0_02, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,       beta_end=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_    )
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=SCREAMING_SNAKE_CASE_       ,       prediction_type=SCREAMING_SNAKE_CASE_       ,       sample_max_value=SCREAMING_SNAKE_CASE_       ,       )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t in [1, 10, 49]:
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, num_inference_steps in zip([1, 10, 50]       ,       [10, 50, 5_00]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for t, eta in zip([1, 10, 49]       ,       [0.0, 0.5, 1.0]    ):
											self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_       ,       eta=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_20       ,       4_00    ) - 0.1_47_71    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_80       ,       9_60    ) - 0.3_24_60    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(0       ,       0    ) - 0.0    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87       ,       4_86    ) - 0.0_09_79    )    ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99       ,       9_98    ) - 0.02    )    ) < 1e-5

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_						 = self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_						 = scheduler_class(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 10, 0.0
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = self.dummy_model()
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter + 0.1
									SCREAMING_SNAKE_CASE_						 = self.dummy_sample_deter - 0.1

									SCREAMING_SNAKE_CASE_						 = samplea.shape[0]
									SCREAMING_SNAKE_CASE_						 = torch.stack([samplea, samplea, samplea]       ,       dim=0    )
									SCREAMING_SNAKE_CASE_						 = torch.arange(SCREAMING_SNAKE_CASE_    )[0:3, None].repeat(1       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = model(samples.flatten(0       ,       1    )       ,       timesteps.flatten(0       ,       1    )    )
									SCREAMING_SNAKE_CASE_						 = scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_       ,       timesteps.flatten(0       ,       1    )       ,       samples.flatten(0       ,       1    )       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
									assert abs(result_mean.item() - 0.49_82    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop()

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
									assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(prediction_type='''v_prediction'''    )

									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 52.53_02    ) < 1e-2
									assert abs(result_mean.item() - 0.06_84    ) < 1e-3

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
									assert abs(result_mean.item() - 0.19_51    ) < 1e-3







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       beta_start=0.01    )
									SCREAMING_SNAKE_CASE_						 = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

									assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
									assert abs(result_mean.item() - 0.19_41    ) < 1e-3