






"""simple docstring"""

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						__magic_name__            =     []
						embed.append(
						    (
						        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
						        f'''stage{idx}.patch_embed.proj.weight''',
						    )   )
						embed.append(
						    (
						        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
						        f'''stage{idx}.patch_embed.proj.bias''',
						    )   )
						embed.append(
						    (
						        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
						        f'''stage{idx}.patch_embed.norm.weight''',
						    )   )
						embed.append(
						    (
						        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
						        f'''stage{idx}.patch_embed.norm.bias''',
						    )   )
						return embed






def _lowerCAmelCase	(  __lowerCamelCase:List[str] ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     []
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
						        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
						        f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
						        f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
						        f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
						        f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
						    )   )
						attention_weights.append(
						    (
						        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
						        f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
						    )   )
						attention_weights.append(
						    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')   )
						attention_weights.append(
						    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')   )
						attention_weights.append(
						    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')   )
						attention_weights.append(
						    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')   )
						attention_weights.append(
						    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''')   )
						attention_weights.append(
						    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''')   )
						attention_weights.append(
						    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''')   )
						attention_weights.append(
						    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''')   )
						return attention_weights






def _lowerCAmelCase	(  __lowerCamelCase:Dict   ):

						'''simple docstring'''






						__magic_name__            =     []
						token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token")   )
						return token






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     []
						head.append(("layernorm.weight", "norm.weight")   )
						head.append(("layernorm.bias", "norm.bias")   )
						head.append(("classifier.weight", "head.weight")   )
						head.append(("classifier.bias", "head.bias")   )
						return head






def _lowerCAmelCase	(  __lowerCamelCase:Dict ,						__lowerCamelCase:List[str] ,						__lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Optional[Any]   ):

						'''simple docstring'''






						__magic_name__            =     "imagenet-1k-id2label.json"
						__magic_name__            =     1_0_0_0

						__magic_name__            =     "huggingface/label-files"
						__magic_name__            =     num_labels
						__magic_name__            =     json.load(open(cached_download(hf_hub_url(lowerCamelCase_ ,						lowerCamelCase_ ,						repo_type="dataset"   )   ) ,						"r"   )   )
						__magic_name__            =     {int(lowerCamelCase_   ): v for k, v in idalabel.items()}

						__magic_name__            =     idalabel
						__magic_name__            =     {v: k for k, v in idalabel.items()}

						__magic_name__            =     __magic_name__            =     CvtConfig(num_labels=lowerCamelCase_ ,						idalabel=lowerCamelCase_ ,						labelaid=lowerCamelCase_   )

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit("/" ,						1   )[-1][4:6] == "13":
												__magic_name__            =     [1, 2, 1_0]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit("/" ,						1   )[-1][4:6] == "21":
												__magic_name__            =     [1, 4, 1_6]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												__magic_name__            =     [2, 2, 2_0]
												__magic_name__            =     [3, 1_2, 1_6]
												__magic_name__            =     [1_9_2, 7_6_8, 1_0_2_4]

						__magic_name__            =     CvtForImageClassification(lowerCamelCase_   )
						__magic_name__            =     AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"   )
						__magic_name__            =     image_size
						__magic_name__            =     torch.load(lowerCamelCase_ ,						map_location=torch.device("cpu"   )   )

						__magic_name__            =     OrderedDict()
						__magic_name__            =     []

						for idx in range(len(config.depth   )   ):
												if config.cls_token[idx]:
																		__magic_name__            =     list_of_state_dict + cls_token(lowerCamelCase_   )
												__magic_name__            =     list_of_state_dict + embeddings(lowerCamelCase_   )
												for cnt in range(config.depth[idx]   ):
																		__magic_name__            =     list_of_state_dict + attention(lowerCamelCase_ ,						lowerCamelCase_   )

						__magic_name__            =     list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(lowerCamelCase_   )
						for i in range(len(lowerCamelCase_   )   ):
												__magic_name__            =     original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(lowerCamelCase_   )
						model.save_pretrained(lowerCamelCase_   )
						image_processor.save_pretrained(lowerCamelCase_   )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
						lowercase    			=       argparse.ArgumentParser()
						parser.add_argument(
						    '''--cvt_model''',
						    default='''cvt-w24''',
						    type=str,
						    help='''Name of the cvt model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--image_size''',
						    default=384,
						    type=int,
						    help='''Input Image Size''',
						)
						parser.add_argument(
						    '''--cvt_file_name''',
						    default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
						    type=str,
						    help='''Input Image Size''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)

						lowercase    			=       parser.parse_args()
						convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)











"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				(UnCLIPScheduler,)







				def     _snake_case					(							self							:						Any						,    **__lowerCamelCase							:						List[Any]	)				->					str:
										__magic_name__            =     {
										    "num_train_timesteps": 1_0_0_0,
										    "variance_type": "fixed_small_log",
										    "clip_sample": True,
										    "clip_sample_range": 1.0,
										    "prediction_type": "epsilon",
										}

										config.update(**__lowerCamelCase	)
										return config







				def     _snake_case					(							self							:						int	)				->					Tuple:
										for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					Any:
										for variance in ["fixed_small_log", "learned_range"]:
																self.check_over_configs(variance_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Tuple:
										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[int]:
										for clip_sample_range in [1, 5, 1_0, 2_0]:
																self.check_over_configs(clip_sample_range=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(prediction_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										for time_step in [0, 5_0_0, 9_9_9]:
																for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
																						if prev_timestep is not None and prev_timestep >= time_step:
																												continue

																						self.check_over_forward(time_step=__lowerCamelCase						,    prev_timestep=__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Tuple:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="fixed_small_log"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.00_00e-10	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_8_7	) - 0.054_9625	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_9_9	) - 0.999_4987	)	) < 1e-5







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[Any]:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="learned_range"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     0.5

										assert scheduler._get_variance(1						,    predicted_variance=__lowerCamelCase	) - -10.171_2790 < 1e-5
										assert scheduler._get_variance(4_8_7						,    predicted_variance=__lowerCamelCase	) - -5.799_8052 < 1e-5
										assert scheduler._get_variance(9_9_9						,    predicted_variance=__lowerCamelCase	) - -0.001_0011 < 1e-5







				def     _snake_case					(							self							:						Optional[Any]	)				->					str:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 252.268_2495	) < 1e-2
										assert abs(result_mean.item() - 0.328_4743	) < 1e-3







				def     _snake_case					(							self							:						int	)				->					int:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										scheduler.set_timesteps(2_5	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																if i + 1 == timesteps.shape[0]:
																						__magic_name__            =     None
																else:
																						__magic_name__            =     timesteps[i + 1]

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(
																    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    prev_timestep=__lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 258.204_4983	) < 1e-2
										assert abs(result_mean.item() - 0.336_2038	) < 1e-3







				def     _snake_case					(							self							:						List[str]	)				->					Any:
										pass







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										pass



