




'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def 						snake_case_       (UpperCamelCase			:    Union[str, Any] ):





							'''simple docstring'''


							_a      	=							SwinvaConfig()
							_a      	=							swinva_name.split('''_''' )

							_a      	=							name_split[1]
							if "to" in name_split[3]:
														_a      	=							int(name_split[3][-3:] )
							else:
														_a      	=							int(name_split[3] )
							if "to" in name_split[2]:
														_a      	=							int(name_split[2][-2:] )
							else:
														_a      	=							int(name_split[2][6:] )

							if model_size == "tiny":
														_a      	=							96
														_a      	=							(2, 2, 6, 2)
														_a      	=							(3, 6, 12, 24)
							elif model_size == "small":
														_a      	=							96
														_a      	=							(2, 2, 18, 2)
														_a      	=							(3, 6, 12, 24)
							elif model_size == "base":
														_a      	=							128
														_a      	=							(2, 2, 18, 2)
														_a      	=							(4, 8, 16, 32)
							else:
														_a      	=							192
														_a      	=							(2, 2, 18, 2)
														_a      	=							(6, 12, 24, 48)

							if "to" in swinva_name:
														_a      	=							(12, 12, 12, 6)

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														_a      	=							2_1841
														_a      	=							'''huggingface/label-files'''
														_a      	=							'''imagenet-22k-id2label.json'''
														_a      	=							json.load(open(hf_hub_download(UpperCamelCase					,		UpperCamelCase					,		repo_type='''dataset''' )					,		'''r''' ) )
														_a      	=							{int(UpperCamelCase ): v for k, v in idalabel.items()}
														_a      	=							idalabel
														_a      	=							{v: k for k, v in idalabel.items()}

							else:
														_a      	=							1000
														_a      	=							'''huggingface/label-files'''
														_a      	=							'''imagenet-1k-id2label.json'''
														_a      	=							json.load(open(hf_hub_download(UpperCamelCase					,		UpperCamelCase					,		repo_type='''dataset''' )					,		'''r''' ) )
														_a      	=							{int(UpperCamelCase ): v for k, v in idalabel.items()}
														_a      	=							idalabel
														_a      	=							{v: k for k, v in idalabel.items()}

							_a      	=							img_size
							_a      	=							num_classes
							_a      	=							embed_dim
							_a      	=							depths
							_a      	=							num_heads
							_a      	=							window_size

							return config
def 						snake_case_       (UpperCamelCase			:    str ):





							'''simple docstring'''


							if "patch_embed.proj" in name:
														_a      	=							name.replace('''patch_embed.proj'''					,		'''embeddings.patch_embeddings.projection''' )
							if "patch_embed.norm" in name:
														_a      	=							name.replace('''patch_embed.norm'''					,		'''embeddings.norm''' )
							if "layers" in name:
														_a      	=							'''encoder.''' + name
							if "attn.proj" in name:
														_a      	=							name.replace('''attn.proj'''					,		'''attention.output.dense''' )
							if "attn" in name:
														_a      	=							name.replace('''attn'''					,		'''attention.self''' )
							if "norm1" in name:
														_a      	=							name.replace('''norm1'''					,		'''layernorm_before''' )
							if "norm2" in name:
														_a      	=							name.replace('''norm2'''					,		'''layernorm_after''' )
							if "mlp.fc1" in name:
														_a      	=							name.replace('''mlp.fc1'''					,		'''intermediate.dense''' )
							if "mlp.fc2" in name:
														_a      	=							name.replace('''mlp.fc2'''					,		'''output.dense''' )
							if "q_bias" in name:
														_a      	=							name.replace('''q_bias'''					,		'''query.bias''' )
							if "k_bias" in name:
														_a      	=							name.replace('''k_bias'''					,		'''key.bias''' )
							if "v_bias" in name:
														_a      	=							name.replace('''v_bias'''					,		'''value.bias''' )
							if "cpb_mlp" in name:
														_a      	=							name.replace('''cpb_mlp'''					,		'''continuous_position_bias_mlp''' )
							if name == "norm.weight":
														_a      	=							'''layernorm.weight'''
							if name == "norm.bias":
														_a      	=							'''layernorm.bias'''

							if "head" in name:
														_a      	=							name.replace('''head'''					,		'''classifier''' )
							else:
														_a      	=							'''swinv2.''' + name

							return name
def 						snake_case_       (UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[Any] ):





							'''simple docstring'''


							for key in orig_state_dict.copy().keys():
														_a      	=							orig_state_dict.pop(UpperCamelCase )

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					_a      	=							key.split('''.''' )
																					_a      	=							int(key_split[1] )
																					_a      	=							int(key_split[3] )
																					_a      	=							model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												_a      	=							val[:dim, :]
																												_a      	=							val[dim : dim * 2, :]
																												_a      	=							val[-dim:, :]
																					else:
																												_a      	=							val[:dim]
																												_a      	=							val[
																												    dim : dim * 2
																												]
																												_a      	=							val[-dim:]
														else:
																					_a      	=							val

							return orig_state_dict
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


							_a      	=							timm.create_model(UpperCamelCase					,		pretrained=UpperCamelCase )
							timm_model.eval()

							_a      	=							get_swinva_config(UpperCamelCase )
							_a      	=							SwinvaForImageClassification(UpperCamelCase )
							model.eval()

							_a      	=							convert_state_dict(timm_model.state_dict()					,		UpperCamelCase )
							model.load_state_dict(UpperCamelCase )

							_a      	=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''

							_a      	=							AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''					,		'''-''' ) ) )
							_a      	=							Image.open(requests.get(UpperCamelCase					,		stream=UpperCamelCase ).raw )
							_a      	=							image_processor(images=UpperCamelCase					,		return_tensors='''pt''' )

							_a      	=							timm_model(inputs['''pixel_values'''] )
							_a      	=							model(**UpperCamelCase ).logits

							assert torch.allclose(UpperCamelCase					,		UpperCamelCase					,		atol=1e-3 )

							print(f'Saving model {swinva_name} to {pytorch_dump_folder_path}' )
							model.save_pretrained(UpperCamelCase )

							print(f'Saving image processor to {pytorch_dump_folder_path}' )
							image_processor.save_pretrained(UpperCamelCase )

							model.push_to_hub(
							    repo_path_or_name=Path(UpperCamelCase					,		UpperCamelCase )					,		organization='''nandwalritik'''					,		commit_message='''Add model'''					,		)


if __name__ == "__main__":
				_snake_case : List[str]  				=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--swinv2_name',
				    default='swinv2_tiny_patch4_window8_256',
				    type=str,
				    help='Name of the Swinv2 timm model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)

				_snake_case : int  				=						parser.parse_args()
				convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (						_a       ):
							lowercase_							     =							(DDPMParallelScheduler,)







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     **lowerCAmelCase_			:    Optional[int]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							{
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														    '''variance_type''': '''fixed_small''',
														    '''clip_sample''': True,
														}

														config.update(**lowerCAmelCase_   )
														return config







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,     [0.0_0_2, 0.0_2, 0.2, 2]   ):
																					self.check_over_configs(beta_start=lowerCAmelCase_				,     beta_end=lowerCAmelCase_   )







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


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														for variance in ["fixed_small", "fixed_large", "other"]:
																					self.check_over_configs(variance_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    str   )     ->				List[str]:


														"""simple docstring"""


														for t in [0, 5_00, 9_99]:
																					self.check_over_forward(time_step=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				Optional[int]:


														"""simple docstring"""


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

														assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_0_9_7_9   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.0_2   )   ) < 1e-5







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


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

														_a      	=							len(lowerCAmelCase_   )

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

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

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

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 1_1_5_3.1_8_3_3   ) < 1e-2
														assert abs(result_mean.item() - 0.5_0_0_5   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Dict:


														"""simple docstring"""


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

														_a      	=							len(lowerCAmelCase_   )

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

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

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

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
														assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config(prediction_type='''v_prediction'''   )
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

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

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

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

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
														assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


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

														_a      	=							[1_00, 87, 50, 1, 0]

														scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

														_a      	=							scheduler.timesteps

														for i, timestep in enumerate(lowerCAmelCase_   ):
																					if i == len(lowerCAmelCase_   ) - 1:
																												_a      	=							-1
																					else:
																												_a      	=							timesteps[i + 1]

																					_a      	=							scheduler.previous_timestep(lowerCAmelCase_   )
																					_a      	=							prev_t.item()

																					self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				List[Any]:


														"""simple docstring"""


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

														_a      	=							[1_00, 87, 50, 51, 0]

														with self.assertRaises(lowerCAmelCase_				,     msg='''`custom_timesteps` must be in descending order.'''   ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


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

														_a      	=							[1_00, 87, 50, 1, 0]
														_a      	=							len(lowerCAmelCase_   )

														with self.assertRaises(lowerCAmelCase_				,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
																					scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_				,     timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


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

														_a      	=							[scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    lowerCAmelCase_				,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,     ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

