

'''simple docstring'''
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	int        =(DEISMultistepScheduler,)
			__A  :	List[str]        =(("num_inference_steps", 25),)






			def 		UpperCamelCase__							(  self			,**_snake_case     ):
				UpperCAmelCase_     :      Optional[Any]						=  {
				    "num_train_timesteps": 10_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				    "solver_order": 2,
				}

				config.update(**_snake_case     )
				return config






			def 		UpperCamelCase__							(  self			,_snake_case=0			,**_snake_case     ):
				UpperCAmelCase_     :      int						=  dict(self.forward_default_kwargs     )
				UpperCAmelCase_     :      Tuple						=  kwargs.pop("num_inference_steps"			,_snake_case     )
				UpperCAmelCase_     :      List[str]						=  self.dummy_sample
				UpperCAmelCase_     :      Optional[int]						=  0.1 * sample
				UpperCAmelCase_     :      Optional[Any]						=  [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      Dict						=  self.get_scheduler_config(**_snake_case     )
					UpperCAmelCase_     :      List[str]						=  scheduler_class(**_snake_case     )
					scheduler.set_timesteps(_snake_case     )
					# copy over dummy past residuals
					UpperCAmelCase_     :      int						=  dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(_snake_case     )
						UpperCAmelCase_     :      Any						=  scheduler_class.from_pretrained(_snake_case     )
						new_scheduler.set_timesteps(_snake_case     )
						# copy over dummy past residuals
						UpperCAmelCase_     :      Any						=  dummy_past_residuals[: new_scheduler.config.solver_order]

					UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[Any]						=  sample, sample
					for t in range(_snake_case			,time_step + scheduler.config.solver_order + 1     ):
						UpperCAmelCase_     :      Tuple						=  scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample
						UpperCAmelCase_     :      Tuple						=  new_scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample

						assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self			,_snake_case=0			,**_snake_case     ):
				UpperCAmelCase_     :      List[str]						=  dict(self.forward_default_kwargs     )
				UpperCAmelCase_     :      List[Any]						=  kwargs.pop("num_inference_steps"			,_snake_case     )
				UpperCAmelCase_     :      Tuple						=  self.dummy_sample
				UpperCAmelCase_     :      Optional[Any]						=  0.1 * sample
				UpperCAmelCase_     :      List[Any]						=  [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      Optional[Any]						=  self.get_scheduler_config()
					UpperCAmelCase_     :      Dict						=  scheduler_class(**_snake_case     )
					scheduler.set_timesteps(_snake_case     )

					# copy over dummy past residuals (must be after setting timesteps)
					UpperCAmelCase_     :      List[str]						=  dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(_snake_case     )
						UpperCAmelCase_     :      int						=  scheduler_class.from_pretrained(_snake_case     )
						# copy over dummy past residuals
						new_scheduler.set_timesteps(_snake_case     )

						# copy over dummy past residual (must be after setting timesteps)
						UpperCAmelCase_     :      int						=  dummy_past_residuals[: new_scheduler.config.solver_order]

					UpperCAmelCase_     :      Any						=  scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample
					UpperCAmelCase_     :      List[str]						=  new_scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample

					assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"






			def 		UpperCamelCase__							(  self			,_snake_case=None			,**_snake_case     ):
				if scheduler is None:
					UpperCAmelCase_     :      int						=  self.scheduler_classes[0]
					UpperCAmelCase_     :      str						=  self.get_scheduler_config(**_snake_case     )
					UpperCAmelCase_     :      Union[str, Any]						=  scheduler_class(**_snake_case     )

				UpperCAmelCase_     :      Tuple						=  self.scheduler_classes[0]
				UpperCAmelCase_     :      Union[str, Any]						=  self.get_scheduler_config(**_snake_case     )
				UpperCAmelCase_     :      Any						=  scheduler_class(**_snake_case     )

				UpperCAmelCase_     :      Union[str, Any]						=  10
				UpperCAmelCase_     :      List[Any]						=  self.dummy_model()
				UpperCAmelCase_     :      Optional[int]						=  self.dummy_sample_deter
				scheduler.set_timesteps(_snake_case     )

				for i, t in enumerate(scheduler.timesteps     ):
					UpperCAmelCase_     :      Tuple						=  model(_snake_case			,_snake_case     )
					UpperCAmelCase_     :      Optional[Any]						=  scheduler.step(_snake_case			,_snake_case			,_snake_case     ).prev_sample

				return sample






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  dict(self.forward_default_kwargs     )

				UpperCAmelCase_     :      List[Any]						=  kwargs.pop("num_inference_steps"			,_snake_case     )

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      Optional[Any]						=  self.get_scheduler_config()
					UpperCAmelCase_     :      Any						=  scheduler_class(**_snake_case     )

					UpperCAmelCase_     :      Optional[Any]						=  self.dummy_sample
					UpperCAmelCase_     :      Union[str, Any]						=  0.1 * sample

					if num_inference_steps is not None and hasattr(_snake_case			,"set_timesteps"     ):
						scheduler.set_timesteps(_snake_case     )
					elif num_inference_steps is not None and not hasattr(_snake_case			,"set_timesteps"     ):
						UpperCAmelCase_     :      Tuple						=  num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					UpperCAmelCase_     :      Optional[int]						=  [residual + 0.2, residual + 0.15, residual + 0.10]
					UpperCAmelCase_     :      Optional[Any]						=  dummy_past_residuals[: scheduler.config.solver_order]

					UpperCAmelCase_     :      Optional[int]						=  scheduler.timesteps[5]
					UpperCAmelCase_     :      Optional[int]						=  scheduler.timesteps[6]

					UpperCAmelCase_     :      Tuple						=  scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample
					UpperCAmelCase_     :      int						=  scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample

					self.assertEqual(output_a.shape			,sample.shape     )
					self.assertEqual(output_a.shape			,output_a.shape     )






			def 		UpperCamelCase__							(  self     ):
				# make sure that iterating over schedulers with same config names gives same results
				# for defaults
				UpperCAmelCase_     :      Tuple						=  DEISMultistepScheduler(**self.get_scheduler_config()     )
				UpperCAmelCase_     :      Union[str, Any]						=  self.full_loop(scheduler=_snake_case     )
				UpperCAmelCase_     :      List[str]						=  torch.mean(torch.abs(_snake_case     )     )

				assert abs(result_mean.item() - 0.23916     ) < 1E-3

				UpperCAmelCase_     :      int						=  DPMSolverSinglestepScheduler.from_config(scheduler.config     )
				UpperCAmelCase_     :      int						=  DPMSolverMultistepScheduler.from_config(scheduler.config     )
				UpperCAmelCase_     :      str						=  UniPCMultistepScheduler.from_config(scheduler.config     )
				UpperCAmelCase_     :      int						=  DEISMultistepScheduler.from_config(scheduler.config     )

				UpperCAmelCase_     :      Union[str, Any]						=  self.full_loop(scheduler=_snake_case     )
				UpperCAmelCase_     :      int						=  torch.mean(torch.abs(_snake_case     )     )

				assert abs(result_mean.item() - 0.23916     ) < 1E-3






			def 		UpperCamelCase__							(  self     ):
				for timesteps in [25, 50, 1_00, 9_99, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				self.check_over_configs(thresholding=_snake_case     )
				for order in [1, 2, 3]:
					for solver_type in ["logrho"]:
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    thresholding=_snake_case			,prediction_type=_snake_case			,sample_max_value=_snake_case			,algorithm_type="deis"			,solver_order=_snake_case			,solver_type=_snake_case			,)






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






			def 		UpperCamelCase__							(  self     ):
				for algorithm_type in ["deis"]:
					for solver_type in ["logrho"]:
						for order in [1, 2, 3]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    solver_order=_snake_case			,solver_type=_snake_case			,prediction_type=_snake_case			,algorithm_type=_snake_case			,)
								UpperCAmelCase_     :      Tuple						=  self.full_loop(
								    solver_order=_snake_case			,solver_type=_snake_case			,prediction_type=_snake_case			,algorithm_type=_snake_case			,)
								assert not torch.isnan(_snake_case     ).any(), "Samples have nan numbers"






			def 		UpperCamelCase__							(  self     ):
				self.check_over_configs(lower_order_final=_snake_case     )
				self.check_over_configs(lower_order_final=_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
					self.check_over_forward(num_inference_steps=_snake_case			,time_step=0     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  self.full_loop()
				UpperCAmelCase_     :      Union[str, Any]						=  torch.mean(torch.abs(_snake_case     )     )

				assert abs(result_mean.item() - 0.23916     ) < 1E-3






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  self.full_loop(prediction_type="v_prediction"     )
				UpperCAmelCase_     :      Tuple						=  torch.mean(torch.abs(_snake_case     )     )

				assert abs(result_mean.item() - 0.091     ) < 1E-3






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[Any]						=  self.scheduler_classes[0]
				UpperCAmelCase_     :      Union[str, Any]						=  self.get_scheduler_config(thresholding=_snake_case			,dynamic_thresholding_ratio=0     )
				UpperCAmelCase_     :      Union[str, Any]						=  scheduler_class(**_snake_case     )

				UpperCAmelCase_     :      Optional[Any]						=  10
				UpperCAmelCase_     :      Dict						=  self.dummy_model()
				UpperCAmelCase_     :      str						=  self.dummy_sample_deter.half()
				scheduler.set_timesteps(_snake_case     )

				for i, t in enumerate(scheduler.timesteps     ):
					UpperCAmelCase_     :      Optional[int]						=  model(_snake_case			,_snake_case     )
					UpperCAmelCase_     :      List[str]						=  scheduler.step(_snake_case			,_snake_case			,_snake_case     ).prev_sample

				assert sample.dtype == torch.floataa











'''simple docstring'''




import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Tuple=False      ,    __lowerCAmelCase       :							Any=False		)		->  Optional[Any]:
   snake_case 						=   """backbone.""" if is_semantic else """"""

   snake_case 						=   []
   for i in range(config.num_hidden_layers		):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')		)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')		)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')		)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           (F'''{prefix}cls_token''', """beit.embeddings.cls_token"""),
           (F'''{prefix}patch_embed.proj.weight''', """beit.embeddings.patch_embeddings.projection.weight"""),
           (F'''{prefix}patch_embed.proj.bias''', """beit.embeddings.patch_embeddings.projection.bias"""),
           (F'''{prefix}pos_embed''', """beit.embeddings.position_embeddings"""),
       ]		)

   if has_lm_head:
      # mask token + layernorm
      rename_keys.extend(
          [
              ("""mask_token""", """beit.embeddings.mask_token"""),
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
          ]		)
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
              ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]		)

   return rename_keys





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							str=False      ,    __lowerCAmelCase       :							List[Any]=False		)		->  Optional[int]:
   for i in range(config.num_hidden_layers		):
      snake_case 						=   """backbone.""" if is_semantic else """"""
      # queries, keys and values
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

      snake_case 						=   in_proj_weight[
          : config.hidden_size, :
      ]
      snake_case 						=   q_bias
      snake_case 						=   in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      snake_case 						=   in_proj_weight[
          -config.hidden_size :, :
      ]
      snake_case 						=   v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

      snake_case 						=   gamma_a
      snake_case 						=   gamma_a





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Tuple		)		->  Any:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      )		->  Dict:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Any=False		)		->  str:
   snake_case 						=   False if """rvlcdip""" in checkpoint_url else True
   snake_case 						=   BeitConfig(use_absolute_position_embeddings=__lowerCAmelCase      ,    use_mask_token=__lowerCAmelCase		)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      snake_case 						=   10_24
      snake_case 						=   40_96
      snake_case 						=   24
      snake_case 						=   16

   # labels
   if "rvlcdip" in checkpoint_url:
      snake_case 						=   16
      snake_case 						=   """huggingface/label-files"""
      snake_case 						=   """rvlcdip-id2label.json"""
      snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
      snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case 						=   idalabel
      snake_case 						=   {v: k for k, v in idalabel.items()}

   # load state_dict of original model, remove and rename some keys
   snake_case 						=   torch.hub.load_state_dict_from_url(__lowerCAmelCase      ,    map_location="""cpu"""		)["""model"""]

   snake_case 						=   create_rename_keys(__lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   read_in_q_k_v(__lowerCAmelCase      ,    __lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)

   # load HuggingFace model
   snake_case 						=   BeitForMaskedImageModeling(__lowerCAmelCase		) if has_lm_head else BeitForImageClassification(__lowerCAmelCase		)
   model.eval()
   model.load_state_dict(__lowerCAmelCase		)

   # Check outputs on an image
   snake_case 						=   BeitImageProcessor(
       size=config.image_size      ,    resample=PILImageResampling.BILINEAR      ,    do_center_crop=__lowerCAmelCase		)
   snake_case 						=   prepare_img()

   snake_case 						=   image_processor(images=__lowerCAmelCase      ,    return_tensors="""pt"""		)
   snake_case 						=   encoding["""pixel_values"""]

   snake_case 						=   model(__lowerCAmelCase		)
   snake_case 						=   outputs.logits

   # verify logits
   snake_case 						=   [1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
   assert logits.shape == torch.Size(__lowerCAmelCase		), "Shape of logits not as expected"

   Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   print(F'''Saving model to {pytorch_dump_folder_path}'''		)
   model.save_pretrained(__lowerCAmelCase		)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
   image_processor.save_pretrained(__lowerCAmelCase		)

   if push_to_hub:
      if has_lm_head:
         snake_case 						=   """dit-base""" if """base""" in checkpoint_url else """dit-large"""
      else:
         snake_case 						=   """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
      image_processor.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add image processor"""      ,    use_temp_dir=__lowerCAmelCase      ,    )
      model.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add model"""      ,    use_temp_dir=__lowerCAmelCase      ,    )


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

 parser.add_argument(
     "--checkpoint_url",
     default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
     type=str,
     help="URL to the original PyTorch checkpoint (.pth file).",
 )
 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",
 )
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

