

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


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







@dataclass
class  __lowerCamelCase				:
      lowerCamelCase_	:      nn.Module
      lowerCamelCase_	:      List[nn.Module]          =					field(default_factory=__snake_case	)
      lowerCamelCase_	:      list          =					field(default_factory=__snake_case	)







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       int:
        snake_case_       						=      len(list(m.modules()   )   ) == 1 or isinstance(lowerCamelCase							,  nn.Convad   ) or isinstance(lowerCamelCase							,  nn.BatchNormad   )
        if has_not_submodules:
          if not isinstance(lowerCamelCase							,  lowerCamelCase   ):
            self.traced.append(lowerCamelCase   )







      def __call__(     self							,  lowerCamelCase   )						->       List[Any]:
        for m in self.module.modules():
          self.handles.append(m.register_forward_hook(self._forward_hook   )   )
        self.module(lowerCamelCase   )
        [x.remove() for x in self.handles]
        return self







      @property
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda lowerCamelCase   : len(list(x.state_dict().keys()   )   ) > 0							,  self.traced   )   )







@dataclass
class  __lowerCamelCase				:
      lowerCamelCase_	:      nn.Module
      lowerCamelCase_	:      nn.Module
      lowerCamelCase_	:      int          =					0
      lowerCamelCase_	:      List          =					field(default_factory=__snake_case	)
      lowerCamelCase_	:      List          =					field(default_factory=__snake_case	)







      def __call__(     self							,  lowerCamelCase   )						->       Optional[Any]:
        snake_case_       						=      Tracker(self.dest   )(lowerCamelCase   ).parametrized
        snake_case_       						=      Tracker(self.src   )(lowerCamelCase   ).parametrized

        snake_case_       						=      list(filter(lambda lowerCamelCase   : type(lowerCamelCase   ) not in self.src_skip							,  lowerCamelCase   )   )
        snake_case_       						=      list(filter(lambda lowerCamelCase   : type(lowerCamelCase   ) not in self.dest_skip							,  lowerCamelCase   )   )

        if len(lowerCamelCase   ) != len(lowerCamelCase   ):
          raise Exception(
              f'''Numbers of operations are different. Source module has {len(lowerCamelCase   )} operations while'''
              f''' destination module has {len(lowerCamelCase   )}.'''   )

        for dest_m, src_m in zip(lowerCamelCase							,  lowerCamelCase   ):
          dest_m.load_state_dict(src_m.state_dict()   )
          if self.verbose == 1:
            print(f'''Transfered from={src_m} to={dest_m}'''   )



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				nn.Module:





  '''simple docstring'''





  snake_case_       						=      from_model.state_dict()
  snake_case_       						=      our_model.state_dict()
  snake_case_       						=      our_model.config
  snake_case_       						=      []
  for stage_idx in range(len(config.hidden_sizes							)							):
    for block_id in range(config.depths[stage_idx]							):
      snake_case_       						=      f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
      snake_case_       						=      f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

      all_keys.append((from_key, to_key)							)
      snake_case_       						=      f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
      snake_case_       						=      f'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

      all_keys.append((from_key, to_key)							)

  for from_key, to_key in all_keys:
    snake_case_       						=      from_state_dict.pop(lowercase_							)

  our_model.load_state_dict(lowercase_							)
  return our_model



def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = True			,					)	->				int:





  '''simple docstring'''





  print(f'''Downloading weights for {name}...'''							)
  snake_case_       						=      cached_download(lowercase_							)
  print(f'''Converting {name}...'''							)
  snake_case_       						=      torch.load(lowercase_							)["""state_dict"""]
  from_model.load_state_dict(lowercase_							)
  from_model.eval()
  with torch.no_grad():
    snake_case_       						=      VanForImageClassification(lowercase_							).eval()
    snake_case_       						=      ModuleTransfer(src=lowercase_			,					dest=lowercase_							)
    snake_case_       						=      torch.randn((1, 3, 224, 224)							)
    module_transfer(lowercase_							)
    snake_case_       						=      copy_parameters(lowercase_			,					lowercase_							)

  if not torch.allclose(from_model(lowercase_							)			,					our_model(lowercase_							).logits							):
    raise ValueError("""The model logits don't match the original one."""							)

  snake_case_       						=      name
  print(lowercase_							)

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name			,					commit_message="""Add model"""			,					use_temp_dir=lowercase_			,					)

    # we can use the convnext one
    snake_case_       						=      AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""							)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name			,					commit_message="""Add image processor"""			,					use_temp_dir=lowercase_			,					)

    print(f'''Pushed {checkpoint_name}'''							)



def    UpperCamelCase(					lowercase_			,					lowercase_ = None			,					lowercase_ = True							)	->				int:





  '''simple docstring'''





  snake_case_       						=      """imagenet-1k-id2label.json"""
  snake_case_       						=      1000

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

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

  snake_case_       						=      partial(lowercase_			,					num_labels=lowercase_			,					idalabel=lowercase_			,					labelaid=lowercase_							)

  snake_case_       						=      {
      """van-tiny""": ImageNetPreTrainedConfig(
          hidden_sizes=[32, 64, 160, 256]			,					depths=[3, 3, 5, 2]			,					mlp_ratios=[8, 8, 4, 4]			,					),
      """van-small""": ImageNetPreTrainedConfig(
          hidden_sizes=[64, 128, 320, 512]			,					depths=[2, 2, 4, 2]			,					mlp_ratios=[8, 8, 4, 4]			,					),
      """van-base""": ImageNetPreTrainedConfig(
          hidden_sizes=[64, 128, 320, 512]			,					depths=[3, 3, 12, 3]			,					mlp_ratios=[8, 8, 4, 4]			,					),
      """van-large""": ImageNetPreTrainedConfig(
          hidden_sizes=[64, 128, 320, 512]			,					depths=[3, 5, 27, 3]			,					mlp_ratios=[8, 8, 4, 4]			,					),
  }

  snake_case_       						=      {
      """van-tiny""": van_tiny,
      """van-small""": van_small,
      """van-base""": van_base,
      """van-large""": van_large,
  }

  snake_case_       						=      {
      """van-tiny""": (
          """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
      ),
      """van-small""": (
          """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
      ),
      """van-base""": (
          """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
      ),
      """van-large""": (
          """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
      ),
  }

  if model_name:
    convert_weight_and_push(
        lowercase_			,					names_to_config[model_name]			,					checkpoint=names_to_original_checkpoints[model_name]			,					from_model=names_to_original_models[model_name]()			,					save_directory=lowercase_			,					push_to_hub=lowercase_			,					)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(
          lowercase_			,					lowercase_			,					checkpoint=names_to_original_checkpoints[model_name]			,					from_model=names_to_original_models[model_name]()			,					save_directory=lowercase_			,					push_to_hub=lowercase_			,					)


if __name__ == "__main__":
    lowerCamelCase_    =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model-name''',
        default=None,
        type=str,
        help=(
            '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
            ''' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default=None,
        type=Path,
        required=True,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument(
        '''--van_dir''',
        required=True,
        type=Path,
        help=(
            '''A path to VAN\'s original implementation directory. You can download from here:'''
            ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
        ),
    )
    parser.add_argument(
        '''--push_to_hub''',
        default=True,
        type=bool,
        required=False,
        help='''If True, push model and image processor to the hub.''',
    )

    lowerCamelCase_    =					parser.parse_args()
    lowerCamelCase_    =					args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    lowerCamelCase_    =					args.van_dir
    # append the path to the parents to maskformer dir
    sys.path.append(str(van_dir.parent))
    from van.models.van import van_base, van_large, van_small, van_tiny

    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					(DDIMScheduler,)
      lowerCamelCase_	:      Union[str, Any]          =					(('eta', 0.0), ('num_inference_steps', 50))







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       Dict:
        snake_case_       						=      {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase   )
        return config







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       List[str]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(**lowerCamelCase   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        snake_case_						,				snake_case_       						=      10, 0.0

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase   )

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

        return sample







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCamelCase   )

        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(steps_offset=1   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )
        scheduler.set_timesteps(5   )
        assert torch.equal(scheduler.timesteps							,  torch.LongTensor([801, 601, 401, 201, 1]   )   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,  [0.002, 0.02, 0.2, 2]   ):
          self.check_over_configs(beta_start=lowerCamelCase							,  beta_end=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        self.check_over_configs(thresholding=lowerCamelCase   )
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=lowerCamelCase							,  prediction_type=lowerCamelCase							,  sample_max_value=lowerCamelCase							,  )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for t, num_inference_steps in zip([1, 10, 50]							,  [10, 50, 500]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  num_inference_steps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for t, eta in zip([1, 10, 49]							,  [0.0, 0.5, 1.0]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  eta=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420							,  400   ) - 0.1_4771   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980							,  960   ) - 0.3_2460   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487							,  486   ) - 0.0_0979   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							,  998   ) - 0.02   )   ) < 1e-5







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.full_loop()

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 172.0067   ) < 1e-2
        assert abs(result_mean.item() - 0.22_3967   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.full_loop(prediction_type="""v_prediction"""   )

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 52.5302   ) < 1e-2
        assert abs(result_mean.item() - 0.0684   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.8295   ) < 1e-2
        assert abs(result_mean.item() - 0.1951   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.0784   ) < 1e-2
        assert abs(result_mean.item() - 0.1941   ) < 1e-3