'''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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__=False    )     ->   List[str]:
    __lowerCamelCase          =  []

    # fmt: off
    # stem:
    rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')    )
    rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')    )

    rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')    )
    rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')    )

    # backbone
    rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')    )
    rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')    )
    rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')    )

    for stage_idx in range(len(config.backbone_config.depths    )    ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
            rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")    )
            rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")    )
            rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")    )
            rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")    )
            rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")    )
            rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")    )
            rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")    )
            rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")    )
            rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")    )

        rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")    )
        rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")    )
        rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")    )

    # transformer encoder
    for i in range(config.num_hidden_layers    ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")    )
        rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")    )
        rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")    )
        rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")    )
        rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")    )
        rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")    )
        rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")    )
        rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")    )
        rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")    )
        rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")    )

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.bias'''),
                ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
            ]    )

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        __lowerCamelCase          =  [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''    ) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''norm.weight''', '''vit.layernorm.weight'''),
                ('''norm.bias''', '''vit.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]    )
    # fmt: on

    return rename_keys






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=False    )     ->   List[str]:
    for i in range(config.num_hidden_layers    ):
        if base_model:
            __lowerCamelCase          =  ''''''
        else:
            __lowerCamelCase          =  '''vit.'''
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        __lowerCamelCase          =  state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""    )
        __lowerCamelCase          =  state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""    )
        # next, add query, keys and values (in that order) to the state dict
        __lowerCamelCase          =  in_proj_weight[
            : config.hidden_size, :
        ]
        __lowerCamelCase          =  in_proj_bias[: config.hidden_size]
        __lowerCamelCase          =  in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __lowerCamelCase          =  in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        __lowerCamelCase          =  in_proj_weight[
            -config.hidden_size :, :
        ]
        __lowerCamelCase          =  in_proj_bias[-config.hidden_size :]






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Optional[Any]:
    __lowerCamelCase          =  ['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase__    ,  UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Tuple:
    __lowerCamelCase          =  dct.pop(UpperCamelCase__    )
    __lowerCamelCase          =  val






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






@torch.no_grad()
def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=False    )     ->   Optional[Any]:
    __lowerCamelCase          =  BitConfig(
        global_padding='''same'''    ,  layer_type='''bottleneck'''    ,  depths=(3, 4, 9)    ,  out_features=['''stage3''']    ,  embedding_dynamic_padding=UpperCamelCase__    ,  )
    __lowerCamelCase          =  ViTHybridConfig(backbone_config=UpperCamelCase__    ,  image_size=3_84    ,  num_labels=10_00    )
    __lowerCamelCase          =  False

    # load original model from timm
    __lowerCamelCase          =  timm.create_model(UpperCamelCase__    ,  pretrained=UpperCamelCase__    )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    __lowerCamelCase          =  timm_model.state_dict()
    if base_model:
        remove_classification_head_(UpperCamelCase__    )
    __lowerCamelCase          =  create_rename_keys(UpperCamelCase__    ,  UpperCamelCase__    )
    for src, dest in rename_keys:
        rename_key(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )
    read_in_q_k_v(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )

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

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        __lowerCamelCase          =  ViTHybridModel(UpperCamelCase__    ).eval()
    else:
        __lowerCamelCase          =  ViTHybridForImageClassification(UpperCamelCase__    ).eval()
    model.load_state_dict(UpperCamelCase__    )

    # create image processor
    __lowerCamelCase          =  create_transform(**resolve_data_config({}    ,  model=UpperCamelCase__    )    )
    __lowerCamelCase          =  transform.transforms

    __lowerCamelCase          =  {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    __lowerCamelCase          =  ViTHybridImageProcessor(
        do_resize=UpperCamelCase__    ,  size={'''shortest_edge''': timm_transforms[0].size}    ,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,  do_center_crop=UpperCamelCase__    ,  crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}    ,  do_normalize=UpperCamelCase__    ,  image_mean=timm_transforms[-1].mean.tolist()    ,  image_std=timm_transforms[-1].std.tolist()    ,  )

    __lowerCamelCase          =  prepare_img()
    __lowerCamelCase          =  transform(UpperCamelCase__    ).unsqueeze(0    )
    __lowerCamelCase          =  processor(UpperCamelCase__    ,  return_tensors='''pt'''    ).pixel_values

    # verify pixel values
    assert torch.allclose(UpperCamelCase__    ,  UpperCamelCase__    )

    # verify logits
    with torch.no_grad():
        __lowerCamelCase          =  model(UpperCamelCase__    )
        __lowerCamelCase          =  outputs.logits

    print('''Predicted class:'''    ,  logits.argmax(-1    ).item()    )
    if base_model:
        __lowerCamelCase          =  timm_model.forward_features(UpperCamelCase__    )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(UpperCamelCase__    ,  outputs.pooler_output    ,  atol=1E-3    )
    else:
        __lowerCamelCase          =  timm_model(UpperCamelCase__    )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(UpperCamelCase__    ,  outputs.logits    ,  atol=1E-3    )
    print('''Looks ok!'''    )

    if pytorch_dump_folder_path is not None:
        Path(UpperCamelCase__    ).mkdir(exist_ok=UpperCamelCase__    )
        print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""    )
        model.save_pretrained(UpperCamelCase__    )
        print(f"""Saving processor to {pytorch_dump_folder_path}"""    )
        processor.save_pretrained(UpperCamelCase__    )

    if push_to_hub:
        print(f"""Pushing model and processor to the hub {vit_name}"""    )
        model.push_to_hub(f"""ybelkada/{vit_name}"""    )
        processor.push_to_hub(f"""ybelkada/{vit_name}"""    )


if __name__ == "__main__":
  __UpperCAmelCase												=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--vit_name",
      default="vit_base_r50_s16_384",
      type=str,
      help="Name of the hybrid ViT 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."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
  )

  __UpperCAmelCase												=parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)

'''simple docstring'''




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest




@require_torchsde
class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=(DPMSolverSDEScheduler,)
     lowerCamelCase  :					List[str]							=1_0






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    **a		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  {
             '''num_train_timesteps''': 11_00,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''noise_sampler_seed''': 0,
         }

         config.update(**a   )
         return config






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         for timesteps in [10, 50, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]  ,    [0.00_02, 0.0_02, 0.02]   ):
             self.check_over_configs(beta_start=a  ,    beta_end=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



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

         scheduler.set_timesteps(self.num_inference_steps   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for i, t in enumerate(scheduler.timesteps   ):
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

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

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75   ) < 1e-2
             assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06   ) < 1e-2
             assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1e-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config(prediction_type='''v_prediction'''   )
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for i, t in enumerate(scheduler.timesteps   ):
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

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

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53   ) < 1e-2
             assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03   ) < 1e-2
             assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25   ) < 1e-2
             assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



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

         scheduler.set_timesteps(self.num_inference_steps  ,    device=a   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

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

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38   ) < 1e-2
             assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12   ) < 1e-2
             assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1e-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a  ,    use_karras_sigmas=a   )

         scheduler.set_timesteps(self.num_inference_steps  ,    device=a   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for t in scheduler.timesteps:
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

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

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
         else:
             assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
