
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()
__lowerCAmelCase					:			Optional[int]			      =		logging.get_logger(__name__)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase=False	) ->						str:
    __lowercase		:			Any	    =       []

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


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						List[Any]:
    __lowercase		:			Any	    =       ['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(__lowerCAmelCase	,					__lowerCAmelCase	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						str:
    __lowercase		:			List[Any]	    =       dct.pop(__lowerCAmelCase	)
    __lowercase		:			List[str]	    =       val


def     UpperCAmelCase_  (						) ->						List[Any]:
    __lowercase		:			Dict	    =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowercase		:			str	    =       Image.open(requests.get(__lowerCAmelCase	,					stream=__lowerCAmelCase	).raw	)
    return im


@torch.no_grad()
def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase=False	) ->						Tuple:
    __lowercase		:			Tuple	    =       BitConfig(
        global_padding='''same'''	,					layer_type='''bottleneck'''	,					depths=(3, 4, 9)	,					out_features=['''stage3''']	,					embedding_dynamic_padding=__lowerCAmelCase	,					)
    __lowercase		:			Any	    =       ViTHybridConfig(backbone_config=__lowerCAmelCase	,					image_size=384	,					num_labels=1_000	)
    __lowercase		:			Dict	    =       False

    # load original model from timm
    __lowercase		:			Tuple	    =       timm.create_model(__lowerCAmelCase	,					pretrained=__lowerCAmelCase	)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    __lowercase		:			Dict	    =       timm_model.state_dict()
    if base_model:
        remove_classification_head_(__lowerCAmelCase	)
    __lowercase		:			List[str]	    =       create_rename_keys(__lowerCAmelCase	,					__lowerCAmelCase	)
    for src, dest in rename_keys:
        rename_key(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    read_in_q_k_v(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)

    __lowercase		:			Union[str, Any]	    =       '''huggingface/label-files'''
    __lowercase		:			Optional[int]	    =       '''imagenet-1k-id2label.json'''
    __lowercase		:			Optional[Any]	    =       json.load(open(hf_hub_download(__lowerCAmelCase	,					__lowerCAmelCase	,					repo_type='''dataset'''	)	,					'''r'''	)	)
    __lowercase		:			Optional[int]	    =       {int(__lowerCAmelCase	): v for k, v in idalabel.items()}
    __lowercase		:			Tuple	    =       idalabel
    __lowercase		:			Dict	    =       {v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        __lowercase		:			Tuple	    =       ViTHybridModel(__lowerCAmelCase	).eval()
    else:
        __lowercase		:			Union[str, Any]	    =       ViTHybridForImageClassification(__lowerCAmelCase	).eval()
    model.load_state_dict(__lowerCAmelCase	)

    # create image processor
    __lowercase		:			List[str]	    =       create_transform(**resolve_data_config({}	,					model=__lowerCAmelCase	)	)
    __lowercase		:			Union[str, Any]	    =       transform.transforms

    __lowercase		:			Optional[int]	    =       {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

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

    __lowercase		:			int	    =       prepare_img()
    __lowercase		:			Optional[int]	    =       transform(__lowerCAmelCase	).unsqueeze(0	)
    __lowercase		:			Union[str, Any]	    =       processor(__lowerCAmelCase	,					return_tensors='''pt'''	).pixel_values

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

    # verify logits
    with torch.no_grad():
        __lowercase		:			str	    =       model(__lowerCAmelCase	)
        __lowercase		:			Tuple	    =       outputs.logits

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

    if pytorch_dump_folder_path is not None:
        Path(__lowerCAmelCase	).mkdir(exist_ok=__lowerCAmelCase	)
        print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'	)
        model.save_pretrained(__lowerCAmelCase	)
        print(F'Saving processor to {pytorch_dump_folder_path}'	)
        processor.save_pretrained(__lowerCAmelCase	)

    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__":
 __lowerCAmelCase					:			Any			      =		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."
 )

 __lowerCAmelCase					:			Optional[int]			      =		parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    (DDPMScheduler,)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       List[str]				):
          __lowercase		:			Tuple	    =       {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**_snake_case				)
          return config




      def 		snake_case_			(      self		:       Union[str, Any]				):
          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,       [0.0_02, 0.02, 0.2, 2]				):
              self.check_over_configs(beta_start=_snake_case				,       beta_end=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_snake_case				)




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




      def 		snake_case_			(      self		:       List[str]				):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for t in [0, 500, 999]:
              self.check_over_forward(time_step=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_09_79				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			Dict	    =       self.dummy_model()
          __lowercase		:			Tuple	    =       self.dummy_sample_deter
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Optional[int]	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			int	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			str	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			Any	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_58.96_06				) < 1E-2
          assert abs(result_mean.item() - 0.33_72				) < 1E-3




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config(prediction_type='''v_prediction'''				)
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			List[Any]	    =       self.dummy_model()
          __lowercase		:			str	    =       self.dummy_sample_deter
          __lowercase		:			Dict	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Tuple	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			Optional[int]	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			int	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			List[Any]	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_02.02_96				) < 1E-2
          assert abs(result_mean.item() - 0.26_31				) < 1E-3




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			List[str]	    =       self.get_scheduler_config()
          __lowercase		:			List[str]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       [100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_snake_case				)

          __lowercase		:			int	    =       scheduler.timesteps

          for i, timestep in enumerate(_snake_case				):
              if i == len(_snake_case				) - 1:
                  __lowercase		:			Optional[Any]	    =       -1
              else:
                  __lowercase		:			List[str]	    =       timesteps[i + 1]

              __lowercase		:			Optional[Any]	    =       scheduler.previous_timestep(_snake_case				)
              __lowercase		:			str	    =       prev_t.item()

              self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[Any]	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Any	    =       [100, 87, 50, 51, 0]

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




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Optional[int]	    =       self.scheduler_classes[0]
          __lowercase		:			Union[str, Any]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Dict	    =       [100, 87, 50, 1, 0]
          __lowercase		:			str	    =       len(_snake_case				)

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




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[str]	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[int]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[int]	    =       [scheduler.config.num_train_timesteps]

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


