





"""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()
lowercase__				:Tuple				   =       logging.get_logger(__name__)


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=False  )						->Optional[int]:





  """simple docstring"""


  __UpperCAmelCase    :						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"
    __UpperCAmelCase    :						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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=False  )						->Optional[int]:





  """simple docstring"""


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


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(UpperCAmelCase_  ,			UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Any       =						dct.pop(UpperCAmelCase_  )
  __UpperCAmelCase    :						List[Any]       =						val


def       lowerCamelCase_       (   )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  __UpperCAmelCase    :						List[str]       =						Image.open(requests.get(UpperCAmelCase_  ,			stream=UpperCAmelCase_  ).raw  )
  return im


@torch.no_grad()
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=False  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						BitConfig(
      global_padding='''same'''  ,			layer_type='''bottleneck'''  ,			depths=(3, 4, 9)  ,			out_features=['''stage3''']  ,			embedding_dynamic_padding=UpperCAmelCase_  ,			)
  __UpperCAmelCase    :						Tuple       =						ViTHybridConfig(backbone_config=UpperCAmelCase_  ,			image_size=3_84  ,			num_labels=10_00  )
  __UpperCAmelCase    :						str       =						False

  # load original model from timm
  __UpperCAmelCase    :						str       =						timm.create_model(UpperCAmelCase_  ,			pretrained=UpperCAmelCase_  )
  timm_model.eval()

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

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

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    __UpperCAmelCase    :						str       =						ViTHybridModel(UpperCAmelCase_  ).eval()
  else:
    __UpperCAmelCase    :						List[Any]       =						ViTHybridForImageClassification(UpperCAmelCase_  ).eval()
  model.load_state_dict(UpperCAmelCase_  )

  # create image processor
  __UpperCAmelCase    :						Optional[Any]       =						create_transform(**resolve_data_config({}  ,			model=UpperCAmelCase_  )  )
  __UpperCAmelCase    :						Optional[Any]       =						transform.transforms

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

  __UpperCAmelCase    :						Optional[int]       =						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()  ,			)

  __UpperCAmelCase    :						int       =						prepare_img()
  __UpperCAmelCase    :						Tuple       =						transform(UpperCAmelCase_  ).unsqueeze(0  )
  __UpperCAmelCase    :						int       =						processor(UpperCAmelCase_  ,			return_tensors='''pt'''  ).pixel_values

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

  # verify logits
  with torch.no_grad():
    __UpperCAmelCase    :						Dict       =						model(UpperCAmelCase_  )
    __UpperCAmelCase    :						Tuple       =						outputs.logits

  print('''Predicted class:'''  ,			logits.argmax(-1  ).item()  )
  if base_model:
    __UpperCAmelCase    :						Dict       =						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:
    __UpperCAmelCase    :						Dict       =						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__":
  lowercase__				:int				   =       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.'
  )

  lowercase__				:int				   =       parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard




@slow
@require_flax
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      str ):


        '''simple docstring'''




        super().tearDown()
        gc.collect()

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						FlaxStableDiffusionPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2'''						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)

        __UpperCAmelCase    :						Dict       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						Any       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						replicate(__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						str       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						Dict       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						Tuple       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Any       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Union[str, Any]       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						str       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						List[Any]       =						jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						'''stabilityai/stable-diffusion-2'''
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						FlaxDPMSolverMultistepScheduler.from_pretrained(__lowercase						,				subfolder='''scheduler''' )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						FlaxStableDiffusionPipeline.from_pretrained(
            __lowercase						,				scheduler=__lowercase						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)
        __UpperCAmelCase    :						Dict       =						scheduler_params

        __UpperCAmelCase    :						int       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						List[Any]       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						replicate(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						List[Any]       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						List[Any]       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Tuple       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Any       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						List[str]       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						Any       =						jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2