






'''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()
__A    	=   logging.get_logger(__name__)




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  ,		A  :       Union[str, Any]=False  )    -> Tuple:







   """simple docstring"""
   __snake_case				:      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"
      __snake_case				:      List[Any]     							= [(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        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       Dict  ,		A  :       Optional[Any]=False  )    -> Any:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  )    -> List[Any]:







   """simple docstring"""
   __snake_case				:      List[str]     							= ['head.weight', 'head.bias']
   for k in ignore_keys:
      state_dict.pop(A  ,		A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Any  ,		A  :       Union[str, Any]  ,		A  :       List[Any]  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Tuple     							= dct.pop(A  )
   __snake_case				:      str     							= val




def        _SCREAMING_SNAKE_CASE  (				)    -> Optional[Any]:







   """simple docstring"""
   __snake_case				:      Any     							= 'http://images.cocodataset.org/val2017/000000039769.jpg'
   __snake_case				:      Union[str, Any]     							= Image.open(requests.get(A  ,		stream=A  ).raw  )
   return im




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       Optional[Any]  ,		A  :       str=False  )    -> Optional[Any]:







   """simple docstring"""
   __snake_case				:      Dict     							= BitConfig(
       global_padding='same'  ,		layer_type='bottleneck'  ,		depths=(3, 4, 9)  ,		out_features=['stage3']  ,		embedding_dynamic_padding=A  ,		)
   __snake_case				:      List[str]     							= ViTHybridConfig(backbone_config=A  ,		image_size=3_84  ,		num_labels=10_00  )
   __snake_case				:      Union[str, Any]     							= False

   # load original model from timm
   __snake_case				:      List[str]     							= timm.create_model(A  ,		pretrained=A  )
   timm_model.eval()

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

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

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      __snake_case				:      str     							= ViTHybridModel(A  ).eval()
   else:
      __snake_case				:      int     							= ViTHybridForImageClassification(A  ).eval()
   model.load_state_dict(A  )

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

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

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

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

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

   # verify logits
   with torch.no_grad():
      __snake_case				:      List[str]     							= model(A  )
      __snake_case				:      str     							= outputs.logits

   print('Predicted class:'  ,		logits.argmax(-1  ).item()  )
   if base_model:
      __snake_case				:      Union[str, Any]     							= timm_model.forward_features(A  )
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(A  ,		outputs.pooler_output  ,		atol=1e-3  )
   else:
      __snake_case				:      str     							= timm_model(A  )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(A  ,		outputs.logits  ,		atol=1e-3  )
   print('Looks ok!'  )

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

   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__":
      __A    	=   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.'''
      )

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







'''simple docstring'''






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
      import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin




@require_torch
class a_    (    unittest.TestCase		,      UpperCamelCase_							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= load_tool('text-to-speech')
      self.tool.setup()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Dict     							= self.tool('hey')
      __snake_case				:      List[Any]     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Any     							= self.tool('hey')
      __snake_case				:      Any     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))