

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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :Dict      =			logging.get_logger(__name__)

def       __snake_case						( __UpperCamelCase	: Dict				,__UpperCamelCase	: Tuple=False    ):



      """simple docstring"""

      A_						  =				[]
      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''')    )

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("cls_token", "vit.embeddings.cls_token"),
              ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
              ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
              ("pos_embed", "vit.embeddings.position_embeddings"),
          ]    )

      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"
            A_						  =				[(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"),
                ]    )

      return rename_keys

def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: Tuple				,__UpperCamelCase	: Any=False    ):



      """simple docstring"""

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

def       __snake_case						( __UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      A_						  =				["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase				,__UpperCamelCase    )

def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: Optional[int]				,__UpperCamelCase	: List[str]    ):



      """simple docstring"""

      A_						  =				dct.pop(__UpperCamelCase    )
      A_						  =				val

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				"http://images.cocodataset.org/val2017/000000039769.jpg"
      A_						  =				Image.open(requests.get(__UpperCamelCase				,stream=__UpperCamelCase    ).raw    )
      return im

@torch.no_grad()
def       __snake_case						( __UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

      A_						  =				ViTConfig()
      A_						  =				False
      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      if vit_name[-5:] == "in21k":
            A_						  =				True
            A_						  =				int(vit_name[-12:-10]    )
            A_						  =				int(vit_name[-9:-6]    )
      else:
            A_						  =				1000
            A_						  =				"huggingface/label-files"
            A_						  =				"imagenet-1k-id2label.json"
            A_						  =				json.load(open(hf_hub_download(__UpperCamelCase				,__UpperCamelCase				,repo_type="dataset"    )				,"r"    )    )
            A_						  =				{int(__UpperCamelCase    ): v for k, v in idalabel.items()}
            A_						  =				idalabel
            A_						  =				{v: k for k, v in idalabel.items()}
            A_						  =				int(vit_name[-6:-4]    )
            A_						  =				int(vit_name[-3:]    )
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith("tiny"    ):
                  A_						  =				192
                  A_						  =				768
                  A_						  =				12
                  A_						  =				3
            elif vit_name[9:].startswith("small"    ):
                  A_						  =				384
                  A_						  =				1536
                  A_						  =				12
                  A_						  =				6
            else:
                  pass
      else:
            if vit_name[4:].startswith("small"    ):
                  A_						  =				768
                  A_						  =				2304
                  A_						  =				8
                  A_						  =				8
            elif vit_name[4:].startswith("base"    ):
                  pass
            elif vit_name[4:].startswith("large"    ):
                  A_						  =				1024
                  A_						  =				4096
                  A_						  =				24
                  A_						  =				16
            elif vit_name[4:].startswith("huge"    ):
                  A_						  =				1280
                  A_						  =				5120
                  A_						  =				32
                  A_						  =				16

    # load original model from timm
      A_						  =				timm.create_model(__UpperCamelCase				,pretrained=__UpperCamelCase    )
      timm_model.eval()

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

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            A_						  =				ViTModel(__UpperCamelCase    ).eval()
      else:
            A_						  =				ViTForImageClassification(__UpperCamelCase    ).eval()
      model.load_state_dict(__UpperCamelCase    )

      # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
      if "deit" in vit_name:
            A_						  =				DeiTImageProcessor(size=config.image_size    )
      else:
            A_						  =				ViTImageProcessor(size=config.image_size    )
      A_						  =				image_processor(images=prepare_img()				,return_tensors="pt"    )
      A_						  =				encoding["pixel_values"]
      A_						  =				model(__UpperCamelCase    )

      if base_model:
            A_						  =				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:
            A_						  =				timm_model(__UpperCamelCase    )
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(__UpperCamelCase				,outputs.logits				,atol=1E-3    )

      Path(__UpperCamelCase    ).mkdir(exist_ok=__UpperCamelCase    )
      print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''    )
      model.save_pretrained(__UpperCamelCase    )
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
      image_processor.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
      __a    :str      =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--vit_name',
          default='vit_base_patch16_224',
          type=str,
          help='Name of the 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.'
      )

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







'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  nn.Conv(
           self.out_channels	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[Any]							     =  hidden_states.shape
       lowercase_						:     Tuple							     =  jax.image.resize(
           a	,  shape=(batch, height * 2, width * 2, channels)	,  method="nearest"	,  )
       lowercase_						:     List[Any]							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  nn.Conv(
           self.out_channels	,  kernel_size=(3, 3)	,  strides=(2, 2)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )





    def __call__(      self	:							Any	,  a	:							int      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: int									=     None
    __lowerCamelCase: float									=     0.0
    __lowerCamelCase: bool									=     None
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.in_channels if self.out_channels is None else self.out_channels

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Tuple							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     List[str]							     =  nn.Dense(a	,  dtype=self.dtype      )

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Any							     =  nn.Dropout(self.dropout_prob      )
       lowercase_						:     Dict							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     Tuple							     =  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       lowercase_						:     Optional[Any]							     =  None
       if use_nin_shortcut:
          lowercase_						:     Union[str, Any]							     =  nn.Conv(
              a	,  kernel_size=(1, 1)	,  strides=(1, 1)	,  padding="VALID"	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							str	,  a	:							Dict	,  a	:							List[str]=True      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  hidden_states
       lowercase_						:     int							     =  self.norma(a      )
       lowercase_						:     List[Any]							     =  nn.swish(a      )
       lowercase_						:     Dict							     =  self.conva(a      )

       lowercase_						:     Optional[int]							     =  self.time_emb_proj(nn.swish(a      )      )
       lowercase_						:     Tuple							     =  jnp.expand_dims(jnp.expand_dims(a	,  1      )	,  1      )
       lowercase_						:     List[str]							     =  hidden_states + temb

       lowercase_						:     Optional[Any]							     =  self.norma(a      )
       lowercase_						:     Any							     =  nn.swish(a      )
       lowercase_						:     List[str]							     =  self.dropout(a	,  a      )
       lowercase_						:     int							     =  self.conva(a      )

       if self.conv_shortcut is not None:
          lowercase_						:     List[str]							     =  self.conv_shortcut(a      )

       return hidden_states + residual




