

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()
_UpperCamelCase 			=		logging.get_logger(__name__)







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







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Dict					=					['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(lowercase__   ,	lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Tuple					=					dct.pop(lowercase__						)
      __lowerCAmelCase					:						List[Any]					=					val







def 		_lowercase (     ):
      __lowerCAmelCase					:						Any					=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __lowerCAmelCase					:						int					=					Image.open(requests.get(lowercase__   ,	stream=lowercase__						).raw						)
      return im







@torch.no_grad()
def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					ViTConfig()
      __lowerCAmelCase					:						List[Any]					=					False
      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      if vit_name[-5:] == "in21k":
            __lowerCAmelCase					:						List[Any]					=					True
            __lowerCAmelCase					:						Dict					=					int(vit_name[-1_2:-1_0]						)
            __lowerCAmelCase					:						Optional[int]					=					int(vit_name[-9:-6]						)
      else:
            __lowerCAmelCase					:						List[str]					=					1_0_0_0
            __lowerCAmelCase					:						int					=					'''huggingface/label-files'''
            __lowerCAmelCase					:						int					=					'''imagenet-1k-id2label.json'''
            __lowerCAmelCase					:						Union[str, Any]					=					json.load(open(hf_hub_download(lowercase__   ,	lowercase__   ,	repo_type='''dataset'''						)   ,	'''r'''						)						)
            __lowerCAmelCase					:						Tuple					=					{int(lowercase__						): v for k, v in idalabel.items()}
            __lowerCAmelCase					:						int					=					idalabel
            __lowerCAmelCase					:						Any					=					{v: k for k, v in idalabel.items()}
            __lowerCAmelCase					:						Tuple					=					int(vit_name[-6:-4]						)
            __lowerCAmelCase					:						Dict					=					int(vit_name[-3:]						)
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith('''tiny'''						):
                  __lowerCAmelCase					:						Tuple					=					1_9_2
                  __lowerCAmelCase					:						Optional[Any]					=					7_6_8
                  __lowerCAmelCase					:						Optional[int]					=					1_2
                  __lowerCAmelCase					:						Union[str, Any]					=					3
            elif vit_name[9:].startswith('''small'''						):
                  __lowerCAmelCase					:						Any					=					3_8_4
                  __lowerCAmelCase					:						Optional[int]					=					1_5_3_6
                  __lowerCAmelCase					:						Dict					=					1_2
                  __lowerCAmelCase					:						int					=					6
            else:
                  pass
      else:
            if vit_name[4:].startswith('''small'''						):
                  __lowerCAmelCase					:						Any					=					7_6_8
                  __lowerCAmelCase					:						List[Any]					=					2_3_0_4
                  __lowerCAmelCase					:						Optional[Any]					=					8
                  __lowerCAmelCase					:						Any					=					8
            elif vit_name[4:].startswith('''base'''						):
                  pass
            elif vit_name[4:].startswith('''large'''						):
                  __lowerCAmelCase					:						Dict					=					1_0_2_4
                  __lowerCAmelCase					:						Tuple					=					4_0_9_6
                  __lowerCAmelCase					:						int					=					2_4
                  __lowerCAmelCase					:						List[str]					=					1_6
            elif vit_name[4:].startswith('''huge'''						):
                  __lowerCAmelCase					:						Optional[int]					=					1_2_8_0
                  __lowerCAmelCase					:						Optional[Any]					=					5_1_2_0
                  __lowerCAmelCase					:						int					=					3_2
                  __lowerCAmelCase					:						Any					=					1_6

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

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

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

      # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
      if "deit" in vit_name:
            __lowerCAmelCase					:						Optional[Any]					=					DeiTImageProcessor(size=config.image_size						)
      else:
            __lowerCAmelCase					:						Union[str, Any]					=					ViTImageProcessor(size=config.image_size						)
      __lowerCAmelCase					:						Union[str, Any]					=					image_processor(images=prepare_img()   ,	return_tensors='''pt'''						)
      __lowerCAmelCase					:						int					=					encoding['''pixel_values''']
      __lowerCAmelCase					:						str					=					model(lowercase__						)

      if base_model:
            __lowerCAmelCase					:						List[str]					=					timm_model.forward_features(lowercase__						)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(lowercase__   ,	outputs.pooler_output   ,	atol=1E-3						)
      else:
            __lowerCAmelCase					:						Any					=					timm_model(lowercase__						)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(lowercase__   ,	outputs.logits   ,	atol=1E-3						)

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


if __name__ == "__main__":
 _UpperCamelCase 			=		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."
 )

 _UpperCamelCase 			=		parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      if isinstance(lowercase__   ,	lowercase__						):
            __lowerCAmelCase					:						Dict					=					np.full((len(lowercase__						), sequence_length, 2)   ,	lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					np.full((len(lowercase__						), sequence_length)   ,	lowercase__						)

      for i, tensor in enumerate(lowercase__						):
            if padding_side == "right":
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						int					=					tensor[:sequence_length]
            else:
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						Optional[Any]					=					tensor[:sequence_length]

      return out_tensor.tolist()







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					ord(lowercase__						)
      if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
            return True
      __lowerCAmelCase					:						int					=					unicodedata.category(lowercase__						)
      if cat.startswith('''P'''						):
            return True
      return False







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  -100
 _UpperCamelCase			  =  "pt"
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						List[str]					=					'''label''' if '''label''' in features[0].keys() else '''labels'''
       __lowerCAmelCase					:						Union[str, Any]					=					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
       __lowerCAmelCase					:						List[Any]					=					self.tokenizer.pad(
           A_      ,							padding=self.padding      ,							max_length=self.max_length      ,							pad_to_multiple_of=self.pad_to_multiple_of      ,							return_tensors='''pt''' if labels is None else None      ,							)

       if labels is None:
             return batch

       __lowerCAmelCase					:						Dict					=					torch.tensor(batch['''entity_ids''']					).shape[1]
       __lowerCAmelCase					:						Optional[int]					=					self.tokenizer.padding_side
       if padding_side == "right":
             __lowerCAmelCase					:						Any					=					[
                 list(A_					) + [self.label_pad_token_id] * (sequence_length - len(A_					)) for label in labels
             ]
       else:
             __lowerCAmelCase					:						Optional[int]					=					[
                 [self.label_pad_token_id] * (sequence_length - len(A_					)) + list(A_					) for label in labels
             ]

       __lowerCAmelCase					:						Tuple					=					[feature['''ner_tags'''] for feature in features]
       __lowerCAmelCase					:						List[Any]					=					padding_tensor(A_      ,							-1      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[int]					=					[feature['''original_entity_spans'''] for feature in features]
       __lowerCAmelCase					:						Any					=					padding_tensor(A_      ,							(-1, -1)      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[Any]					=					{k: torch.tensor(A_      ,							dtype=torch.intaa					) for k, v in batch.items()}

       return batch


