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


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



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



def 			__snake_case  (     _UpperCAmelCase       :							List[Any]):
   UpperCamelCase	  =	['head.weight', 'head.bias']
   for k in ignore_keys:
      state_dict.pop(__snake_case,   __snake_case)



def 			__snake_case  (     _UpperCAmelCase       :							Optional[int],   _UpperCAmelCase       :							Optional[Any],   _UpperCAmelCase       :							Union[str, Any]):
   UpperCamelCase	  =	dct.pop(__snake_case)
   UpperCamelCase	  =	val



def 			__snake_case  (     ):
   UpperCamelCase	  =	'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase	  =	Image.open(requests.get(__snake_case,   stream=__snake_case).raw)
   return im



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

    # load original model from timm
   UpperCamelCase	  =	timm.create_model(__snake_case,   pretrained=__snake_case)
   timm_model.eval()

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

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

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

   if base_model:
      UpperCamelCase	  =	timm_model.forward_features(__snake_case)
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(__snake_case,   outputs.pooler_output,   atol=1E-3)
   else:
      UpperCamelCase	  =	timm_model(__snake_case)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__snake_case,   outputs.logits,   atol=1E-3)

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


if __name__ == "__main__":
   snake_case_			:	Optional[int] 	=       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.'
   )

   snake_case_			:	Tuple 	=       parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)












'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d