


"""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    					=					logging.get_logger(__name__)


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Dict=False  ):
 lowercase__       :       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"
  lowercase__       :       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"""),
      ]  )

 return rename_keys


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


def    _lowerCamelCase   (     lowerCamelCase__	:							Tuple  ):
 lowercase__       :       Optional[Any]				=						["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(lowerCamelCase__       ,		lowerCamelCase__  )


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Dict       ,		lowerCamelCase__	:							Optional[Any]  ):
 lowercase__       :       Tuple				=						dct.pop(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						val


def    _lowerCamelCase   (     ):
 lowercase__       :       List[str]				=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowercase__       :       List[str]				=						Image.open(requests.get(lowerCamelCase__       ,		stream=lowerCamelCase__  ).raw  )
 return im


@torch.no_grad()
def    _lowerCamelCase   (     lowerCamelCase__	:							List[str]       ,		lowerCamelCase__	:							Any  ):
 lowercase__       :       Any				=						ViTConfig()
 lowercase__       :       Dict				=						False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  lowercase__       :       Optional[Any]				=						True
  lowercase__       :       Union[str, Any]				=						int(vit_name[-12:-10]  )
  lowercase__       :       List[str]				=						int(vit_name[-9:-6]  )
 else:
  lowercase__       :       Union[str, Any]				=						10_00
  lowercase__       :       str				=						"""huggingface/label-files"""
  lowercase__       :       Dict				=						"""imagenet-1k-id2label.json"""
  lowercase__       :       Optional[int]				=						json.load(open(hf_hub_download(lowerCamelCase__       ,		lowerCamelCase__       ,		repo_type="""dataset"""  )       ,		"""r"""  )  )
  lowercase__       :       List[Any]				=						{int(lowerCamelCase__  ): v for k, v in idalabel.items()}
  lowercase__       :       int				=						idalabel
  lowercase__       :       List[str]				=						{v: k for k, v in idalabel.items()}
  lowercase__       :       List[Any]				=						int(vit_name[-6:-4]  )
  lowercase__       :       Union[str, Any]				=						int(vit_name[-3:]  )
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("""tiny"""  ):
   lowercase__       :       Tuple				=						1_92
   lowercase__       :       Optional[int]				=						7_68
   lowercase__       :       int				=						12
   lowercase__       :       Dict				=						3
  elif vit_name[9:].startswith("""small"""  ):
   lowercase__       :       Any				=						3_84
   lowercase__       :       Optional[int]				=						15_36
   lowercase__       :       List[Any]				=						12
   lowercase__       :       Dict				=						6
  else:
   pass
 else:
  if vit_name[4:].startswith("""small"""  ):
   lowercase__       :       Optional[int]				=						7_68
   lowercase__       :       List[str]				=						23_04
   lowercase__       :       Dict				=						8
   lowercase__       :       str				=						8
  elif vit_name[4:].startswith("""base"""  ):
   pass
  elif vit_name[4:].startswith("""large"""  ):
   lowercase__       :       List[str]				=						10_24
   lowercase__       :       Any				=						40_96
   lowercase__       :       Dict				=						24
   lowercase__       :       List[str]				=						16
  elif vit_name[4:].startswith("""huge"""  ):
   lowercase__       :       Any				=						12_80
   lowercase__       :       Dict				=						51_20
   lowercase__       :       Union[str, Any]				=						32
   lowercase__       :       Dict				=						16

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

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

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  lowercase__       :       Optional[int]				=						ViTModel(lowerCamelCase__  ).eval()
 else:
  lowercase__       :       str				=						ViTForImageClassification(lowerCamelCase__  ).eval()
 model.load_state_dict(lowerCamelCase__  )

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

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

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


if __name__ == "__main__":
   __snake_case    					=					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    					=					parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       str				=						tempfile.mkdtemp()

    lowercase__       :       List[Any]				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """[PAD]""",
        """[MASK]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       str				=						os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)

    lowercase__       :       Optional[Any]				=						{
        """do_resize""": True,
        """size""": 20,
        """do_center_crop""": True,
        """crop_size""": 18,
        """do_normalize""": True,
        """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
        """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
    }
    lowercase__       :       Any				=						os.path.join(self.tmpdirname ,     lowerCamelCase__						)
    with open(self.image_processor_file ,     """w""" ,     encoding="""utf-8"""						) as fp:
     json.dump(lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							str:
    return BertTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							str:
    return BertTokenizerFast.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							List[Any]:
    return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    shutil.rmtree(self.tmpdirname						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Tuple				=						[np.random.randint(255 ,     size=(3, 30, 400) ,     dtype=np.uinta						)]
    lowercase__       :       Any				=						[Image.fromarray(np.moveaxis(lowerCamelCase__ ,     0 ,     -1						)						) for x in image_inputs]
    return image_inputs


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Union[str, Any]				=						self.get_tokenizer()
    lowercase__       :       Union[str, Any]				=						self.get_rust_tokenizer()
    lowercase__       :       Any				=						self.get_image_processor()

    lowercase__       :       Union[str, Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor_slow.save_pretrained(self.tmpdirname						)
    lowercase__       :       Tuple				=						AlignProcessor.from_pretrained(self.tmpdirname ,     use_fast=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)
    processor_fast.save_pretrained(self.tmpdirname						)
    lowercase__       :       str				=						AlignProcessor.from_pretrained(self.tmpdirname						)

    self.assertEqual(processor_slow.tokenizer.get_vocab() ,     tokenizer_slow.get_vocab()						)
    self.assertEqual(processor_fast.tokenizer.get_vocab() ,     tokenizer_fast.get_vocab()						)
    self.assertEqual(tokenizer_slow.get_vocab() ,     tokenizer_fast.get_vocab()						)
    self.assertIsInstance(processor_slow.tokenizer ,     lowerCamelCase__						)
    self.assertIsInstance(processor_fast.tokenizer ,     lowerCamelCase__						)

    self.assertEqual(processor_slow.image_processor.to_json_string() ,     image_processor.to_json_string()						)
    self.assertEqual(processor_fast.image_processor.to_json_string() ,     image_processor.to_json_string()						)
    self.assertIsInstance(processor_slow.image_processor ,     lowerCamelCase__						)
    self.assertIsInstance(processor_fast.image_processor ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Optional[int]				=						AlignProcessor(tokenizer=self.get_tokenizer() ,     image_processor=self.get_image_processor()						)
    processor.save_pretrained(self.tmpdirname						)

    lowercase__       :       Dict				=						self.get_tokenizer(bos_token="""(BOS)""" ,     eos_token="""(EOS)"""						)
    lowercase__       :       Any				=						self.get_image_processor(do_normalize=lowerCamelCase__ ,     padding_value=1.0						)

    lowercase__       :       Union[str, Any]				=						AlignProcessor.from_pretrained(
        self.tmpdirname ,     bos_token="""(BOS)""" ,     eos_token="""(EOS)""" ,     do_normalize=lowerCamelCase__ ,     padding_value=1.0						)

    self.assertEqual(processor.tokenizer.get_vocab() ,     tokenizer_add_kwargs.get_vocab()						)
    self.assertIsInstance(processor.tokenizer ,     lowerCamelCase__						)

    self.assertEqual(processor.image_processor.to_json_string() ,     image_processor_add_kwargs.to_json_string()						)
    self.assertIsInstance(processor.image_processor ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Optional[int]				=						self.get_image_processor()
    lowercase__       :       Dict				=						self.get_tokenizer()

    lowercase__       :       Tuple				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Optional[int]				=						self.prepare_image_inputs()

    lowercase__       :       Tuple				=						image_processor(lowerCamelCase__ ,     return_tensors="""np"""						)
    lowercase__       :       str				=						processor(images=lowerCamelCase__ ,     return_tensors="""np"""						)

    for key in input_image_proc.keys():
     self.assertAlmostEqual(input_image_proc[key].sum() ,     input_processor[key].sum() ,     delta=1E-2						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Union[str, Any]				=						self.get_image_processor()
    lowercase__       :       Tuple				=						self.get_tokenizer()

    lowercase__       :       List[str]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Any				=						"""lower newer"""

    lowercase__       :       List[Any]				=						processor(text=lowerCamelCase__						)

    lowercase__       :       Tuple				=						tokenizer(lowerCamelCase__ ,     padding="""max_length""" ,     max_length=64						)

    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key] ,     encoded_processor[key]						)


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       List[Any]				=						self.get_image_processor()
    lowercase__       :       Any				=						self.get_tokenizer()

    lowercase__       :       Union[str, Any]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       List[Any]				=						"""lower newer"""
    lowercase__       :       List[str]				=						self.prepare_image_inputs()

    lowercase__       :       List[Any]				=						processor(text=lowerCamelCase__ ,     images=lowerCamelCase__						)

    self.assertListEqual(list(inputs.keys()						) ,     ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]						)

    # test if it raises when no input is passed
    with pytest.raises(lowerCamelCase__						):
     processor()


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       int				=						self.get_image_processor()
    lowercase__       :       Union[str, Any]				=						self.get_tokenizer()

    lowercase__       :       Tuple				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       Union[str, Any]				=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    lowercase__       :       Tuple				=						processor.batch_decode(lowerCamelCase__						)
    lowercase__       :       Optional[Any]				=						tokenizer.batch_decode(lowerCamelCase__						)

    self.assertListEqual(lowerCamelCase__ ,     lowerCamelCase__						)







   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       List[str]				=						self.get_image_processor()
    lowercase__       :       Optional[Any]				=						self.get_tokenizer()

    lowercase__       :       Optional[int]				=						AlignProcessor(tokenizer=lowerCamelCase__ ,     image_processor=lowerCamelCase__						)

    lowercase__       :       str				=						"""lower newer"""
    lowercase__       :       List[Any]				=						self.prepare_image_inputs()

    lowercase__       :       int				=						processor(text=lowerCamelCase__ ,     images=lowerCamelCase__						)

    self.assertListEqual(list(inputs.keys()						) ,     processor.model_input_names						)