'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				Tuple=False     )							->	List[str]:
  __UpperCAmelCase:					Union[str, Any]		= []
  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"""),
        ]     )

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    __UpperCAmelCase:					Optional[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  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				int   ,    _lowercase    :				Dict=False     )							->	int:
  for i in range(config.num_hidden_layers     ):
    if base_model:
      __UpperCAmelCase:					Union[str, Any]		= """"""
    else:
      __UpperCAmelCase:					Union[str, Any]		= """vit."""
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    __UpperCAmelCase:					Any		= state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''     )
    __UpperCAmelCase:					str		= state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''     )
    # next, add query, keys and values (in that order) to the state dict
    __UpperCAmelCase:					int		= in_proj_weight[
        : config.hidden_size, :
    ]
    __UpperCAmelCase:					Tuple		= in_proj_bias[: config.hidden_size]
    __UpperCAmelCase:					Union[str, Any]		= in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    __UpperCAmelCase:					str		= in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    __UpperCAmelCase:					str		= in_proj_weight[
        -config.hidden_size :, :
    ]
    __UpperCAmelCase:					int		= in_proj_bias[-config.hidden_size :]






def  UpperCamelCase__					(					_lowercase    :				List[str]     )							->	Union[str, Any]:
  __UpperCAmelCase:					int		= ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(_lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				Any   ,    _lowercase    :				Union[str, Any]     )							->	Optional[Any]:
  __UpperCAmelCase:					Any		= dct.pop(_lowercase     )
  __UpperCAmelCase:					Dict		= val






def  UpperCamelCase__					(					)							->	Tuple:
  __UpperCAmelCase:					str		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase:					List[Any]		= Image.open(requests.get(_lowercase   ,    stream=_lowercase     ).raw     )
  return im






@torch.no_grad()
def  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				Any   ,    _lowercase    :				Any=True     )							->	List[Any]:
  __UpperCAmelCase:					Dict		= ViTConfig()
  # patch_size
  if model_name[-1] == "8":
    __UpperCAmelCase:					Optional[Any]		= 8
  # set labels if required
  if not base_model:
    __UpperCAmelCase:					Union[str, Any]		= 1_0_0_0
    __UpperCAmelCase:					Tuple		= """huggingface/label-files"""
    __UpperCAmelCase:					List[str]		= """imagenet-1k-id2label.json"""
    __UpperCAmelCase:					Tuple		= json.load(open(hf_hub_download(_lowercase   ,    _lowercase   ,    repo_type="""dataset"""     )   ,    """r"""     )     )
    __UpperCAmelCase:					Optional[Any]		= {int(_lowercase     ): v for k, v in idalabel.items()}
    __UpperCAmelCase:					List[Any]		= idalabel
    __UpperCAmelCase:					List[str]		= {v: k for k, v in idalabel.items()}
  # size of the architecture
  if model_name in ["dino_vits8", "dino_vits16"]:
    __UpperCAmelCase:					Optional[Any]		= 3_8_4
    __UpperCAmelCase:					Tuple		= 1_5_3_6
    __UpperCAmelCase:					Any		= 1_2
    __UpperCAmelCase:					Union[str, Any]		= 6

  # load original model from torch hub
  __UpperCAmelCase:					Optional[Any]		= torch.hub.load("""facebookresearch/dino:main"""   ,    _lowercase     )
  original_model.eval()

  # load state_dict of original model, remove and rename some keys
  __UpperCAmelCase:					Optional[int]		= original_model.state_dict()
  if base_model:
    remove_classification_head_(_lowercase     )
  __UpperCAmelCase:					Optional[Any]		= create_rename_keys(_lowercase   ,    base_model=_lowercase     )
  for src, dest in rename_keys:
    rename_key(_lowercase   ,    _lowercase   ,    _lowercase     )
  read_in_q_k_v(_lowercase   ,    _lowercase   ,    _lowercase     )

  # load HuggingFace model
  if base_model:
    __UpperCAmelCase:					Optional[Any]		= ViTModel(_lowercase   ,    add_pooling_layer=_lowercase     ).eval()
  else:
    __UpperCAmelCase:					Optional[Any]		= ViTForImageClassification(_lowercase     ).eval()
  model.load_state_dict(_lowercase     )

  # Check outputs on an image, prepared by ViTImageProcessor
  __UpperCAmelCase:					Tuple		= ViTImageProcessor()
  __UpperCAmelCase:					Union[str, Any]		= image_processor(images=prepare_img()   ,    return_tensors="""pt"""     )
  __UpperCAmelCase:					Tuple		= encoding["""pixel_values"""]
  __UpperCAmelCase:					Tuple		= model(_lowercase     )

  if base_model:
    __UpperCAmelCase:					List[Any]		= original_model(_lowercase     )
    assert torch.allclose(_lowercase   ,    outputs.last_hidden_state[:, 0, :]   ,    atol=1E-1     )
  else:
    __UpperCAmelCase:					str		= original_model(_lowercase     )
    assert logits.shape == outputs.logits.shape
    assert torch.allclose(_lowercase   ,    outputs.logits   ,    atol=1E-3     )

  Path(_lowercase     ).mkdir(exist_ok=_lowercase     )
  print(F'''Saving model {model_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__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='dino_vitb16',
        type=str,
        help='Name of the model trained with DINO 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(
        '--base_model',
        action='store_true',
        help='Whether to only convert the base model (no projection head weights).',
    )

    parser.set_defaults(base_model=True)
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
'''simple docstring'''






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=18   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= size if size is not None else {"""height""": 18, """width""": 18}
    __UpperCAmelCase:					Tuple		= parent
    __UpperCAmelCase:					Any		= batch_size
    __UpperCAmelCase:					str		= num_channels
    __UpperCAmelCase:					Any		= image_size
    __UpperCAmelCase:					Optional[int]		= min_resolution
    __UpperCAmelCase:					Tuple		= max_resolution
    __UpperCAmelCase:					Any		= do_resize
    __UpperCAmelCase:					Any		= size
    __UpperCAmelCase:					Optional[Any]		= do_normalize







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
            ]					),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }




@require_torch
@require_vision
class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					ImageGPTImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= ImageGPTImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(snake_case_   ,    """clusters"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_resize"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """size"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_normalize"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size   ,    {"""height""": 18, """width""": 18}					)

    __UpperCAmelCase:					int		= self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42					)
    self.assertEqual(image_processor.size   ,    {"""height""": 42, """width""": 42}					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.image_processing_class(**self.image_processor_dict					)
    __UpperCAmelCase:					str		= json.loads(image_processor.to_json_string()					)
    for key, value in self.image_processor_dict.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    obj[key]					)					)
      else:
        self.assertEqual(obj[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase:					List[Any]		= os.path.join(snake_case_   ,    """image_processor.json"""					)
      image_processor_first.to_json_file(snake_case_					)
      __UpperCAmelCase:					List[str]		= self.image_processing_class.from_json_file(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(snake_case_					)
      __UpperCAmelCase:					Any		= self.image_processing_class.from_pretrained(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  @unittest.skip("""ImageGPT requires clusters at initialization"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass






def  UpperCamelCase__					(					)							->	List[str]:
  __UpperCAmelCase:					Any		= load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,    split="""test"""     )

  __UpperCAmelCase:					List[Any]		= Image.open(dataset[4]["""file"""]     )
  __UpperCAmelCase:					Dict		= Image.open(dataset[5]["""file"""]     )

  __UpperCAmelCase:					Dict		= [imagea, imagea]

  return images







@require_vision
@require_torch
class    a (       unittest.TestCase				):



  """simple docstring"""







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""					)

    __UpperCAmelCase:					Dict		= prepare_images()

    # test non-batched
    __UpperCAmelCase:					Tuple		= image_processing(images[0]   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (1, 1024)					)

    __UpperCAmelCase:					int		= [306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist()   ,    snake_case_					)

    # test batched
    __UpperCAmelCase:					Union[str, Any]		= image_processing(snake_case_   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (2, 1024)					)

    __UpperCAmelCase:					Tuple		= [303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,    snake_case_					)