



'''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()
lowercase_	 =						logging.get_logger(__name__)




def lowerCAmelCase						(__A						,			__A=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'''),
              ])

          # 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 lowerCAmelCase						(__A						,			__A						,			__A=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 lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(__A						,			__A)




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						dct.pop(__A)
     _a											=						val




def lowerCAmelCase						():



     """simple docstring"""



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




@torch.no_grad()
def lowerCAmelCase						(__A						,			__A						,			__A=True):



     """simple docstring"""



     _a											=						ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          _a											=						8
     # set labels if required
     if not base_model:
          _a											=						1_000
          _a											=						'''huggingface/label-files'''
          _a											=						'''imagenet-1k-id2label.json'''
          _a											=						json.load(open(hf_hub_download(__A						,			__A						,			repo_type='''dataset''')						,			'''r'''))
          _a											=						{int(__A): v for k, v in idalabel.items()}
          _a											=						idalabel
          _a											=						{v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          _a											=						384
          _a											=						1_536
          _a											=						12
          _a											=						6

     # load original model from torch hub
     _a											=						torch.hub.load('''facebookresearch/dino:main'''						,			__A)
     original_model.eval()

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

     # load HuggingFace model
     if base_model:
          _a											=						ViTModel(__A						,			add_pooling_layer=__A).eval()
     else:
          _a											=						ViTForImageClassification(__A).eval()
     model.load_state_dict(__A)

     # Check outputs on an image, prepared by ViTImageProcessor
     _a											=						ViTImageProcessor()
     _a											=						image_processor(images=prepare_img()						,			return_tensors='''pt''')
     _a											=						encoding['''pixel_values''']
     _a											=						model(__A)

     if base_model:
          _a											=						original_model(__A)
          assert torch.allclose(__A						,			outputs.last_hidden_state[:, 0, :]						,			atol=1e-1)
     else:
          _a											=						original_model(__A)
          assert logits.shape == outputs.logits.shape
          assert torch.allclose(__A						,			outputs.logits						,			atol=1e-3)

     Path(__A).mkdir(exist_ok=__A)
     print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''')
     model.save_pretrained(__A)
     print(F'''Saving image processor to {pytorch_dump_folder_path}''')
     image_processor.save_pretrained(__A)


if __name__ == "__main__":
  lowercase_	 =						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)
  lowercase_	 =						parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)








'''simple docstring'''





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = KandinskyVaaPriorPipeline
 __lowerCamelCase					: List[str]    = ['prompt']
 __lowerCamelCase					: str    = ['prompt', 'negative_prompt']
 __lowerCamelCase					: Union[str, Any]    = [
     'num_images_per_prompt',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 __lowerCamelCase					: Union[str, Any]    = False



 @property
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return 32



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.time_input_dim



 @property
 def       a__						(self )					->  Any:


      """simple docstring"""



      return self.time_input_dim * 4



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return 100



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
      return tokenizer



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						CLIPTextConfig(
          bos_token_id=0				,     eos_token_id=2				,     hidden_size=self.text_embedder_hidden_size				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_000				,     )
      return CLIPTextModelWithProjection(A )



 @property
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      torch.manual_seed(0 )

      _a											=						{
          '''num_attention_heads''': 2,
          '''attention_head_dim''': 12,
          '''embedding_dim''': self.text_embedder_hidden_size,
          '''num_layers''': 1,
      }

      _a											=						PriorTransformer(**A )
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      _a											=						nn.Parameter(torch.ones(model.clip_std.shape ) )
      return model



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      torch.manual_seed(0 )
      _a											=						CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size				,     image_size=224				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_channels=3				,     num_hidden_layers=5				,     patch_size=14				,     )

      _a											=						CLIPVisionModelWithProjection(A )
      return model



 @property
 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						CLIPImageProcessor(
          crop_size=224				,     do_center_crop=A				,     do_normalize=A				,     do_resize=A				,     image_mean=[0.48145466, 0.4578275, 0.40821073]				,     image_std=[0.26862954, 0.26130258, 0.27577711]				,     resample=3				,     size=224				,     )

      return image_processor



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						self.dummy_prior
      _a											=						self.dummy_image_encoder
      _a											=						self.dummy_text_encoder
      _a											=						self.dummy_tokenizer
      _a											=						self.dummy_image_processor

      _a											=						UnCLIPScheduler(
          variance_type='''fixed_small_log'''				,     prediction_type='''sample'''				,     num_train_timesteps=1_000				,     clip_sample=A				,     clip_sample_range=10.0				,     )

      _a											=						{
          '''prior''': prior,
          '''image_encoder''': image_encoder,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''scheduler''': scheduler,
          '''image_processor''': image_processor,
      }

      return components



 def       a__						(self				,     A				,     A=0 )					->  Optional[int]:


      """simple docstring"""



      if str(A ).startswith('''mps''' ):
           _a											=						torch.manual_seed(A )
      else:
           _a											=						torch.Generator(device=A ).manual_seed(A )
      _a											=						{
          '''prompt''': '''horse''',
          '''generator''': generator,
          '''guidance_scale''': 4.0,
          '''num_inference_steps''': 2,
          '''output_type''': '''np''',
      }
      return inputs



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						'''cpu'''

      _a											=						self.get_dummy_components()

      _a											=						self.pipeline_class(**A )
      _a											=						pipe.to(A )

      pipe.set_progress_bar_config(disable=A )

      _a											=						pipe(**self.get_dummy_inputs(A ) )
      _a											=						output.image_embeds

      _a											=						pipe(
          **self.get_dummy_inputs(A )				,     return_dict=A				,     )[0]

      _a											=						image[0, -10:]
      _a											=						image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      _a											=						np.array(
          [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156] )

      assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2



 @skip_mps
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						True
      _a											=						False

      self._test_inference_batch_single_identical(
          test_max_difference=A				,     relax_max_difference=A				,     test_mean_pixel_difference=A				,     )



 @skip_mps
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						torch_device == '''cpu'''
      _a											=						False

      self._test_attention_slicing_forward_pass(
          test_max_difference=A				,     test_mean_pixel_difference=A				,     )



