



'''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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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





def   a_   (		_UpperCAmelCase    :      int ,_UpperCAmelCase    :      Tuple=False				)    ->  Optional[int]:
      __snake_case :   Optional[int]													=    []
      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'''deit.encoder.layer.{i}.layernorm_before.weight''')				)
            rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')				)
            rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')				)
            rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')				)
            rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')				)
            rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')				)
            rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')				)
            rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')				)
            rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')				)
            rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')				)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('cls_token', 'deit.embeddings.cls_token'),
              ('dist_token', 'deit.embeddings.distillation_token'),
              ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
              ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
              ('pos_embed', 'deit.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 "deit" from all keys that start with "deit"
            __snake_case :   List[str]													=    [(pair[0], pair[1][4:]) if pair[1].startswith('deit'				) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ('norm.weight', 'deit.layernorm.weight'),
                    ('norm.bias', 'deit.layernorm.bias'),
                    ('head.weight', 'cls_classifier.weight'),
                    ('head.bias', 'cls_classifier.bias'),
                    ('head_dist.weight', 'distillation_classifier.weight'),
                    ('head_dist.bias', 'distillation_classifier.bias'),
                ]				)

      return rename_keys





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





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





def   a_   (		)    ->  List[str]:
      __snake_case :   Any													=    'http://images.cocodataset.org/val2017/000000039769.jpg'
      __snake_case :   Any													=    Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase				).raw				)
      return im





@torch.no_grad()
def   a_   (		_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      str				)    ->  List[str]:
      __snake_case :   List[Any]													=    DeiTConfig()
      # all deit models have fine-tuned heads
      __snake_case :   List[Any]													=    False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      __snake_case :   int													=    10_00
      __snake_case :   Optional[Any]													=    'huggingface/label-files'
      __snake_case :   Union[str, Any]													=    'imagenet-1k-id2label.json'
      __snake_case :   Union[str, Any]													=    json.load(open(hf_hub_download(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset'				) ,'r'				)				)
      __snake_case :   Dict													=    {int(_UpperCAmelCase				): v for k, v in idalabel.items()}
      __snake_case :   Dict													=    idalabel
      __snake_case :   Tuple													=    {v: k for k, v in idalabel.items()}
      __snake_case :   str													=    int(deit_name[-6:-4]				)
      __snake_case :   Optional[Any]													=    int(deit_name[-3:]				)
      # size of the architecture
      if deit_name[9:].startswith('tiny'				):
            __snake_case :   Optional[Any]													=    1_92
            __snake_case :   Optional[int]													=    7_68
            __snake_case :   Dict													=    12
            __snake_case :   Tuple													=    3
      elif deit_name[9:].startswith('small'				):
            __snake_case :   List[Any]													=    3_84
            __snake_case :   Any													=    15_36
            __snake_case :   str													=    12
            __snake_case :   str													=    6
      if deit_name[9:].startswith('base'				):
            pass
      elif deit_name[4:].startswith('large'				):
            __snake_case :   int													=    10_24
            __snake_case :   int													=    40_96
            __snake_case :   Tuple													=    24
            __snake_case :   Optional[int]													=    16

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

      # load state_dict of original model, remove and rename some keys
      __snake_case :   Any													=    timm_model.state_dict()
      __snake_case :   Optional[int]													=    create_rename_keys(_UpperCAmelCase ,_UpperCAmelCase				)
      for src, dest in rename_keys:
            rename_key(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase				)
      read_in_q_k_v(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase				)

      # load HuggingFace model
      __snake_case :   Union[str, Any]													=    DeiTForImageClassificationWithTeacher(_UpperCAmelCase				).eval()
      model.load_state_dict(_UpperCAmelCase				)

      # Check outputs on an image, prepared by DeiTImageProcessor
      __snake_case :   Optional[Any]													=    int(
          (2_56 / 2_24) * config.image_size				)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      __snake_case :   int													=    DeiTImageProcessor(size=_UpperCAmelCase ,crop_size=config.image_size				)
      __snake_case :   Tuple													=    image_processor(images=prepare_img() ,return_tensors='pt'				)
      __snake_case :   Union[str, Any]													=    encoding['pixel_values']
      __snake_case :   Optional[int]													=    model(_UpperCAmelCase				)

      __snake_case :   Dict													=    timm_model(_UpperCAmelCase				)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_UpperCAmelCase ,outputs.logits ,atol=1E-3				)

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


if __name__ == "__main__":
    A__    :   Tuple           =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--deit_name''',
        default='''vit_deit_base_distilled_patch16_224''',
        type=str,
        help='''Name of the DeiT 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.'''
    )

    A__    :   str           =					parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= KandinskyInpaintPipeline
 A__				= ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 A__				= [
     '''prompt''',
     '''negative_prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 A__				= [
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 A__				= False
 @property
 def A_   (   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       return self.time_input_dim
 @property
 def A_   (   self						:							Dict  )      ->   Any:







       '''simple docstring'''
       return self.time_input_dim * 4
 @property
 def A_   (   self						:							List[Any]  )      ->   List[str]:







       '''simple docstring'''
       return 100
 @property
 def A_   (   self						:							int  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
       return tokenizer
 @property
 def A_   (   self						:							Tuple  )      ->   str:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   str													=    MCLIPConfig(
           numDims=self.cross_attention_dim      ,     transformerDimensions=self.text_embedder_hidden_size      ,     hidden_size=self.text_embedder_hidden_size      ,     intermediate_size=37      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     vocab_size=1005      ,     )

       __snake_case :   List[Any]													=    MultilingualCLIP(__a  )
       __snake_case :   List[str]													=    text_encoder.eval()

       return text_encoder
 @property
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       torch.manual_seed(0  )

       __snake_case :   Tuple													=    {
           'in_channels': 9,
           # Out channels is double in channels because predicts mean and variance
           'out_channels': 8,
           'addition_embed_type': 'text_image',
           'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
           'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
           'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
           'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
           'layers_per_block': 1,
           'encoder_hid_dim': self.text_embedder_hidden_size,
           'encoder_hid_dim_type': 'text_image_proj',
           'cross_attention_dim': self.cross_attention_dim,
           'attention_head_dim': 4,
           'resnet_time_scale_shift': 'scale_shift',
           'class_embed_type': None,
       }

       __snake_case :   Any													=    UNetaDConditionModel(**__a  )
       return model
 @property
 def A_   (   self						:							List[str]  )      ->   List[Any]:







       '''simple docstring'''
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }
 @property
 def A_   (   self						:							int  )      ->   List[str]:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   Optional[int]													=    VQModel(**self.dummy_movq_kwargs  )
       return model
 def A_   (   self						:							Dict  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.dummy_text_encoder
       __snake_case :   str													=    self.dummy_tokenizer
       __snake_case :   Any													=    self.dummy_unet
       __snake_case :   Optional[Any]													=    self.dummy_movq

       __snake_case :   Optional[Any]													=    DDIMScheduler(
           num_train_timesteps=1000      ,     beta_schedule='linear'      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=__a      ,     set_alpha_to_one=__a      ,     steps_offset=1      ,     prediction_type='epsilon'      ,     thresholding=__a      ,     )

       __snake_case :   List[str]													=    {
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components
 def A_   (   self						:							List[str]      ,     __a						:							Dict      ,     __a						:							int=0  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   int													=    floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   str													=    floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(seed + 1  )  ).to(__a  )
       # create init_image
       __snake_case :   Any													=    floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   Optional[int]													=    image.cpu().permute(0      ,     2      ,     3      ,     1  )[0]
       __snake_case :   List[str]													=    Image.fromarray(np.uinta(__a  )  ).convert('RGB'  ).resize((256, 256)  )
       # create mask
       __snake_case :   Optional[int]													=    np.ones((64, 64)      ,     dtype=np.floataa  )
       __snake_case :   List[Any]													=    0

       if str(__a  ).startswith('mps'  ):
             __snake_case :   Any													=    torch.manual_seed(__a  )
       else:
             __snake_case :   Optional[int]													=    torch.Generator(device=__a  ).manual_seed(__a  )
       __snake_case :   int													=    {
           'prompt': 'horse',
           'image': init_image,
           'mask_image': mask,
           'image_embeds': image_embeds,
           'negative_image_embeds': negative_image_embeds,
           'generator': generator,
           'height': 64,
           'width': 64,
           'num_inference_steps': 2,
           'guidance_scale': 4.0,
           'output_type': 'np',
       }
       return inputs
 def A_   (   self						:							Any  )      ->   int:







       '''simple docstring'''
       __snake_case :   List[str]													=    'cpu'

       __snake_case :   Tuple													=    self.get_dummy_components()

       __snake_case :   Optional[int]													=    self.pipeline_class(**__a  )
       __snake_case :   Union[str, Any]													=    pipe.to(__a  )

       pipe.set_progress_bar_config(disable=__a  )

       __snake_case :   Optional[Any]													=    pipe(**self.get_dummy_inputs(__a  )  )
       __snake_case :   Dict													=    output.images

       __snake_case :   Union[str, Any]													=    pipe(
           **self.get_dummy_inputs(__a  )      ,     return_dict=__a      ,     )[0]

       __snake_case :   int													=    image[0, -3:, -3:, -1]
       __snake_case :   Optional[int]													=    image_from_tuple[0, -3:, -3:, -1]

       print(f'''image.shape {image.shape}'''  )

       assert image.shape == (1, 64, 64, 3)

       __snake_case :   List[Any]													=    np.array(
           [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]  )

       assert (
           np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





 def A_   (   self						:							List[Any]  )      ->   List[str]:







       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def A_   (   self						:							Optional[int]  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'  )

       __snake_case :   Tuple													=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
       __snake_case :   Tuple													=    np.ones((768, 768)      ,     dtype=np.floataa  )
       __snake_case :   Tuple													=    0

       __snake_case :   Tuple													=    'a hat'

       __snake_case :   Optional[Any]													=    KandinskyPriorPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-1-prior'      ,     torch_dtype=torch.floataa  )
       pipe_prior.to(__a  )

       __snake_case :   int													=    KandinskyInpaintPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-1-inpaint'      ,     torch_dtype=torch.floataa  )
       __snake_case :   Optional[Any]													=    pipeline.to(__a  )
       pipeline.set_progress_bar_config(disable=__a  )

       __snake_case :   Any													=    torch.Generator(device='cpu'  ).manual_seed(0  )
       __snake_case							,     __snake_case :   Union[str, Any]													=    pipe_prior(
           __a      ,     generator=__a      ,     num_inference_steps=5      ,     negative_prompt=''      ,     ).to_tuple()

       __snake_case :   List[str]													=    pipeline(
           __a      ,     image=__a      ,     mask_image=__a      ,     image_embeds=__a      ,     negative_image_embeds=__a      ,     generator=__a      ,     num_inference_steps=100      ,     height=768      ,     width=768      ,     output_type='np'      ,     )

       __snake_case :   int													=    output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__a      ,     __a  )



