

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()
lowerCAmelCase_			      =      logging.get_logger(__name__)


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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      ['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      dct.pop(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      val


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


@torch.no_grad()
def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=True			):
						snake_case_  =      ViTConfig()
						# patch_size
						if model_name[-1] == "8":
												snake_case_  =      8
						# set labels if required
						if not base_model:
												snake_case_  =      1000
												snake_case_  =      '''huggingface/label-files'''
												snake_case_  =      '''imagenet-1k-id2label.json'''
												snake_case_  =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						repo_type='''dataset'''			)    ,						'''r'''			)			)
												snake_case_  =      {int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
												snake_case_  =      idalabel
												snake_case_  =      {v: k for k, v in idalabel.items()}
						# size of the architecture
						if model_name in ["dino_vits8", "dino_vits16"]:
												snake_case_  =      384
												snake_case_  =      1536
												snake_case_  =      12
												snake_case_  =      6

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

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

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

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

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

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


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







'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Dict							     =  [tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:]				)


class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Union[str, Any]									=     StableDiffusionLatentUpscalePipeline
    __lowerCamelCase: List[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        'height',
        'width',
        'cross_attention_kwargs',
        'negative_prompt_embeds',
        'prompt_embeds',
    }
    __lowerCamelCase: List[Any]									=     PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
    __lowerCamelCase: List[Any]									=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowerCamelCase: Union[str, Any]									=     frozenset(
        []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __lowerCamelCase: List[Any]									=     frozenset([]      )

    __lowerCamelCase: Dict									=     True




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  1
       lowercase_						:     Any							     =  4
       lowercase_						:     int							     =  (1_6, 1_6)

       lowercase_						:     List[Any]							     =  floats_tensor((batch_size, num_channels) + sizes	,  rng=random.Random(0      )      ).to(a      )
       return image




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       torch.manual_seed(0      )
       lowercase_						:     int							     =  UNetaDConditionModel(
           act_fn="gelu"	,  attention_head_dim=8	,  norm_num_groups=a	,  block_out_channels=[3_2, 3_2, 6_4, 6_4]	,  time_cond_proj_dim=1_6_0	,  conv_in_kernel=1	,  conv_out_kernel=1	,  cross_attention_dim=3_2	,  down_block_types=(
               "KDownBlock2D",
               "KCrossAttnDownBlock2D",
               "KCrossAttnDownBlock2D",
               "KCrossAttnDownBlock2D",
           )	,  in_channels=8	,  mid_block_type=a	,  only_cross_attention=a	,  out_channels=5	,  resnet_time_scale_shift="scale_shift"	,  time_embedding_type="fourier"	,  timestep_post_act="gelu"	,  up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")	,  )
       lowercase_						:     List[str]							     =  AutoencoderKL(
           block_out_channels=[3_2, 3_2, 6_4, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=[
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
           ]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
       lowercase_						:     Optional[int]							     =  EulerDiscreteScheduler(prediction_type="sample"      )
       lowercase_						:     List[str]							     =  CLIPTextConfig(
           bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  hidden_act="quick_gelu"	,  projection_dim=5_1_2	,  )
       lowercase_						:     Optional[int]							     =  CLIPTextModel(a      )
       lowercase_						:     List[str]							     =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

       lowercase_						:     int							     =  {
           "unet": model.eval(),
           "vae": vae.eval(),
           "scheduler": scheduler,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
       }

       return components




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							int	,  a	:							List[str]=0      ):






       '''simple docstring'''






       if str(a      ).startswith("mps"      ):
          lowercase_						:     Optional[int]							     =  torch.manual_seed(a      )
       else:
          lowercase_						:     Optional[int]							     =  torch.Generator(device=a      ).manual_seed(a      )
       lowercase_						:     Optional[Any]							     =  {
           "prompt": "A painting of a squirrel eating a burger",
           "image": self.dummy_image.cpu(),
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }
       return inputs




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  "cpu"

       lowercase_						:     Any							     =  self.get_dummy_components()
       lowercase_						:     Tuple							     =  self.pipeline_class(**a      )
       pipe.to(a      )
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     List[Any]							     =  self.get_dummy_inputs(a      )
       lowercase_						:     List[str]							     =  pipe(**a      ).images
       lowercase_						:     int							     =  image[0, -3:, -3:, -1]

       self.assertEqual(image.shape	,  (1, 2_5_6, 2_5_6, 3)      )
       lowercase_						:     int							     =  np.array(
           [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]      )
       lowercase_						:     int							     =  np.abs(image_slice.flatten() - expected_slice      ).max()
       self.assertLessEqual(a	,  1e-3      )




    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       super().test_attention_slicing_forward_pass(expected_max_diff=7e-3      )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       super().test_cpu_offload_forward_pass(expected_max_diff=3e-3      )




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       super().test_inference_batch_single_identical(expected_max_diff=7e-3      )




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3      )




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       super().test_save_load_local(expected_max_difference=3e-3      )




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       super().test_save_load_optional_components(expected_max_difference=3e-3      )





    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  [
           "DDIMScheduler",
           "DDPMScheduler",
           "PNDMScheduler",
           "HeunDiscreteScheduler",
           "EulerAncestralDiscreteScheduler",
           "KDPM2DiscreteScheduler",
           "KDPM2AncestralDiscreteScheduler",
           "DPMSolverSDEScheduler",
       ]
       lowercase_						:     Optional[int]							     =  self.get_dummy_components()
       lowercase_						:     int							     =  self.pipeline_class(**a      )

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=a      )

       pipe.to(a      )
       pipe.set_progress_bar_config(disable=a      )
       lowercase_						:     Dict							     =  self.get_dummy_inputs(a      )
       lowercase_						:     int							     =  2

       lowercase_						:     Dict							     =  []
       for scheduler_enum in KarrasDiffusionSchedulers:
          if scheduler_enum.name in skip_schedulers:
             # no sigma schedulers are not supported
             # no schedulers
             continue

          lowercase_						:     Optional[int]							     =  getattr(a	,  scheduler_enum.name      )
          lowercase_						:     Union[str, Any]							     =  scheduler_cls.from_config(pipe.scheduler.config      )
          lowercase_						:     Tuple							     =  pipe(**a      )[0]
          outputs.append(a      )

       assert check_same_shape(a      )


@require_torch_gpu
@slow
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     str							     =  torch.manual_seed(3_3      )

       lowercase_						:     Union[str, Any]							     =  StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"	,  torch_dtype=torch.floataa      )
       pipe.to("cuda"      )

       lowercase_						:     List[Any]							     =  StableDiffusionLatentUpscalePipeline.from_pretrained(
           "stabilityai/sd-x2-latent-upscaler"	,  torch_dtype=torch.floataa      )
       upscaler.to("cuda"      )

       lowercase_						:     Dict							     =  "a photo of an astronaut high resolution, unreal engine, ultra realistic"

       lowercase_						:     Optional[Any]							     =  pipe(a	,  generator=a	,  output_type="latent"      ).images

       lowercase_						:     Tuple							     =  upscaler(
           prompt=a	,  image=a	,  num_inference_steps=2_0	,  guidance_scale=0	,  generator=a	,  output_type="np"	,  ).images[0]

       lowercase_						:     Any							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"      )
       assert np.abs((expected_image - image).mean()      ) < 5e-2





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  torch.manual_seed(3_3      )

       lowercase_						:     Optional[Any]							     =  StableDiffusionLatentUpscalePipeline.from_pretrained(
           "stabilityai/sd-x2-latent-upscaler"	,  torch_dtype=torch.floataa      )
       upscaler.to("cuda"      )

       lowercase_						:     Any							     =  "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

       lowercase_						:     str							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"      )

       lowercase_						:     int							     =  upscaler(
           prompt=a	,  image=a	,  num_inference_steps=2_0	,  guidance_scale=0	,  generator=a	,  output_type="np"	,  ).images[0]

       lowercase_						:     Union[str, Any]							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"      )
       assert np.abs((expected_image - image).max()      ) < 5e-2




