




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      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[Any]:
       lowerCamelCase   :      Tuple			 =					[tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]    )




class 		UpperCamelCase__	(lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Union[str, Any]	     =      StableDiffusionLatentUpscalePipeline
    lowerCamelCase_      :	Union[str, Any]	     =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        """height""",
        """width""",
        """cross_attention_kwargs""",
        """negative_prompt_embeds""",
        """prompt_embeds""",
    }
    lowerCamelCase_      :	Tuple	     =      PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
    lowerCamelCase_      :	Optional[int]	     =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    lowerCamelCase_      :	Tuple	     =      frozenset(
        []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowerCamelCase_      :	Dict	     =      frozenset([]     )

    lowerCamelCase_      :	Tuple	     =      True







    @property
    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      Optional[int]			 =					1
           lowerCamelCase   :      List[str]			 =					4
           lowerCamelCase   :      List[str]			 =					(16, 16)

           lowerCamelCase   :      str			 =					floats_tensor((batch_size, num_channels) + sizes     ,  rng=random.Random(0     )     ).to(UpperCamelCase__     )
           return image







    def     _lowercase     (    self     )     ->      List[str]:
           torch.manual_seed(0     )
           lowerCamelCase   :      Dict			 =					UNetaDConditionModel(
               act_fn="gelu"     ,  attention_head_dim=8     ,  norm_num_groups=UpperCamelCase__     ,  block_out_channels=[32, 32, 64, 64]     ,  time_cond_proj_dim=160     ,  conv_in_kernel=1     ,  conv_out_kernel=1     ,  cross_attention_dim=32     ,  down_block_types=(
                   "KDownBlock2D",
                   "KCrossAttnDownBlock2D",
                   "KCrossAttnDownBlock2D",
                   "KCrossAttnDownBlock2D",
               )     ,  in_channels=8     ,  mid_block_type=UpperCamelCase__     ,  only_cross_attention=UpperCamelCase__     ,  out_channels=5     ,  resnet_time_scale_shift="scale_shift"     ,  time_embedding_type="fourier"     ,  timestep_post_act="gelu"     ,  up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")     ,  )
           lowerCamelCase   :      Tuple			 =					AutoencoderKL(
               block_out_channels=[32, 32, 64, 64]     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=[
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
                   "DownEncoderBlock2D",
               ]     ,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]     ,  latent_channels=4     ,  )
           lowerCamelCase   :      int			 =					EulerDiscreteScheduler(prediction_type="sample"     )
           lowerCamelCase   :      List[str]			 =					CLIPTextConfig(
               bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=32     ,  intermediate_size=37     ,  layer_norm_eps=1e-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=1000     ,  hidden_act="quick_gelu"     ,  projection_dim=512     ,  )
           lowerCamelCase   :      List[str]			 =					CLIPTextModel(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

           lowerCamelCase   :      Optional[Any]			 =					{
               "unet": model.eval(),
               "vae": vae.eval(),
               "scheduler": scheduler,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
           }

           return components







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__=0     )     ->      Optional[Any]:
           if str(UpperCamelCase__     ).startswith("mps"     ):
                  lowerCamelCase   :      Any			 =					torch.manual_seed(UpperCamelCase__     )
           else:
                  lowerCamelCase   :      Optional[Any]			 =					torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )
           lowerCamelCase   :      int			 =					{
               "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     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      List[str]			 =					"cpu"

           lowerCamelCase   :      Any			 =					self.get_dummy_components()
           lowerCamelCase   :      Union[str, Any]			 =					self.pipeline_class(**UpperCamelCase__     )
           pipe.to(UpperCamelCase__     )
           pipe.set_progress_bar_config(disable=UpperCamelCase__     )

           lowerCamelCase   :      str			 =					self.get_dummy_inputs(UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					pipe(**UpperCamelCase__     ).images
           lowerCamelCase   :      Optional[int]			 =					image[0, -3:, -3:, -1]

           self.assertEqual(image.shape     ,  (1, 256, 256, 3)     )
           lowerCamelCase   :      str			 =					np.array(
               [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]     )
           lowerCamelCase   :      List[Any]			 =					np.abs(image_slice.flatten() - expected_slice     ).max()
           self.assertLessEqual(UpperCamelCase__     ,  1e-3     )







    def     _lowercase     (    self     )     ->      str:
           super().test_attention_slicing_forward_pass(expected_max_diff=7e-3     )







    def     _lowercase     (    self     )     ->      Any:
           super().test_cpu_offload_forward_pass(expected_max_diff=3e-3     )







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )







    def     _lowercase     (    self     )     ->      Tuple:
           super().test_inference_batch_single_identical(expected_max_diff=7e-3     )







    def     _lowercase     (    self     )     ->      Dict:
           super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           super().test_save_load_local(expected_max_difference=3e-3     )







    def     _lowercase     (    self     )     ->      Any:
           super().test_save_load_optional_components(expected_max_difference=3e-3     )







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      List[str]			 =					[
               "DDIMScheduler",
               "DDPMScheduler",
               "PNDMScheduler",
               "HeunDiscreteScheduler",
               "EulerAncestralDiscreteScheduler",
               "KDPM2DiscreteScheduler",
               "KDPM2AncestralDiscreteScheduler",
               "DPMSolverSDEScheduler",
           ]
           lowerCamelCase   :      Optional[Any]			 =					self.get_dummy_components()
           lowerCamelCase   :      Tuple			 =					self.pipeline_class(**UpperCamelCase__     )

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

           pipe.to(UpperCamelCase__     )
           pipe.set_progress_bar_config(disable=UpperCamelCase__     )
           lowerCamelCase   :      Optional[Any]			 =					self.get_dummy_inputs(UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					2

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

                  lowerCamelCase   :      int			 =					getattr(UpperCamelCase__     ,  scheduler_enum.name     )
                  lowerCamelCase   :      Optional[Any]			 =					scheduler_cls.from_config(pipe.scheduler.config     )
                  lowerCamelCase   :      Optional[int]			 =					pipe(**UpperCamelCase__     )[0]
                  outputs.append(UpperCamelCase__     )

           assert check_same_shape(UpperCamelCase__     )




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

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      List[Any]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      int			 =					torch.manual_seed(33     )

           lowerCamelCase   :      Tuple			 =					StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"     ,  torch_dtype=torch.floataa     )
           pipe.to("cuda"     )

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

           lowerCamelCase   :      List[Any]			 =					"a photo of an astronaut high resolution, unreal engine, ultra realistic"

           lowerCamelCase   :      int			 =					pipe(UpperCamelCase__     ,  generator=UpperCamelCase__     ,  output_type="latent"     ).images

           lowerCamelCase   :      str			 =					upscaler(
               prompt=UpperCamelCase__     ,  image=UpperCamelCase__     ,  num_inference_steps=20     ,  guidance_scale=0     ,  generator=UpperCamelCase__     ,  output_type="np"     ,  ).images[0]

           lowerCamelCase   :      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     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      int			 =					torch.manual_seed(33     )

           lowerCamelCase   :      str			 =					StableDiffusionLatentUpscalePipeline.from_pretrained(
               "stabilityai/sd-x2-latent-upscaler"     ,  torch_dtype=torch.floataa     )
           upscaler.to("cuda"     )

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

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

           lowerCamelCase   :      str			 =					upscaler(
               prompt=UpperCamelCase__     ,  image=UpperCamelCase__     ,  num_inference_steps=20     ,  guidance_scale=0     ,  generator=UpperCamelCase__     ,  output_type="np"     ,  ).images[0]

           lowerCamelCase   :      List[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












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()
SCREAMING_SNAKE_CASE__       :      str  =   logging.get_logger(__name__)

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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      List[str]			 =					dct.pop(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Any			 =					val

def      A      (      )	-> List[str]:
       lowerCamelCase   :      Union[str, Any]			 =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase   :      str			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    )
       return im

@torch.no_grad()
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[Any]:
       lowerCamelCase   :      Union[str, Any]			 =					DeiTConfig()
       # all deit models have fine-tuned heads
       lowerCamelCase   :      Optional[int]			 =					False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       lowerCamelCase   :      Dict			 =					1000
       lowerCamelCase   :      Tuple			 =					"huggingface/label-files"
       lowerCamelCase   :      List[str]			 =					"imagenet-1k-id2label.json"
       lowerCamelCase   :      List[Any]			 =					json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,repo_type="dataset"    ) ,"r"    )    )
       lowerCamelCase   :      Optional[int]			 =					{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
       lowerCamelCase   :      Tuple			 =					idalabel
       lowerCamelCase   :      str			 =					{v: k for k, v in idalabel.items()}
       lowerCamelCase   :      Dict			 =					int(deit_name[-6:-4]    )
       lowerCamelCase   :      Optional[Any]			 =					int(deit_name[-3:]    )
       # size of the architecture
       if deit_name[9:].startswith("tiny"    ):
              lowerCamelCase   :      Optional[Any]			 =					192
              lowerCamelCase   :      List[str]			 =					768
              lowerCamelCase   :      Tuple			 =					12
              lowerCamelCase   :      Optional[Any]			 =					3
       elif deit_name[9:].startswith("small"    ):
              lowerCamelCase   :      str			 =					384
              lowerCamelCase   :      Optional[Any]			 =					1536
              lowerCamelCase   :      Dict			 =					12
              lowerCamelCase   :      Optional[int]			 =					6
       if deit_name[9:].startswith("base"    ):
              pass
       elif deit_name[4:].startswith("large"    ):
              lowerCamelCase   :      str			 =					1024
              lowerCamelCase   :      List[str]			 =					4096
              lowerCamelCase   :      Any			 =					24
              lowerCamelCase   :      Dict			 =					16

       # load original model from timm
       lowerCamelCase   :      List[Any]			 =					timm.create_model(_SCREAMING_SNAKE_CASE ,pretrained=_SCREAMING_SNAKE_CASE    )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowerCamelCase   :      Dict			 =					timm_model.state_dict()
       lowerCamelCase   :      Dict			 =					create_rename_keys(_SCREAMING_SNAKE_CASE ,_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
       lowerCamelCase   :      Optional[Any]			 =					DeiTForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE    ).eval()
       model.load_state_dict(_SCREAMING_SNAKE_CASE    )

       # Check outputs on an image, prepared by DeiTImageProcessor
       lowerCamelCase   :      Any			 =					int(
           (256 / 224) * config.image_size    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       lowerCamelCase   :      Union[str, Any]			 =					DeiTImageProcessor(size=_SCREAMING_SNAKE_CASE ,crop_size=config.image_size    )
       lowerCamelCase   :      str			 =					image_processor(images=prepare_img() ,return_tensors="pt"    )
       lowerCamelCase   :      int			 =					encoding["pixel_values"]
       lowerCamelCase   :      Optional[Any]			 =					model(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Union[str, Any]			 =					timm_model(_SCREAMING_SNAKE_CASE    )
       assert timm_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 {deit_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__":
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   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.'
       )

       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






