


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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE       :		int         =				logging.get_logger(__name__)




def     UpperCamelCase    (      _a       ,		_a=False		)					->							Tuple:
     '''simple docstring'''





     lowercase_       :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'''),
                  ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                  ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
              ]		)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          lowercase_       :int     =       [(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    (      _a       ,		_a       ,		_a=False		)					->							List[str]:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a		)					->							List[Any]:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :Any     =       dct.pop(_a		)
     lowercase_       :List[Any]     =       val




def     UpperCamelCase    (      )					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :int     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     lowercase_       :Optional[Any]     =       Image.open(requests.get(_a       ,		stream=_a		).raw		)
     return im




@torch.no_grad()
def     UpperCamelCase    (      _a       ,		_a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       ViTConfig()
     lowercase_       :Any     =       False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          lowercase_       :Dict     =       True
          lowercase_       :Optional[int]     =       int(vit_name[-1_2:-1_0]		)
          lowercase_       :List[Any]     =       int(vit_name[-9:-6]		)
     else:
          lowercase_       :Tuple     =       1_0_0_0
          lowercase_       :Optional[Any]     =       '''huggingface/label-files'''
          lowercase_       :int     =       '''imagenet-1k-id2label.json'''
          lowercase_       :int     =       json.load(open(hf_hub_download(_a       ,		_a       ,		repo_type='''dataset'''		)       ,		'''r'''		)		)
          lowercase_       :str     =       {int(_a		): v for k, v in idalabel.items()}
          lowercase_       :Any     =       idalabel
          lowercase_       :Dict     =       {v: k for k, v in idalabel.items()}
          lowercase_       :int     =       int(vit_name[-6:-4]		)
          lowercase_       :List[Any]     =       int(vit_name[-3:]		)
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith('''tiny'''		):
               lowercase_       :Any     =       1_9_2
               lowercase_       :List[Any]     =       7_6_8
               lowercase_       :Any     =       1_2
               lowercase_       :Dict     =       3
          elif vit_name[9:].startswith('''small'''		):
               lowercase_       :List[str]     =       3_8_4
               lowercase_       :List[Any]     =       1_5_3_6
               lowercase_       :Optional[int]     =       1_2
               lowercase_       :Optional[int]     =       6
          else:
               pass
     else:
          if vit_name[4:].startswith('''small'''		):
               lowercase_       :Optional[Any]     =       7_6_8
               lowercase_       :Tuple     =       2_3_0_4
               lowercase_       :Union[str, Any]     =       8
               lowercase_       :Optional[Any]     =       8
          elif vit_name[4:].startswith('''base'''		):
               pass
          elif vit_name[4:].startswith('''large'''		):
               lowercase_       :List[str]     =       1_0_2_4
               lowercase_       :List[str]     =       4_0_9_6
               lowercase_       :Any     =       2_4
               lowercase_       :List[Any]     =       1_6
          elif vit_name[4:].startswith('''huge'''		):
               lowercase_       :Any     =       1_2_8_0
               lowercase_       :Optional[int]     =       5_1_2_0
               lowercase_       :Any     =       3_2
               lowercase_       :List[Any]     =       1_6

    # load original model from timm
     lowercase_       :Optional[Any]     =       timm.create_model(_a       ,		pretrained=_a		)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     lowercase_       :Union[str, Any]     =       timm_model.state_dict()
     if base_model:
          remove_classification_head_(_a		)
     lowercase_       :List[Any]     =       create_rename_keys(_a       ,		_a		)
     for src, dest in rename_keys:
          rename_key(_a       ,		_a       ,		_a		)
     read_in_q_k_v(_a       ,		_a       ,		_a		)

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          lowercase_       :Optional[Any]     =       ViTModel(_a		).eval()
     else:
          lowercase_       :Any     =       ViTForImageClassification(_a		).eval()
     model.load_state_dict(_a		)

     # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
     if "deit" in vit_name:
          lowercase_       :int     =       DeiTImageProcessor(size=config.image_size		)
     else:
          lowercase_       :Any     =       ViTImageProcessor(size=config.image_size		)
     lowercase_       :Tuple     =       image_processor(images=prepare_img()       ,		return_tensors='''pt'''		)
     lowercase_       :Any     =       encoding['''pixel_values''']
     lowercase_       :Optional[int]     =       model(_a		)

     if base_model:
          lowercase_       :Any     =       timm_model.forward_features(_a		)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(_a       ,		outputs.pooler_output       ,		atol=1E-3		)
     else:
          lowercase_       :Union[str, Any]     =       timm_model(_a		)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(_a       ,		outputs.logits       ,		atol=1E-3		)

     Path(_a		).mkdir(exist_ok=_a		)
     print(f"Saving model {vit_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__":
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--vit_name",
       default="vit_base_patch16_224",
       type=str,
       help="Name of the ViT 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       :		Optional[Any]         =				parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @property
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       1
        lowercase_       :Optional[Any]     =       3
        lowercase_       :Optional[int]     =       (32, 32)

        lowercase_       :Dict     =       floats_tensor((batch_size, num_channels) + sizes   ,							rng=random.Random(0		)		).to(UpperCamelCase_		)
        return image
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :Optional[int]     =       UNetaDConditionModel(
            block_out_channels=(32, 64)   ,							layers_per_block=2   ,							sample_size=32   ,							in_channels=4   ,							out_channels=4   ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,							cross_attention_dim=32   ,							)
        return model
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :Dict     =       AutoencoderKL(
            block_out_channels=[32, 64]   ,							in_channels=3   ,							out_channels=3   ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,							latent_channels=4   ,							)
        return model
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :str     =       RobertaSeriesConfig(
            hidden_size=32   ,							project_dim=32   ,							intermediate_size=37   ,							layer_norm_eps=1E-05   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=5006   ,							)
        return RobertaSeriesModelWithTransformation(UpperCamelCase_		)
   @property
   def 		UpperCamelCase  (     self		):
        def extract(*UpperCamelCase_   ,							**UpperCamelCase_		):






             class      UpperCamelCase					:


                '''simple docstring'''







                def __init__(     self		):
                     lowercase_       :List[str]     =       torch.ones([0]		)




                def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
                     self.pixel_values.to(UpperCamelCase_		)
                     return self
             return Out()

        return extract
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        lowercase_       :Dict     =       self.dummy_cond_unet
        lowercase_       :Optional[Any]     =       PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
        lowercase_       :Union[str, Any]     =       self.dummy_vae
        lowercase_       :Any     =       self.dummy_text_encoder
        lowercase_       :List[Any]     =       XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
        lowercase_       :List[str]     =       77

        lowercase_       :int     =       self.dummy_image.to(UpperCamelCase_		)
        lowercase_       :Optional[Any]     =       init_image / 2 + 0.5

        # make sure here that pndm scheduler skips prk
        lowercase_       :str     =       AltDiffusionImgaImgPipeline(
            unet=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							vae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							feature_extractor=self.dummy_extractor   ,							)
        lowercase_       :List[str]     =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,							do_normalize=UpperCamelCase_		)
        lowercase_       :int     =       alt_pipe.to(UpperCamelCase_		)
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :Any     =       '''A painting of a squirrel eating a burger'''
        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       alt_pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='''np'''   ,							image=UpperCamelCase_   ,							)

        lowercase_       :Dict     =       output.images

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       alt_pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='''np'''   ,							image=UpperCamelCase_   ,							return_dict=UpperCamelCase_   ,							)[0]

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

        assert image.shape == (1, 32, 32, 3)
        lowercase_       :int     =       np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 5E-3
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 5E-3
   @unittest.skipIf(torch_device != '''cuda'''   ,							'''This test requires a GPU'''		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       self.dummy_cond_unet
        lowercase_       :Dict     =       PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
        lowercase_       :Tuple     =       self.dummy_vae
        lowercase_       :Dict     =       self.dummy_text_encoder
        lowercase_       :Tuple     =       XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
        lowercase_       :str     =       77

        lowercase_       :str     =       self.dummy_image.to(UpperCamelCase_		)

        # put models in fp16
        lowercase_       :Union[str, Any]     =       unet.half()
        lowercase_       :Union[str, Any]     =       vae.half()
        lowercase_       :List[str]     =       bert.half()

        # make sure here that pndm scheduler skips prk
        lowercase_       :List[Any]     =       AltDiffusionImgaImgPipeline(
            unet=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							vae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							feature_extractor=self.dummy_extractor   ,							)
        lowercase_       :List[Any]     =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,							do_normalize=UpperCamelCase_		)
        lowercase_       :List[str]     =       alt_pipe.to(UpperCamelCase_		)
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :str     =       '''A painting of a squirrel eating a burger'''
        lowercase_       :Union[str, Any]     =       torch.manual_seed(0		)
        lowercase_       :Any     =       alt_pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''   ,							image=UpperCamelCase_   ,							).images

        assert image.shape == (1, 32, 32, 3)




   @unittest.skipIf(torch_device != '''cuda'''   ,							'''This test requires a GPU'''		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''		)
        # resize to resolution that is divisible by 8 but not 16 or 32
        lowercase_       :Optional[Any]     =       init_image.resize((760, 504)		)

        lowercase_       :List[str]     =       '''BAAI/AltDiffusion'''
        lowercase_       :Optional[Any]     =       AltDiffusionImgaImgPipeline.from_pretrained(
            UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							)
        pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)
        pipe.enable_attention_slicing()

        lowercase_       :Optional[Any]     =       '''A fantasy landscape, trending on artstation'''

        lowercase_       :Optional[Any]     =       torch.manual_seed(0		)
        lowercase_       :str     =       pipe(
            prompt=UpperCamelCase_   ,							image=UpperCamelCase_   ,							strength=0.75   ,							guidance_scale=7.5   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							)
        lowercase_       :Optional[Any]     =       output.images[0]

        lowercase_       :Optional[int]     =       image[255:258, 383:386, -1]

        assert image.shape == (504, 760, 3)
        lowercase_       :Any     =       np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]		)

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






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


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''		)
        lowercase_       :Any     =       init_image.resize((768, 512)		)
        lowercase_       :List[str]     =       load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''		)

        lowercase_       :List[Any]     =       '''BAAI/AltDiffusion'''
        lowercase_       :Tuple     =       AltDiffusionImgaImgPipeline.from_pretrained(
            UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							)
        pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)
        pipe.enable_attention_slicing()

        lowercase_       :List[str]     =       '''A fantasy landscape, trending on artstation'''

        lowercase_       :Optional[int]     =       torch.manual_seed(0		)
        lowercase_       :Tuple     =       pipe(
            prompt=UpperCamelCase_   ,							image=UpperCamelCase_   ,							strength=0.75   ,							guidance_scale=7.5   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							)
        lowercase_       :int     =       output.images[0]

        assert image.shape == (512, 768, 3)
        # img2img is flaky across GPUs even in fp32, so using MAE here
        assert np.abs(expected_image - image		).max() < 1E-2

