




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()
__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)
def      a_ (      _A      ,   _A=False  )			->			Union[str, Any]:







				"""simple docstring"""
				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'''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__									=     [(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_ (      _A      ,   _A      ,   _A=False  )			->			Dict:







				"""simple docstring"""
				for i in range(config.num_hidden_layers  ):
								if base_model:
												snake_case__									=     ''
								else:
												snake_case__									=     'deit.'
								# 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      a_ (      _A      ,   _A      ,   _A  )			->			Union[str, Any]:







				"""simple docstring"""
				snake_case__									=     dct.pop(_A  )
				snake_case__									=     val
def      a_ (      )			->			Tuple:







				"""simple docstring"""
				snake_case__									=     'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case__									=     Image.open(requests.get(_A      ,   stream=_A  ).raw  )
				return im
@torch.no_grad()
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				snake_case__									=     DeiTConfig()
				# all deit models have fine-tuned heads
				snake_case__									=     False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				snake_case__									=     1000
				snake_case__									=     'huggingface/label-files'
				snake_case__									=     'imagenet-1k-id2label.json'
				snake_case__									=     json.load(open(hf_hub_download(_A      ,   _A      ,   repo_type='dataset'  )      ,   'r'  )  )
				snake_case__									=     {int(_A  ): v for k, v in idalabel.items()}
				snake_case__									=     idalabel
				snake_case__									=     {v: k for k, v in idalabel.items()}
				snake_case__									=     int(deit_name[-6:-4]  )
				snake_case__									=     int(deit_name[-3:]  )
				# size of the architecture
				if deit_name[9:].startswith('tiny'  ):
								snake_case__									=     192
								snake_case__									=     768
								snake_case__									=     12
								snake_case__									=     3
				elif deit_name[9:].startswith('small'  ):
								snake_case__									=     384
								snake_case__									=     1536
								snake_case__									=     12
								snake_case__									=     6
				if deit_name[9:].startswith('base'  ):
								pass
				elif deit_name[4:].startswith('large'  ):
								snake_case__									=     1024
								snake_case__									=     4096
								snake_case__									=     24
								snake_case__									=     16

				# load original model from timm
				snake_case__									=     timm.create_model(_A      ,   pretrained=_A  )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				snake_case__									=     timm_model.state_dict()
				snake_case__									=     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
				snake_case__									=     DeiTForImageClassificationWithTeacher(_A  ).eval()
				model.load_state_dict(_A  )

				# Check outputs on an image, prepared by DeiTImageProcessor
				snake_case__									=     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
				snake_case__									=     DeiTImageProcessor(size=_A      ,   crop_size=config.image_size  )
				snake_case__									=     image_processor(images=prepare_img()      ,   return_tensors='pt'  )
				snake_case__									=     encoding['pixel_values']
				snake_case__									=     model(_A  )

				snake_case__									=     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 {deit_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__":
							__UpperCamelCase					:		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."""
							)

							__UpperCamelCase					:		List[Any]	  =						parser.parse_args()
							convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)












import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableUnCLIPImgaImgPipeline
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_UpperCAmelCase     =       frozenset(
							    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							_UpperCAmelCase     =       frozenset([]	)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Union[str, Any]:
											snake_case__									=     32
											snake_case__									=     embedder_hidden_size

											# image encoding components

											snake_case__									=     CLIPImageProcessor(crop_size=32       ,	size=32						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPVisionModelWithProjection(
											    CLIPVisionConfig(
											        hidden_size=UpperCamelCase       ,	projection_dim=UpperCamelCase       ,	num_hidden_layers=5       ,	num_attention_heads=4       ,	image_size=32       ,	intermediate_size=37       ,	patch_size=1       ,	)						)

											# regular denoising components

											torch.manual_seed(0						)
											snake_case__									=     StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase						)
											snake_case__									=     DDPMScheduler(beta_schedule='squaredcos_cap_v2'						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPTextModel(
											    CLIPTextConfig(
											        bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=UpperCamelCase       ,	projection_dim=32       ,	intermediate_size=37       ,	layer_norm_eps=1e-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=10_00       ,	)						)

											torch.manual_seed(0						)
											snake_case__									=     UNetaDConditionModel(
											    sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,	up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,	block_out_channels=(32, 64)       ,	attention_head_dim=(2, 4)       ,	class_embed_type='projection'       ,	projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,	cross_attention_dim=UpperCamelCase       ,	layers_per_block=1       ,	upcast_attention=UpperCamelCase       ,	use_linear_projection=UpperCamelCase       ,	)

											torch.manual_seed(0						)
											snake_case__									=     DDIMScheduler(
											    beta_schedule='scaled_linear'       ,	beta_start=0.00_085       ,	beta_end=0.012       ,	prediction_type='v_prediction'       ,	set_alpha_to_one=UpperCamelCase       ,	steps_offset=1       ,	)

											torch.manual_seed(0						)
											snake_case__									=     AutoencoderKL()

											snake_case__									=     {
											    # image encoding components
											    'feature_extractor': feature_extractor,
											    'image_encoder': image_encoder.eval(),
											    # image noising components
											    'image_normalizer': image_normalizer.eval(),
											    'image_noising_scheduler': image_noising_scheduler,
											    # regular denoising components
											    'tokenizer': tokenizer,
											    'text_encoder': text_encoder.eval(),
											    'unet': unet.eval(),
											    'scheduler': scheduler,
											    'vae': vae.eval(),
											}

											return components
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Tuple=0       ,	UpperCamelCase:      Union[str, Any]=True						)		->		Any:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)

											snake_case__									=     floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)

											if pil_image:
															snake_case__									=     input_image * 0.5 + 0.5
															snake_case__									=     input_image.clamp(0       ,	1						)
															snake_case__									=     input_image.cpu().permute(0       ,	2       ,	3       ,	1						).float().numpy()
															snake_case__									=     DiffusionPipeline.numpy_to_pil(UpperCamelCase						)[0]

											return {
											    "prompt": "An anime racoon running a marathon",
											    "image": input_image,
											    "generator": generator,
											    "num_inference_steps": 2,
											    "output_type": "np",
											}
							@skip_mps
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[Any]:
											snake_case__									=     'cpu'  # ensure determinism for the device-dependent torch.Generator
											snake_case__									=     self.get_dummy_components()
											snake_case__									=     StableUnCLIPImgaImgPipeline(**UpperCamelCase						)
											snake_case__									=     sd_pipe.to(UpperCamelCase						)
											sd_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     self.get_dummy_inputs(UpperCamelCase						)
											inputs.update({'image_embeds': None}						)
											snake_case__									=     sd_pipe(**UpperCamelCase						).images
											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case__									=     np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, Any]:
											snake_case__									=     torch_device in ['cpu', 'mps']

											self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		int:
											snake_case__									=     torch_device in ['cpu', 'mps']

											self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase						)


							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()       ,	reason='XFormers attention is only available with CUDA and `xformers` installed'       ,	)
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Dict:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'						)

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-l-img2img'       ,	torch_dtype=torch.floataa						)
											pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__									=     pipe(UpperCamelCase       ,	'anime turle'       ,	generator=UpperCamelCase       ,	output_type='np'						)

											snake_case__									=     output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Dict:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'						)

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-h-img2img'       ,	torch_dtype=torch.floataa						)
											pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__									=     pipe(UpperCamelCase       ,	'anime turle'       ,	generator=UpperCamelCase       ,	output_type='np'						)

											snake_case__									=     output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		str:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-h-img2img'       ,	torch_dtype=torch.floataa						)
											snake_case__									=     pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     pipe(
											    UpperCamelCase       ,	'anime turtle'       ,	num_inference_steps=2       ,	output_type='np'       ,	)

											snake_case__									=     torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 10**9




