



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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        UpperCAmelCase_  (       A_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			int      =  KandinskyVaaControlnetPipeline
						UpperCamelCase__      :			str      =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
						UpperCamelCase__      :			Optional[Any]      =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
						UpperCamelCase__      :			str      =  [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''latents''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						UpperCamelCase__      :			Tuple      =  False






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return 32






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return 32






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.time_input_dim






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.time_input_dim * 4






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return 100






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)

													__SCREAMING_SNAKE_CASE									= {
													    'in_channels': 8,
													    # Out channels is double in channels because predicts mean and variance
													    'out_channels': 8,
													    'addition_embed_type': 'image_hint',
													    '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': 'image_proj',
													    'cross_attention_dim': self.cross_attention_dim,
													    'attention_head_dim': 4,
													    'resnet_time_scale_shift': 'scale_shift',
													    'class_embed_type': None,
													}

													__SCREAMING_SNAKE_CASE									= UNetaDConditionModel(**_lowerCamelCase		)
													return model






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return {
													    "block_out_channels": [32, 32, 64, 64],
													    "down_block_types": [
													        "DownEncoderBlock2D",
													        "DownEncoderBlock2D",
													        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
													    "vq_embed_dim": 4,
													}






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= VQModel(**self.dummy_movq_kwargs		)
													return model






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.dummy_unet
													__SCREAMING_SNAKE_CASE									= self.dummy_movq

													__SCREAMING_SNAKE_CASE									= DDIMScheduler(
													    num_train_timesteps=1_000				,							beta_schedule='linear'				,							beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							clip_sample=_lowerCamelCase				,							set_alpha_to_one=_lowerCamelCase				,							steps_offset=1				,							prediction_type='epsilon'				,							thresholding=_lowerCamelCase				,							)

													__SCREAMING_SNAKE_CASE									= {
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}
													return components






						def 	_A     (   self				,							_A				,							_A=0		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= floats_tensor((1, self.text_embedder_hidden_size)				,							rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)
													__SCREAMING_SNAKE_CASE									= floats_tensor((1, self.text_embedder_hidden_size)				,							rng=random.Random(seed + 1		)		).to(
													    _lowerCamelCase		)

													# create hint
													__SCREAMING_SNAKE_CASE									= floats_tensor((1, 3, 64, 64)				,							rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)

													if str(_lowerCamelCase		).startswith('mps'		):
																				__SCREAMING_SNAKE_CASE									= torch.manual_seed(_lowerCamelCase		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.Generator(device=_lowerCamelCase		).manual_seed(_lowerCamelCase		)
													__SCREAMING_SNAKE_CASE									= {
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'hint': hint,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'guidance_scale': 4.0,
													    'num_inference_steps': 2,
													    'output_type': 'np',
													}
													return inputs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'cpu'

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()

													__SCREAMING_SNAKE_CASE									= self.pipeline_class(**_lowerCamelCase		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_lowerCamelCase		)

													pipe.set_progress_bar_config(disable=_lowerCamelCase		)

													__SCREAMING_SNAKE_CASE									= pipe(**self.get_dummy_inputs(_lowerCamelCase		)		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= pipe(
													    **self.get_dummy_inputs(_lowerCamelCase		)				,							return_dict=_lowerCamelCase				,							)[0]

													__SCREAMING_SNAKE_CASE									= image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE									= image_from_tuple[0, -3:, -3:, -1]

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

													__SCREAMING_SNAKE_CASE									= np.array(
													    [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]		)

													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()}"""







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

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'		)

													__SCREAMING_SNAKE_CASE									= load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinskyv22/hint_image_cat.png'		)
													__SCREAMING_SNAKE_CASE									= torch.from_numpy(np.array(_lowerCamelCase		)		).float() / 255.0
													__SCREAMING_SNAKE_CASE									= hint.permute(2				,							0				,							1		).unsqueeze(0		)

													__SCREAMING_SNAKE_CASE									= KandinskyVaaPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-2-prior'				,							torch_dtype=torch.floataa		)
													pipe_prior.to(_lowerCamelCase		)

													__SCREAMING_SNAKE_CASE									= KandinskyVaaControlnetPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-2-controlnet-depth'				,							torch_dtype=torch.floataa		)
													__SCREAMING_SNAKE_CASE									= pipeline.to(_lowerCamelCase		)
													pipeline.set_progress_bar_config(disable=_lowerCamelCase		)

													__SCREAMING_SNAKE_CASE									= 'A robot, 4k photo'

													__SCREAMING_SNAKE_CASE									= torch.Generator(device='cuda'		).manual_seed(0		)
													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= pipe_prior(
													    _lowerCamelCase				,							generator=_lowerCamelCase				,							num_inference_steps=5				,							negative_prompt=''				,							).to_tuple()

													__SCREAMING_SNAKE_CASE									= torch.Generator(device='cuda'		).manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipeline(
													    image_embeds=_lowerCamelCase				,							negative_image_embeds=_lowerCamelCase				,							hint=_lowerCamelCase				,							generator=_lowerCamelCase				,							num_inference_steps=100				,							output_type='np'				,							)

													__SCREAMING_SNAKE_CASE									= output.images[0]

													assert image.shape == (512, 512, 3)

													assert_mean_pixel_difference(_lowerCamelCase				,							_lowerCamelCase		)











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






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






def      __lowercase       (			a__							,       a__							,       a__       )    ->			str:
							__SCREAMING_SNAKE_CASE									= dct.pop(a__       )
							__SCREAMING_SNAKE_CASE									= val






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






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

							# load original model from timm
							__SCREAMING_SNAKE_CASE									= timm.create_model(a__							,       pretrained=a__       )
							timm_model.eval()

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

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

							__SCREAMING_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__":
			lowerCAmelCase__		:					Union[str, 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.'''
			)

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






