

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_        =      logging.get_logger(__name__)

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       Optional[int]:
				_UpperCAmelCase       :		Optional[int] 		=    ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(lowerCAmelCase  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  Optional[Any]  ,      lowerCAmelCase:  Optional[Any] )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Optional[Any] 		=    dct.pop(lowerCAmelCase )
				_UpperCAmelCase       :		Dict 		=    val

def 							__SCREAMING_SNAKE_CASE							(				)   ->       Dict:
				_UpperCAmelCase       :		Tuple 		=    "http://images.cocodataset.org/val2017/000000039769.jpg"
				_UpperCAmelCase       :		List[str] 		=    Image.open(requests.get(lowerCAmelCase  ,      stream=lowerCAmelCase ).raw )
				return im

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  Any )   ->       List[Any]:
				_UpperCAmelCase       :		Optional[int] 		=    ViTConfig()
				_UpperCAmelCase       :		str 		=    False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								_UpperCAmelCase       :		Any 		=    True
								_UpperCAmelCase       :		Tuple 		=    int(vit_name[-12:-10] )
								_UpperCAmelCase       :		List[Any] 		=    int(vit_name[-9:-6] )
				else:
								_UpperCAmelCase       :		Optional[int] 		=    1000
								_UpperCAmelCase       :		Optional[int] 		=    "huggingface/label-files"
								_UpperCAmelCase       :		Optional[Any] 		=    "imagenet-1k-id2label.json"
								_UpperCAmelCase       :		List[Any] 		=    json.load(open(hf_hub_download(lowerCAmelCase  ,      lowerCAmelCase  ,      repo_type="dataset" )  ,      "r" ) )
								_UpperCAmelCase       :		List[Any] 		=    {int(lowerCAmelCase ): v for k, v in idalabel.items()}
								_UpperCAmelCase       :		Union[str, Any] 		=    idalabel
								_UpperCAmelCase       :		Tuple 		=    {v: k for k, v in idalabel.items()}
								_UpperCAmelCase       :		Tuple 		=    int(vit_name[-6:-4] )
								_UpperCAmelCase       :		Optional[Any] 		=    int(vit_name[-3:] )
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith("tiny" ):
												_UpperCAmelCase       :		int 		=    192
												_UpperCAmelCase       :		str 		=    768
												_UpperCAmelCase       :		Dict 		=    12
												_UpperCAmelCase       :		int 		=    3
								elif vit_name[9:].startswith("small" ):
												_UpperCAmelCase       :		List[str] 		=    384
												_UpperCAmelCase       :		Any 		=    1536
												_UpperCAmelCase       :		Union[str, Any] 		=    12
												_UpperCAmelCase       :		Optional[Any] 		=    6
								else:
												pass
				else:
								if vit_name[4:].startswith("small" ):
												_UpperCAmelCase       :		List[str] 		=    768
												_UpperCAmelCase       :		Union[str, Any] 		=    2304
												_UpperCAmelCase       :		Optional[Any] 		=    8
												_UpperCAmelCase       :		List[Any] 		=    8
								elif vit_name[4:].startswith("base" ):
												pass
								elif vit_name[4:].startswith("large" ):
												_UpperCAmelCase       :		Any 		=    1024
												_UpperCAmelCase       :		Optional[int] 		=    4096
												_UpperCAmelCase       :		Tuple 		=    24
												_UpperCAmelCase       :		int 		=    16
								elif vit_name[4:].startswith("huge" ):
												_UpperCAmelCase       :		Tuple 		=    1280
												_UpperCAmelCase       :		Optional[Any] 		=    5120
												_UpperCAmelCase       :		List[Any] 		=    32
												_UpperCAmelCase       :		Dict 		=    16

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

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

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

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

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_        =      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_        =      parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


SCREAMING_SNAKE_CASE_        =      False

class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 32


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		Optional[int] 		=    VQModel(
										    block_out_channels=[32, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,					latent_channels=3						,					num_vq_embeddings=self.num_embed						,					vq_embed_dim=3						,					)
										return model


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
										return tokenizer


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		int 		=    CLIPTextConfig(
										    bos_token_id=0						,					eos_token_id=2						,					hidden_size=self.text_embedder_hidden_size						,					intermediate_size=37						,					layer_norm_eps=1e-05						,					num_attention_heads=4						,					num_hidden_layers=5						,					pad_token_id=1						,					vocab_size=1000						,					)
										return CLIPTextModel(A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)

										_UpperCAmelCase       :		Optional[int] 		=    12
										_UpperCAmelCase       :		Union[str, Any] 		=    12

										_UpperCAmelCase       :		str 		=    {
										    "attention_bias": True,
										    "cross_attention_dim": 32,
										    "attention_head_dim": height * width,
										    "num_attention_heads": 1,
										    "num_vector_embeds": self.num_embed,
										    "num_embeds_ada_norm": self.num_embeds_ada_norm,
										    "norm_num_groups": 32,
										    "sample_size": width,
										    "activation_fn": "geglu-approximate",
										}

										_UpperCAmelCase       :		Any 		=    TransformeraDModel(**A_	)
										return model


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    "cpu"

										_UpperCAmelCase       :		List[str] 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Any 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		Optional[int] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Union[str, Any] 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Union[str, Any] 		=    LearnedClassifierFreeSamplingEmbeddings(learnable=A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Optional[int] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		List[Any] 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		List[Any] 		=    output.images

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Optional[Any] 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		int 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		List[str] 		=    np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]	)

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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    "cpu"

										_UpperCAmelCase       :		Tuple 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Optional[Any] 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		List[str] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Dict 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Dict 		=    LearnedClassifierFreeSamplingEmbeddings(
										    learnable=A_						,					hidden_size=self.text_embedder_hidden_size						,					length=tokenizer.model_max_length	)

										_UpperCAmelCase       :		Optional[int] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		int 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    output.images

										_UpperCAmelCase       :		Optional[Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Any 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		Optional[int] 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		Union[str, Any] 		=    np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"	)

										_UpperCAmelCase       :		Dict 		=    VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"	)
										_UpperCAmelCase       :		Optional[int] 		=    pipeline.to(A_	)
										pipeline.set_progress_bar_config(disable=A_	)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										_UpperCAmelCase       :		Optional[int] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    pipeline(
										    "teddy bear playing in the pool"						,					num_images_per_prompt=1						,					generator=A_						,					output_type="np"						,					)

										_UpperCAmelCase       :		str 		=    output.images[0]

										assert image.shape == (256, 256, 3)
										assert np.abs(expected_image - image	).max() < 2.0


