
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


logging.set_verbosity_info()
__lowerCAmelCase    :	Tuple						=						logging.get_logger(__name__)


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase=False		)       ->  Optional[int]:
    __lowercase						:	Optional[int]					=				[]
    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'''),
            ]		)

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        __lowercase						:	Union[str, Any]					=				[(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_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=False		)       ->  Optional[int]:
    for i in range(config.num_hidden_layers		):
        if base_model:
            __lowercase						:	Union[str, Any]					=				''''''
        else:
            __lowercase						:	List[Any]					=				'''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						:	int					=				state_dict.pop(F'blocks.{i}.attn.qkv.bias'		)
        # next, add query, keys and values (in that order) to the state dict
        __lowercase						:	Any					=				in_proj_weight[
            : config.hidden_size, :
        ]
        __lowercase						:	Tuple					=				in_proj_bias[: config.hidden_size]
        __lowercase						:	Union[str, Any]					=				in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __lowercase						:	Any					=				in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        __lowercase						:	int					=				in_proj_weight[
            -config.hidden_size :, :
        ]
        __lowercase						:	Optional[Any]					=				in_proj_bias[-config.hidden_size :]


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  int:
    __lowercase						:	Dict					=				['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase__		,    UpperCamelCase__		)


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  List[Any]:
    __lowercase						:	List[Any]					=				dct.pop(UpperCamelCase__		)
    __lowercase						:	Union[str, Any]					=				val


def 			UpperCAmelCase_     (  )       ->  int:
    __lowercase						:	Tuple					=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowercase						:	int					=				Image.open(requests.get(UpperCamelCase__		,    stream=UpperCamelCase__		).raw		)
    return im


@torch.no_grad()
def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=True		)       ->  str:
    __lowercase						:	List[Any]					=				ViTConfig()
    # patch_size
    if model_name[-1] == "8":
        __lowercase						:	int					=				8
    # set labels if required
    if not base_model:
        __lowercase						:	Optional[Any]					=				1_000
        __lowercase						:	str					=				'''huggingface/label-files'''
        __lowercase						:	str					=				'''imagenet-1k-id2label.json'''
        __lowercase						:	Tuple					=				json.load(open(hf_hub_download(UpperCamelCase__		,    UpperCamelCase__		,    repo_type='''dataset'''		)		,    '''r'''		)		)
        __lowercase						:	Union[str, Any]					=				{int(UpperCamelCase__		): v for k, v in idalabel.items()}
        __lowercase						:	Tuple					=				idalabel
        __lowercase						:	Tuple					=				{v: k for k, v in idalabel.items()}
    # size of the architecture
    if model_name in ["dino_vits8", "dino_vits16"]:
        __lowercase						:	Tuple					=				384
        __lowercase						:	Optional[Any]					=				1_536
        __lowercase						:	str					=				12
        __lowercase						:	Union[str, Any]					=				6

    # load original model from torch hub
    __lowercase						:	Optional[Any]					=				torch.hub.load('''facebookresearch/dino:main'''		,    UpperCamelCase__		)
    original_model.eval()

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

    # load HuggingFace model
    if base_model:
        __lowercase						:	int					=				ViTModel(UpperCamelCase__		,    add_pooling_layer=UpperCamelCase__		).eval()
    else:
        __lowercase						:	List[Any]					=				ViTForImageClassification(UpperCamelCase__		).eval()
    model.load_state_dict(UpperCamelCase__		)

    # Check outputs on an image, prepared by ViTImageProcessor
    __lowercase						:	Tuple					=				ViTImageProcessor()
    __lowercase						:	Dict					=				image_processor(images=prepare_img()		,    return_tensors='''pt'''		)
    __lowercase						:	int					=				encoding['''pixel_values''']
    __lowercase						:	Tuple					=				model(UpperCamelCase__		)

    if base_model:
        __lowercase						:	Optional[int]					=				original_model(UpperCamelCase__		)
        assert torch.allclose(UpperCamelCase__		,    outputs.last_hidden_state[:, 0, :]		,    atol=1E-1		)
    else:
        __lowercase						:	Dict					=				original_model(UpperCamelCase__		)
        assert logits.shape == outputs.logits.shape
        assert torch.allclose(UpperCamelCase__		,    outputs.logits		,    atol=1E-3		)

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


if __name__ == "__main__":
 __lowerCAmelCase    :	Tuple						=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="dino_vitb16",
     type=str,
     help="Name of the model trained with DINO you\'d like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--base_model",
     action="store_true",
     help="Whether to only convert the base model (no projection head weights).",
 )

 parser.set_defaults(base_model=True)
 __lowerCAmelCase    :	Union[str, Any]						=						parser.parse_args()
 convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







"""simple docstring"""
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


_lowerCAmelCase    :Any     =					False
@skip_mps
class 							_UpperCAmelCase							( a		,a		,a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=StableDiffusionAttendAndExcitePipeline
     a__									=False
     a__									=TEXT_TO_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}      )
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					List[str]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Union[str, Any]:
         super().tearDownClass()
         torch.use_deterministic_algorithms(A		)





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)       ,	layers_per_block=1       ,	sample_size=3_2       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,	cross_attention_dim=3_2       ,	attention_head_dim=(2, 4)       ,	use_linear_projection=A       ,	)
         _UpperCAmelCase							:  List[Any]											=    DDIMScheduler(
             beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='''scaled_linear'''       ,	clip_sample=A       ,	set_alpha_to_one=A       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    AutoencoderKL(
             block_out_channels=[3_2, 6_4]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,	latent_channels=4       ,	sample_size=1_2_8       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    CLIPTextConfig(
             bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=3_2       ,	intermediate_size=3_7       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=1_0_0_0       ,	hidden_act='''gelu'''       ,	projection_dim=5_1_2       ,	)
         _UpperCAmelCase							:  List[str]											=    CLIPTextModel(A		)
         _UpperCAmelCase							:  str											=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

         _UpperCAmelCase							:  Union[str, Any]											=    {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }

         return components





     def  __lowerCAmelCase	(					self       ,	A       ,	A=0		)							->					List[Any]:
         if str(A		).startswith('''mps'''		):
             _UpperCAmelCase							:  Optional[int]											=    torch.manual_seed(A		)
         else:
             _UpperCAmelCase							:  Union[str, Any]											=    torch.Generator(device=A		).manual_seed(A		)
         _UpperCAmelCase							:  List[str]											=    {
             '''prompt''': '''a cat and a frog''',
             '''token_indices''': [2, 5],
             '''generator''': generator,
             '''num_inference_steps''': 1,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
             '''max_iter_to_alter''': 2,
             '''thresholds''': {0: 0.7},
         }
         return inputs





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  List[str]											=    '''cpu'''

         _UpperCAmelCase							:  Tuple											=    self.get_dummy_components()
         _UpperCAmelCase							:  int											=    self.pipeline_class(**A		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  Dict											=    self.get_dummy_inputs(A		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe(**A		).images
         _UpperCAmelCase							:  Tuple											=    image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,	(1, 6_4, 6_4, 3)		)
         _UpperCAmelCase							:  int											=    np.array(
             [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
         _UpperCAmelCase							:  Tuple											=    np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(A       ,	1E-3		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
         self._test_inference_batch_consistent(batch_sizes=[1, 2]		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         self._test_inference_batch_single_identical(batch_size=2       ,	expected_max_diff=7E-4		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					str:
         super().test_save_load_local(expected_max_difference=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         super().test_save_load_optional_components(expected_max_difference=4E-4		)



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





     '''simple docstring'''



     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Union[str, Any]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Optional[int]:
         super().tearDownClass()
         torch.use_deterministic_algorithms(A		)





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





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    torch.manual_seed(5_1		)

         _UpperCAmelCase							:  Optional[Any]											=    StableDiffusionAttendAndExcitePipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''       ,	safety_checker=A       ,	torch_dtype=torch.floataa		)
         pipe.to('''cuda'''		)

         _UpperCAmelCase							:  Optional[int]											=    '''a painting of an elephant with glasses'''
         _UpperCAmelCase							:  int											=    [5, 7]

         _UpperCAmelCase							:  Dict											=    pipe(
             prompt=A       ,	token_indices=A       ,	guidance_scale=7.5       ,	generator=A       ,	num_inference_steps=5       ,	max_iter_to_alter=5       ,	output_type='''numpy'''       ,	).images[0]

         _UpperCAmelCase							:  List[Any]											=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'''		)
         assert np.abs((expected_image - image).max()		) < 5E-1


