


'''simple docstring'''

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase:		Optional[Any]    						=  logging.get_logger(__name__)







@dataclass
class 			UpperCAmelCase			:
 _lowerCamelCase			:       nn.Module
 _lowerCamelCase			:       List[nn.Module]            =       field(default_factory=SCREAMING_SNAKE_CASE__)
 _lowerCamelCase			:       list            =       field(default_factory=SCREAMING_SNAKE_CASE__)

 def 		lowercase_    (			self			:						int,       a_			:						Dict,       a_			:						Tensor,       a_			:						Tensor       ):





    """simple docstring"""



    UpperCamelCase__			    =    len(list(m.modules()       )       ) == 1 or isinstance(a_,       nn.Convad       ) or isinstance(a_,       nn.BatchNormad       )
    if has_not_submodules:
       if not isinstance(a_,       a_       ):
          self.traced.append(a_       )

 def __call__(			self			:						Tuple,       a_			:						Tensor       ):





    """simple docstring"""



    for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook       )       )
    self.module(a_       )
    [x.remove() for x in self.handles]
    return self

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    return list(filter(lambda a_       : len(list(x.state_dict().keys()       )       ) > 0,       self.traced       )       )







@dataclass
class 			UpperCAmelCase			:
 _lowerCamelCase			:       nn.Module
 _lowerCamelCase			:       nn.Module
 _lowerCamelCase			:       int            =       0
 _lowerCamelCase			:       List            =       field(default_factory=SCREAMING_SNAKE_CASE__)
 _lowerCamelCase			:       List            =       field(default_factory=SCREAMING_SNAKE_CASE__)

 def __call__(			self			:						List[str],       a_			:						Tensor       ):





    """simple docstring"""



    UpperCamelCase__			    =    Tracker(self.dest       )(a_       ).parametrized
    UpperCamelCase__			    =    Tracker(self.src       )(a_       ).parametrized

    UpperCamelCase__			    =    list(filter(lambda a_       : type(a_       ) not in self.src_skip,       a_       )       )
    UpperCamelCase__			    =    list(filter(lambda a_       : type(a_       ) not in self.dest_skip,       a_       )       )

    if len(a_       ) != len(a_       ):
       raise Exception(
           f'Numbers of operations are different. Source module has {len(a_       )} operations while'
           f' destination module has {len(a_       )}.'       )

    for dest_m, src_m in zip(a_,       a_       ):
       dest_m.load_state_dict(src_m.state_dict()       )
       if self.verbose == 1:
          print(f'Transfered from={src_m} to={dest_m}'       )







def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					nn.Module     ,		_UpperCamelCase   :					nn.Module )		->       nn.Module:





   '''simple docstring'''







   UpperCamelCase__			    =    from_model.state_dict()
   UpperCamelCase__			    =    our_model.state_dict()
   UpperCamelCase__			    =    our_model.config
   UpperCamelCase__			    =    []
   for stage_idx in range(len(config.hidden_sizes ) ):
      for block_id in range(config.depths[stage_idx] ):
         UpperCamelCase__			    =    F'block{stage_idx + 1}.{block_id}.layer_scale_1'
         UpperCamelCase__			    =    F'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

         all_keys.append((from_key, to_key) )
         UpperCamelCase__			    =    F'block{stage_idx + 1}.{block_id}.layer_scale_2'
         UpperCamelCase__			    =    F'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'

         all_keys.append((from_key, to_key) )

   for from_key, to_key in all_keys:
      UpperCamelCase__			    =    from_state_dict.pop(_UpperCamelCase )

   our_model.load_state_dict(_UpperCamelCase )
   return our_model



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					VanConfig     ,		_UpperCamelCase   :					str     ,		_UpperCamelCase   :					nn.Module     ,		_UpperCamelCase   :					Path     ,		_UpperCamelCase   :					bool = True     ,		)		->       Tuple:





   '''simple docstring'''







   print(F'Downloading weights for {name}...' )
   UpperCamelCase__			    =    cached_download(_UpperCamelCase )
   print(F'Converting {name}...' )
   UpperCamelCase__			    =    torch.load(_UpperCamelCase )["state_dict"]
   from_model.load_state_dict(_UpperCamelCase )
   from_model.eval()
   with torch.no_grad():
      UpperCamelCase__			    =    VanForImageClassification(_UpperCamelCase ).eval()
      UpperCamelCase__			    =    ModuleTransfer(src=_UpperCamelCase     ,		dest=_UpperCamelCase )
      UpperCamelCase__			    =    torch.randn((1, 3, 2_24, 2_24) )
      module_transfer(_UpperCamelCase )
      UpperCamelCase__			    =    copy_parameters(_UpperCamelCase     ,		_UpperCamelCase )

   if not torch.allclose(from_model(_UpperCamelCase )     ,		our_model(_UpperCamelCase ).logits ):
      raise ValueError("The model logits don't match the original one." )

   UpperCamelCase__			    =    name
   print(_UpperCamelCase )

   if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name     ,		commit_message="Add model"     ,		use_temp_dir=_UpperCamelCase     ,		)

      # we can use the convnext one
      UpperCamelCase__			    =    AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name     ,		commit_message="Add image processor"     ,		use_temp_dir=_UpperCamelCase     ,		)

      print(F'Pushed {checkpoint_name}' )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Path     ,		_UpperCamelCase   :					str = None     ,		_UpperCamelCase   :					bool = True )		->       Union[str, Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    "imagenet-1k-id2label.json"
   UpperCamelCase__			    =    10_00

   UpperCamelCase__			    =    "huggingface/label-files"
   UpperCamelCase__			    =    num_labels
   UpperCamelCase__			    =    json.load(open(hf_hub_download(_UpperCamelCase     ,		_UpperCamelCase     ,		repo_type="dataset" )     ,		"r" ) )
   UpperCamelCase__			    =    {int(_UpperCamelCase ): v for k, v in idalabel.items()}

   UpperCamelCase__			    =    idalabel
   UpperCamelCase__			    =    {v: k for k, v in idalabel.items()}

   UpperCamelCase__			    =    partial(_UpperCamelCase     ,		num_labels=_UpperCamelCase     ,		idalabel=_UpperCamelCase     ,		labelaid=_UpperCamelCase )

   UpperCamelCase__			    =    {
       "van-tiny": ImageNetPreTrainedConfig(
           hidden_sizes=[32, 64, 1_60, 2_56]     ,		depths=[3, 3, 5, 2]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
       "van-small": ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]     ,		depths=[2, 2, 4, 2]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
       "van-base": ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]     ,		depths=[3, 3, 12, 3]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
       "van-large": ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]     ,		depths=[3, 5, 27, 3]     ,		mlp_ratios=[8, 8, 4, 4]     ,		),
   }

   UpperCamelCase__			    =    {
       "van-tiny": van_tiny,
       "van-small": van_small,
       "van-base": van_base,
       "van-large": van_large,
   }

   UpperCamelCase__			    =    {
       "van-tiny": (
           "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
       ),
       "van-small": (
           "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
       ),
       "van-base": (
           "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
       ),
       "van-large": (
           "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
       ),
   }

   if model_name:
      convert_weight_and_push(
          _UpperCamelCase     ,		names_to_config[model_name]     ,		checkpoint=names_to_original_checkpoints[model_name]     ,		from_model=names_to_original_models[model_name]()     ,		save_directory=_UpperCamelCase     ,		push_to_hub=_UpperCamelCase     ,		)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(
             _UpperCamelCase     ,		_UpperCamelCase     ,		checkpoint=names_to_original_checkpoints[model_name]     ,		from_model=names_to_original_models[model_name]()     ,		save_directory=_UpperCamelCase     ,		push_to_hub=_UpperCamelCase     ,		)


if __name__ == "__main__":
       __lowercase:		Optional[Any]    						=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model-name",
           default=None,
           type=str,
           help=(
               "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
               " currently: van-tiny/small/base/large. If `None`, all of them will the converted."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=Path,
           required=True,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument(
           "--van_dir",
           required=True,
           type=Path,
           help=(
               "A path to VAN's original implementation directory. You can download from here:"
               " https://github.com/Visual-Attention-Network/VAN-Classification"
           ),
       )
       parser.add_argument(
           "--push_to_hub",
           default=True,
           type=bool,
           required=False,
           help="If True, push model and image processor to the hub.",
       )

       __lowercase:		Tuple    						=  parser.parse_args()
       __lowercase:		Path    						=  args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       __lowercase:		Dict    						=  args.van_dir
       # append the path to the parents to maskformer dir
       sys.path.append(str(van_dir.parent))
       from van.models.van import van_base, van_large, van_small, van_tiny

       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



'''simple docstring'''

import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       List[Any]            =       KandinskyVaaPriorEmbaEmbPipeline
 _lowerCamelCase			:       Tuple            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       [
     'num_images_per_prompt',
     'strength',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 _lowerCamelCase			:       List[str]            =       False

 @property
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    return self.time_input_dim

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    return self.time_input_dim * 4

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    return 100

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
    return tokenizer

 @property
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    CLIPTextConfig(
        bos_token_id=0,       eos_token_id=2,       hidden_size=self.text_embedder_hidden_size,       projection_dim=self.text_embedder_hidden_size,       intermediate_size=37,       layer_norm_eps=1e-0_5,       num_attention_heads=4,       num_hidden_layers=5,       pad_token_id=1,       vocab_size=1000,       )
    return CLIPTextModelWithProjection(a_       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    torch.manual_seed(0       )

    UpperCamelCase__			    =    {
        "num_attention_heads": 2,
        "attention_head_dim": 12,
        "embedding_dim": self.text_embedder_hidden_size,
        "num_layers": 1,
    }

    UpperCamelCase__			    =    PriorTransformer(**a_       )
    # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
    UpperCamelCase__			    =    nn.Parameter(torch.ones(model.clip_std.shape       )       )
    return model

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    CLIPVisionConfig(
        hidden_size=self.text_embedder_hidden_size,       image_size=224,       projection_dim=self.text_embedder_hidden_size,       intermediate_size=37,       num_attention_heads=4,       num_channels=3,       num_hidden_layers=5,       patch_size=14,       )

    UpperCamelCase__			    =    CLIPVisionModelWithProjection(a_       )
    return model

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    CLIPImageProcessor(
        crop_size=224,       do_center_crop=a_,       do_normalize=a_,       do_resize=a_,       image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],       image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],       resample=3,       size=224,       )

    return image_processor

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.dummy_prior
    UpperCamelCase__			    =    self.dummy_image_encoder
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    self.dummy_tokenizer
    UpperCamelCase__			    =    self.dummy_image_processor

    UpperCamelCase__			    =    UnCLIPScheduler(
        variance_type="fixed_small_log",       prediction_type="sample",       num_train_timesteps=1000,       clip_sample=a_,       clip_sample_range=10.0,       )

    UpperCamelCase__			    =    {
        "prior": prior,
        "image_encoder": image_encoder,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "scheduler": scheduler,
        "image_processor": image_processor,
    }

    return components

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						List[str],       a_			:						Union[str, Any]=0       ):





    """simple docstring"""



    if str(a_       ).startswith("mps"       ):
       UpperCamelCase__			    =    torch.manual_seed(a_       )
    else:
       UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(a_       )

    UpperCamelCase__			    =    floats_tensor((1, 3, 64, 64),       rng=random.Random(a_       )       ).to(a_       )
    UpperCamelCase__			    =    image.cpu().permute(0,       2,       3,       1       )[0]
    UpperCamelCase__			    =    Image.fromarray(np.uinta(a_       )       ).convert("RGB"       ).resize((256, 256)       )

    UpperCamelCase__			    =    {
        "prompt": "horse",
        "image": init_image,
        "strength": 0.5,
        "generator": generator,
        "guidance_scale": 4.0,
        "num_inference_steps": 2,
        "output_type": "np",
    }
    return inputs

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"

    UpperCamelCase__			    =    self.get_dummy_components()

    UpperCamelCase__			    =    self.pipeline_class(**a_       )
    UpperCamelCase__			    =    pipe.to(a_       )

    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    pipe(**self.get_dummy_inputs(a_       )       )
    UpperCamelCase__			    =    output.image_embeds

    UpperCamelCase__			    =    pipe(
        **self.get_dummy_inputs(a_       ),       return_dict=a_,       )[0]

    UpperCamelCase__			    =    image[0, -10:]
    UpperCamelCase__			    =    image_from_tuple[0, -10:]

    assert image.shape == (1, 32)

    UpperCamelCase__			    =    np.array(
        [
            0.1_071_284,
            1.3_330_271,
            0.61_260_223,
            -0.6_691_065,
            -0.3_846_852,
            -1.0_303_661,
            0.22_716_111,
            0.03_348_901,
            0.30_040_675,
            -0.24_805_029,
        ]       )

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

 @skip_mps
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    True
    UpperCamelCase__			    =    False

    self._test_inference_batch_single_identical(
        test_max_difference=a_,       relax_max_difference=a_,       test_mean_pixel_difference=a_,       )

 @skip_mps
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    False

    self._test_attention_slicing_forward_pass(
        test_max_difference=a_,       test_mean_pixel_difference=a_,       )