






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 			__a  (    A__       ,   unittest.TestCase       ):
 _lowerCAmelCase       :       Optional[int]        = KandinskyImgaImgPipeline
 _lowerCAmelCase       :       List[Any]        = ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
 _lowerCAmelCase       :       Union[str, Any]        = [
     '''prompt''',
     '''negative_prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
 ]
 _lowerCAmelCase       :       Optional[Any]        = [
     '''generator''',
     '''height''',
     '''width''',
     '''strength''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 _lowerCAmelCase       :       Union[str, Any]        = False







 @property
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      return 32







 @property
 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      return 32







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return self.time_input_dim







 @property
 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      return self.time_input_dim * 4







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return 1_00







 @property
 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"						)
      return tokenizer







 @property
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   Union[str, Any]       				=  MCLIPConfig(
          numDims=self.cross_attention_dim							,   transformerDimensions=self.text_embedder_hidden_size							,   hidden_size=self.text_embedder_hidden_size							,   intermediate_size=37							,   num_attention_heads=4							,   num_hidden_layers=5							,   vocab_size=10_05							,   )

      UpperCamelCase__			:   str       				=  MultilingualCLIP(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  text_encoder.eval()

      return text_encoder







 @property
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      torch.manual_seed(0						)

      UpperCamelCase__			:   Optional[int]       				=  {
          "in_channels": 4,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "text_image",
          "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": "text_image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      UpperCamelCase__			:   str       				=  UNetaDConditionModel(**SCREAMING_SNAKE_CASE						)
      return model







 @property
 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






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







 @property
 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   Tuple       				=  VQModel(**self.dummy_movq_kwargs						)
      return model







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.dummy_text_encoder
      UpperCamelCase__			:   List[str]       				=  self.dummy_tokenizer
      UpperCamelCase__			:   List[str]       				=  self.dummy_unet
      UpperCamelCase__			:   int       				=  self.dummy_movq

      UpperCamelCase__			:   Optional[int]       				=  {
          "num_train_timesteps": 10_00,
          "beta_schedule": "linear",
          "beta_start": 0.0_0_0_8_5,
          "beta_end": 0.0_1_2,
          "clip_sample": False,
          "set_alpha_to_one": False,
          "steps_offset": 0,
          "prediction_type": "epsilon",
          "thresholding": False,
      }

      UpperCamelCase__			:   int       				=  DDIMScheduler(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  {
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=0						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  floats_tensor((1, self.cross_attention_dim)							,   rng=random.Random(SCREAMING_SNAKE_CASE						)						).to(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  floats_tensor((1, self.cross_attention_dim)							,   rng=random.Random(seed + 1						)						).to(SCREAMING_SNAKE_CASE						)
      # create init_image
      UpperCamelCase__			:   Tuple       				=  floats_tensor((1, 3, 64, 64)							,   rng=random.Random(SCREAMING_SNAKE_CASE						)						).to(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  image.cpu().permute(0							,   2							,   3							,   1						)[0]
      UpperCamelCase__			:   Dict       				=  Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE						)						).convert("RGB"						).resize((2_56, 2_56)						)

      if str(SCREAMING_SNAKE_CASE						).startswith("mps"						):
           UpperCamelCase__			:   int       				=  torch.manual_seed(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__			:   str       				=  torch.Generator(device=SCREAMING_SNAKE_CASE						).manual_seed(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   int       				=  {
          "prompt": "horse",
          "image": init_image,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 64,
          "width": 64,
          "num_inference_steps": 10,
          "guidance_scale": 7.0,
          "strength": 0.2,
          "output_type": "np",
      }
      return inputs







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  "cpu"

      UpperCamelCase__			:   Dict       				=  self.get_dummy_components()

      UpperCamelCase__			:   int       				=  self.pipeline_class(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  pipe.to(SCREAMING_SNAKE_CASE						)

      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[int]       				=  pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   Optional[int]       				=  output.images

      UpperCamelCase__			:   int       				=  pipe(
          **self.get_dummy_inputs(SCREAMING_SNAKE_CASE						)							,   return_dict=SCREAMING_SNAKE_CASE							,   )[0]

      UpperCamelCase__			:   List[Any]       				=  image[0, -3:, -3:, -1]
      UpperCamelCase__			:   Optional[Any]       				=  image_from_tuple[0, -3:, -3:, -1]

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

      UpperCamelCase__			:   List[str]       				=  np.array(
          [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]						)
      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 			__a  (    unittest.TestCase       ):







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinsky/kandinsky_img2img_frog.npy"						)

      UpperCamelCase__			:   int       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"						)
      UpperCamelCase__			:   List[str]       				=  "A red cartoon frog, 4k"

      UpperCamelCase__			:   Any       				=  KandinskyPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1-prior"							,   torch_dtype=torch.floataa						)
      pipe_prior.to(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  KandinskyImgaImgPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-1"							,   torch_dtype=torch.floataa						)
      UpperCamelCase__			:   Dict       				=  pipeline.to(SCREAMING_SNAKE_CASE						)

      pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  torch.Generator(device="cpu"						).manual_seed(0						)
      UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  pipe_prior(
          SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   num_inference_steps=5							,   negative_prompt=""							,   ).to_tuple()

      UpperCamelCase__			:   Optional[Any]       				=  pipeline(
          SCREAMING_SNAKE_CASE							,   image=SCREAMING_SNAKE_CASE							,   image_embeds=SCREAMING_SNAKE_CASE							,   negative_image_embeds=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   num_inference_steps=1_00							,   height=7_68							,   width=7_68							,   strength=0.2							,   output_type="np"							,   )

      UpperCamelCase__			:   Tuple       				=  output.images[0]

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

      assert_mean_pixel_difference(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	:							Optional[int]				=logging.get_logger()





@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       List[nn.Module]        = field(default_factory=A__       )
 _lowerCAmelCase       :       list        = field(default_factory=A__       )







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Tensor							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  len(list(m.modules()						)						) == 1 or isinstance(SCREAMING_SNAKE_CASE							,   nn.Convad						) or isinstance(SCREAMING_SNAKE_CASE							,   nn.BatchNormad						)
      if has_not_submodules:
           self.traced.append(SCREAMING_SNAKE_CASE						)







 def __call__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






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







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






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







@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       int        = 0
 _lowerCAmelCase       :       List        = field(default_factory=A__       )
 _lowerCAmelCase       :       List        = field(default_factory=A__       )







 def __call__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  Tracker(self.dest						)(SCREAMING_SNAKE_CASE						).parametrized
      UpperCamelCase__			:   Any       				=  Tracker(self.src						)(SCREAMING_SNAKE_CASE						).parametrized

      UpperCamelCase__			:   str       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.src_skip							,   SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   List[Any]       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.dest_skip							,   SCREAMING_SNAKE_CASE						)						)

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

      for dest_m, src_m in zip(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           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							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = True			) ->	Optional[int]:
     print(f'Converting {name}...'			)
     with torch.no_grad():
          UpperCamelCase__			:   Union[str, Any]       				=  timm.create_model(__lowerCAmelCase					,    pretrained=__lowerCAmelCase			).eval()
          UpperCamelCase__			:   List[Any]       				=  ResNetForImageClassification(__lowerCAmelCase			).eval()
          UpperCamelCase__			:   Optional[Any]       				=  ModuleTransfer(src=__lowerCAmelCase					,    dest=__lowerCAmelCase			)
          UpperCamelCase__			:   int       				=  torch.randn((1, 3, 224, 224)			)
          module_transfer(__lowerCAmelCase			)

     assert torch.allclose(from_model(__lowerCAmelCase			)					,    our_model(__lowerCAmelCase			).logits			), "The model logits don't match the original one."

     UpperCamelCase__			:   List[Any]       				=  f'resnet{"-".join(name.split("resnet"			)			)}'
     print(__lowerCAmelCase			)

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

          # we can use the convnext one
          UpperCamelCase__			:   List[Any]       				=  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=__lowerCAmelCase					,    )

          print(f'Pushed {checkpoint_name}'			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = None					,    __lowerCAmelCase = True			) ->	Dict:
     UpperCamelCase__			:   Dict       				=  "imagenet-1k-id2label.json"
     UpperCamelCase__			:   Optional[int]       				=  1000
     UpperCamelCase__			:   Any       				=  (1, num_labels)

     UpperCamelCase__			:   Union[str, Any]       				=  "huggingface/label-files"
     UpperCamelCase__			:   Optional[int]       				=  num_labels
     UpperCamelCase__			:   str       				=  json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type="dataset"			)					,    "r"			)			)
     UpperCamelCase__			:   Optional[int]       				=  {int(__lowerCAmelCase			): v for k, v in idalabel.items()}

     UpperCamelCase__			:   Tuple       				=  idalabel
     UpperCamelCase__			:   Union[str, Any]       				=  {v: k for k, v in idalabel.items()}

     UpperCamelCase__			:   Union[str, Any]       				=  partial(__lowerCAmelCase					,    num_labels=__lowerCAmelCase					,    idalabel=__lowerCAmelCase					,    labelaid=__lowerCAmelCase			)

     UpperCamelCase__			:   Union[str, Any]       				=  {
         "resnet18": ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,    hidden_sizes=[64, 128, 256, 512]					,    layer_type="basic"			),
         "resnet26": ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet34": ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,    hidden_sizes=[64, 128, 256, 512]					,    layer_type="basic"			),
         "resnet50": ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet101": ImageNetPreTrainedConfig(
             depths=[3, 4, 23, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet152": ImageNetPreTrainedConfig(
             depths=[3, 8, 36, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
     }

     if model_name:
          convert_weight_and_push(__lowerCAmelCase					,    names_to_config[model_name]					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     return config, expected_shape


if __name__ == "__main__":
 lowerCamelCase	:							Union[str, 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: resnet18,26,34,50,101,152. 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(
     '''--push_to_hub''',
     default=True,
     type=bool,
     required=False,
     help='''If True, push model and image processor to the hub.''',
 )

 lowerCamelCase	:							int				=parser.parse_args()
 lowerCamelCase	:							Path				=args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)