
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()
_snake_case				:   Dict     =				logging.get_logger()


@dataclass
class 			lowerCAmelCase :
		a							:					nn.Module
		a							:					List[nn.Module]       =					field(default_factory=__UpperCAmelCase			)
		a							:					list       =					field(default_factory=__UpperCAmelCase			)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= len(list(m.modules()      )      ) == 1 or isinstance(UpperCamelCase					,						nn.Convad      ) or isinstance(UpperCamelCase					,						nn.BatchNormad      )
								if has_not_submodules:
														self.traced.append(UpperCamelCase      )




		def __call__(				self					,						UpperCamelCase      ):
								for m in self.module.modules():
														self.handles.append(m.register_forward_hook(self._forward_hook      )      )
								self.module(UpperCamelCase      )
								[x.remove() for x in self.handles]
								return self




		@property
		def     lowercase       (				self      ):
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda UpperCamelCase      : len(list(x.state_dict().keys()      )      ) > 0					,						self.traced      )      )


@dataclass
class 			lowerCAmelCase :
		a							:					nn.Module
		a							:					nn.Module
		a							:					int       =					0
		a							:					List       =					field(default_factory=__UpperCAmelCase			)
		a							:					List       =					field(default_factory=__UpperCAmelCase			)




		def __call__(				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= Tracker(self.dest      )(UpperCamelCase      ).parametrized
								_SCREAMING_SNAKE_CASE				= Tracker(self.src      )(UpperCamelCase      ).parametrized

								_SCREAMING_SNAKE_CASE				= list(filter(lambda UpperCamelCase      : type(UpperCamelCase      ) not in self.src_skip					,						UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= list(filter(lambda UpperCamelCase      : type(UpperCamelCase      ) not in self.dest_skip					,						UpperCamelCase      )      )

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

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   ResNetConfig	,			_SCREAMING_SNAKE_CASE		:   Path	,			_SCREAMING_SNAKE_CASE		:   bool = True  ):
						print(F'Converting {name}...'  )
						with torch.no_grad():
												_SCREAMING_SNAKE_CASE				= timm.create_model(_SCREAMING_SNAKE_CASE	,			pretrained=_SCREAMING_SNAKE_CASE  ).eval()
												_SCREAMING_SNAKE_CASE				= ResNetForImageClassification(_SCREAMING_SNAKE_CASE  ).eval()
												_SCREAMING_SNAKE_CASE				= ModuleTransfer(src=_SCREAMING_SNAKE_CASE	,			dest=_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= torch.randn((1, 3, 224, 224)  )
												module_transfer(_SCREAMING_SNAKE_CASE  )

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

						_SCREAMING_SNAKE_CASE				= F'resnet{"-".join(name.split("resnet"  )  )}'
						print(_SCREAMING_SNAKE_CASE  )

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

												# we can use the convnext one
												_SCREAMING_SNAKE_CASE				= 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=_SCREAMING_SNAKE_CASE	,			)

												print(F'Pushed {checkpoint_name}'  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Path	,			_SCREAMING_SNAKE_CASE		:   str = None	,			_SCREAMING_SNAKE_CASE		:   bool = True  ):
						_SCREAMING_SNAKE_CASE				= "imagenet-1k-id2label.json"
						_SCREAMING_SNAKE_CASE				= 1000
						_SCREAMING_SNAKE_CASE				= (1, num_labels)

						_SCREAMING_SNAKE_CASE				= "huggingface/label-files"
						_SCREAMING_SNAKE_CASE				= num_labels
						_SCREAMING_SNAKE_CASE				= json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			repo_type="dataset"  )	,			"r"  )  )
						_SCREAMING_SNAKE_CASE				= {int(_SCREAMING_SNAKE_CASE  ): 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				= partial(_SCREAMING_SNAKE_CASE	,			num_labels=_SCREAMING_SNAKE_CASE	,			idalabel=_SCREAMING_SNAKE_CASE	,			labelaid=_SCREAMING_SNAKE_CASE  )

						_SCREAMING_SNAKE_CASE				= {
						    "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(_SCREAMING_SNAKE_CASE	,			names_to_config[model_name]	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						return config, expected_shape


if __name__ == "__main__":
						_snake_case				:   Optional[int]     =				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.""",
						)

						_snake_case				:   Optional[int]     =				parser.parse_args()
						_snake_case				:   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)

'''simple docstring'''







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 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Optional[int]       =					KandinskyImgaImgPipeline
		a							:					Union[str, Any]       =					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		a							:					Union[str, Any]       =					[
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		a							:					int       =					[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		a							:					Tuple       =					False




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return 32




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim




		@property
		def     lowercase       (				self      ):
								return self.time_input_dim * 4




		@property
		def     lowercase       (				self      ):
								return 100




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
								return tokenizer




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= 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=1_005					,						)

								_SCREAMING_SNAKE_CASE				= MultilingualCLIP(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= text_encoder.eval()

								return text_encoder




		@property
		def     lowercase       (				self      ):
								torch.manual_seed(0      )

								_SCREAMING_SNAKE_CASE				= {
								    "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,
								}

								_SCREAMING_SNAKE_CASE				= UNetaDConditionModel(**UpperCamelCase      )
								return model




		@property
		def     lowercase       (				self      ):
								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      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= VQModel(**self.dummy_movq_kwargs      )
								return model




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.dummy_text_encoder
								_SCREAMING_SNAKE_CASE				= self.dummy_tokenizer
								_SCREAMING_SNAKE_CASE				= self.dummy_unet
								_SCREAMING_SNAKE_CASE				= self.dummy_movq

								_SCREAMING_SNAKE_CASE				= {
								    "num_train_timesteps": 1_000,
								    "beta_schedule": "linear",
								    "beta_start": 0.0_00_85,
								    "beta_end": 0.0_12,
								    "clip_sample": False,
								    "set_alpha_to_one": False,
								    "steps_offset": 0,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								}

								_SCREAMING_SNAKE_CASE				= DDIMScheduler(**UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "movq": movq,
								}

								return components




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, self.cross_attention_dim)					,						rng=random.Random(seed + 1      )      ).to(UpperCamelCase      )
								# create init_image
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, 3, 64, 64)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= image.cpu().permute(0					,						2					,						3					,						1      )[0]
								_SCREAMING_SNAKE_CASE				= Image.fromarray(np.uinta(UpperCamelCase      )      ).convert("RGB"      ).resize((256, 256)      )

								if str(UpperCamelCase      ).startswith("mps"      ):
														_SCREAMING_SNAKE_CASE				= torch.manual_seed(UpperCamelCase      )
								else:
														_SCREAMING_SNAKE_CASE				= torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {
								    "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      ):
								_SCREAMING_SNAKE_CASE				= "cpu"

								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()

								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe.to(UpperCamelCase      )

								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= pipe(**self.get_dummy_inputs(UpperCamelCase      )      )
								_SCREAMING_SNAKE_CASE				= output.images

								_SCREAMING_SNAKE_CASE				= pipe(
								    **self.get_dummy_inputs(UpperCamelCase      )					,						return_dict=UpperCamelCase					,						)[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(
								    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]      )
								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 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/kandinsky/kandinsky_img2img_frog.npy"      )

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

								_SCREAMING_SNAKE_CASE				= KandinskyPriorPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1-prior"					,						torch_dtype=torch.floataa      )
								pipe_prior.to(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= KandinskyImgaImgPipeline.from_pretrained(
								    "kandinsky-community/kandinsky-2-1"					,						torch_dtype=torch.floataa      )
								_SCREAMING_SNAKE_CASE				= pipeline.to(UpperCamelCase      )

								pipeline.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= torch.Generator(device="cpu"      ).manual_seed(0      )
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= pipe_prior(
								    UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=5					,						negative_prompt=""					,						).to_tuple()

								_SCREAMING_SNAKE_CASE				= pipeline(
								    UpperCamelCase					,						image=UpperCamelCase					,						image_embeds=UpperCamelCase					,						negative_image_embeds=UpperCamelCase					,						generator=UpperCamelCase					,						num_inference_steps=100					,						height=768					,						width=768					,						strength=0.2					,						output_type="np"					,						)

								_SCREAMING_SNAKE_CASE				= output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(UpperCamelCase					,						UpperCamelCase      )