"""simple docstring"""




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase									=	logging.get_logger()
@dataclass
class      UpperCamelCase__:
						lowerCAmelCase__	:    nn.Module
						lowerCAmelCase__	:    List[nn.Module]    				=		field(default_factory=__A			)
						lowerCAmelCase__	:    list    				=		field(default_factory=__A			)
						lowerCAmelCase__	:    Dict[str, nn.Module]    				=		field(default_factory=__A			)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					int:
													A__						  =				len(list(m.modules()  )  ) == 1 or isinstance(__UpperCAmelCase		,nn.Convad  ) or isinstance(__UpperCAmelCase		,nn.BatchNormad  )
													if has_not_submodules:
																				self.traced.append(__UpperCAmelCase  )
																				A__						  =				m





						def __call__(							self		,__UpperCAmelCase  )       ->					Any:
													for name, m in self.module.named_modules():
																				self.handles.append(m.register_forward_hook(partial(self._forward_hook		,name=__UpperCAmelCase  )  )  )
													self.module(__UpperCAmelCase  )
													[x.remove() for x in self.handles]
													return self





						@property
						def 					snake_case__						(							self  )       ->					str:
													# check the len of the state_dict keys to see if we have learnable params
													return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()  )  ) > 0}






class      UpperCamelCase__(      nn.Module			):





						def __init__(							self		,__UpperCAmelCase  )       ->					int:
													super().__init__()

													A__						  =				[]
													# - get the stem
													feature_blocks.append(('conv1', model.stem)  )
													# - get all the feature blocks
													for k, v in model.trunk_output.named_children():
																				assert k.startswith('block'  ), f'''Unexpected layer name {k}'''
																				A__						  =				len(__UpperCAmelCase  ) + 1
																				feature_blocks.append((f'''res{block_index}''', v)  )

													A__						  =				nn.ModuleDict(__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					str:
													return get_trunk_forward_outputs(
													    __UpperCAmelCase		,out_feat_keys=__UpperCAmelCase		,feature_blocks=self._feature_blocks		,)






class      UpperCamelCase__(      __A			):





						def 					snake_case__						(							self  )       ->					List[Any]:
													return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				RegNetConfig(depths=[2, 7, 17, 1]							,     hidden_sizes=[8, 8, 8, 8]							,     groups_width=8					)
							if "in1k" in model_name:
														A__						  =				RegNetForImageClassification(UpperCamelCase__					)
							else:
														A__						  =				RegNetModel(UpperCamelCase__					)
							# create from model (with small weights)
							A__						  =				FakeRegNetVisslWrapper(
							    RegNet(FakeRegNetParams(depth=27							,     group_width=1_010							,     w_a=1_744							,     w_a=6_2_0.8_3							,     w_m=2.5_2					)					)					)

							with torch.no_grad():
														A__						  =				from_model.eval()
														A__						  =				our_model.eval()

														A__						  =				torch.randn((1, 3, 32, 32)					)
														# trace both
														A__						  =				Tracker(UpperCamelCase__					)
														A__						  =				dest_tracker(UpperCamelCase__					).parametrized

														pprint(dest_tracker.nameamodule					)
														A__						  =				Tracker(UpperCamelCase__					)
														A__						  =				src_tracker(UpperCamelCase__					).parametrized

							# convert the keys -> module dict to keys -> params
							def to_params_dict(UpperCamelCase__					):
														A__						  =				OrderedDict()
														for name, module in dict_with_modules.items():
																					for param_name, param in module.state_dict().items():
																												A__						  =				param
														return params_dict

							A__						  =				{}

							A__						  =				to_params_dict(UpperCamelCase__					)
							A__						  =				to_params_dict(UpperCamelCase__					)

							for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()							,     dst_state_dict.items()					):
														A__						  =				dest_key
														logger.info(F'''{src_key} -> {dest_key}'''					)
							# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
							if "in1k" in model_name:
														A__						  =				'classifier.1.weight'
														A__						  =				'classifier.1.bias'

							return from_to_ours_keys






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__ = None							,     UpperCamelCase__ = True					):






							"""simple docstring"""





							A__						  =				'imagenet-1k-id2label.json'
							A__						  =				1_000

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

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

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

							A__						  =				{
							    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]							,     hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,     groups_width=1_010					),
							    # finetuned on imagenet
							    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]							,     hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,     groups_width=1_010					),
							}

							# add seer weights logic
							def load_using_classy_vision(UpperCamelCase__					) -> Tuple[Dict, Dict]:
														A__						  =				torch.hub.load_state_dict_from_url(UpperCamelCase__							,     model_dir=str(UpperCamelCase__					)							,     map_location='cpu'					)
														# check if we have a head, if yes add it
														A__						  =				files['classy_state_dict']['base_model']['model']
														return model_state_dict["trunk"], model_state_dict["heads"]

							A__						  =				{
							    'regnet-y-10b-seer': partial(
							        UpperCamelCase__							,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'							,     ),
							    'regnet-y-10b-seer-in1k': partial(
							        UpperCamelCase__							,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'							,     ),
							}

							A__						  =				get_from_to_our_keys(UpperCamelCase__					)

							if not (save_directory / F'''{model_name}.pth''').exists():
														logger.info('Loading original state_dict.'					)
														A__  ,	A__						  =				names_to_from_model[model_name]()
														A__						  =				from_state_dict_trunk
														if "in1k" in model_name:
																					# add the head
																					A__						  =				{**from_state_dict_trunk, **from_state_dict_head}
														logger.info('Done!'					)

														A__						  =				{}

														A__						  =				list(from_state_dict.keys()					)
														A__						  =				r'\.block.-part.'
														# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
														for key in from_state_dict.keys():
																					# remove the weird "block[0,1]-part" from the key
																					A__						  =				re.sub(UpperCamelCase__							,     ''							,     UpperCamelCase__					)
																					# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
																					A__						  =				from_to_ours_keys[src_key]
																					# store the parameter with our key
																					A__						  =				from_state_dict[key]
																					not_used_keys.remove(UpperCamelCase__					)
														# check that all keys have been updated
														assert len(UpperCamelCase__					) == 0, F'''Some keys where not used {",".join(UpperCamelCase__					)}'''

														logger.info(F'''The following keys were not used: {",".join(UpperCamelCase__					)}'''					)

														# save our state dict to disk
														torch.save(UpperCamelCase__							,     save_directory / F'''{model_name}.pth'''					)

														del converted_state_dict
							else:
														logger.info('The state_dict was already stored on disk.'					)
							if push_to_hub:
														logger.info(F'''Token is {os.environ["HF_TOKEN"]}'''					)
														logger.info('Loading our model.'					)
														# create our model
														A__						  =				names_to_config[model_name]
														A__						  =				RegNetModel
														if "in1k" in model_name:
																					A__						  =				RegNetForImageClassification
														A__						  =				our_model_func(UpperCamelCase__					)
														# place our model to the meta device (so remove all the weights)
														our_model.to(torch.device('meta'					)					)
														logger.info('Loading state_dict in our model.'					)
														# load state dict
														A__						  =				our_model.state_dict().keys()
														PreTrainedModel._load_pretrained_model_low_mem(
														    UpperCamelCase__							,     UpperCamelCase__							,     [save_directory / F'''{model_name}.pth''']					)
														logger.info('Finally, pushing!'					)
														# push it to hub
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / model_name							,     commit_message='Add model'							,     output_dir=save_directory / model_name							,     )
														A__						  =				384
														# we can use the convnext one
														A__						  =				AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'							,     size=UpperCamelCase__					)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / model_name							,     commit_message='Add image processor'							,     output_dir=save_directory / model_name							,     )


if __name__ == "__main__":
				__lowerCamelCase									=	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 regnet* architecture,"
				        " currently: regnetx-*, regnety-*. 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									=	parser.parse_args()

				__lowerCamelCase									=	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 transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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      UpperCamelCase__(      __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Tuple    				=		KandinskyPipeline
						lowerCAmelCase__	:    int    				=		[
						    'prompt',
						    'image_embeds',
						    'negative_image_embeds',
						]
						lowerCAmelCase__	:    str    				=		['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
						lowerCAmelCase__	:    Any    				=		[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						lowerCAmelCase__	:    Dict    				=		False





						@property
						def 					snake_case__						(							self  )       ->					List[Any]:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Any:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Dict:
													return self.time_input_dim





						@property
						def 					snake_case__						(							self  )       ->					Optional[Any]:
													return self.time_input_dim * 4





						@property
						def 					snake_case__						(							self  )       ->					List[str]:
													return 1_00





						@property
						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
													return tokenizer





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													torch.manual_seed(0  )
													A__						  =				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		,)

													A__						  =				MultilingualCLIP(__UpperCAmelCase  )
													A__						  =				text_encoder.eval()

													return text_encoder





						@property
						def 					snake_case__						(							self  )       ->					Optional[Any]:
													torch.manual_seed(0  )

													A__						  =				{
													    '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,
													}

													A__						  =				UNetaDConditionModel(**__UpperCAmelCase  )
													return model





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													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 					snake_case__						(							self  )       ->					Any:
													torch.manual_seed(0  )
													A__						  =				VQModel(**self.dummy_movq_kwargs  )
													return model





						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				self.dummy_text_encoder
													A__						  =				self.dummy_tokenizer
													A__						  =				self.dummy_unet
													A__						  =				self.dummy_movq

													A__						  =				DDIMScheduler(
													    num_train_timesteps=10_00		,beta_schedule='linear'		,beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,clip_sample=__UpperCAmelCase		,set_alpha_to_one=__UpperCAmelCase		,steps_offset=1		,prediction_type='epsilon'		,thresholding=__UpperCAmelCase		,)

													A__						  =				{
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}
													return components





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase=0  )       ->					Optional[Any]:
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(__UpperCAmelCase  )  ).to(__UpperCAmelCase  )
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(seed + 1  )  ).to(__UpperCAmelCase  )
													if str(__UpperCAmelCase  ).startswith('mps'  ):
																				A__						  =				torch.manual_seed(__UpperCAmelCase  )
													else:
																				A__						  =				torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
													A__						  =				{
													    'prompt': 'horse',
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'guidance_scale': 4.0,
													    'num_inference_steps': 2,
													    'output_type': 'np',
													}
													return inputs





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				'cpu'

													A__						  =				self.get_dummy_components()

													A__						  =				self.pipeline_class(**__UpperCAmelCase  )
													A__						  =				pipe.to(__UpperCAmelCase  )

													pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
													A__						  =				output.images

													A__						  =				pipe(
													    **self.get_dummy_inputs(__UpperCAmelCase  )		,return_dict=__UpperCAmelCase		,)[0]

													A__						  =				image[0, -3:, -3:, -1]
													A__						  =				image_from_tuple[0, -3:, -3:, -1]

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

													A__						  =				np.array(
													    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_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      UpperCamelCase__(      unittest.TestCase			):





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





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

													A__						  =				KandinskyPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1-prior'		,torch_dtype=torch.floataa  )
													pipe_prior.to(__UpperCAmelCase  )

													A__						  =				KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'		,torch_dtype=torch.floataa  )
													A__						  =				pipeline.to(__UpperCAmelCase  )
													pipeline.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				'red cat, 4k photo'

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__  ,	A__						  =				pipe_prior(
													    __UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=5		,negative_prompt=''		,).to_tuple()

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__						  =				pipeline(
													    __UpperCAmelCase		,image_embeds=__UpperCAmelCase		,negative_image_embeds=__UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=1_00		,output_type='np'		,)

													A__						  =				output.images[0]

													assert image.shape == (5_12, 5_12, 3)

													assert_mean_pixel_difference(__UpperCAmelCase		,__UpperCAmelCase  )





