






"""simple docstring"""



import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger





class  _UpperCAmelCase	(			snake_case__							):
					SCREAMING_SNAKE_CASE_		:    str							   =       '''config.json'''




					@register_to_config
					def __init__(    self					:						Tuple   ,						A					:						Union[str, Any]=2   ,						A					:						Union[str, Any]=5   ,						A					:						Dict=(2, 5)   ,						A					:						List[str]="for diffusion"   ,						A					:						Union[str, Any]=[1, 3]   ,						)  ->  Tuple:
							pass





class  _UpperCAmelCase	(			snake_case__							):
					SCREAMING_SNAKE_CASE_		:    Tuple							   =       '''config.json'''




					@register_to_config
					def __init__(    self					:						Optional[Any]   ,						A					:						Dict=2   ,						A					:						Optional[int]=5   ,						A					:						Dict=(2, 5)   ,						A					:						Optional[int]="for diffusion"   ,						A					:						Optional[int]=[1, 3]   ,						)  ->  Tuple:
							pass





class  _UpperCAmelCase	(			snake_case__							):
					SCREAMING_SNAKE_CASE_		:    List[Any]							   =       '''config.json'''




					@register_to_config
					def __init__(    self					:						Optional[int]   ,						A					:						int=2   ,						A					:						Optional[int]=5   ,						A					:						List[str]=(2, 5)   ,						A					:						List[str]="for diffusion"   ,						A					:						Union[str, Any]=[1, 3]   ,						A					:						Any=[1, 3]   ,						)  ->  str:
							pass





class  _UpperCAmelCase	(			snake_case__							):
					SCREAMING_SNAKE_CASE_		:    Dict							   =       '''config.json'''




					@register_to_config
					def __init__(    self					:						Union[str, Any]   ,						A					:						Dict=2   ,						A					:						Union[str, Any]=5   ,						A					:						Union[str, Any]=(2, 5)   ,						A					:						Optional[int]="for diffusion"   ,						A					:						Tuple=[1, 5]   ,						A					:						Any=[5, 4]   ,						)  ->  Dict:
							pass





class  _UpperCAmelCase	(			unittest.TestCase							):




					def   A  (    self					:						Any       )  ->  Union[str, Any]:
							with self.assertRaises(_A       ):
									ConfigMixin.load_config('''dummy_path'''       )




					def   A  (    self					:						List[Any]       )  ->  Any:
							lowercase_			:			List[Any]								=       SampleObject()
							lowercase_			:			Dict								=       obj.config
							assert config["a"] == 2
							assert config["b"] == 5
							assert config["c"] == (2, 5)
							assert config["d"] == "for diffusion"
							assert config["e"] == [1, 3]

							# init ignore private arguments
							lowercase_			:			Optional[int]								=       SampleObject(_name_or_path='''lalala'''       )
							lowercase_			:			Tuple								=       obj.config
							assert config["a"] == 2
							assert config["b"] == 5
							assert config["c"] == (2, 5)
							assert config["d"] == "for diffusion"
							assert config["e"] == [1, 3]

							# can override default
							lowercase_			:			Union[str, Any]								=       SampleObject(c=6       )
							lowercase_			:			List[str]								=       obj.config
							assert config["a"] == 2
							assert config["b"] == 5
							assert config["c"] == 6
							assert config["d"] == "for diffusion"
							assert config["e"] == [1, 3]

							# can use positional arguments.
							lowercase_			:			int								=       SampleObject(1   ,						c=6       )
							lowercase_			:			Optional[Any]								=       obj.config
							assert config["a"] == 1
							assert config["b"] == 5
							assert config["c"] == 6
							assert config["d"] == "for diffusion"
							assert config["e"] == [1, 3]




					def   A  (    self					:						Optional[Any]       )  ->  Union[str, Any]:
							lowercase_			:			str								=       SampleObject()
							lowercase_			:			Optional[Any]								=       obj.config

							assert config["a"] == 2
							assert config["b"] == 5
							assert config["c"] == (2, 5)
							assert config["d"] == "for diffusion"
							assert config["e"] == [1, 3]

							with tempfile.TemporaryDirectory() as tmpdirname:
									obj.save_config(_A       )
									lowercase_			:			int								=       SampleObject.from_config(SampleObject.load_config(_A       )       )
									lowercase_			:			Any								=       new_obj.config

							# unfreeze configs
							lowercase_			:			str								=       dict(_A       )
							lowercase_			:			Tuple								=       dict(_A       )

							assert config.pop('''c'''       ) == (2, 5)  # instantiated as tuple
							assert new_config.pop('''c'''       ) == [2, 5]  # saved & loaded as list because of json
							config.pop('''_use_default_values'''       )
							assert config == new_config




					def   A  (    self					:						List[Any]       )  ->  Union[str, Any]:
							lowercase_			:			Union[str, Any]								=       logging.get_logger('''diffusers.configuration_utils'''       )
							# 30 for warning
							logger.setLevel(30       )

							with CaptureLogger(_A       ) as cap_logger:
									lowercase_			:			Optional[Any]								=       DDIMScheduler.from_pretrained(
									    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,						subfolder='''scheduler'''       )

							assert ddim.__class__ == DDIMScheduler
							# no warning should be thrown
							assert cap_logger.out == ""




					def   A  (    self					:						List[str]       )  ->  Optional[int]:
							lowercase_			:			List[Any]								=       logging.get_logger('''diffusers.configuration_utils'''       )
							# 30 for warning
							logger.setLevel(30       )

							with CaptureLogger(_A       ) as cap_logger:
									lowercase_			:			Optional[Any]								=       EulerDiscreteScheduler.from_pretrained(
									    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,						subfolder='''scheduler'''       )

							assert euler.__class__ == EulerDiscreteScheduler
							# no warning should be thrown
							assert cap_logger.out == ""




					def   A  (    self					:						str       )  ->  Dict:
							lowercase_			:			Tuple								=       logging.get_logger('''diffusers.configuration_utils'''       )
							# 30 for warning
							logger.setLevel(30       )

							with CaptureLogger(_A       ) as cap_logger:
									lowercase_			:			Any								=       EulerAncestralDiscreteScheduler.from_pretrained(
									    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,						subfolder='''scheduler'''       )

							assert euler.__class__ == EulerAncestralDiscreteScheduler
							# no warning should be thrown
							assert cap_logger.out == ""




					def   A  (    self					:						Any       )  ->  Optional[Any]:
							lowercase_			:			Tuple								=       logging.get_logger('''diffusers.configuration_utils'''       )
							# 30 for warning
							logger.setLevel(30       )

							with CaptureLogger(_A       ) as cap_logger:
									lowercase_			:			Optional[int]								=       PNDMScheduler.from_pretrained(
									    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,						subfolder='''scheduler'''       )

							assert pndm.__class__ == PNDMScheduler
							# no warning should be thrown
							assert cap_logger.out == ""




					def   A  (    self					:						int       )  ->  Dict:
							lowercase_			:			List[str]								=       logging.get_logger('''diffusers.configuration_utils'''       )
							# 30 for warning
							logger.setLevel(30       )

							with CaptureLogger(_A       ) as cap_logger:
									lowercase_			:			Optional[int]								=       DDPMScheduler.from_pretrained(
									    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,						subfolder='''scheduler'''   ,						prediction_type='''sample'''   ,						beta_end=8   ,						)

							with CaptureLogger(_A       ) as cap_logger_a:
									lowercase_			:			Any								=       DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''   ,						beta_start=88       )

							assert ddpm.__class__ == DDPMScheduler
							assert ddpm.config.prediction_type == "sample"
							assert ddpm.config.beta_end == 8
							assert ddpm_a.config.beta_start == 88

							# no warning should be thrown
							assert cap_logger.out == ""
							assert cap_logger_a.out == ""




					def   A  (    self					:						Optional[int]       )  ->  str:
							lowercase_			:			List[str]								=       logging.get_logger('''diffusers.configuration_utils'''       )
							# 30 for warning
							logger.setLevel(30       )

							with CaptureLogger(_A       ) as cap_logger:
									lowercase_			:			Union[str, Any]								=       DPMSolverMultistepScheduler.from_pretrained(
									    '''hf-internal-testing/tiny-stable-diffusion-torch'''   ,						subfolder='''scheduler'''       )

							assert dpm.__class__ == DPMSolverMultistepScheduler
							# no warning should be thrown
							assert cap_logger.out == ""






					def   A  (    self					:						Tuple       )  ->  Dict:
							# let's first save a config that should be in the form
							#    a=2,
							#    b=5,
							#    c=(2, 5),
							#    d="for diffusion",
							#    e=[1, 3],

							lowercase_			:			Dict								=       SampleObject()

							lowercase_			:			Optional[Any]								=       {k: v for k, v in config.config.items() if not k.startswith('''_'''       )}

							# make sure that default config has all keys in `_use_default_values`
							assert set(config_dict.keys()       ) == set(config.config._use_default_values       )

							with tempfile.TemporaryDirectory() as tmpdirname:
									config.save_config(_A       )

									# now loading it with SampleObject2 should put f into `_use_default_values`
									lowercase_			:			int								=       SampleObjecta.from_config(_A       )

									assert "f" in config._use_default_values
									assert config.f == [1, 3]

							# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
							# **BECAUSE** it is part of `config._use_default_values`
							lowercase_			:			Union[str, Any]								=       SampleObjecta.from_config(config.config       )
							assert new_config.f == [5, 4]

							config.config._use_default_values.pop()
							lowercase_			:			Tuple								=       SampleObjecta.from_config(config.config       )
							assert new_config_a.f == [1, 3]

							# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
							assert new_config_a.e == [1, 3]











"""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()
__A		:	List[str]      =		logging.get_logger()





@dataclass
class  _UpperCAmelCase	:
					SCREAMING_SNAKE_CASE_		:    nn.Module
					SCREAMING_SNAKE_CASE_		:    List[nn.Module]							   =       field(default_factory=_A							)
					SCREAMING_SNAKE_CASE_		:    list							   =       field(default_factory=_A							)
					SCREAMING_SNAKE_CASE_		:    Dict[str, nn.Module]							   =       field(default_factory=_A							)




					def   A  (    self					:						Tuple   ,						A					:						List[str]   ,						A					:						Tensor   ,						A					:						Tensor   ,						A					:						str       )  ->  Optional[int]:
							lowercase_			:			Optional[int]								=       len(list(m.modules()       )       ) == 1 or isinstance(A   ,						nn.Convad       ) or isinstance(A   ,						nn.BatchNormad       )
							if has_not_submodules:
									self.traced.append(A       )
									lowercase_			:			Tuple								=       m




					def __call__(    self					:						Optional[int]   ,						A					:						Tensor       )  ->  Tuple:
							for name, m in self.module.named_modules():
									self.handles.append(m.register_forward_hook(partial(self._forward_hook   ,						name=A       )       )       )
							self.module(A       )
							[x.remove() for x in self.handles]
							return self






					@property
					def   A  (    self					:						int       )  ->  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					:						Optional[int]   ,						A					:						nn.Module       )  ->  Dict:
							super().__init__()

							lowercase_			:			List[Tuple[str, nn.Module]]								=       []
							# - 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}'''
									lowercase_			:			Dict								=       len(A       ) + 1
									feature_blocks.append((F'''res{block_index}''', v)       )

							lowercase_			:			int								=       nn.ModuleDict(A       )






					def   A  (    self					:						Optional[int]   ,						A					:						Tensor       )  ->  List[Any]:
							return get_trunk_forward_outputs(
							    A   ,						out_feat_keys=A   ,						feature_blocks=self._feature_blocks   ,						)





class  _UpperCAmelCase	(			_A							):




					def   A  (    self					:						Tuple       )  ->  Tuple:
							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 						lowercase   (    __snake_case    :			str     ):
		lowercase_			:			Dict								=       RegNetConfig(depths=[2, 7, 1_7, 1]		, hidden_sizes=[8, 8, 8, 8]		, groups_width=8     )
		if "in1k" in model_name:
				lowercase_			:			List[str]								=       RegNetForImageClassification(__snake_case     )
		else:
				lowercase_			:			Union[str, Any]								=       RegNetModel(__snake_case     )
		# create from model (with small weights)
		lowercase_			:			Dict								=       FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=2_7		, group_width=1_0_1_0		, w_a=1_7_4_4		, w_a=620.83		, w_m=2.52     )     )     )

		with torch.no_grad():
				lowercase_			:			Tuple								=       from_model.eval()
				lowercase_			:			int								=       our_model.eval()

				lowercase_			:			List[Any]								=       torch.randn((1, 3, 3_2, 3_2)     )
				# trace both
				lowercase_			:			Optional[int]								=       Tracker(__snake_case     )
				lowercase_			:			List[Any]								=       dest_tracker(__snake_case     ).parametrized

				pprint(dest_tracker.nameamodule     )
				lowercase_			:			str								=       Tracker(__snake_case     )
				lowercase_			:			str								=       src_tracker(__snake_case     ).parametrized

		# convert the keys -> module dict to keys -> params
		def to_params_dict(__snake_case    :			Dict     ):
				lowercase_			:			Optional[int]								=       OrderedDict()
				for name, module in dict_with_modules.items():
						for param_name, param in module.state_dict().items():
								lowercase_			:			int								=       param
				return params_dict

		lowercase_			:			Tuple								=       {}

		lowercase_			:			Optional[Any]								=       to_params_dict(__snake_case     )
		lowercase_			:			Dict								=       to_params_dict(__snake_case     )

		for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()		, dst_state_dict.items()     ):
				lowercase_			:			List[str]								=       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:
				lowercase_			:			Optional[Any]								=       '''classifier.1.weight'''
				lowercase_			:			int								=       '''classifier.1.bias'''

		return from_to_ours_keys



def 						lowercase   (    __snake_case    :			Path		, __snake_case    :			str = None		, __snake_case    :			bool = True     ):
		lowercase_			:			Optional[Any]								=       '''imagenet-1k-id2label.json'''
		lowercase_			:			str								=       1_0_0_0

		lowercase_			:			Any								=       '''huggingface/label-files'''
		lowercase_			:			Union[str, Any]								=       num_labels
		lowercase_			:			Any								=       json.load(open(cached_download(hf_hub_url(__snake_case		, __snake_case		, repo_type='''dataset'''     )     )		, '''r'''     )     )
		lowercase_			:			Union[str, Any]								=       {int(__snake_case     ): v for k, v in idalabel.items()}

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

		lowercase_			:			Optional[Any]								=       partial(__snake_case		, num_labels=__snake_case		, idalabel=__snake_case		, labelaid=__snake_case     )

		lowercase_			:			int								=       {
		    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
		        depths=[2, 7, 1_7, 1]		, hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]		, groups_width=1_0_1_0     ),
		    # finetuned on imagenet
		    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
		        depths=[2, 7, 1_7, 1]		, hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]		, groups_width=1_0_1_0     ),
		}

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

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

		lowercase_			:			List[Any]								=       get_from_to_our_keys(__snake_case     )

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

				lowercase_			:			List[Any]								=       {}

				lowercase_			:			int								=       list(from_state_dict.keys()     )
				lowercase_			:			int								=       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
						lowercase_			:			List[str]								=       re.sub(__snake_case		, ''''''		, __snake_case     )
						# 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
						lowercase_			:			Dict								=       from_to_ours_keys[src_key]
						# store the parameter with our key
						lowercase_			:			int								=       from_state_dict[key]
						not_used_keys.remove(__snake_case     )
				# check that all keys have been updated
				assert len(__snake_case     ) == 0, F'''Some keys where not used {','.join(__snake_case     )}'''

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

				# save our state dict to disk
				torch.save(__snake_case		, 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
				lowercase_			:			List[Any]								=       names_to_config[model_name]
				lowercase_			:			Union[str, Any]								=       RegNetModel
				if "in1k" in model_name:
						lowercase_			:			Optional[int]								=       RegNetForImageClassification
				lowercase_			:			Union[str, Any]								=       our_model_func(__snake_case     )
				# 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
				lowercase_			:			Tuple								=       our_model.state_dict().keys()
				PreTrainedModel._load_pretrained_model_low_mem(
				    __snake_case		, __snake_case		, [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		, )
				lowercase_			:			Union[str, Any]								=       3_8_4
				# we can use the convnext one
				lowercase_			:			int								=       AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		, size=__snake_case     )
				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__":
						__A		:	Tuple      =		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.''',
						)

						__A		:	Tuple      =		parser.parse_args()

						__A		:	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)



