



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   Tuple						 =						(UniPCMultistepScheduler,)
       UpperCamelCase__  :   Optional[Any]						 =						(('''num_inference_steps''', 25),)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    {
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
               '''solver_type''': '''bh2''',
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					List[Any]=0					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    dict(self.forward_default_kwargs)
           __a							=    kwargs.pop('''num_inference_steps'''					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.dummy_sample
           __a							=    0.1 * sample
           __a							=    [residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
               __a							=    self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)
               scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
               # copy over dummy past residuals
               __a							=    dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(__SCREAMING_SNAKE_CASE)
                   __a							=    scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE)
                   new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
                   # copy over dummy past residuals
                   __a							=    dummy_past_residuals[: new_scheduler.config.solver_order]

               __a	,       __a							=    sample, sample
               for t in range(__SCREAMING_SNAKE_CASE					,		time_step + scheduler.config.solver_order + 1):
                   __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample
                   __a							=    new_scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample

                   assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=0					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    dict(self.forward_default_kwargs)
           __a							=    kwargs.pop('''num_inference_steps'''					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.dummy_sample
           __a							=    0.1 * sample
           __a							=    [residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
               __a							=    self.get_scheduler_config()
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)
               scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

               # copy over dummy past residuals (must be after setting timesteps)
               __a							=    dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(__SCREAMING_SNAKE_CASE)
                   __a							=    scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

                   # copy over dummy past residual (must be after setting timesteps)
                   __a							=    dummy_past_residuals[: new_scheduler.config.solver_order]

               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample
               __a							=    new_scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any=None					,		**__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           if scheduler is None:
               __a							=    self.scheduler_classes[0]
               __a							=    self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a							=    self.scheduler_classes[0]
           __a							=    self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
           __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a							=    10
           __a							=    self.dummy_model()
           __a							=    self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
               __a							=    model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE).prev_sample

           return sample



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    dict(self.forward_default_kwargs)

           __a							=    kwargs.pop('''num_inference_steps'''					,		__SCREAMING_SNAKE_CASE)

           for scheduler_class in self.scheduler_classes:
               __a							=    self.get_scheduler_config()
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

               __a							=    self.dummy_sample
               __a							=    0.1 * sample

               if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE					,		'''set_timesteps'''):
                   scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)
               elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE					,		'''set_timesteps'''):
                   __a							=    num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               __a							=    [residual + 0.2, residual + 0.15, residual + 0.10]
               __a							=    dummy_past_residuals[: scheduler.config.solver_order]

               __a							=    scheduler.timesteps[5]
               __a							=    scheduler.timesteps[6]

               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample
               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE).prev_sample

               self.assertEqual(output_a.shape					,		sample.shape)
               self.assertEqual(output_a.shape					,		output_a.shape)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    UniPCMultistepScheduler(**self.get_scheduler_config())
           __a							=    self.full_loop(scheduler=__SCREAMING_SNAKE_CASE)
           __a							=    torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_mean.item() - 0.24_64) < 1E-3

           __a							=    DPMSolverSinglestepScheduler.from_config(scheduler.config)
           __a							=    DEISMultistepScheduler.from_config(scheduler.config)
           __a							=    DPMSolverMultistepScheduler.from_config(scheduler.config)
           __a							=    UniPCMultistepScheduler.from_config(scheduler.config)

           __a							=    self.full_loop(scheduler=__SCREAMING_SNAKE_CASE)
           __a							=    torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_mean.item() - 0.24_64) < 1E-3



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           for timesteps in [25, 50, 100, 999, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
           for order in [1, 2, 3]:
               for solver_type in ["bh1", "bh2"]:
                   for threshold in [0.5, 1.0, 2.0]:
                       for prediction_type in ["epsilon", "sample"]:
                           self.check_over_configs(
                               thresholding=__SCREAMING_SNAKE_CASE					,		prediction_type=__SCREAMING_SNAKE_CASE					,		sample_max_value=__SCREAMING_SNAKE_CASE					,		solver_order=__SCREAMING_SNAKE_CASE					,		solver_type=__SCREAMING_SNAKE_CASE					,		)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           for solver_type in ["bh1", "bh2"]:
               for order in [1, 2, 3]:
                   for prediction_type in ["epsilon", "sample"]:
                       self.check_over_configs(
                           solver_order=__SCREAMING_SNAKE_CASE					,		solver_type=__SCREAMING_SNAKE_CASE					,		prediction_type=__SCREAMING_SNAKE_CASE					,		)
                       __a							=    self.full_loop(
                           solver_order=__SCREAMING_SNAKE_CASE					,		solver_type=__SCREAMING_SNAKE_CASE					,		prediction_type=__SCREAMING_SNAKE_CASE					,		)
                       assert not torch.isnan(__SCREAMING_SNAKE_CASE).any(), "Samples have nan numbers"



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
               self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE					,		time_step=0)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.full_loop()
           __a							=    torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_mean.item() - 0.24_64) < 1E-3



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.full_loop(prediction_type='''v_prediction''')
           __a							=    torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_mean.item() - 0.10_14) < 1E-3



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.scheduler_classes[0]
           __a							=    self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE					,		dynamic_thresholding_ratio=0)
           __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a							=    10
           __a							=    self.dummy_model()
           __a							=    self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
               __a							=    model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
               __a							=    scheduler.step(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE).prev_sample

           assert sample.dtype == torch.floataa





       def 		_lowerCamelCase      (		self							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           for scheduler_class in self.scheduler_classes:
               __a							=    self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
               __a							=    scheduler_class(**__SCREAMING_SNAKE_CASE)

               scheduler.set_timesteps(scheduler.config.num_train_timesteps)
               assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps










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

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
__snake_case						:Dict						=					logging.get_logger()

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



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Tensor					,		__SCREAMING_SNAKE_CASE							:					Tensor):


           '''simple docstring'''

           __a							=    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							:					Optional[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 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

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


@dataclass
class    _A	:
       UpperCamelCase__  :   nn.Module
       UpperCamelCase__  :   nn.Module
       UpperCamelCase__  :   int						 =						1
       UpperCamelCase__  :   List						 =						field(default_factory=__UpperCAmelCase   )
       UpperCamelCase__  :   List						 =						field(default_factory=__UpperCAmelCase   )
       UpperCamelCase__  :   bool						 =						True



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


           '''simple docstring'''

           __a							=    Tracker(self.dest)(__SCREAMING_SNAKE_CASE).parametrized
           __a							=    Tracker(self.src)(__SCREAMING_SNAKE_CASE).parametrized

           __a							=    list(filter(lambda __SCREAMING_SNAKE_CASE: type(__SCREAMING_SNAKE_CASE) not in self.src_skip					,		__SCREAMING_SNAKE_CASE))
           __a							=    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) and self.raise_if_mismatch:
               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}')


class    _A	(     nn.Module   ):



       def __init__(		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					nn.Module):


           '''simple docstring'''

           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(__SCREAMING_SNAKE_CASE) + 1
               feature_blocks.append((F'res{block_index}', v))

           __a							=    nn.ModuleDict(__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Tensor):


           '''simple docstring'''

           return get_trunk_forward_outputs(
               __SCREAMING_SNAKE_CASE					,		out_feat_keys=__SCREAMING_SNAKE_CASE					,		feature_blocks=self._feature_blocks					,		)


class    _A	(     __UpperCAmelCase   ):



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    x.split('''-''')
           return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])





       def __getitem__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           if x not in self:
               __a							=    self.convert_name_to_timm(__SCREAMING_SNAKE_CASE)
               __a							=    partial(lambda: (timm.create_model(__SCREAMING_SNAKE_CASE					,		pretrained=__SCREAMING_SNAKE_CASE).eval(), None))

           else:
               __a							=    super().__getitem__(__SCREAMING_SNAKE_CASE)

           return val


class    _A	(     __UpperCAmelCase   ):



       def __getitem__(		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           if "seer" in x and "in1k" not in x:
               __a							=    RegNetModel
           else:
               __a							=    RegNetForImageClassification
           return val





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    for from_key, to_key in keys:
        __a							=    from_state_dict[from_key].clone()
        print(f'Copied key={from_key} to={to_key}'    )
    return to_state_dict





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase = True       ,			):
    print(f'Converting {name}...'    )
    with torch.no_grad():
        __a	,       __a							=    from_model_func()
        __a							=    our_model_func(_UpperCAmelCase    ).eval()
        __a							=    ModuleTransfer(src=_UpperCAmelCase       ,			dest=_UpperCAmelCase       ,			raise_if_mismatch=_UpperCAmelCase    )
        __a							=    torch.randn((1, 3, 224, 224)    )
        module_transfer(_UpperCAmelCase    )

    if from_state_dict is not None:
        __a							=    []
        # for seer - in1k finetuned we have to manually copy the head
        if "seer" in name and "in1k" in name:
            __a							=    [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
        __a							=    manually_copy_vissl_head(_UpperCAmelCase       ,			our_model.state_dict()       ,			_UpperCAmelCase    )
        our_model.load_state_dict(_UpperCAmelCase    )

    __a							=    our_model(_UpperCAmelCase       ,			output_hidden_states=_UpperCAmelCase    )
    __a							=    (
        our_outputs.logits if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ) else our_outputs.last_hidden_state
    )

    __a							=    from_model(_UpperCAmelCase    )
    __a							=    from_output[-1] if type(_UpperCAmelCase    ) is list else from_output

    # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
    if "seer" in name and "in1k" in name:
        __a							=    our_outputs.hidden_states[-1]

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

    if push_to_hub:
        our_model.push_to_hub(
            repo_path_or_name=save_directory / name       ,			commit_message='''Add model'''       ,			use_temp_dir=_UpperCAmelCase       ,			)

        __a							=    224 if '''seer''' not in name else 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 / name       ,			commit_message='''Add image processor'''       ,			use_temp_dir=_UpperCAmelCase       ,			)

        print(f'Pushed {name}'    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = True    ):
    __a							=    '''imagenet-1k-id2label.json'''
    __a							=    1000
    __a							=    (1, num_labels)

    __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-x-002''': ImageNetPreTrainedConfig(
            depths=[1, 1, 4, 7]       ,			hidden_sizes=[24, 56, 152, 368]       ,			groups_width=8       ,			layer_type='''x'''    ),
        '''regnet-x-004''': ImageNetPreTrainedConfig(
            depths=[1, 2, 7, 12]       ,			hidden_sizes=[32, 64, 160, 384]       ,			groups_width=16       ,			layer_type='''x'''    ),
        '''regnet-x-006''': ImageNetPreTrainedConfig(
            depths=[1, 3, 5, 7]       ,			hidden_sizes=[48, 96, 240, 528]       ,			groups_width=24       ,			layer_type='''x'''    ),
        '''regnet-x-008''': ImageNetPreTrainedConfig(
            depths=[1, 3, 7, 5]       ,			hidden_sizes=[64, 128, 288, 672]       ,			groups_width=16       ,			layer_type='''x'''    ),
        '''regnet-x-016''': ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 2]       ,			hidden_sizes=[72, 168, 408, 912]       ,			groups_width=24       ,			layer_type='''x'''    ),
        '''regnet-x-032''': ImageNetPreTrainedConfig(
            depths=[2, 6, 15, 2]       ,			hidden_sizes=[96, 192, 432, 1008]       ,			groups_width=48       ,			layer_type='''x'''    ),
        '''regnet-x-040''': ImageNetPreTrainedConfig(
            depths=[2, 5, 14, 2]       ,			hidden_sizes=[80, 240, 560, 1360]       ,			groups_width=40       ,			layer_type='''x'''    ),
        '''regnet-x-064''': ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 1]       ,			hidden_sizes=[168, 392, 784, 1624]       ,			groups_width=56       ,			layer_type='''x'''    ),
        '''regnet-x-080''': ImageNetPreTrainedConfig(
            depths=[2, 5, 15, 1]       ,			hidden_sizes=[80, 240, 720, 1920]       ,			groups_width=120       ,			layer_type='''x'''    ),
        '''regnet-x-120''': ImageNetPreTrainedConfig(
            depths=[2, 5, 11, 1]       ,			hidden_sizes=[224, 448, 896, 2240]       ,			groups_width=112       ,			layer_type='''x'''    ),
        '''regnet-x-160''': ImageNetPreTrainedConfig(
            depths=[2, 6, 13, 1]       ,			hidden_sizes=[256, 512, 896, 2048]       ,			groups_width=128       ,			layer_type='''x'''    ),
        '''regnet-x-320''': ImageNetPreTrainedConfig(
            depths=[2, 7, 13, 1]       ,			hidden_sizes=[336, 672, 1344, 2520]       ,			groups_width=168       ,			layer_type='''x'''    ),
        # y variant
        '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]       ,			hidden_sizes=[24, 56, 152, 368]       ,			groups_width=8    ),
        '''regnet-y-004''': ImageNetPreTrainedConfig(
            depths=[1, 3, 6, 6]       ,			hidden_sizes=[48, 104, 208, 440]       ,			groups_width=8    ),
        '''regnet-y-006''': ImageNetPreTrainedConfig(
            depths=[1, 3, 7, 4]       ,			hidden_sizes=[48, 112, 256, 608]       ,			groups_width=16    ),
        '''regnet-y-008''': ImageNetPreTrainedConfig(
            depths=[1, 3, 8, 2]       ,			hidden_sizes=[64, 128, 320, 768]       ,			groups_width=16    ),
        '''regnet-y-016''': ImageNetPreTrainedConfig(
            depths=[2, 6, 17, 2]       ,			hidden_sizes=[48, 120, 336, 888]       ,			groups_width=24    ),
        '''regnet-y-032''': ImageNetPreTrainedConfig(
            depths=[2, 5, 13, 1]       ,			hidden_sizes=[72, 216, 576, 1512]       ,			groups_width=24    ),
        '''regnet-y-040''': ImageNetPreTrainedConfig(
            depths=[2, 6, 12, 2]       ,			hidden_sizes=[128, 192, 512, 1088]       ,			groups_width=64    ),
        '''regnet-y-064''': ImageNetPreTrainedConfig(
            depths=[2, 7, 14, 2]       ,			hidden_sizes=[144, 288, 576, 1296]       ,			groups_width=72    ),
        '''regnet-y-080''': ImageNetPreTrainedConfig(
            depths=[2, 4, 10, 1]       ,			hidden_sizes=[168, 448, 896, 2016]       ,			groups_width=56    ),
        '''regnet-y-120''': ImageNetPreTrainedConfig(
            depths=[2, 5, 11, 1]       ,			hidden_sizes=[224, 448, 896, 2240]       ,			groups_width=112    ),
        '''regnet-y-160''': ImageNetPreTrainedConfig(
            depths=[2, 4, 11, 1]       ,			hidden_sizes=[224, 448, 1232, 3024]       ,			groups_width=112    ),
        '''regnet-y-320''': ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]       ,			hidden_sizes=[232, 696, 1392, 3712]       ,			groups_width=232    ),
        # models created by SEER -> https://arxiv.org/abs/2202.08360
        '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]       ,			hidden_sizes=[232, 696, 1392, 3712]       ,			groups_width=232    ),
        '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]       ,			hidden_sizes=[328, 984, 1968, 4920]       ,			groups_width=328    ),
        '''regnet-y-1280-seer''': RegNetConfig(
            depths=[2, 7, 17, 1]       ,			hidden_sizes=[528, 1056, 2904, 7392]       ,			groups_width=264    ),
        '''regnet-y-2560-seer''': RegNetConfig(
            depths=[3, 7, 16, 1]       ,			hidden_sizes=[640, 1696, 2544, 5088]       ,			groups_width=640    ),
        '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]       ,			hidden_sizes=[2020, 4040, 11110, 28280]       ,			groups_width=1010    ),
        # finetuned on imagenet
        '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]       ,			hidden_sizes=[232, 696, 1392, 3712]       ,			groups_width=232    ),
        '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
            depths=[2, 5, 12, 1]       ,			hidden_sizes=[328, 984, 1968, 4920]       ,			groups_width=328    ),
        '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]       ,			hidden_sizes=[528, 1056, 2904, 7392]       ,			groups_width=264    ),
        '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
            depths=[3, 7, 16, 1]       ,			hidden_sizes=[640, 1696, 2544, 5088]       ,			groups_width=640    ),
        '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]       ,			hidden_sizes=[2020, 4040, 11110, 28280]       ,			groups_width=1010    ),
    }

    __a							=    NameToOurModelFuncMap()
    __a							=    NameToFromModelFuncMap()
    # add seer weights logic

    def load_using_classy_vision(_UpperCAmelCase       ,			_UpperCAmelCase    ) -> Tuple[nn.Module, Dict]:
        __a							=    torch.hub.load_state_dict_from_url(_UpperCAmelCase       ,			model_dir=str(_UpperCAmelCase    )       ,			map_location='''cpu'''    )
        __a							=    model_func()
        # check if we have a head, if yes add it
        __a							=    files['''classy_state_dict''']['''base_model''']['''model''']
        __a							=    model_state_dict['''trunk''']
        model.load_state_dict(_UpperCAmelCase    )
        return model.eval(), model_state_dict["heads"]

    # pretrained
    __a							=    partial(
        _UpperCAmelCase       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )       ,			)

    __a							=    partial(
        _UpperCAmelCase       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )       ,			)

    __a							=    partial(
        _UpperCAmelCase       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )       ,			)

    __a							=    partial(
        _UpperCAmelCase       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''       ,			lambda: FakeRegNetVisslWrapper(
            RegNet(RegNetParams(depth=27       ,			group_width=1010       ,			w_a=1744       ,			w_a=6_20.83       ,			w_m=2.52    )    )    )       ,			)

    # IN1K finetuned
    __a							=    partial(
        _UpperCAmelCase       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )       ,			)

    __a							=    partial(
        _UpperCAmelCase       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )       ,			)

    __a							=    partial(
        _UpperCAmelCase       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )       ,			)

    __a							=    partial(
        _UpperCAmelCase       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''       ,			lambda: FakeRegNetVisslWrapper(
            RegNet(RegNetParams(depth=27       ,			group_width=1010       ,			w_a=1744       ,			w_a=6_20.83       ,			w_m=2.52    )    )    )       ,			)

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


if __name__ == "__main__":
 __snake_case						: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.''',
 )

 __snake_case						:Tuple						=					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)





