



import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    int(_UpperCAmelCase    )
    __a	,       __a	,       __a							=    t // 3600, (t // 60) % 60, t % 60
    return f'{h}:{m:02d}:{s:02d}' if h != 0 else f'{m:02d}:{s:02d}'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=300    ):
    # docstyle-ignore
    return f'\n    <div>\n      {prefix}\n      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>\n      {label}\n    </div>\n    '





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    '''<table border="1" class="dataframe">\n'''
    html_code += """  <thead>\n <tr style="text-align: left;">\n"""
    for i in items[0]:
        html_code += f'      <th>{i}</th>\n'
    html_code += "    </tr>\n  </thead>\n  <tbody>\n"
    for line in items[1:]:
        html_code += "    <tr>\n"
        for elt in line:
            __a							=    f'{elt:.6f}' if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ) else str(_UpperCAmelCase    )
            html_code += f'      <td>{elt}</td>\n'
        html_code += "    </tr>\n"
    html_code += "  </tbody>\n</table><p>"
    return html_code

class    _A	:
       UpperCamelCase__  :   Tuple						 =						5
       UpperCamelCase__  :   List[Any]						 =						0.2



       def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None					,		__SCREAMING_SNAKE_CASE							:					bool = True					,		__SCREAMING_SNAKE_CASE							:					Optional["NotebookTrainingTracker"] = None					,		__SCREAMING_SNAKE_CASE							:					int = 300					,		):


           '''simple docstring'''

           __a							=    total
           __a							=    '''''' if prefix is None else prefix
           __a							=    leave
           __a							=    parent
           __a							=    width
           __a							=    None
           __a							=    None
           __a							=    None



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					str = None):


           '''simple docstring'''

           __a							=    value
           if comment is not None:
               __a							=    comment
           if self.last_value is None:
               __a							=    __a							=    time.time()
               __a							=    __a							=    value
               __a							=    __a							=    None
               __a							=    self.warmup
               __a							=    1
               self.update_bar(__SCREAMING_SNAKE_CASE)
           elif value <= self.last_value and not force_update:
               return
           elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for					,		self.total):
               if self.first_calls > 0:
                   self.first_calls -= 1
               __a							=    time.time()
               __a							=    current_time - self.start_time
               # We could have value = self.start_value if the update is called twixe with the same start value.
               if value > self.start_value:
                   __a							=    self.elapsed_time / (value - self.start_value)
               else:
                   __a							=    None
               if value >= self.total:
                   __a							=    self.total
                   __a							=    None
                   if not self.leave:
                       self.close()
               elif self.average_time_per_item is not None:
                   __a							=    self.average_time_per_item * (self.total - value)
               self.update_bar(__SCREAMING_SNAKE_CASE)
               __a							=    value
               __a							=    current_time
               if self.average_time_per_item is None:
                   __a							=    1
               else:
                   __a							=    max(int(self.update_every / self.average_time_per_item)					,		1)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[str]=None):


           '''simple docstring'''

           __a							=    ''' ''' * (len(str(self.total)) - len(str(__SCREAMING_SNAKE_CASE))) + str(__SCREAMING_SNAKE_CASE)
           if self.elapsed_time is None:
               __a							=    F'[{spaced_value}/{self.total} : < :'
           elif self.predicted_remaining is None:
               __a							=    F'[{spaced_value}/{self.total} {format_time(self.elapsed_time)}'
           else:
               __a							=    (
                   F'[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <'
                   F' {format_time(self.predicted_remaining)}'
               )
               self.label += F', {1/self.average_time_per_item:.2f} it/s'
           self.label += "]" if self.comment is None or len(self.comment) == 0 else F', {self.comment}]'
           self.display()



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    html_progress_bar(self.value					,		self.total					,		self.prefix					,		self.label					,		self.width)
           if self.parent is not None:
               # If this is a child bar, the parent will take care of the display.
               self.parent.display()
               return
           if self.output is None:
               __a							=    disp.display(disp.HTML(self.html_code)					,		display_id=__SCREAMING_SNAKE_CASE)
           else:
               self.output.update(disp.HTML(self.html_code))





       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           if self.parent is None and self.output is not None:
               self.output.update(disp.HTML(''''''))


class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=None):


           '''simple docstring'''

           super().__init__(__SCREAMING_SNAKE_CASE)
           __a							=    None if column_names is None else [column_names]
           __a							=    None



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


           '''simple docstring'''

           __a							=    html_progress_bar(self.value					,		self.total					,		self.prefix					,		self.label					,		self.width)
           if self.inner_table is not None:
               self.html_code += text_to_html_table(self.inner_table)
           if self.child_bar is not None:
               self.html_code += self.child_bar.html_code
           if self.output is None:
               __a							=    disp.display(disp.HTML(self.html_code)					,		display_id=__SCREAMING_SNAKE_CASE)
           else:
               self.output.update(disp.HTML(self.html_code))



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


           '''simple docstring'''

           if self.inner_table is None:
               __a							=    [list(values.keys()), list(values.values())]
           else:
               __a							=    self.inner_table[0]
               if len(self.inner_table) == 1:
                   # We give a chance to update the column names at the first iteration
                   for key in values.keys():
                       if key not in columns:
                           columns.append(__SCREAMING_SNAKE_CASE)
                   __a							=    columns
               self.inner_table.append([values[c] for c in columns])



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple=None					,		__SCREAMING_SNAKE_CASE							:					Tuple=300):


           '''simple docstring'''

           __a							=    NotebookProgressBar(__SCREAMING_SNAKE_CASE					,		prefix=__SCREAMING_SNAKE_CASE					,		parent=self					,		width=__SCREAMING_SNAKE_CASE)
           return self.child_bar





       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    None
           self.display()


class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Tuple):


           '''simple docstring'''

           __a							=    None
           __a							=    None
           __a							=    False



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


           '''simple docstring'''

           __a							=    '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
           __a							=    0
           __a							=    0
           __a							=    [self.first_column] + ['''Training Loss''']
           if args.evaluation_strategy != IntervalStrategy.NO:
               column_names.append('''Validation Loss''')
           __a							=    NotebookTrainingTracker(state.max_steps					,		__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           __a							=    int(state.epoch) if int(state.epoch) == state.epoch else F'{state.epoch:.2f}'
           self.training_tracker.update(
               state.global_step + 1					,		comment=F'Epoch {epoch}/{state.num_train_epochs}'					,		force_update=self._force_next_update					,		)
           __a							=    False



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					List[str]=None					,		**__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           if not has_length(__SCREAMING_SNAKE_CASE):
               return
           if self.prediction_bar is None:
               if self.training_tracker is not None:
                   __a							=    self.training_tracker.add_child(len(__SCREAMING_SNAKE_CASE))
               else:
                   __a							=    NotebookProgressBar(len(__SCREAMING_SNAKE_CASE))
               self.prediction_bar.update(1)
           else:
               self.prediction_bar.update(self.prediction_bar.value + 1)



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


           '''simple docstring'''

           if self.prediction_bar is not None:
               self.prediction_bar.close()
           __a							=    None



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[str]=None					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
               __a							=    {'''Training Loss''': logs['''loss''']}
               # First column is necessarily Step sine we're not in epoch eval strategy
               __a							=    state.global_step
               self.training_tracker.write_line(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[Any]=None					,		**__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           if self.training_tracker is not None:
               __a							=    {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
               for log in reversed(state.log_history):
                   if "loss" in log:
                       __a							=    log['''loss''']
                       break

               if self.first_column == "Epoch":
                   __a							=    int(state.epoch)
               else:
                   __a							=    state.global_step
               __a							=    '''eval'''
               for k in metrics:
                   if k.endswith('''_loss'''):
                       __a							=    re.sub(r'''\_loss$'''					,		''''''					,		__SCREAMING_SNAKE_CASE)
               __a							=    metrics.pop('''total_flos'''					,		__SCREAMING_SNAKE_CASE)
               __a							=    metrics.pop('''epoch'''					,		__SCREAMING_SNAKE_CASE)
               __a							=    metrics.pop(F'{metric_key_prefix}_runtime'					,		__SCREAMING_SNAKE_CASE)
               __a							=    metrics.pop(F'{metric_key_prefix}_samples_per_second'					,		__SCREAMING_SNAKE_CASE)
               __a							=    metrics.pop(F'{metric_key_prefix}_steps_per_second'					,		__SCREAMING_SNAKE_CASE)
               __a							=    metrics.pop(F'{metric_key_prefix}_jit_compilation_time'					,		__SCREAMING_SNAKE_CASE)
               for k, v in metrics.items():
                   if k == F'{metric_key_prefix}_loss':
                       __a							=    v
                   else:
                       __a							=    k.split('''_''')
                       __a							=    ''' '''.join([part.capitalize() for part in splits[1:]])
                       __a							=    v
               self.training_tracker.write_line(__SCREAMING_SNAKE_CASE)
               self.training_tracker.remove_child()
               __a							=    None
               # Evaluation takes a long time so we should force the next update.
               __a							=    True





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


           '''simple docstring'''

           self.training_tracker.update(
               state.global_step					,		comment=F'Epoch {int(state.epoch)}/{state.num_train_epochs}'					,		force_update=__SCREAMING_SNAKE_CASE)
           __a							=    None










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)





