



import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__snake_case						:List[Any]						=					logging.get_logger(__name__)





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=False    ):
    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
            ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
            ''' instructions.'''    )
        raise

    if not is_sharded:
        __a							=    os.path.abspath(_UpperCAmelCase    )
        logger.info(f'Loading PyTorch weights from {pt_path}'    )

        __a							=    torch.load(_UpperCAmelCase       ,			map_location='''cpu'''    )
        logger.info(f'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()    ):,} parameters.'    )

        __a							=    convert_pytorch_state_dict_to_flax(_UpperCAmelCase       ,			_UpperCAmelCase    )
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        __a							=    convert_pytorch_sharded_state_dict_to_flax(_UpperCAmelCase       ,			_UpperCAmelCase    )
    return flax_state_dict





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			):

    def is_key_or_prefix_key_in_dict(_UpperCAmelCase    ) -> bool:
        return len(set(_UpperCAmelCase    ) & {key, (model_prefix,) + key}    ) > 0

    # layer norm
    __a							=    pt_tuple_key[:-1] + ('''scale''',)
    if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer mean
    __a							=    pt_tuple_key[:-1] + ('''mean''',)
    if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer var
    __a							=    pt_tuple_key[:-1] + ('''var''',)
    if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    __a							=    pt_tuple_key[:-1] + ('''embedding''',)
    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    __a							=    pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
        __a							=    pt_tensor.transpose(2       ,			3       ,			1       ,			0    )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    __a							=    pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase    ):
        __a							=    pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    __a							=    pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    __a							=    pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    __a							=    None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        __a							=    pt_tuple_key[-2] + '''_g'''
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        __a							=    pt_tuple_key[-2] + '''_v'''
    if name is not None:
        __a							=    pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    # convert pytorch tensor to numpy
    __a							=    {k: v.numpy() for k, v in pt_state_dict.items()}

    __a							=    flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers
    if "params" in flax_model.params:
        __a							=    flax_model.params['''params''']
    else:
        __a							=    flax_model.params
    __a							=    flatten_dict(_UpperCAmelCase    )

    # add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
        __a							=    flatten_dict(flax_model.params['''batch_stats''']    )
        random_flax_state_dict.update(_UpperCAmelCase    )

    __a							=    {}

    __a							=    (model_prefix not in flax_model_params) and (
        model_prefix in {k.split('''.'''    )[0] for k in pt_state_dict.keys()}
    )
    __a							=    (model_prefix in flax_model_params) and (
        model_prefix not in {k.split('''.'''    )[0] for k in pt_state_dict.keys()}
    )

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        __a							=    tuple(pt_key.split('''.'''    )    )

        # remove base model prefix if necessary
        __a							=    pt_tuple_key[0] == model_prefix
        if load_model_with_head_into_base_model and has_base_model_prefix:
            __a							=    pt_tuple_key[1:]

        # Correctly rename weight parameters
        __a	,       __a							=    rename_key_and_reshape_tensor(
            _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )

        # add model prefix if necessary
        __a							=    (model_prefix,) + flax_key in random_flax_state_dict
        if load_base_model_into_model_with_head and require_base_model_prefix:
            __a							=    (model_prefix,) + flax_key

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                    f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'    )

        # add batch stats if the model contains batchnorm layers
        if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                __a							=    jnp.asarray(_UpperCAmelCase    )
                continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
                flax_state_dict.pop(_UpperCAmelCase       ,			_UpperCAmelCase    )
                continue

            # also add unexpected weight so that warning is thrown
            __a							=    jnp.asarray(_UpperCAmelCase    )

        else:
            # also add unexpected weight so that warning is thrown
            __a							=    jnp.asarray(_UpperCAmelCase    )

    return unflatten_dict(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    import torch

    # Load the index
    __a							=    {}
    for shard_file in shard_filenames:
        # load using msgpack utils
        __a							=    torch.load(_UpperCAmelCase    )
        __a							=    {k: v.numpy() for k, v in pt_state_dict.items()}

        __a							=    flax_model.base_model_prefix

        # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
        if "batch_stats" in flax_model.params:
            __a							=    flax_model.params['''params''']

            __a							=    flatten_dict(_UpperCAmelCase    )
            random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']    )    )
        else:
            __a							=    flax_model.params
            __a							=    flatten_dict(_UpperCAmelCase    )

        __a							=    (model_prefix not in flax_model_params) and (
            model_prefix in {k.split('''.'''    )[0] for k in pt_state_dict.keys()}
        )
        __a							=    (model_prefix in flax_model_params) and (
            model_prefix not in {k.split('''.'''    )[0] for k in pt_state_dict.keys()}
        )
        # Need to change some parameters name to match Flax names
        for pt_key, pt_tensor in pt_state_dict.items():
            __a							=    tuple(pt_key.split('''.'''    )    )

            # remove base model prefix if necessary
            __a							=    pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                __a							=    pt_tuple_key[1:]

            # Correctly rename weight parameters
            __a	,       __a							=    rename_key_and_reshape_tensor(
                _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )
            # add model prefix if necessary
            __a							=    (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                __a							=    (model_prefix,) + flax_key

            if flax_key in random_flax_state_dict:
                if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                        f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'    )

            # add batch stats if the model contains batchnorm layers
            if "batch_stats" in flax_model.params:
                if "mean" in flax_key[-1]:
                    __a							=    jnp.asarray(_UpperCAmelCase    )
                    continue
                if "var" in flax_key[-1]:
                    __a							=    jnp.asarray(_UpperCAmelCase    )
                    continue
                # remove num_batches_tracked key
                if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(_UpperCAmelCase       ,			_UpperCAmelCase    )
                    continue

                # also add unexpected weight so that warning is thrown
                __a							=    jnp.asarray(_UpperCAmelCase    )

            else:
                # also add unexpected weight so that warning is thrown
                __a							=    jnp.asarray(_UpperCAmelCase    )
    return unflatten_dict(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    os.path.abspath(_UpperCAmelCase    )
    logger.info(f'Loading Flax weights from {flax_checkpoint_path}'    )

    # import correct flax class
    __a							=    getattr(_UpperCAmelCase       ,			'''Flax''' + model.__class__.__name__    )

    # load flax weight dict
    with open(_UpperCAmelCase       ,			'''rb'''    ) as state_f:
        try:
            __a							=    from_bytes(_UpperCAmelCase       ,			state_f.read()    )
        except UnpicklingError:
            raise EnvironmentError(f'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '    )

    return load_flax_weights_in_pytorch_model(_UpperCAmelCase       ,			_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):

    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
            ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
            ''' instructions.'''    )
        raise

    # check if we have bf16 weights
    __a							=    flatten_dict(jax.tree_util.tree_map(lambda _UpperCAmelCase    : x.dtype == jnp.bfloataa       ,			_UpperCAmelCase    )    ).values()
    if any(_UpperCAmelCase    ):
        # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
        # and bf16 is not fully supported in PT yet.
        logger.warning(
            '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
            '''before loading those in PyTorch model.'''    )
        __a							=    jax.tree_util.tree_map(
            lambda _UpperCAmelCase    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params       ,			_UpperCAmelCase    )

    __a							=    flatten_dict(_UpperCAmelCase    )
    __a							=    pt_model.state_dict()

    __a							=    (pt_model.base_model_prefix in flax_state) and (
        pt_model.base_model_prefix not in {k.split('''.'''    )[0] for k in pt_model_dict.keys()}
    )
    __a							=    (pt_model.base_model_prefix not in flax_state) and (
        pt_model.base_model_prefix in {k.split('''.'''    )[0] for k in pt_model_dict.keys()}
    )

    # keep track of unexpected & missing keys
    __a							=    []
    __a							=    set(pt_model_dict.keys()    )

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        __a							=    flax_key_tuple[0] == pt_model.base_model_prefix
        __a							=    '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple    ) in pt_model_dict

        # adapt flax_key to prepare for loading from/to base model only
        if load_model_with_head_into_base_model and has_base_model_prefix:
            __a							=    flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            __a							=    (pt_model.base_model_prefix,) + flax_key_tuple

        # rename flax weights to PyTorch format
        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_UpperCAmelCase    ) not in pt_model_dict:
            # conv layer
            __a							=    flax_key_tuple[:-1] + ('''weight''',)
            __a							=    jnp.transpose(_UpperCAmelCase       ,			(3, 2, 0, 1)    )
        elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase    ) not in pt_model_dict:
            # linear layer
            __a							=    flax_key_tuple[:-1] + ('''weight''',)
            __a							=    flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            __a							=    flax_key_tuple[:-1] + ('''weight''',)

        # adding batch stats from flax batch norm to pt
        elif "mean" in flax_key_tuple[-1]:
            __a							=    flax_key_tuple[:-1] + ('''running_mean''',)
        elif "var" in flax_key_tuple[-1]:
            __a							=    flax_key_tuple[:-1] + ('''running_var''',)

        if "batch_stats" in flax_state:
            __a							=    '''.'''.join(flax_key_tuple[1:]    )  # Remove the params/batch_stats header
        else:
            __a							=    '''.'''.join(_UpperCAmelCase    )

        # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
        __a							=    {}
        # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
        for key in pt_model_dict:
            __a							=    key.split('''.'''    )
            __a							=    None
            if key_components[-3::2] == ["parametrizations", "original0"]:
                __a							=    key_components[-2] + '''_g'''
            elif key_components[-3::2] == ["parametrizations", "original1"]:
                __a							=    key_components[-2] + '''_v'''
            if name is not None:
                __a							=    key_components[:-3] + [name]
                __a							=    '''.'''.join(_UpperCAmelCase    )
                __a							=    key

        if flax_key in special_pt_names:
            __a							=    special_pt_names[flax_key]

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                    f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'    )
            else:
                # add weight to pytorch dict
                __a							=    np.asarray(_UpperCAmelCase    ) if not isinstance(_UpperCAmelCase       ,			np.ndarray    ) else flax_tensor
                __a							=    torch.from_numpy(_UpperCAmelCase    )
                # remove from missing keys
                missing_keys.remove(_UpperCAmelCase    )
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(_UpperCAmelCase    )

    pt_model.load_state_dict(_UpperCAmelCase    )

    # re-transform missing_keys to list
    __a							=    list(_UpperCAmelCase    )

    if len(_UpperCAmelCase    ) > 0:
        logger.warning(
            '''Some weights of the Flax model were not used when initializing the PyTorch model'''
            f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
            f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
            ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
            f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
            ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
            ''' FlaxBertForSequenceClassification model).'''    )
    else:
        logger.warning(f'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'    )
    if len(_UpperCAmelCase    ) > 0:
        logger.warning(
            f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
            f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
            ''' use it for predictions and inference.'''    )
    else:
        logger.warning(
            f'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
            '''If your task is similar to the task the model of the checkpoint was trained on, '''
            f'you can already use {pt_model.__class__.__name__} for predictions without further training.'    )

    return pt_model










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)





