



import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.framework.errors_impl import ResourceExhaustedError

 from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
 import pyanvml.pyanvml as nvml

__snake_case						:Dict						=					logging.get_logger(__name__)





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    def run_func(_UpperCAmelCase    ):
        @wraps(_UpperCAmelCase    )
        def run_in_eager_mode(*_UpperCAmelCase       ,			**_UpperCAmelCase    ):
            return func(*_UpperCAmelCase       ,			**_UpperCAmelCase    )

        @wraps(_UpperCAmelCase    )
        @tf.function(experimental_compile=_UpperCAmelCase    )
        def run_in_graph_mode(*_UpperCAmelCase       ,			**_UpperCAmelCase    ):
            return func(*_UpperCAmelCase       ,			**_UpperCAmelCase    )

        if do_eager_mode is True:
            if use_xla is not False:
                raise ValueError(
                    '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''    )
            return run_in_eager_mode
        else:
            return run_in_graph_mode

    return run_func





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    random.Random()
    __a							=    [rng.randint(0       ,			vocab_size - 1    ) for i in range(batch_size * sequence_length    )]
    return tf.constant(_UpperCAmelCase       ,			shape=(batch_size, sequence_length)       ,			dtype=tf.intaa    )

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   TensorFlowBenchmarkArguments
       UpperCamelCase__  :   PretrainedConfig
       UpperCamelCase__  :   str						 =						"TensorFlow"



       @property
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return tf.__version__



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           __a							=    self.args.strategy
           if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')
           __a							=    self._prepare_inference_func(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return self._measure_speed(_inference)



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


           '''simple docstring'''

           __a							=    self.args.strategy
           if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')
           __a							=    self._prepare_train_func(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return self._measure_speed(_train)



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.args.strategy
           if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')
           __a							=    self._prepare_inference_func(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return self._measure_memory(_inference)



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.args.strategy
           if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.''')

           __a							=    self._prepare_train_func(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           return self._measure_memory(_train)



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


           '''simple docstring'''

           __a							=    self.config_dict[model_name]

           if self.args.fpaa:
               raise NotImplementedError('''Mixed precision is currently not supported.''')

           __a							=    (
               hasattr(__SCREAMING_SNAKE_CASE					,		'''architectures''')
               and isinstance(config.architectures					,		__SCREAMING_SNAKE_CASE)
               and len(config.architectures) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                   __a							=    '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   __a							=    __import__('''transformers'''					,		fromlist=[model_class])
                   __a							=    getattr(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    model_cls(__SCREAMING_SNAKE_CASE)
               except ImportError:
                   raise ImportError(
                       F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                       ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''')
           else:
               __a							=    TF_MODEL_MAPPING[config.__class__](__SCREAMING_SNAKE_CASE)

           # encoder-decoder has vocab size saved differently
           __a							=    config.vocab_size if hasattr(__SCREAMING_SNAKE_CASE					,		'''vocab_size''') else config.encoder.vocab_size
           __a							=    random_input_ids(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

           @run_with_tf_optimizations(self.args.eager_mode					,		self.args.use_xla)
           def encoder_decoder_forward():
               return model(__SCREAMING_SNAKE_CASE					,		decoder_input_ids=__SCREAMING_SNAKE_CASE					,		training=__SCREAMING_SNAKE_CASE)

           @run_with_tf_optimizations(self.args.eager_mode					,		self.args.use_xla)
           def encoder_forward():
               return model(__SCREAMING_SNAKE_CASE					,		training=__SCREAMING_SNAKE_CASE)

           __a							=    encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

           return _inference



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           __a							=    self.config_dict[model_name]

           if self.args.eager_mode is not False:
               raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.''')

           if self.args.fpaa:
               raise NotImplementedError('''Mixed precision is currently not supported.''')

           __a							=    (
               hasattr(__SCREAMING_SNAKE_CASE					,		'''architectures''')
               and isinstance(config.architectures					,		__SCREAMING_SNAKE_CASE)
               and len(config.architectures) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                   __a							=    '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   __a							=    __import__('''transformers'''					,		fromlist=[model_class])
                   __a							=    getattr(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    model_cls(__SCREAMING_SNAKE_CASE)
               except ImportError:
                   raise ImportError(
                       F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                       ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''')
           else:
               __a							=    TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__SCREAMING_SNAKE_CASE)

           # encoder-decoder has vocab size saved differently
           __a							=    config.vocab_size if hasattr(__SCREAMING_SNAKE_CASE					,		'''vocab_size''') else config.encoder.vocab_size
           __a							=    random_input_ids(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

           @run_with_tf_optimizations(self.args.eager_mode					,		self.args.use_xla)
           def encoder_decoder_train():
               __a							=    model(__SCREAMING_SNAKE_CASE					,		decoder_input_ids=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE					,		training=__SCREAMING_SNAKE_CASE)[0]
               __a							=    tf.gradients(__SCREAMING_SNAKE_CASE					,		model.trainable_variables)
               return gradients

           @run_with_tf_optimizations(self.args.eager_mode					,		self.args.use_xla)
           def encoder_train():
               __a							=    model(__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE					,		training=__SCREAMING_SNAKE_CASE)[0]
               __a							=    tf.gradients(__SCREAMING_SNAKE_CASE					,		model.trainable_variables)
               return gradients

           __a							=    encoder_decoder_train if config.is_encoder_decoder else encoder_train

           return _train



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           with self.args.strategy.scope():
               try:
                   if self.args.is_tpu or self.args.use_xla:
                       # run additional 10 times to stabilize compilation for tpu
                       logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation''')
                       timeit.repeat(__SCREAMING_SNAKE_CASE					,		repeat=1					,		number=5)

                   # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                   __a							=    timeit.repeat(
                       __SCREAMING_SNAKE_CASE					,		repeat=self.args.repeat					,		number=10					,		)

                   return min(__SCREAMING_SNAKE_CASE) / 10.0
               except ResourceExhaustedError as e:
                   self.print_fn(F'Doesn\'t fit on GPU. {e}')





       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Callable[[], None]):


           '''simple docstring'''

           logger.info(
               '''Note that TensorFlow allocates more memory than '''
               '''it might need to speed up computation. '''
               '''The memory reported here corresponds to the memory '''
               '''reported by `nvidia-smi`, which can vary depending '''
               '''on total available memory on the GPU that is used.''')
           with self.args.strategy.scope():
               try:
                   if self.args.trace_memory_line_by_line:
                       if not self.args.eager_mode:
                           raise ValueError(
                               '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
                               ''' consumption line by line.''')
                       __a							=    start_memory_tracing('''transformers''')

                   if self.args.is_tpu:
                       # tpu
                       raise NotImplementedError(
                           '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
                           ''' with `args.memory=False`''')
                   elif self.args.is_gpu:
                       # gpu
                       if not is_pyanvml_available():
                           logger.warning(
                               '''py3nvml not installed, we won\'t log GPU memory usage. '''
                               '''Install py3nvml (pip install py3nvml) to log information about GPU.''')
                           __a							=    '''N/A'''
                       else:
                           logger.info(
                               '''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
                               ''' running on the same GPU.''')
                           # init nvml
                           nvml.nvmlInit()
                           func()
                           __a							=    nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
                           __a							=    nvml.nvmlDeviceGetMemoryInfo(__SCREAMING_SNAKE_CASE)
                           __a							=    meminfo.used
                           __a							=    Memory(__SCREAMING_SNAKE_CASE)
                           # shutdown nvml
                           nvml.nvmlShutdown()
                   else:
                       # cpu
                       if self.args.trace_memory_line_by_line:
                           logger.info(
                               '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
                               ''' TensorFlow.''')
                           __a							=    None
                       else:
                           __a							=    measure_peak_memory_cpu(__SCREAMING_SNAKE_CASE)
                           __a							=    Memory(__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else memory_bytes
                   if self.args.trace_memory_line_by_line:
                       __a							=    stop_memory_tracing(__SCREAMING_SNAKE_CASE)
                       if memory is None:
                           __a							=    summary.total
                   else:
                       __a							=    None

                   return memory, summary
               except ResourceExhaustedError as e:
                   self.print_fn(F'Doesn\'t fit on GPU. {e}')
                   return "N/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)





