





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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowercase				:	List[Any]      						=  logging.get_logger()





@dataclass
class 			A__   :




   """simple docstring"""



   __A       :    nn.Module
   __A       :    List[nn.Module]	  =  field(default_factory=__UpperCAmelCase     )
   __A       :    list	  =  field(default_factory=__UpperCAmelCase     )

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Dict			=    len(list(m.modules())) == 1 or isinstance(lowercase ,  nn.Convad) or isinstance(lowercase ,  nn.BatchNormad)
          if has_not_submodules:
                 self.traced.append(lowercase)

   def __call__( self ,  lowercase)      ->						Dict:







          '''simple docstring'''

          for m in self.module.modules():
                 self.handles.append(m.register_forward_hook(self._forward_hook))
          self.module(lowercase)
          [x.remove() for x in self.handles]
          return self

   @property
   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

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





@dataclass
class 			A__   :




   """simple docstring"""



   __A       :    nn.Module
   __A       :    nn.Module
   __A       :    int	  =  0
   __A       :    List	  =  field(default_factory=__UpperCAmelCase     )
   __A       :    List	  =  field(default_factory=__UpperCAmelCase     )

   def __call__( self ,  lowercase)      ->						int:







          '''simple docstring'''

          a__						:     List[str]			=    Tracker(self.dest)(lowercase).parametrized
          a__						:     Optional[Any]			=    Tracker(self.src)(lowercase).parametrized

          a__						:     Tuple			=    list(filter(lambda lowercase: type(lowercase) not in self.src_skip ,  lowercase))
          a__						:     Optional[Any]			=    list(filter(lambda lowercase: type(lowercase) not in self.dest_skip ,  lowercase))

          if len(lowercase) != len(lowercase):
                 raise Exception(
                     F'Numbers of operations are different. Source module has {len(lowercase)} operations while'
                     F' destination module has {len(lowercase)}.')

          for dest_m, src_m in zip(lowercase ,  lowercase):
                 dest_m.load_state_dict(src_m.state_dict())
                 if self.verbose == 1:
                        print(F'Transfered from={src_m} to={dest_m}')



def A_     (		A__		,				A__		,				A__		,				A__ = True    )  ->		Tuple:
       print(F'Converting {name}...'    )
       with torch.no_grad():
              a__						:     int			=    timm.create_model(A__		,				pretrained=A__    ).eval()
              a__						:     List[str]			=    ResNetForImageClassification(A__    ).eval()
              a__						:     Union[str, Any]			=    ModuleTransfer(src=A__		,				dest=A__    )
              a__						:     List[Any]			=    torch.randn((1, 3, 224, 224)    )
              module_transfer(A__    )

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

       a__						:     Optional[int]			=    F'resnet{"-".join(name.split("resnet"    )    )}'
       print(A__    )

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

              # we can use the convnext one
              a__						:     Union[str, Any]			=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name		,				commit_message='Add image processor'		,				use_temp_dir=A__		,				)

              print(F'Pushed {checkpoint_name}'    )



def A_     (		A__		,				A__ = None		,				A__ = True    )  ->		Tuple:
       a__						:     List[Any]			=    'imagenet-1k-id2label.json'
       a__						:     List[Any]			=    1000
       a__						:     str			=    (1, num_labels)

       a__						:     Union[str, Any]			=    'huggingface/label-files'
       a__						:     Dict			=    num_labels
       a__						:     Tuple			=    json.load(open(hf_hub_download(A__		,				A__		,				repo_type='dataset'    )		,				'r'    )    )
       a__						:     Optional[Any]			=    {int(A__    ): v for k, v in idalabel.items()}

       a__						:     str			=    idalabel
       a__						:     int			=    {v: k for k, v in idalabel.items()}

       a__						:     Any			=    partial(A__		,				num_labels=A__		,				idalabel=A__		,				labelaid=A__    )

       a__						:     str			=    {
           'resnet18': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]		,				hidden_sizes=[64, 128, 256, 512]		,				layer_type='basic'    ),
           'resnet26': ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]		,				hidden_sizes=[256, 512, 1024, 2048]		,				layer_type='bottleneck'    ),
           'resnet34': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]		,				hidden_sizes=[64, 128, 256, 512]		,				layer_type='basic'    ),
           'resnet50': ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]		,				hidden_sizes=[256, 512, 1024, 2048]		,				layer_type='bottleneck'    ),
           'resnet101': ImageNetPreTrainedConfig(
               depths=[3, 4, 23, 3]		,				hidden_sizes=[256, 512, 1024, 2048]		,				layer_type='bottleneck'    ),
           'resnet152': ImageNetPreTrainedConfig(
               depths=[3, 8, 36, 3]		,				hidden_sizes=[256, 512, 1024, 2048]		,				layer_type='bottleneck'    ),
       }

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


if __name__ == "__main__":
    lowercase				:	int      						=  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 resnet* architecture,"""
            """ currently: resnet18,26,34,50,101,152. 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.""",
    )

    lowercase				:	Optional[int]      						=  parser.parse_args()
    lowercase				:	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)









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.eager import context
    from tensorflow.python.framework import ops

    from transformers import GradientAccumulator, create_optimizer





@require_tf
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          self.assertEqual(len(lowercase) ,  len(lowercase))
          for a, b in zip(lowercase ,  lowercase):
                 self.assertAlmostEqual(lowercase ,  lowercase ,  delta=lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    GradientAccumulator()
          accumulator([tf.constant([1.0, 2.0])])
          accumulator([tf.constant([-2.0, 1.0])])
          accumulator([tf.constant([-1.0, 2.0])])
          with self.assertRaises(lowercase):
                 accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])])
          self.assertEqual(accumulator.step ,  3)
          self.assertEqual(len(accumulator.gradients) ,  1)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [-2.0, 5.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [0.0, 0.0] ,  tol=1e-2)

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    None
          ops.enable_eager_execution_internal()
          a__						:     Optional[int]			=    tf.config.list_physical_devices('CPU')
          if len(lowercase) == 1:
                 tf.config.set_logical_device_configuration(
                     physical_devices[0] ,  [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()])
          a__						:     int			=    tf.config.list_logical_devices(device_type='CPU')
          a__						:     Union[str, Any]			=    tf.distribute.MirroredStrategy(devices=devices[:2])

          with strategy.scope():
                 a__						:     str			=    GradientAccumulator()
                 a__						:     Tuple			=    tf.Variable([4.0, 3.0])
                 a__			,							a__						:     Tuple			=    create_optimizer(5e-5 ,  10 ,  5)
                 a__						:     Tuple			=    tf.Variable([0.0, 0.0] ,  trainable=lowercase)

          def accumulate_on_replica(lowercase):
                 accumulator([gradient])

          def apply_on_replica():
                 optimizer.apply_gradients(list(zip(accumulator.gradients ,  [variable])))

          @tf.function
          def accumulate(lowercase ,  lowercase):
                 with strategy.scope():
                        a__						:     Union[str, Any]			=    strategy.experimental_local_results(lowercase)
                        local_variables[0].assign(lowercase)
                        local_variables[1].assign(lowercase)
                        strategy.run(lowercase ,  args=(gradient_placeholder,))

          @tf.function
          def apply_grad():
                 with strategy.scope():
                        strategy.run(lowercase)

          def _check_local_values(lowercase ,  lowercase):
                 a__						:     Union[str, Any]			=    strategy.experimental_local_results(accumulator._gradients[0])
                 self.assertListAlmostEqual(values[0].value() ,  lowercase ,  tol=1e-2)
                 self.assertListAlmostEqual(values[1].value() ,  lowercase ,  tol=1e-2)

          accumulate([1.0, 2.0] ,  [-1.0, 1.0])
          accumulate([3.0, -1.0] ,  [-1.0, -1.0])
          accumulate([-2.0, 2.0] ,  [3.0, -2.0])
          self.assertEqual(accumulator.step ,  3)
          _check_local_values([2.0, 3.0] ,  [1.0, -2.0])
          apply_grad()
          self.assertListAlmostEqual(variable.value() ,  [4.0, 3.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          _check_local_values([0.0, 0.0] ,  [0.0, 0.0])


