

'''simple docstring'''




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()
_lowerCAmelCase         =						logging.get_logger()






@dataclass
class   _SCREAMING_SNAKE_CASE   :
 __SCREAMING_SNAKE_CASE					:nn.Module
 __SCREAMING_SNAKE_CASE					:List[nn.Module]      	=					field(default_factory=__a     )
 __SCREAMING_SNAKE_CASE					:list      	=					field(default_factory=__a     )



 def 			snake_case__  (   self		:   List[str]			,				a__		:   str			,				a__		:   Tensor			,				a__		:   Tensor      ):
      __magic_name__						       =    len(list(m.modules()      )      ) == 1 or isinstance(a__			,				nn.Convad      ) or isinstance(a__			,				nn.BatchNormad      )
      if has_not_submodules:
           self.traced.append(a__      )



 def __call__(   self		:   List[Any]			,				a__		:   Tensor      ):
      for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook      )      )
      self.module(a__      )
      [x.remove() for x in self.handles]
      return self



 @property
 def 			snake_case__  (   self		:   Dict      ):
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda a__      : len(list(x.state_dict().keys()      )      ) > 0			,				self.traced      )      )






@dataclass
class   _SCREAMING_SNAKE_CASE   :
 __SCREAMING_SNAKE_CASE					:nn.Module
 __SCREAMING_SNAKE_CASE					:nn.Module
 __SCREAMING_SNAKE_CASE					:int      	=					0
 __SCREAMING_SNAKE_CASE					:List      	=					field(default_factory=__a     )
 __SCREAMING_SNAKE_CASE					:List      	=					field(default_factory=__a     )



 def __call__(   self		:   int			,				a__		:   Tensor      ):
      __magic_name__						       =    Tracker(self.dest      )(a__      ).parametrized
      __magic_name__						       =    Tracker(self.src      )(a__      ).parametrized

      __magic_name__						       =    list(filter(lambda a__      : type(a__      ) not in self.src_skip			,				a__      )      )
      __magic_name__						       =    list(filter(lambda a__      : type(a__      ) not in self.dest_skip			,				a__      )      )

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

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




def 						UpperCamelCase	(			a		,						a		,						a		,						a = True			)				->     Any:






     '''simple docstring'''
     print(F'''Converting {name}...'''			)
     with torch.no_grad():
          __magic_name__						       =    timm.create_model(a		,						pretrained=a			).eval()
          __magic_name__						       =    ResNetForImageClassification(a			).eval()
          __magic_name__						       =    ModuleTransfer(src=a		,						dest=a			)
          __magic_name__						       =    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."

     __magic_name__						       =    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
          __magic_name__						       =    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 						UpperCamelCase	(			a		,						a = None		,						a = True			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    '''imagenet-1k-id2label.json'''
     __magic_name__						       =    1000
     __magic_name__						       =    (1, num_labels)

     __magic_name__						       =    '''huggingface/label-files'''
     __magic_name__						       =    num_labels
     __magic_name__						       =    json.load(open(hf_hub_download(a		,						a		,						repo_type='''dataset'''			)		,						'''r'''			)			)
     __magic_name__						       =    {int(a			): v for k, v in idalabel.items()}

     __magic_name__						       =    idalabel
     __magic_name__						       =    {v: k for k, v in idalabel.items()}

     __magic_name__						       =    partial(a		,						num_labels=a		,						idalabel=a		,						labelaid=a			)

     __magic_name__						       =    {
         '''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__":
      _lowerCAmelCase         =						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.",
      )

      _lowerCAmelCase         =						parser.parse_args()
      _lowerCAmelCase         =						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)









'''simple docstring'''




import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def __init__(   self		:   Tuple			,				a__		:   Union[str, Any]			,				a__		:   Union[str, Any]=7			,				a__		:   Dict=3			,				a__		:   Optional[Any]=18			,				a__		:   Optional[Any]=30			,				a__		:   Tuple=400			,				a__		:   Optional[int]=True			,				a__		:   int=None			,				a__		:   Union[str, Any]=True			,				a__		:   Optional[Any]=None			,				a__		:   str=True			,				a__		:   List[Any]=[0.5, 0.5, 0.5]			,				a__		:   Tuple=[0.5, 0.5, 0.5]			,				a__		:   Union[str, Any]=False			,				):
      __magic_name__						       =    size if size is not None else {'''height''': 20, '''width''': 20}
      __magic_name__						       =    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      __magic_name__						       =    parent
      __magic_name__						       =    batch_size
      __magic_name__						       =    num_channels
      __magic_name__						       =    image_size
      __magic_name__						       =    min_resolution
      __magic_name__						       =    max_resolution
      __magic_name__						       =    do_resize
      __magic_name__						       =    size
      __magic_name__						       =    do_center_crop
      __magic_name__						       =    crop_size
      __magic_name__						       =    do_normalize
      __magic_name__						       =    image_mean
      __magic_name__						       =    image_std
      __magic_name__						       =    do_reduce_labels



 def 			snake_case__  (   self		:   Optional[Any]      ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }




def 						UpperCamelCase	(			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(dataset[0]['''file''']			)
     __magic_name__						       =    Image.open(dataset[1]['''file''']			)

     return image, map




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    load_dataset('''hf-internal-testing/fixtures_ade20k'''		,						split='''test'''			)

     __magic_name__						       =    Image.open(ds[0]['''file''']			)
     __magic_name__						       =    Image.open(ds[1]['''file''']			)
     __magic_name__						       =    Image.open(ds[2]['''file''']			)
     __magic_name__						       =    Image.open(ds[3]['''file''']			)

     return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					BeitImageProcessor if is_vision_available() else None



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    BeitImageProcessingTester(self      )



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return self.image_processor_tester.prepare_image_processor_dict()



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(a__			,				'''do_resize'''      )      )
      self.assertTrue(hasattr(a__			,				'''size'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''center_crop'''      )      )
      self.assertTrue(hasattr(a__			,				'''do_normalize'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_mean'''      )      )
      self.assertTrue(hasattr(a__			,				'''image_std'''      )      )



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    self.image_processing_class.from_dict(self.image_processor_dict      )
      self.assertEqual(image_processor.size			,				{'''height''': 20, '''width''': 20}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 18, '''width''': 18}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )

      __magic_name__						       =    self.image_processing_class.from_dict(
          self.image_processor_dict			,				size=42			,				crop_size=84			,				reduce_labels=a__      )
      self.assertEqual(image_processor.size			,				{'''height''': 42, '''width''': 42}      )
      self.assertEqual(image_processor.crop_size			,				{'''height''': 84, '''width''': 84}      )
      self.assertEqual(image_processor.do_reduce_labels			,				a__      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      pass



 def 			snake_case__  (   self		:   Dict      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				Image.Image      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Optional[int]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				numpify=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				np.ndarray      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   List[str]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				torchify=a__      )
      for image in image_inputs:
           self.assertIsInstance(a__			,				torch.Tensor      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)

      # Test batched
      __magic_name__						       =    image_processing(a__			,				return_tensors='''pt'''      ).pixel_values
      self.assertEqual(
          encoded_images.shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      __magic_name__						       =    prepare_image_inputs(self.image_processor_tester			,				equal_resolution=a__			,				torchify=a__      )
      __magic_name__						       =    []
      for image in image_inputs:
           self.assertIsInstance(a__			,				torch.Tensor      )
           maps.append(torch.zeros(image.shape[-2:]      ).long()      )

      # Test not batched input
      __magic_name__						       =    image_processing(image_inputs[0]			,				maps[0]			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test not batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              1,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )

      # Test batched input (PIL images)
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_batch_inputs()

      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertEqual(
          encoding['''pixel_values'''].shape			,				(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(
          encoding['''labels'''].shape			,				(
              2,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )			,				)
      self.assertEqual(encoding['''labels'''].dtype			,				torch.long      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )



 def 			snake_case__  (   self		:   Any      ):
      # Initialize image_processing
      __magic_name__						       =    self.image_processing_class(**self.image_processor_dict      )

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      __magic_name__       ,  __magic_name__						       =    prepare_semantic_single_inputs()
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 150      )

      __magic_name__						       =    True
      __magic_name__						       =    image_processing(a__			,				a__			,				return_tensors='''pt'''      )
      self.assertTrue(encoding['''labels'''].min().item() >= 0      )
      self.assertTrue(encoding['''labels'''].max().item() <= 255      )






