



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()
__snake_case		:Optional[Any]   	=  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							:  str  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__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							:  str  ,	__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							:  Optional[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		      =		0
       UpperCamelCase__						:					List		      =		field(default_factory=__UpperCAmelCase					)
       UpperCamelCase__						:					List		      =		field(default_factory=__UpperCAmelCase					)



       def __call__(	self							:  int  ,	__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):
               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}')





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase = True       ):
    print(f'Converting {name}...'       )
    with torch.no_grad():
        __a		 =       timm.create_model(_UpperCAmelCase     ,				pretrained=_UpperCAmelCase       ).eval()
        __a		 =       ResNetForImageClassification(_UpperCAmelCase       ).eval()
        __a		 =       ModuleTransfer(src=_UpperCAmelCase     ,				dest=_UpperCAmelCase       )
        __a		 =       torch.randn((1, 3, 224, 224)       )
        module_transfer(_UpperCAmelCase       )

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

    __a		 =       f'resnet{"-".join(name.split("resnet"       )       )}'
    print(_UpperCAmelCase       )

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

        # we can use the convnext one
        __a		 =       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=_UpperCAmelCase     ,				)

        print(f'Pushed {checkpoint_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(hf_hub_download(_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		 =       {
        '''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(_UpperCAmelCase     ,				names_to_config[model_name]     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    return config, expected_shape


if __name__ == "__main__":
 __snake_case		:List[str]   	=  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.''',
 )

 __snake_case		:Optional[Any]   	=  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)










import os
import sys
import unittest


__snake_case		:Union[str, Any]   	=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__snake_case		:List[str]   	=  os.path.join(git_repo_path, '''src''', '''transformers''')

__snake_case		:Any   	=  '''
{0} = None
'''

__snake_case		:Dict   	=  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__snake_case		:str   	=  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''

class 					_A (			unittest.TestCase					):



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")''')
           self.assertIsNone(__SCREAMING_SNAKE_CASE)

           __a		 =       find_backend('''    if not is_tokenizers_available():''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''tokenizers''')

           __a		 =       find_backend('''    if not is_tensorflow_text_available():''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''tensorflow_text''')

           __a		 =       find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tokenizers''')

           __a		 =       find_backend(
               '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tensorflow_text''')

           __a		 =       find_backend(
               '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tokenizers_and_vision''')



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn('''torch'''  ,	__SCREAMING_SNAKE_CASE)
           self.assertIn('''tensorflow_text'''  ,	__SCREAMING_SNAKE_CASE)
           self.assertIn('''sentencepiece_and_tokenizers'''  ,	__SCREAMING_SNAKE_CASE)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn('''BertModel'''  ,	objects['''torch'''])
           self.assertIn('''TFBertModel'''  ,	objects['''tf'''])
           self.assertIn('''FlaxBertModel'''  ,	objects['''flax'''])
           self.assertIn('''BertModel'''  ,	objects['''torch'''])
           self.assertIn('''TFBertTokenizer'''  ,	objects['''tensorflow_text'''])
           self.assertIn('''convert_slow_tokenizer'''  ,	objects['''sentencepiece_and_tokenizers'''])



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       create_dummy_object('''CONSTANT'''  ,	'''\'torch\'''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''\nCONSTANT = None\n''')

           __a		 =       create_dummy_object('''function'''  ,	'''\'torch\'''')
           self.assertEqual(
               __SCREAMING_SNAKE_CASE  ,	'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n''')

           __a		 =       '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
           __a		 =       create_dummy_object('''FakeClass'''  ,	'''\'torch\'''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)





       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
           __a		 =       create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']})
           self.assertEqual(dummy_files['''torch''']  ,	__SCREAMING_SNAKE_CASE)





