




"""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()
lowercase__     						=						logging.get_logger()






@dataclass
class lowerCAmelCase__	:






			'''simple docstring'''



			lowerCamelCase__      =			42
			lowerCamelCase__      =			field(default_factory=lowercase						)
			lowerCamelCase__      =			field(default_factory=lowercase						)
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Any    =    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  ):
							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 					A_    (  self  ):
							# check the len of the state_dict keys to see if we have learnable params
							return list(filter(lambda lowercase  : len(list(x.state_dict().keys()  )  ) > 0  ,      self.traced  )  )






@dataclass
class lowerCAmelCase__	:






			'''simple docstring'''



			lowerCamelCase__      =			42
			lowerCamelCase__      =			42
			lowerCamelCase__      =			0
			lowerCamelCase__      =			field(default_factory=lowercase						)
			lowerCamelCase__      =			field(default_factory=lowercase						)
			def __call__(  self  ,      lowercase  ):
							_lowerCamelCase     :			Union[str, Any]    =    Tracker(self.dest  )(lowercase  ).parametrized
							_lowerCamelCase     :			Optional[Any]    =    Tracker(self.src  )(lowercase  ).parametrized

							_lowerCamelCase     :			Tuple    =    list(filter(lambda lowercase  : type(lowercase  ) not in self.src_skip  ,      lowercase  )  )
							_lowerCamelCase     :			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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = True		):
				print(f'''Converting {name}...'''		)
				with torch.no_grad():
								_lowerCamelCase     :			List[str]    =    timm.create_model(lowercase__							,    pretrained=lowercase__		).eval()
								_lowerCamelCase     :			List[Any]    =    ResNetForImageClassification(lowercase__		).eval()
								_lowerCamelCase     :			Union[str, Any]    =    ModuleTransfer(src=lowercase__							,    dest=lowercase__		)
								_lowerCamelCase     :			int    =    torch.randn((1, 3, 224, 224)		)
								module_transfer(lowercase__		)

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

				_lowerCamelCase     :			Optional[int]    =    f'''resnet{'-'.join(name.split('resnet'		)		)}'''
				print(lowercase__		)

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

								# we can use the convnext one
								_lowerCamelCase     :			Optional[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=lowercase__							,    )

								print(f'''Pushed {checkpoint_name}'''		)







def      _snake_case			(				lowercase__							,    lowercase__ = None							,    lowercase__ = True		):
				_lowerCamelCase     :			Dict    =    'imagenet-1k-id2label.json'
				_lowerCamelCase     :			List[Any]    =    1000
				_lowerCamelCase     :			List[str]    =    (1, num_labels)

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

				_lowerCamelCase     :			Union[str, Any]    =    idalabel
				_lowerCamelCase     :			Any    =    {v: k for k, v in idalabel.items()}

				_lowerCamelCase     :			str    =    partial(lowercase__							,    num_labels=lowercase__							,    idalabel=lowercase__							,    labelaid=lowercase__		)

				_lowerCamelCase     :			Optional[Any]    =    {
				    '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(lowercase__							,    names_to_config[model_name]							,    lowercase__							,    lowercase__		)
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)
				return config, expected_shape


if __name__ == "__main__":
			lowercase__     						=						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__     						=						parser.parse_args()
			lowercase__     						=						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 argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		SCREAMING_SNAKE_CASE													=					to_tf_var_name(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose  ):
																								SCREAMING_SNAKE_CASE													=					torch_tensor.T
																		SCREAMING_SNAKE_CASE													=					create_tf_var(tensor=SCREAMING_SNAKE_CASE_ ,			name=SCREAMING_SNAKE_CASE_ ,			session=SCREAMING_SNAKE_CASE_  )
																		tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					session.run(SCREAMING_SNAKE_CASE_  )
																		print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )}'  )

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path  ) ,			cache_dir=args.cache_dir ,			)

						convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


