

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     a__  :
			"""simple docstring"""







			__lowerCamelCase       				=				42
			__lowerCamelCase       				=				field(default_factory=lowercase_	)
			__lowerCamelCase       				=				field(default_factory=lowercase_	)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	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				)     ->  Tuple:
							'''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    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






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

@dataclass
class     a__  :
			"""simple docstring"""







			__lowerCamelCase       				=				42
			__lowerCamelCase       				=				42
			__lowerCamelCase       				=				0
			__lowerCamelCase       				=				field(default_factory=lowercase_	)
			__lowerCamelCase       				=				field(default_factory=lowercase_	)


			def __call__(			self					,					lowercase				)     ->  int:
							'''simple docstring'''






							A__						=	Tracker(self.dest				)(lowercase				).parametrized
							A__						=	Tracker(self.src				)(lowercase				).parametrized

							A__						=	list(filter(lambda lowercase				: type(lowercase				) not in self.src_skip					,					lowercase				)				)
							A__						=	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     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[int]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							Any = True					)	->					Dict:




				'''simple docstring'''





				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, 2_2_4, 2_2_4)					)
								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     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							Optional[Any] = None		,				SCREAMING_SNAKE_CASE_:							Any = True					)	->					str:




				'''simple docstring'''





				A__						=	"imagenet-1k-id2label.json"
				A__						=	1_0_0_0
				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=[6_4, 1_2_8, 2_5_6, 5_1_2]		,				layer_type="basic"					),
				    "resnet26": ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]		,				hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]		,				layer_type="bottleneck"					),
				    "resnet34": ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]		,				hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]		,				layer_type="basic"					),
				    "resnet50": ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]		,				hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]		,				layer_type="bottleneck"					),
				    "resnet101": ImageNetPreTrainedConfig(
				        depths=[3, 4, 2_3, 3]		,				hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]		,				layer_type="bottleneck"					),
				    "resnet152": ImageNetPreTrainedConfig(
				        depths=[3, 8, 3_6, 3]		,				hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]		,				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__":
	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)




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_UpperCAmelCase		:   Optional[int]										=       5_0000
_UpperCAmelCase		:   Dict										=       5000

_UpperCAmelCase				,						_UpperCAmelCase		:   Optional[int]										=       os.path.split(__file__)
_UpperCAmelCase		:   List[str]										=       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(UpperCamelCase__      ):
														snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(0   ,							len(UpperCamelCase__      )   ,							UpperCamelCase__      ):
														snake_case_							=    dataset[i : i + batch_size]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(UpperCamelCase__      ):
																					snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(0   ,							UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    dataset[i : i + batch_size]





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    {'num examples': SPEED_TEST_N_EXAMPLES}
							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]

							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print('generating dataset'      )
														snake_case_							=    datasets.Features(
														    {'list': datasets.Sequence(datasets.Value('float32'      )      ), 'numbers': datasets.Value('float32'      )}      )
														snake_case_							=    generate_example_dataset(
														    os.path.join(UpperCamelCase__   ,							'dataset.arrow'      )   ,							UpperCamelCase__   ,							num_examples=UpperCamelCase__   ,							seq_shapes={'list': (100,)}   ,							)
														print('first set of iterations'      )
														for func, kwargs in functions:
																					print(func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(UpperCamelCase__   ,							**UpperCamelCase__      )

														print('shuffling dataset'      )
														snake_case_							=    dataset.shuffle()
														print('Second set of iterations (after shuffling'      )
														for func, kwargs in functions_shuffled:
																					print('shuffled '   ,							func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(
																					    UpperCamelCase__   ,							**UpperCamelCase__      )

							with open(UpperCamelCase__   ,							'wb'      ) as f:
														f.write(json.dumps(UpperCamelCase__      ).encode('utf-8'      )      )


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()




