


"""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 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      nn.Module
				_lowerCAmelCase      :      List[nn.Module]      							=					field(default_factory=lowercase   )
				_lowerCAmelCase      :      list      							=					field(default_factory=lowercase   )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				len(list(m.modules()     )     ) == 1 or isinstance(UpperCAmelCase__       ,		nn.Convad     ) or isinstance(UpperCAmelCase__       ,		nn.BatchNormad     )
								if has_not_submodules:
												self.traced.append(UpperCAmelCase__     )



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






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


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      nn.Module
				_lowerCAmelCase      :      nn.Module
				_lowerCAmelCase      :      int      							=					0
				_lowerCAmelCase      :      List      							=					field(default_factory=lowercase   )
				_lowerCAmelCase      :      List      							=					field(default_factory=lowercase   )



				def __call__(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				Tracker(self.dest     )(UpperCAmelCase__     ).parametrized
								SCREAMING_SNAKE_CASE__     							=				Tracker(self.src     )(UpperCAmelCase__     ).parametrized

								SCREAMING_SNAKE_CASE__     							=				list(filter(lambda UpperCAmelCase__     : type(UpperCAmelCase__     ) not in self.src_skip       ,		UpperCAmelCase__     )     )
								SCREAMING_SNAKE_CASE__     							=				list(filter(lambda UpperCAmelCase__     : type(UpperCAmelCase__     ) not in self.dest_skip       ,		UpperCAmelCase__     )     )

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

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


def __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      ResNetConfig						,					lowerCamelCase_  :      Path						,					lowerCamelCase_  :      bool = True  ):
				print(F'''Converting {name}...'''  )
				with torch.no_grad():
								SCREAMING_SNAKE_CASE__     							=				timm.create_model(lowerCamelCase_						,					pretrained=lowerCamelCase_  ).eval()
								SCREAMING_SNAKE_CASE__     							=				ResNetForImageClassification(lowerCamelCase_  ).eval()
								SCREAMING_SNAKE_CASE__     							=				ModuleTransfer(src=lowerCamelCase_						,					dest=lowerCamelCase_  )
								SCREAMING_SNAKE_CASE__     							=				torch.randn((1, 3, 224, 224)  )
								module_transfer(lowerCamelCase_  )

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

				SCREAMING_SNAKE_CASE__     							=				F'''resnet{"-".join(name.split("resnet"  )  )}'''
				print(lowerCamelCase_  )

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

								# we can use the convnext one
								SCREAMING_SNAKE_CASE__     							=				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=lowerCamelCase_						,					)

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


def __lowercase     (      lowerCamelCase_  :      Path						,					lowerCamelCase_  :      str = None						,					lowerCamelCase_  :      bool = True  ):
				SCREAMING_SNAKE_CASE__     							=				"imagenet-1k-id2label.json"
				SCREAMING_SNAKE_CASE__     							=				1000
				SCREAMING_SNAKE_CASE__     							=				(1, num_labels)

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

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

				SCREAMING_SNAKE_CASE__     							=				partial(lowerCamelCase_						,					num_labels=lowerCamelCase_						,					idalabel=lowerCamelCase_						,					labelaid=lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				{
				    "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(lowerCamelCase_						,					names_to_config[model_name]						,					lowerCamelCase_						,					lowerCamelCase_  )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  )
				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 json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace


@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"       ,  "False"   )   ) is not True       ,  reason="Skipping test because should only be run when releasing minor transformers version"       ,  )
@pytest.mark.usefixtures("sm_env"   )
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]   )
class 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								if self.framework == "pytorch":
												subprocess.run(
												    f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()       ,		encoding="utf-8"       ,		check=UpperCAmelCase__       ,		)
								assert hasattr(self       ,		"env"     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=1     ):
								# creates estimator
								return HuggingFace(
								    entry_point=self.script       ,		source_dir=self.env.test_path       ,		role=self.env.role       ,		image_uri=self.env.image_uri       ,		base_job_name=f'''{self.env.base_job_name}-single'''       ,		instance_count=UpperCAmelCase__       ,		instance_type=self.instance_type       ,		debugger_hook_config=UpperCAmelCase__       ,		hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,		metric_definitions=self.env.metric_definitions       ,		py_version="py36"       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								TrainingJobAnalytics(UpperCAmelCase__     ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''     )






				def   lowerCAmelCase__		(						self     ):
								# create estimator
								SCREAMING_SNAKE_CASE__     							=				self.create_estimator()

								# run training
								estimator.fit()

								# result dataframe
								SCREAMING_SNAKE_CASE__     							=				TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

								# extract kpis
								SCREAMING_SNAKE_CASE__     							=				list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
								SCREAMING_SNAKE_CASE__     							=				list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								SCREAMING_SNAKE_CASE__     							=				(
								    Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds"       ,		99_9999     )
								)

								# assert kpis
								assert train_runtime <= self.results["train_runtime"]
								assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy     )
								assert all(t <= self.results["eval_loss"] for t in eval_loss     )

								# dump tests result into json file to share in PR
								with open(f'''{estimator.latest_training_job.name}.json'''       ,		"w"     ) as outfile:
												json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}       ,		UpperCAmelCase__     )
