
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()
_SCREAMING_SNAKE_CASE    :      int          =			logging.get_logger()







@dataclass
class  UpperCamelCase__		:







 '''simple docstring'''
 _lowerCAmelCase							       =				42
 _lowerCAmelCase							       =				field(default_factory=SCREAMING_SNAKE_CASE_   )
 _lowerCAmelCase							       =				field(default_factory=SCREAMING_SNAKE_CASE_   )





 def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
     A__  :	List[str]         =							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   __snake_case				(			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  UpperCamelCase__		:







 '''simple docstring'''
 _lowerCAmelCase							       =				42
 _lowerCAmelCase							       =				42
 _lowerCAmelCase							       =				0
 _lowerCAmelCase							       =				field(default_factory=SCREAMING_SNAKE_CASE_   )
 _lowerCAmelCase							       =				field(default_factory=SCREAMING_SNAKE_CASE_   )





 def __call__(			self				,       UpperCamelCase__		):
     A__  :	Any         =							Tracker(self.dest		)(UpperCamelCase__		).parametrized
     A__  :	Optional[Any]         =							Tracker(self.src		)(UpperCamelCase__		).parametrized

     A__  :	Tuple         =							list(filter(lambda UpperCamelCase__		: type(UpperCamelCase__		) not in self.src_skip				,       UpperCamelCase__		)		)
     A__  :	Union[str, Any]         =							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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str							,						__UpperCamelCase :						ResNetConfig							,						__UpperCamelCase :						Path							,						__UpperCamelCase :						bool = True       ) ->       Union[str, Any]:

    """simple docstring"""







    print(F"Converting {name}..."       )
    with torch.no_grad():
        A__  :	List[str]         =							timm.create_model(__UpperCamelCase							,						pretrained=__UpperCamelCase       ).eval()
        A__  :	List[str]         =							ResNetForImageClassification(__UpperCamelCase       ).eval()
        A__  :	List[Any]         =							ModuleTransfer(src=__UpperCamelCase							,						dest=__UpperCamelCase       )
        A__  :	Any         =							torch.randn((1, 3, 2_24, 2_24)       )
        module_transfer(__UpperCamelCase       )

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

    A__  :	Union[str, Any]         =							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__  :	int         =							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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Path							,						__UpperCamelCase :						str = None							,						__UpperCamelCase :						bool = True       ) ->       Dict:

    """simple docstring"""







    A__  :	Optional[int]         =							'''imagenet-1k-id2label.json'''
    A__  :	Optional[int]         =							10_00
    A__  :	Optional[int]         =							(1, num_labels)

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

    A__  :	str         =							idalabel
    A__  :	List[str]         =							{v: k for k, v in idalabel.items()}

    A__  :	Dict         =							partial(__UpperCamelCase							,						num_labels=__UpperCamelCase							,						idalabel=__UpperCamelCase							,						labelaid=__UpperCamelCase       )

    A__  :	str         =							{
        '''resnet18''': ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]							,						hidden_sizes=[64, 1_28, 2_56, 5_12]							,						layer_type='''basic'''       ),
        '''resnet26''': ImageNetPreTrainedConfig(
            depths=[2, 2, 2, 2]							,						hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,						layer_type='''bottleneck'''       ),
        '''resnet34''': ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]							,						hidden_sizes=[64, 1_28, 2_56, 5_12]							,						layer_type='''basic'''       ),
        '''resnet50''': ImageNetPreTrainedConfig(
            depths=[3, 4, 6, 3]							,						hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,						layer_type='''bottleneck'''       ),
        '''resnet101''': ImageNetPreTrainedConfig(
            depths=[3, 4, 23, 3]							,						hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,						layer_type='''bottleneck'''       ),
        '''resnet152''': ImageNetPreTrainedConfig(
            depths=[3, 8, 36, 3]							,						hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,						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__":
     _SCREAMING_SNAKE_CASE    :      Dict          =			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.',
     )

     _SCREAMING_SNAKE_CASE    :      Optional[Any]          =			parser.parse_args()
     _SCREAMING_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 argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_SCREAMING_SNAKE_CASE    :      str          =			1_6
_SCREAMING_SNAKE_CASE    :      Tuple          =			3_2

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Accelerator							,						__UpperCamelCase :						int = 16       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	List[str]         =							AutoTokenizer.from_pretrained('''bert-base-cased'''       )
				A__  :	Optional[int]         =							load_dataset('''glue'''							,						'''mrpc'''       )

				def tokenize_function(__UpperCamelCase :						Union[str, Any]       ):
								# max_length=None => use the model max length (it's actually the default)
								A__  :	int         =							tokenizer(examples['''sentence1''']							,						examples['''sentence2''']							,						truncation=__UpperCamelCase							,						max_length=__UpperCamelCase       )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								A__  :	Optional[int]         =							datasets.map(
								    __UpperCamelCase							,						batched=__UpperCamelCase							,						remove_columns=['''idx''', '''sentence1''', '''sentence2''']							,						)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				A__  :	List[Any]         =							tokenized_datasets.rename_column('''label'''							,						'''labels'''       )

				def collate_fn(__UpperCamelCase :						Any       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__  :	Optional[Any]         =							1_28 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												A__  :	int         =							16
								elif accelerator.mixed_precision != "no":
												A__  :	Any         =							8
								else:
												A__  :	Union[str, Any]         =							None

								return tokenizer.pad(
								    __UpperCamelCase							,						padding='''longest'''							,						max_length=__UpperCamelCase							,						pad_to_multiple_of=__UpperCamelCase							,						return_tensors='''pt'''							,						)

				# Instantiate dataloaders.
				A__  :	Optional[int]         =							DataLoader(
				    tokenized_datasets['''train''']							,						shuffle=__UpperCamelCase							,						collate_fn=__UpperCamelCase							,						batch_size=__UpperCamelCase       )
				A__  :	Tuple         =							DataLoader(
				    tokenized_datasets['''validation''']							,						shuffle=__UpperCamelCase							,						collate_fn=__UpperCamelCase							,						batch_size=__UpperCamelCase       )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					_SCREAMING_SNAKE_CASE    :      Dict          =			mocked_dataloaders  # noqa: F811

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						List[Any]       ) ->       Optional[Any]:

				"""simple docstring"""







				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,						__UpperCamelCase       ) == "1":
								A__  :	List[str]         =							2
				# Initialize accelerator
				A__  :	Optional[Any]         =							Accelerator(cpu=args.cpu							,						mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__  :	Tuple         =							config['''lr''']
				A__  :	Dict         =							int(config['''num_epochs''']       )
				A__  :	int         =							int(config['''seed''']       )
				A__  :	Optional[Any]         =							int(config['''batch_size''']       )

				A__  :	int         =							evaluate.load('''glue'''							,						'''mrpc'''       )

				# If the batch size is too big we use gradient accumulation
				A__  :	Union[str, Any]         =							1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								A__  :	List[Any]         =							batch_size // MAX_GPU_BATCH_SIZE
								A__  :	Dict         =							MAX_GPU_BATCH_SIZE

				set_seed(__UpperCamelCase       )
				A__       ,							A__  :	int         =							get_dataloaders(__UpperCamelCase							,						__UpperCamelCase       )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__  :	Optional[int]         =							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							,						return_dict=__UpperCamelCase       )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				A__  :	Tuple         =							model.to(accelerator.device       )

				# Instantiate optimizer
				A__  :	Optional[int]         =							AdamW(params=model.parameters()							,						lr=__UpperCamelCase       )

				# Instantiate scheduler
				A__  :	Any         =							get_linear_schedule_with_warmup(
				    optimizer=__UpperCamelCase							,						num_warmup_steps=1_00							,						num_training_steps=(len(__UpperCamelCase       ) * num_epochs) // gradient_accumulation_steps							,						)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				A__       ,							A__       ,							A__       ,							A__       ,							A__  :	Dict         =							accelerator.prepare(
				    __UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				# Now we train the model
				for epoch in range(__UpperCamelCase       ):
								model.train()
								for step, batch in enumerate(__UpperCamelCase       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												A__  :	Dict         =							model(**__UpperCamelCase       )
												A__  :	Dict         =							outputs.loss
												A__  :	List[str]         =							loss / gradient_accumulation_steps
												accelerator.backward(__UpperCamelCase       )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								A__  :	Optional[int]         =							0
								for step, batch in enumerate(__UpperCamelCase       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												with torch.no_grad():
																A__  :	Union[str, Any]         =							model(**__UpperCamelCase       )
												A__  :	int         =							outputs.logits.argmax(dim=-1       )
												A__       ,							A__  :	Optional[Any]         =							accelerator.gather((predictions, batch['''labels'''])       )
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(__UpperCamelCase       ) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				A__  :	Tuple         =							predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																				A__  :	int         =							references[: len(eval_dataloader.dataset       ) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=__UpperCamelCase							,						references=__UpperCamelCase							,						)

								A__  :	Union[str, Any]         =							metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"							,						__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      ) ->       Union[str, Any]:

				"""simple docstring"""







				A__  :	Tuple         =							argparse.ArgumentParser(description='''Simple example of training script.'''       )
				parser.add_argument(
				    '''--mixed_precision'''							,						type=__UpperCamelCase							,						default=__UpperCamelCase							,						choices=['''no''', '''fp16''', '''bf16''', '''fp8''']							,						help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''							,						)
				parser.add_argument('''--cpu'''							,						action='''store_true'''							,						help='''If passed, will train on the CPU.'''       )
				A__  :	Dict         =							parser.parse_args()
				A__  :	Any         =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__UpperCamelCase							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()