






'''simple docstring'''







import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_A      :     List[str]       	=logging.get_logger()

@dataclass
class _lowercase      :
							a							 =			42
							a							 =			field(default_factory=__a						)
							a							 =			field(default_factory=__a						)
							a							 =			field(default_factory=__a						)




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				UpperCamelCase__:							Optional[Any]     ,				UpperCamelCase__:							Tensor     ,				UpperCamelCase__:							Tensor     ,				UpperCamelCase__:							str	):
														lowerCamelCase__   :      List[Any]			    =			len(list(m.modules()	)	) == 1 or isinstance(UpperCamelCase__     ,				nn.Convad	) or isinstance(UpperCamelCase__     ,				nn.BatchNormad	)
														if has_not_submodules:
																					self.traced.append(UpperCamelCase__	)
																					lowerCamelCase__   :      str			    =			m




							def __call__(				self:							Optional[int]     ,				UpperCamelCase__:							Tensor	):

														for name, m in self.module.named_modules():
																					self.handles.append(m.register_forward_hook(partial(self._forward_hook     ,				name=UpperCamelCase__	)	)	)
														self.module(UpperCamelCase__	)
														[x.remove() for x in self.handles]
														return self




							@property
							def 						lowerCamelCase_							(				self:							Tuple	):
														return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()	)	) > 0}



class _lowercase      (							nn.Module						):




							def __init__(				self:							List[Any]     ,				UpperCamelCase__:							nn.Module	):
														super().__init__()

														lowerCamelCase__   :      List[str]			    =			[]
														# - get the stem
														feature_blocks.append(("""conv1""", model.stem)	)
														# - get all the feature blocks
														for k, v in model.trunk_output.named_children():
																					assert k.startswith("""block"""	), F'''Unexpected layer name {k}'''
																					lowerCamelCase__   :      List[Any]			    =			len(UpperCamelCase__	) + 1
																					feature_blocks.append((F'''res{block_index}''', v)	)

														lowerCamelCase__   :      Any			    =			nn.ModuleDict(UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							Tensor	):
														return get_trunk_forward_outputs(
														    UpperCamelCase__     ,				out_feat_keys=UpperCamelCase__     ,				feature_blocks=self._feature_blocks     ,				)



class _lowercase      (							__a						):




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Optional[int]:
							lowerCamelCase__   :      Any			    =			RegNetConfig(depths=[2, 7, 17, 1]							,     hidden_sizes=[8, 8, 8, 8]							,     groups_width=8 )
							if "in1k" in model_name:
														lowerCamelCase__   :      Optional[Any]			    =			RegNetForImageClassification(_a )
							else:
														lowerCamelCase__   :      Optional[Any]			    =			RegNetModel(_a )
							# create from model (with small weights)
							lowerCamelCase__   :      Any			    =			FakeRegNetVisslWrapper(
							    RegNet(FakeRegNetParams(depth=27							,     group_width=1010							,     w_a=1744							,     w_a=620.83							,     w_m=2.52 ) ) )

							with torch.no_grad():
														lowerCamelCase__   :      List[Any]			    =			from_model.eval()
														lowerCamelCase__   :      Union[str, Any]			    =			our_model.eval()

														lowerCamelCase__   :      Any			    =			torch.randn((1, 3, 32, 32) )
														# trace both
														lowerCamelCase__   :      Dict			    =			Tracker(_a )
														lowerCamelCase__   :      int			    =			dest_tracker(_a ).parametrized

														pprint(dest_tracker.nameamodule )
														lowerCamelCase__   :      List[str]			    =			Tracker(_a )
														lowerCamelCase__   :      Optional[Any]			    =			src_tracker(_a ).parametrized

							# convert the keys -> module dict to keys -> params
							def to_params_dict(UpperCamelCase ):
														lowerCamelCase__   :      str			    =			OrderedDict()
														for name, module in dict_with_modules.items():
																					for param_name, param in module.state_dict().items():
																												lowerCamelCase__   :      int			    =			param
														return params_dict

							lowerCamelCase__   :      Optional[Any]			    =			{}

							lowerCamelCase__   :      List[Any]			    =			to_params_dict(_a )
							lowerCamelCase__   :      Any			    =			to_params_dict(_a )

							for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()							,     dst_state_dict.items() ):
														lowerCamelCase__   :      Tuple			    =			dest_key
														logger.info(f'''{src_key} -> {dest_key}''' )
							# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
							if "in1k" in model_name:
														lowerCamelCase__   :      Optional[Any]			    =			"""classifier.1.weight"""
														lowerCamelCase__   :      Tuple			    =			"""classifier.1.bias"""

							return from_to_ours_keys





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase = None							,     UpperCamelCase = True ) ->			Optional[Any]:
							lowerCamelCase__   :      int			    =			"""imagenet-1k-id2label.json"""
							lowerCamelCase__   :      Any			    =			1000

							lowerCamelCase__   :      Optional[int]			    =			"""huggingface/label-files"""
							lowerCamelCase__   :      Any			    =			num_labels
							lowerCamelCase__   :      Dict			    =			json.load(open(cached_download(hf_hub_url(_a							,     _a							,     repo_type="""dataset""" ) )							,     """r""" ) )
							lowerCamelCase__   :      str			    =			{int(_a ): v for k, v in idalabel.items()}

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

							lowerCamelCase__   :      Any			    =			partial(_a							,     num_labels=_a							,     idalabel=_a							,     labelaid=_a )

							lowerCamelCase__   :      Optional[int]			    =			{
							    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]							,     hidden_sizes=[2020, 4040, 11110, 28280]							,     groups_width=1010 ),
							    # finetuned on imagenet
							    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]							,     hidden_sizes=[2020, 4040, 11110, 28280]							,     groups_width=1010 ),
							}

							# add seer weights logic
							def load_using_classy_vision(UpperCamelCase ) -> Tuple[Dict, Dict]:
														lowerCamelCase__   :      int			    =			torch.hub.load_state_dict_from_url(_a							,     model_dir=str(_a )							,     map_location="""cpu""" )
														# check if we have a head, if yes add it
														lowerCamelCase__   :      int			    =			files["""classy_state_dict"""]["""base_model"""]["""model"""]
														return model_state_dict["trunk"], model_state_dict["heads"]

							lowerCamelCase__   :      int			    =			{
							    """regnet-y-10b-seer""": partial(
							        _a							,     """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""							,     ),
							    """regnet-y-10b-seer-in1k""": partial(
							        _a							,     """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""							,     ),
							}

							lowerCamelCase__   :      List[Any]			    =			get_from_to_our_keys(_a )

							if not (save_directory / f'''{model_name}.pth''').exists():
														logger.info("""Loading original state_dict.""" )
														lowerCamelCase__    ,						lowerCamelCase__   :      Any			    =			names_to_from_model[model_name]()
														lowerCamelCase__   :      Union[str, Any]			    =			from_state_dict_trunk
														if "in1k" in model_name:
																					# add the head
																					lowerCamelCase__   :      Union[str, Any]			    =			{**from_state_dict_trunk, **from_state_dict_head}
														logger.info("""Done!""" )

														lowerCamelCase__   :      Tuple			    =			{}

														lowerCamelCase__   :      List[Any]			    =			list(from_state_dict.keys() )
														lowerCamelCase__   :      str			    =			r"""\.block.-part."""
														# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
														for key in from_state_dict.keys():
																					# remove the weird "block[0,1]-part" from the key
																					lowerCamelCase__   :      Any			    =			re.sub(_a							,     """"""							,     _a )
																					# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
																					lowerCamelCase__   :      Dict			    =			from_to_ours_keys[src_key]
																					# store the parameter with our key
																					lowerCamelCase__   :      Dict			    =			from_state_dict[key]
																					not_used_keys.remove(_a )
														# check that all keys have been updated
														assert len(_a ) == 0, f'''Some keys where not used {','.join(_a )}'''

														logger.info(f'''The following keys were not used: {','.join(_a )}''' )

														# save our state dict to disk
														torch.save(_a							,     save_directory / f'''{model_name}.pth''' )

														del converted_state_dict
							else:
														logger.info("""The state_dict was already stored on disk.""" )
							if push_to_hub:
														logger.info(f'''Token is {os.environ['HF_TOKEN']}''' )
														logger.info("""Loading our model.""" )
														# create our model
														lowerCamelCase__   :      Tuple			    =			names_to_config[model_name]
														lowerCamelCase__   :      int			    =			RegNetModel
														if "in1k" in model_name:
																					lowerCamelCase__   :      Union[str, Any]			    =			RegNetForImageClassification
														lowerCamelCase__   :      str			    =			our_model_func(_a )
														# place our model to the meta device (so remove all the weights)
														our_model.to(torch.device("""meta""" ) )
														logger.info("""Loading state_dict in our model.""" )
														# load state dict
														lowerCamelCase__   :      Union[str, Any]			    =			our_model.state_dict().keys()
														PreTrainedModel._load_pretrained_model_low_mem(
														    _a							,     _a							,     [save_directory / f'''{model_name}.pth'''] )
														logger.info("""Finally, pushing!""" )
														# push it to hub
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / model_name							,     commit_message="""Add model"""							,     output_dir=save_directory / model_name							,     )
														lowerCamelCase__   :      Dict			    =			384
														# we can use the convnext one
														lowerCamelCase__   :      Optional[int]			    =			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""							,     size=_a )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / model_name							,     commit_message="""Add image processor"""							,     output_dir=save_directory / model_name							,     )


if __name__ == "__main__":
			_A      :     Tuple       	=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 regnet* architecture,'''
			        ''' currently: regnetx-*, regnety-*. 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.''',
			)

			_A      :     List[str]       	=parser.parse_args()

			_A      :     Optional[Any]       	=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 logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
   A					       =argparse.ArgumentParser()

   # Hyperparameters sent by the client are passed as command-line arguments to the script.
   parser.add_argument('--epochs', type=int, default=1)
   parser.add_argument('--per_device_train_batch_size', type=int, default=16)
   parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
   parser.add_argument('--model_name_or_path', type=str)
   parser.add_argument('--learning_rate', type=str, default=5E-5)
   parser.add_argument('--do_train', type=bool, default=True)
   parser.add_argument('--do_eval', type=bool, default=True)
   parser.add_argument('--output_dir', type=str)

   A       , A					       =parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A					       =16
   A					       =16

   # Set up logging
   A					       =logging.getLogger(__name__)

   logging.basicConfig(
       level=logging.getLevelName('INFO'),
       handlers=[logging.StreamHandler(sys.stdout)],
       format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
   )

   # Load model and tokenizer
   A					       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A					       =AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A       , A					       =load_dataset('imdb', split=['train', 'test'])
   A					       =train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
   A					       =test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   A					       =train_dataset.map(
       lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
   )
   train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

   A					       ={
       x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   A					       =tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
       args.per_device_train_batch_size
   )

   # Preprocess test dataset
   A					       =test_dataset.map(
       lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
   )
   test_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

   A					       ={
       x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['input_ids', 'attention_mask']
   }
   A					       =tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
       args.per_device_eval_batch_size
   )

   # fine optimizer and loss
   A					       =tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   A					       =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   A					       =[tf.keras.metrics.SparseCategoricalAccuracy()]
   model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

   A					       =time.time()
   A					       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A					       =time.time() - start_train_time

   logger.info('*** Train ***')
   logger.info(f"""train_runtime = {end_train_time}""")
   for key, value in train_results.history.items():
      logger.info(f"""  {key} = {value}""")




