




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Union[str, Any]								=SwinConfig(image_size=192   )

				if "base" in model_name:
								__magic_name__	:	Tuple								=6
								__magic_name__	:	List[str]								=128
								__magic_name__	:	Any								=(2, 2, 18, 2)
								__magic_name__	:	Dict								=(4, 8, 16, 32)
				elif "large" in model_name:
								__magic_name__	:	Union[str, Any]								=12
								__magic_name__	:	Tuple								=192
								__magic_name__	:	int								=(2, 2, 18, 2)
								__magic_name__	:	Any								=(6, 12, 24, 48)
				else:
								raise ValueError("""Model not supported, only supports base and large variants"""   )

				__magic_name__	:	Union[str, Any]								=window_size
				__magic_name__	:	Optional[Any]								=embed_dim
				__magic_name__	:	Optional[int]								=depths
				__magic_name__	:	int								=num_heads

				return config




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				if "encoder.mask_token" in name:
								__magic_name__	:	Union[str, Any]								=name.replace("""encoder.mask_token"""	,     """embeddings.mask_token"""   )
				if "encoder.patch_embed.proj" in name:
								__magic_name__	:	Tuple								=name.replace("""encoder.patch_embed.proj"""	,     """embeddings.patch_embeddings.projection"""   )
				if "encoder.patch_embed.norm" in name:
								__magic_name__	:	Tuple								=name.replace("""encoder.patch_embed.norm"""	,     """embeddings.norm"""   )
				if "attn.proj" in name:
								__magic_name__	:	str								=name.replace("""attn.proj"""	,     """attention.output.dense"""   )
				if "attn" in name:
								__magic_name__	:	Tuple								=name.replace("""attn"""	,     """attention.self"""   )
				if "norm1" in name:
								__magic_name__	:	Any								=name.replace("""norm1"""	,     """layernorm_before"""   )
				if "norm2" in name:
								__magic_name__	:	Dict								=name.replace("""norm2"""	,     """layernorm_after"""   )
				if "mlp.fc1" in name:
								__magic_name__	:	Dict								=name.replace("""mlp.fc1"""	,     """intermediate.dense"""   )
				if "mlp.fc2" in name:
								__magic_name__	:	Dict								=name.replace("""mlp.fc2"""	,     """output.dense"""   )

				if name == "encoder.norm.weight":
								__magic_name__	:	Any								="""layernorm.weight"""
				if name == "encoder.norm.bias":
								__magic_name__	:	Any								="""layernorm.bias"""

				if "decoder" in name:
								pass
				else:
								__magic_name__	:	str								="""swin.""" + name

				return name




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				for key in orig_state_dict.copy().keys():
								__magic_name__	:	str								=orig_state_dict.pop(lowerCamelCase   )

								if "attn_mask" in key:
												pass
								elif "qkv" in key:
												__magic_name__	:	Union[str, Any]								=key.split("""."""   )
												__magic_name__	:	str								=int(key_split[2]   )
												__magic_name__	:	Optional[Any]								=int(key_split[4]   )
												__magic_name__	:	List[str]								=model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

												if "weight" in key:
																__magic_name__	:	Any								=val[:dim, :]
																__magic_name__	:	Dict								=val[
																    dim : dim * 2, :
																]
																__magic_name__	:	Dict								=val[-dim:, :]
												else:
																__magic_name__	:	Any								=val[
																    :dim
																]
																__magic_name__	:	Union[str, Any]								=val[
																    dim : dim * 2
																]
																__magic_name__	:	Optional[int]								=val[
																    -dim:
																]
								else:
												__magic_name__	:	Union[str, Any]								=val

				return orig_state_dict




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Dict								=torch.load(lowerCamelCase	,     map_location="""cpu"""   )["""model"""]

				__magic_name__	:	Union[str, Any]								=get_swin_config(lowerCamelCase   )
				__magic_name__	:	List[Any]								=SwinForMaskedImageModeling(lowerCamelCase   )
				model.eval()

				__magic_name__	:	Union[str, Any]								=convert_state_dict(lowerCamelCase	,     lowerCamelCase   )
				model.load_state_dict(lowerCamelCase   )

				__magic_name__	:	List[str]								="""http://images.cocodataset.org/val2017/000000039769.jpg"""

				__magic_name__	:	Dict								=ViTImageProcessor(size={"""height""": 192, """width""": 192}   )
				__magic_name__	:	Optional[Any]								=Image.open(requests.get(lowerCamelCase	,     stream=lowerCamelCase   ).raw   )
				__magic_name__	:	Union[str, Any]								=image_processor(images=lowerCamelCase	,     return_tensors="""pt"""   )

				with torch.no_grad():
								__magic_name__	:	List[str]								=model(**lowerCamelCase   ).logits

				print(outputs.keys()   )
				print("""Looks ok!"""   )

				if pytorch_dump_folder_path is not None:
								print(F"Saving model {model_name} to {pytorch_dump_folder_path}"   )
								model.save_pretrained(lowerCamelCase   )

								print(F"Saving image processor to {pytorch_dump_folder_path}"   )
								image_processor.save_pretrained(lowerCamelCase   )

				if push_to_hub:
								print(F"Pushing model and image processor for {model_name} to hub"   )
								model.push_to_hub(F"microsoft/{model_name}"   )
								image_processor.push_to_hub(F"microsoft/{model_name}"   )


if __name__ == "__main__":
							UpperCAmelCase_		:				Tuple									=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="swin-base-simmim-window6-192",
							    type=str,
							    choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
							    help="Name of the Swin SimMIM model you'd like to convert.",
							)
							parser.add_argument(
							    "--checkpoint_path",
							    default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
							    type=str,
							    help="Path to the original PyTorch checkpoint (.pth file).",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
							)

							UpperCAmelCase_		:				str									=     parser.parse_args()
							convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = None	,     ):
				__magic_name__	:	Optional[int]								={}

				if train_file is not None:
								__magic_name__	:	Optional[int]								=[train_file]
				if eval_file is not None:
								__magic_name__	:	Any								=[eval_file]
				if test_file is not None:
								__magic_name__	:	int								=[test_file]

				__magic_name__	:	Any								=datasets.load_dataset("""csv"""	,     data_files=lowerCamelCase   )
				__magic_name__	:	Optional[Any]								=list(ds[list(files.keys()   )[0]].features.keys()   )
				__magic_name__	:	Optional[Any]								=features_name.pop(lowerCamelCase   )
				__magic_name__	:	str								=list(set(ds[list(files.keys()   )[0]][label_name]   )   )
				__magic_name__	:	Union[str, Any]								={label: i for i, label in enumerate(lowerCamelCase   )}
				__magic_name__	:	Dict								=tokenizer.model_input_names
				__magic_name__	:	Any								={}

				if len(lowerCamelCase   ) == 1:
								for k in files.keys():
												__magic_name__	:	Dict								=ds[k].map(
												    lambda lowerCamelCase   : tokenizer.batch_encode_plus(
												        example[features_name[0]]	,     truncation=lowerCamelCase	,     max_length=lowerCamelCase	,     padding="""max_length"""   )	,     batched=lowerCamelCase	,     )
				elif len(lowerCamelCase   ) == 2:
								for k in files.keys():
												__magic_name__	:	Optional[Any]								=ds[k].map(
												    lambda lowerCamelCase   : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]])	,     truncation=lowerCamelCase	,     max_length=lowerCamelCase	,     padding="""max_length"""	,     )	,     batched=lowerCamelCase	,     )

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												__magic_name__	:	Any								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Any								=labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												__magic_name__	:	Dict								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	str								=labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												__magic_name__	:	Union[str, Any]								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Optional[int]								=labelaid[ex[label_name]]
												yield (d, label)

				__magic_name__	:	Union[str, Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								__magic_name__	:	Optional[Any]								=train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

				__magic_name__	:	Optional[Any]								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								__magic_name__	:	Any								=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

				__magic_name__	:	Any								=(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase	,     ({k: tf.intaa for k in input_names}, tf.intaa)	,     ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))	,     )
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								__magic_name__	:	Optional[int]								=test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

				return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_		:				int									=     logging.getLogger(__name__)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(metadata={"""help""": """Which column contains the label"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the training file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the development file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the test file"""}			)
						UpperCamelCase     						=					field(
						    default=128					,						metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }					,						)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """Set this flag to use fast tokenization."""}			)
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}					,						)




def 		lowerCAmelCase_   (	):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.
				__magic_name__	:	List[Any]								=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
				__magic_name__				, __magic_name__				, __magic_name__	:	Union[str, Any]								=parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir   )
				    and os.listdir(training_args.output_dir   )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
								    """ --overwrite_output_dir to overcome."""   )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,     datefmt="""%m/%d/%Y %H:%M:%S"""	,     level=logging.INFO	,     )
				logger.info(
				    F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1   )}, "
				    F"16-bits training: {training_args.fpaa}"   )
				logger.info(F"Training/evaluation parameters {training_args}"   )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__magic_name__	:	Dict								=AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,     cache_dir=model_args.cache_dir	,     )

				__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Optional[Any]								=get_tfds(
				    train_file=data_args.train_file	,     eval_file=data_args.dev_file	,     test_file=data_args.test_file	,     tokenizer=lowerCamelCase	,     label_column_id=data_args.label_column_id	,     max_seq_length=data_args.max_seq_length	,     )

				__magic_name__	:	Any								=AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,     num_labels=len(lowerCamelCase   )	,     labelaid=lowerCamelCase	,     idalabel={id: label for label, id in labelaid.items()}	,     finetuning_task="""text-classification"""	,     cache_dir=model_args.cache_dir	,     )

				with training_args.strategy.scope():
								__magic_name__	:	Any								=TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path	,     from_pt=bool(""".bin""" in model_args.model_name_or_path   )	,     config=lowerCamelCase	,     cache_dir=model_args.cache_dir	,     )

				def compute_metrics(lowerCamelCase   ) -> Dict:
								__magic_name__	:	Tuple								=np.argmax(p.predictions	,     axis=1   )

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				__magic_name__	:	int								=TFTrainer(
				    model=lowerCamelCase	,     args=lowerCamelCase	,     train_dataset=lowerCamelCase	,     eval_dataset=lowerCamelCase	,     compute_metrics=lowerCamelCase	,     )

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir   )

				# Evaluation
				__magic_name__	:	List[str]								={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""   )
								__magic_name__	:	List[str]								=trainer.evaluate()
								__magic_name__	:	Optional[Any]								=os.path.join(training_args.output_dir	,     """eval_results.txt"""   )

								with open(lowerCamelCase	,     """w"""   ) as writer:
												logger.info("""***** Eval results *****"""   )

												for key, value in result.items():
																logger.info(F"  {key} = {value}"   )
																writer.write(F"{key} = {value}\n"   )

												results.update(lowerCamelCase   )

				return results


if __name__ == "__main__":
							main()




