





'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[Any]				):
			__lowercase										=							SwinConfig(image_size=1_9_2				)

			if "base" in model_name:
						__lowercase										=							6
						__lowercase										=							1_2_8
						__lowercase										=							(2, 2, 1_8, 2)
						__lowercase										=							(4, 8, 1_6, 3_2)
			elif "large" in model_name:
						__lowercase										=							1_2
						__lowercase										=							1_9_2
						__lowercase										=							(2, 2, 1_8, 2)
						__lowercase										=							(6, 1_2, 2_4, 4_8)
			else:
						raise ValueError('''Model not supported, only supports base and large variants'''				)

			__lowercase										=							window_size
			__lowercase										=							embed_dim
			__lowercase										=							depths
			__lowercase										=							num_heads

			return config




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]				):
			if "encoder.mask_token" in name:
						__lowercase										=							name.replace('''encoder.mask_token'''		,       '''embeddings.mask_token'''				)
			if "encoder.patch_embed.proj" in name:
						__lowercase										=							name.replace('''encoder.patch_embed.proj'''		,       '''embeddings.patch_embeddings.projection'''				)
			if "encoder.patch_embed.norm" in name:
						__lowercase										=							name.replace('''encoder.patch_embed.norm'''		,       '''embeddings.norm'''				)
			if "attn.proj" in name:
						__lowercase										=							name.replace('''attn.proj'''		,       '''attention.output.dense'''				)
			if "attn" in name:
						__lowercase										=							name.replace('''attn'''		,       '''attention.self'''				)
			if "norm1" in name:
						__lowercase										=							name.replace('''norm1'''		,       '''layernorm_before'''				)
			if "norm2" in name:
						__lowercase										=							name.replace('''norm2'''		,       '''layernorm_after'''				)
			if "mlp.fc1" in name:
						__lowercase										=							name.replace('''mlp.fc1'''		,       '''intermediate.dense'''				)
			if "mlp.fc2" in name:
						__lowercase										=							name.replace('''mlp.fc2'''		,       '''output.dense'''				)

			if name == "encoder.norm.weight":
						__lowercase										=							'''layernorm.weight'''
			if name == "encoder.norm.bias":
						__lowercase										=							'''layernorm.bias'''

			if "decoder" in name:
						pass
			else:
						__lowercase										=							'''swin.''' + name

			return name




def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Optional[int]				):
			for key in orig_state_dict.copy().keys():
						__lowercase										=							orig_state_dict.pop(__UpperCamelCase				)

						if "attn_mask" in key:
									pass
						elif "qkv" in key:
									__lowercase										=							key.split('''.'''				)
									__lowercase										=							int(key_split[2]				)
									__lowercase										=							int(key_split[4]				)
									__lowercase										=							model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												__lowercase										=							val[:dim, :]
												__lowercase										=							val[
												    dim : dim * 2, :
												]
												__lowercase										=							val[-dim:, :]
									else:
												__lowercase										=							val[
												    :dim
												]
												__lowercase										=							val[
												    dim : dim * 2
												]
												__lowercase										=							val[
												    -dim:
												]
						else:
									__lowercase										=							val

			return orig_state_dict




def    _lowerCAmelCase						(				lowerCamelCase_			:  Any		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  int				):
			__lowercase										=							torch.load(__UpperCamelCase		,       map_location='''cpu'''				)['''model''']

			__lowercase										=							get_swin_config(__UpperCamelCase				)
			__lowercase										=							SwinForMaskedImageModeling(__UpperCamelCase				)
			model.eval()

			__lowercase										=							convert_state_dict(__UpperCamelCase		,       __UpperCamelCase				)
			model.load_state_dict(__UpperCamelCase				)

			__lowercase										=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''

			__lowercase										=							ViTImageProcessor(size={'''height''': 1_9_2, '''width''': 1_9_2}				)
			__lowercase										=							Image.open(requests.get(__UpperCamelCase		,       stream=__UpperCamelCase				).raw				)
			__lowercase										=							image_processor(images=__UpperCamelCase		,       return_tensors='''pt'''				)

			with torch.no_grad():
						__lowercase										=							model(**__UpperCamelCase				).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(__UpperCamelCase				)

						print(f"Saving image processor to {pytorch_dump_folder_path}"				)
						image_processor.save_pretrained(__UpperCamelCase				)

			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__":
							_SCREAMING_SNAKE_CASE					=    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.'''
							)

							_SCREAMING_SNAKE_CASE					=    parser.parse_args()
							convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''



import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE					=    logging.getLogger(__name__)


_SCREAMING_SNAKE_CASE					=    list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_SCREAMING_SNAKE_CASE					=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 							__lowercase       :







	'''simple docstring'''

	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "The model checkpoint for weights initialization. Leave None if you want to train a model from"
	            " scratch."
	        )
	    } ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowerCAmelCase__						)} ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,      )






@dataclass
class 							__lowercase       :







	'''simple docstring'''

	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "The input training data file (a text file)."}						)
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "The input training data files (multiple files in glob format). "
	            "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
	        )
	    } ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."} ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "An optional input train ref data file for whole word mask in Chinese."} ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."} ,      )
	a    :				bool											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."} ,      )

	a    :				bool											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Train with masked-language modeling loss instead of language modeling."}						)
	a    :				bool											=       field(default=lowerCAmelCase__ ,      metadata={"help": "Whether ot not to use whole word mask."}						)
	a    :				float											=       field(
	    default=0.15 ,      metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}						)
	a    :				float											=       field(
	    default=1 / 6 ,      metadata={
	        "help": (
	            "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
	            " modeling."
	        )
	    } ,      )
	a    :				int											=       field(
	    default=5 ,      metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}						)

	a    :				int											=       field(
	    default=-1 ,      metadata={
	        "help": (
	            "Optional input sequence length after tokenization."
	            "The training dataset will be truncated in block of this size for training."
	            "Default to the model max input length for single sentence inputs (take into account special tokens)."
	        )
	    } ,      )
	a    :				bool											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Overwrite the cached training and evaluation sets"}						)





def    _lowerCAmelCase						(				lowerCamelCase_			:  DataTrainingArguments		,       lowerCamelCase_			:  PreTrainedTokenizer		,       lowerCamelCase_			:  bool = False		,       lowerCamelCase_			:  Optional[str] = None		,       ):
			def _dataset(lowerCamelCase_			:  str		,       lowerCamelCase_			:  Union[str, Any]=None				):
						if args.line_by_line:
									if ref_path is not None:
												if not args.whole_word_mask or not args.mlm:
															raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask'''				)
												return LineByLineWithRefDataset(
												    tokenizer=lowerCamelCase_		,       file_path=lowerCamelCase_		,       block_size=args.block_size		,       ref_path=lowerCamelCase_		,       )

									return LineByLineTextDataset(tokenizer=lowerCamelCase_		,       file_path=lowerCamelCase_		,       block_size=args.block_size				)
						else:
									return TextDataset(
									    tokenizer=lowerCamelCase_		,       file_path=lowerCamelCase_		,       block_size=args.block_size		,       overwrite_cache=args.overwrite_cache		,       cache_dir=lowerCamelCase_		,       )

			if evaluate:
						return _dataset(args.eval_data_file		,       args.eval_ref_file				)
			elif args.train_data_files:
						return ConcatDataset([_dataset(lowerCamelCase_				) for f in glob(args.train_data_files				)]				)
			else:
						return _dataset(args.train_data_file		,       args.train_ref_file				)




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.

			__lowercase										=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
			__lowercase						,		__lowercase						,		__lowercase										=							parser.parse_args_into_dataclasses()

			if data_args.eval_data_file is None and training_args.do_eval:
						raise ValueError(
						    '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
						    '''or remove the --do_eval argument.'''				)
			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 if training_args.local_rank in [-1, 0] else logging.WARN		,       )
			logger.warning(
			    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,       training_args.local_rank		,       training_args.device		,       training_args.n_gpu		,       bool(training_args.local_rank != -1				)		,       training_args.fpaa		,       )
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank				):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('''Training/evaluation parameters %s'''		,       lowerCamelCase_				)

			# Set seed
			set_seed(training_args.seed				)

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

			if model_args.config_name:
						__lowercase										=							AutoConfig.from_pretrained(model_args.config_name		,       cache_dir=model_args.cache_dir				)
			elif model_args.model_name_or_path:
						__lowercase										=							AutoConfig.from_pretrained(model_args.model_name_or_path		,       cache_dir=model_args.cache_dir				)
			else:
						__lowercase										=							CONFIG_MAPPING[model_args.model_type]()
						logger.warning('''You are instantiating a new config instance from scratch.'''				)

			if model_args.tokenizer_name:
						__lowercase										=							AutoTokenizer.from_pretrained(model_args.tokenizer_name		,       cache_dir=model_args.cache_dir				)
			elif model_args.model_name_or_path:
						__lowercase										=							AutoTokenizer.from_pretrained(model_args.model_name_or_path		,       cache_dir=model_args.cache_dir				)
			else:
						raise ValueError(
						    '''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
						    ''' script, save it,and load it from here, using --tokenizer_name'''				)

			if model_args.model_name_or_path:
						__lowercase										=							AutoModelWithLMHead.from_pretrained(
						    model_args.model_name_or_path		,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)		,       config=lowerCamelCase_		,       cache_dir=model_args.cache_dir		,       )
			else:
						logger.info('''Training new model from scratch'''				)
						__lowercase										=							AutoModelWithLMHead.from_config(lowerCamelCase_				)

			model.resize_token_embeddings(len(lowerCamelCase_				)				)

			if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
						raise ValueError(
						    '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
						    '''--mlm flag (masked language modeling).'''				)

			if data_args.block_size <= 0:
						__lowercase										=							tokenizer.max_len
						# Our input block size will be the max possible for the model
			else:
						__lowercase										=							min(data_args.block_size		,       tokenizer.max_len				)

			# Get datasets

			__lowercase										=							(
			    get_dataset(lowerCamelCase_		,       tokenizer=lowerCamelCase_		,       cache_dir=model_args.cache_dir				) if training_args.do_train else None
			)
			__lowercase										=							(
			    get_dataset(lowerCamelCase_		,       tokenizer=lowerCamelCase_		,       evaluate=lowerCamelCase_		,       cache_dir=model_args.cache_dir				)
			    if training_args.do_eval
			    else None
			)
			if config.model_type == "xlnet":
						__lowercase										=							DataCollatorForPermutationLanguageModeling(
						    tokenizer=lowerCamelCase_		,       plm_probability=data_args.plm_probability		,       max_span_length=data_args.max_span_length		,       )
			else:
						if data_args.mlm and data_args.whole_word_mask:
									__lowercase										=							DataCollatorForWholeWordMask(
									    tokenizer=lowerCamelCase_		,       mlm_probability=data_args.mlm_probability				)
						else:
									__lowercase										=							DataCollatorForLanguageModeling(
									    tokenizer=lowerCamelCase_		,       mlm=data_args.mlm		,       mlm_probability=data_args.mlm_probability				)

    # Initialize our Trainer
			__lowercase										=							Trainer(
			    model=lowerCamelCase_		,       args=lowerCamelCase_		,       data_collator=lowerCamelCase_		,       train_dataset=lowerCamelCase_		,       eval_dataset=lowerCamelCase_		,       prediction_loss_only=lowerCamelCase_		,       )

			# Training
			if training_args.do_train:
						__lowercase										=							(
						    model_args.model_name_or_path
						    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path				)
						    else None
						)
						trainer.train(model_path=lowerCamelCase_				)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_master():
									tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
			__lowercase										=							{}
			if training_args.do_eval:
						logger.info('''*** Evaluate ***'''				)

						__lowercase										=							trainer.evaluate()

						__lowercase										=							math.exp(eval_output['''eval_loss''']				)
						__lowercase										=							{'''perplexity''': perplexity}

						__lowercase										=							os.path.join(training_args.output_dir		,       '''eval_results_lm.txt'''				)
						if trainer.is_world_master():
									with open(lowerCamelCase_		,       '''w'''				) as writer:
												logger.info('''***** Eval results *****'''				)
												for key in sorted(result.keys()				):
															logger.info('''  %s = %s'''		,       lowerCamelCase_		,       str(result[key]				)				)
															writer.write('''%s = %s\n''' % (key, str(result[key]				))				)

						results.update(lowerCamelCase_				)

			return results




def    _lowerCAmelCase						(				lowerCamelCase_			:  str				):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()





