



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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase_							:      str      =  logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

UpperCAmelCase_							:      str      =  list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase_							:      List[Any]      =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class      UpperCamelCase    :
	lowerCAmelCase	:   Optional[str]				=	field(
	    default="""cifar10"""       ,    metadata={"""help""": """Name of a dataset from the datasets package"""}     )
	lowerCAmelCase	:   Optional[str]				=	field(
	    default=_UpperCAmelCase       ,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}     )
	lowerCAmelCase	:   Optional[str]				=	field(
	    default=_UpperCAmelCase       ,    metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}       ,    )
	lowerCAmelCase	:   Optional[str]				=	field(default=_UpperCAmelCase       ,    metadata={"""help""": """A folder containing the training data."""}     )
	lowerCAmelCase	:   Optional[str]				=	field(default=_UpperCAmelCase       ,    metadata={"""help""": """A folder containing the validation data."""}     )
	lowerCAmelCase	:   Optional[float]				=	field(
	    default=0.15       ,    metadata={"""help""": """Percent to split off of train for validation."""}     )
	lowerCAmelCase	:   int				=	field(default=32       ,    metadata={"""help""": """The size of the square patches to use for masking."""}     )
	lowerCAmelCase	:   float				=	field(
	    default=0.6       ,    metadata={"""help""": """Percentage of patches to mask."""}       ,    )
	lowerCAmelCase	:   Optional[int]				=	field(
	    default=_UpperCAmelCase       ,    metadata={
	        """help""": (
	            """For debugging purposes or quicker training, truncate the number of training examples to this """
	            """value if set."""
	        )
	    }       ,    )
	lowerCAmelCase	:   Optional[int]				=	field(
	    default=_UpperCAmelCase       ,    metadata={
	        """help""": (
	            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
	            """value if set."""
	        )
	    }       ,    )



	def 			__A							(       self		):
							A__							=       {}
							if self.train_dir is not None:
													A__							=       self.train_dir
							if self.validation_dir is not None:
													A__							=       self.validation_dir
							A__							=       data_files if data_files else None






@dataclass
class      UpperCamelCase    :
	lowerCAmelCase	:   str				=	field(
	    default=_UpperCAmelCase       ,    metadata={
	        """help""": (
	            """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
	            """checkpoint identifier on the hub. """
	            """Don't set if you want to train a model from scratch."""
	        )
	    }       ,    )
	lowerCAmelCase	:   Optional[str]				=	field(
	    default=_UpperCAmelCase       ,    metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_UpperCAmelCase     )}       ,    )
	lowerCAmelCase	:   Optional[str]				=	field(
	    default=_UpperCAmelCase       ,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
	lowerCAmelCase	:   Optional[str]				=	field(
	    default=_UpperCAmelCase       ,    metadata={
	        """help""": (
	            """Override some existing default config settings when a model is trained from scratch. Example: """
	            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
	        )
	    }       ,    )
	lowerCAmelCase	:   Optional[str]				=	field(
	    default=_UpperCAmelCase       ,    metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}       ,    )
	lowerCAmelCase	:   str				=	field(
	    default="""main"""       ,    metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}       ,    )
	lowerCAmelCase	:   str				=	field(default=_UpperCAmelCase       ,    metadata={"""help""": """Name or path of preprocessor config."""}     )
	lowerCAmelCase	:   bool				=	field(
	    default=_UpperCAmelCase       ,    metadata={
	        """help""": (
	            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
	            """with private models)."""
	        )
	    }       ,    )
	lowerCAmelCase	:   Optional[int]				=	field(
	    default=_UpperCAmelCase       ,    metadata={
	        """help""": (
	            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
	        )
	    }       ,    )
	lowerCAmelCase	:   Optional[int]				=	field(
	    default=_UpperCAmelCase       ,    metadata={
	        """help""": (
	            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
	        )
	    }       ,    )
	lowerCAmelCase	:   Optional[int]				=	field(
	    default=_UpperCAmelCase       ,    metadata={"""help""": """Stride to use for the encoder."""}       ,    )






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__=192			,	UpperCAmelCase__=32			,	UpperCAmelCase__=4			,	UpperCAmelCase__=0.6		):
							A__							=       input_size
							A__							=       mask_patch_size
							A__							=       model_patch_size
							A__							=       mask_ratio

							if self.input_size % self.mask_patch_size != 0:
													raise ValueError("Input size must be divisible by mask patch size"		)
							if self.mask_patch_size % self.model_patch_size != 0:
													raise ValueError("Mask patch size must be divisible by model patch size"		)

							A__							=       self.input_size // self.mask_patch_size
							A__							=       self.mask_patch_size // self.model_patch_size

							A__							=       self.rand_size**2
							A__							=       int(np.ceil(self.token_count * self.mask_ratio		)		)



	def __call__(       self		):
							A__							=       np.random.permutation(self.token_count		)[: self.mask_count]
							A__							=       np.zeros(self.token_count			,	dtype=UpperCAmelCase__		)
							A__							=       1

							A__							=       mask.reshape((self.rand_size, self.rand_size)		)
							A__							=       mask.repeat(self.scale			,	axis=0		).repeat(self.scale			,	axis=1		)

							return torch.tensor(mask.flatten()		)





def  UpperCamelCase						(       _A			:				Tuple    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       torch.stack([example["pixel_values"] for example in examples]    )
						A__							=       torch.stack([example["mask"] for example in examples]    )
						return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						A__							=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
						if len(sys.argv    ) == 2 and sys.argv[1].endswith(".json"    ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												A__						,       A__						,       A__							=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
						else:
												A__						,       A__						,       A__							=       parser.parse_args_into_dataclasses()

						# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
						# information sent is the one passed as arguments along with your Python/PyTorch versions.
						send_example_telemetry("run_mim"			,							_A			,							_A    )

						# Setup logging
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,							datefmt="%m/%d/%Y %H:%M:%S"			,							handlers=[logging.StreamHandler(sys.stdout    )]			,							)

						if training_args.should_log:
												# The default of training_args.log_level is passive, so we set log level at info here to have that default.
												transformers.utils.logging.set_verbosity_info()

						A__							=       training_args.get_process_log_level()
						logger.setLevel(_A    )
						transformers.utils.logging.set_verbosity(_A    )
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()

						# Log on each process the small summary:
						logger.warning(
						    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
						    + f"""distributed training: {bool(training_args.local_rank != -1    )}, 16-bits training: {training_args.fpaa}"""    )
						logger.info(f"""Training/evaluation parameters {training_args}"""    )

						# Detecting last checkpoint.
						A__							=       None
						if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
												A__							=       get_last_checkpoint(training_args.output_dir    )
												if last_checkpoint is None and len(os.listdir(training_args.output_dir    )    ) > 0:
																		raise ValueError(
																		    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
																		    "Use --overwrite_output_dir to overcome."    )
												elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																		logger.info(
																		    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
																		    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."    )

    # Initialize our dataset.
						A__							=       load_dataset(
						    data_args.dataset_name			,							data_args.dataset_config_name			,							data_files=data_args.data_files			,							cache_dir=model_args.cache_dir			,							use_auth_token=True if model_args.use_auth_token else None			,							)

						# If we don't have a validation split, split off a percentage of train as validation.
						A__							=       None if "validation" in ds.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split			,							_A    ) and data_args.train_val_split > 0.0:
												A__							=       ds["train"].train_test_split(data_args.train_val_split    )
												A__							=       split["train"]
												A__							=       split["test"]

						# Create config
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						A__							=       {
						    "cache_dir": model_args.cache_dir,
						    "revision": model_args.model_revision,
						    "use_auth_token": True if model_args.use_auth_token else None,
						}
						if model_args.config_name_or_path:
												A__							=       AutoConfig.from_pretrained(model_args.config_name_or_path			,							**_A    )
						elif model_args.model_name_or_path:
												A__							=       AutoConfig.from_pretrained(model_args.model_name_or_path			,							**_A    )
						else:
												A__							=       CONFIG_MAPPING[model_args.model_type]()
												logger.warning("You are instantiating a new config instance from scratch."    )
												if model_args.config_overrides is not None:
																		logger.info(f"""Overriding config: {model_args.config_overrides}"""    )
																		config.update_from_string(model_args.config_overrides    )
																		logger.info(f"""New config: {config}"""    )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
						if hasattr(_A			,							"decoder_type"    ):
												A__							=       "simmim"

						# adapt config
						A__							=       model_args.image_size if model_args.image_size is not None else config.image_size
						A__							=       model_args.patch_size if model_args.patch_size is not None else config.patch_size
						A__							=       (
						    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
						)

						config.update(
						    {
						        "image_size": model_args.image_size,
						        "patch_size": model_args.patch_size,
						        "encoder_stride": model_args.encoder_stride,
						    }    )

						# create image processor
						if model_args.image_processor_name:
												A__							=       AutoImageProcessor.from_pretrained(model_args.image_processor_name			,							**_A    )
						elif model_args.model_name_or_path:
												A__							=       AutoImageProcessor.from_pretrained(model_args.model_name_or_path			,							**_A    )
						else:
												A__							=       {
												    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
												}
												A__							=       IMAGE_PROCESSOR_TYPES[model_args.model_type]()

						# create model
						if model_args.model_name_or_path:
												A__							=       AutoModelForMaskedImageModeling.from_pretrained(
												    model_args.model_name_or_path			,							from_tf=bool(".ckpt" in model_args.model_name_or_path    )			,							config=_A			,							cache_dir=model_args.cache_dir			,							revision=model_args.model_revision			,							use_auth_token=True if model_args.use_auth_token else None			,							)
						else:
												logger.info("Training new model from scratch"    )
												A__							=       AutoModelForMaskedImageModeling.from_config(_A    )

						if training_args.do_train:
												A__							=       ds["train"].column_names
						else:
												A__							=       ds["validation"].column_names

						if data_args.image_column_name is not None:
												A__							=       data_args.image_column_name
						elif "image" in column_names:
												A__							=       "image"
						elif "img" in column_names:
												A__							=       "img"
						else:
												A__							=       column_names[0]

						# transformations as done in original SimMIM paper
						# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
						A__							=       Compose(
						    [
						        Lambda(lambda _A    : img.convert("RGB"    ) if img.mode != "RGB" else img    ),
						        RandomResizedCrop(model_args.image_size			,							scale=(0.67, 1.0)			,							ratio=(3.0 / 4.0, 4.0 / 3.0)    ),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        Normalize(mean=image_processor.image_mean			,							std=image_processor.image_std    ),
						    ]    )

						# create mask generator
						A__							=       MaskGenerator(
						    input_size=model_args.image_size			,							mask_patch_size=data_args.mask_patch_size			,							model_patch_size=model_args.patch_size			,							mask_ratio=data_args.mask_ratio			,							)

						def preprocess_images(_A			:				Tuple    ):
												A__							=       [transforms(_A    ) for image in examples[image_column_name]]
												A__							=       [mask_generator() for i in range(len(examples[image_column_name]    )    )]

												return examples

						if training_args.do_train:
												if "train" not in ds:
																		raise ValueError("--do_train requires a train dataset"    )
												if data_args.max_train_samples is not None:
																		A__							=       ds["train"].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
												# Set the training transforms
												ds["train"].set_transform(_A    )

						if training_args.do_eval:
												if "validation" not in ds:
																		raise ValueError("--do_eval requires a validation dataset"    )
												if data_args.max_eval_samples is not None:
																		A__							=       (
																		    ds["validation"].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
																		)
												# Set the validation transforms
												ds["validation"].set_transform(_A    )

						# Initialize our trainer
						A__							=       Trainer(
						    model=_A			,							args=_A			,							train_dataset=ds["train"] if training_args.do_train else None			,							eval_dataset=ds["validation"] if training_args.do_eval else None			,							tokenizer=_A			,							data_collator=_A			,							)

						# Training
						if training_args.do_train:
												A__							=       None
												if training_args.resume_from_checkpoint is not None:
																		A__							=       training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		A__							=       last_checkpoint
												A__							=       trainer.train(resume_from_checkpoint=_A    )
												trainer.save_model()
												trainer.log_metrics("train"			,							train_result.metrics    )
												trainer.save_metrics("train"			,							train_result.metrics    )
												trainer.save_state()

						# Evaluation
						if training_args.do_eval:
												A__							=       trainer.evaluate()
												trainer.log_metrics("eval"			,							_A    )
												trainer.save_metrics("eval"			,							_A    )

						# Write model card and (optionally) push to hub
						A__							=       {
						    "finetuned_from": model_args.model_name_or_path,
						    "tasks": "masked-image-modeling",
						    "dataset": data_args.dataset_name,
						    "tags": ["masked-image-modeling"],
						}
						if training_args.push_to_hub:
												trainer.push_to_hub(**_A    )
						else:
												trainer.create_model_card(**_A    )


if __name__ == "__main__":
						main()










import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def  UpperCamelCase						(       _A			:				Tuple    )->       Dict:





						"""simple docstring"""





						A__							=       [
						    "encoder.version",
						    "decoder.version",
						    "model.encoder.version",
						    "model.decoder.version",
						    "decoder.output_projection.weight",
						    "_float_tensor",
						    "encoder.embed_positions._float_tensor",
						    "decoder.embed_positions._float_tensor",
						]
						for k in ignore_keys:
												state_dict.pop(_A			,							_A    )
def  UpperCamelCase						(       _A			:				int    )->       Optional[Any]:





						"""simple docstring"""





						A__						,       A__							=       emb.weight.shape
						A__							=       nn.Linear(_A			,							_A			,							bias=_A    )
						A__							=       emb.weight.data
						return lin_layer
def  UpperCamelCase						(       _A			:				str			,							_A			:				Optional[Any]=None    )->       str:





						"""simple docstring"""





						A__							=       {}
						for old_key in state_dict.keys():
												A__							=       old_key
												if "moe_layer.experts." in key:
																		if expert_idx is not None:
																								A__							=       key.replace("moe_layer.experts.0"			,							f"""ffn.experts.expert_{expert_idx}"""    )
																		else:
																								A__							=       key.replace("moe_layer.experts."			,							"ffn.experts.expert_"    )
												if "gate" in key:
																		A__							=       key.replace(".moe_layer.gate.wg"			,							".ffn.router.classifier"    )
												if "fc2" and "experts" not in key:
																		A__							=       key.replace(".fc2."			,							".ffn.fc2."    )
												if "fc1" and "experts" not in key:
																		A__							=       key.replace(".fc1."			,							".ffn.fc1."    )
												if ".encoder_attn." in key:
																		A__							=       key.replace(".encoder_attn."			,							".cross_attention."    )
												if "encoder_attn_layer_norm" in key:
																		A__							=       key.replace("encoder_attn_layer_norm"			,							"cross_attention_layer_norm"    )
												if "final_layer_norm" in key:
																		A__							=       key.replace("final_layer_norm"			,							"ff_layer_norm"    )
												A__							=       state_dict[old_key]
						return new_dict
def  UpperCamelCase						(       _A			:				Tuple			,							_A			:				Tuple			,							_A			:				int			,							_A			:				str			,							_A			:				str = WEIGHTS_NAME    )->       List[str]:





						"""simple docstring"""





						A__							=       []
						A__							=       0
						os.makedirs(_A			,							exist_ok=_A    )

						for expert in range(_A    ):
												A__							=       switch_checkpoint_path + f"""-rank-{expert}.pt"""
												if os.path.isfile(_A    ):
																		A__							=       torch.load(_A    )["model"]
																		remove_ignore_keys_(_A    )
																		A__							=       rename_fairseq_keys(_A			,							_A    )
																		A__							=       os.path.join(
																		    _A			,							weights_name.replace(".bin"			,							f"""-{len(_A    )+1:05d}-of-???.bin"""    )    )
																		torch.save(_A			,							_A    )
																		sharded_state_dicts.append(expert_state.keys()    )
																		total_size += sum([value.numel() for key, value in expert_state.items()]    ) * dtype_byte_size(
																		    expert_state[list(_A    )[0]].dtype    )

    # Add the last block
						A__							=       os.path.join(_A			,							weights_name.replace(".bin"			,							f"""-{len(_A    )+1:05d}-of-???.bin"""    )    )
						A__							=       torch.load(switch_checkpoint_path + "-shared.pt"    )["model"]
						remove_ignore_keys_(_A    )
						A__							=       rename_fairseq_keys(_A			,							_A    )
						A__							=       shared_weights["decoder.embed_tokens.weight"]
						sharded_state_dicts.append(shared_weights.keys()    )

						# If we only have the shared weights (dummy model/experts saved on the same file)
						if len(_A    ) == 1:
												A__							=       os.path.join(_A			,							_A    )
												torch.save(_A			,							_A    )
												return {weights_name: sharded_state_dicts[0]}, None
						else:
												torch.save(_A			,							_A    )
						# Otherwise, let's build the index
						A__							=       {}
						for idx, shard in enumerate(_A    ):
												A__							=       weights_name.replace(".bin"			,							f"""-{idx+1:05d}-of-{len(_A    ):05d}.bin"""    )
												A__							=       os.path.join(_A			,							weights_name.replace(".bin"			,							f"""-{idx+1:05d}-of-???.bin"""    )    )
												os.rename(_A			,							os.path.join(_A			,							_A    )    )
												for key in shard:
																		A__							=       shard_file

    # Add the metadata
						A__							=       {"total_size": total_size}
						A__							=       {"metadata": metadata, "weight_map": weight_map}

						with open(os.path.join(_A			,							_A    )			,							"w"			,							encoding="utf-8"    ) as f:
												A__							=       json.dumps(_A			,							indent=2			,							sort_keys=_A    ) + "\n"
												f.write(_A    )

						return metadata, index


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--nllb_moe_checkpoint_path",
						    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
						    type=str,
						    required=False,
						    help="Path to a directory containing a folder per layer. Follows the original Google format.",
						)
						parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
						    type=str,
						    required=False,
						    help="Path to the output pytorch model.",
						)
						UpperCAmelCase_							:      Union[str, Any]      =  parser.parse_args()
						UpperCAmelCase_   ,	UpperCAmelCase_							:      Optional[int]      =  shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						UpperCAmelCase_							:      Any      =  NllbMoeConfig.from_pretrained(
						    "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						UpperCAmelCase_							:      Tuple      =  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)





