



'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__a  			=						logging.get_logger(__name__)

__a  			=						{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__a  			=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Optional[int]:
      for attribute in key.split("""."""						):
            snake_case__    :       int       		= getattr(_lowerCAmelCase	,  _lowerCAmelCase						)

      if weight_type is not None:
            snake_case__    :       Optional[int]       		= getattr(_lowerCAmelCase	,  _lowerCAmelCase						).shape
      else:
            snake_case__    :       Union[str, Any]       		= hf_pointer.shape

      assert hf_shape == value.shape, (
          f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
          f" {value.shape} for {full_name}"
      )

      if weight_type == "weight":
            snake_case__    :       Optional[int]       		= value
      elif weight_type == "weight_g":
            snake_case__    :       int       		= value
      elif weight_type == "weight_v":
            snake_case__    :       int       		= value
      elif weight_type == "bias":
            snake_case__    :       Optional[int]       		= value
      else:
            snake_case__    :       Tuple       		= value

      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."						)





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Dict:
      snake_case__    :       Union[str, Any]       		= []
      snake_case__    :       List[Any]       		= fairseq_model.state_dict()

      snake_case__    :       Optional[int]       		= hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      snake_case__    :       Tuple       		= None

      for name, value in fairseq_dict.items():
            snake_case__    :       Union[str, Any]       		= False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  hf_model.config.feat_extract_norm == """group"""	,  )
                  snake_case__    :       Tuple       		= True
            elif name.split("""."""						)[0] == "proj":
                  snake_case__    :       Tuple       		= fairseq_model.proj
                  snake_case__    :       Tuple       		= True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
                              snake_case__    :       Tuple       		= True
                              if "*" in mapped_key:
                                    snake_case__    :       Union[str, Any]       		= name.split(_lowerCAmelCase						)[0].split("""."""						)[-2]
                                    snake_case__    :       Any       		= mapped_key.replace("""*"""	,  _lowerCAmelCase						)
                              if "weight_g" in name:
                                    snake_case__    :       List[str]       		= """weight_g"""
                              elif "weight_v" in name:
                                    snake_case__    :       Optional[int]       		= """weight_v"""
                              elif "bias" in name:
                                    snake_case__    :       List[str]       		= """bias"""
                              elif "weight" in name:
                                    snake_case__    :       Tuple       		= """weight"""
                              else:
                                    snake_case__    :       List[str]       		= None
                              set_recursively(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
                        continue
            if not is_used:
                  unused_weights.append(_lowerCAmelCase						)

      logger.warning(f"Unused weights: {unused_weights}"						)

      return proj_weight





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Tuple:
      snake_case__    :       Optional[int]       		= full_name.split("""conv_layers."""						)[-1]
      snake_case__    :       Optional[int]       		= name.split("""."""						)
      snake_case__    :       Any       		= int(items[0]						)
      snake_case__    :       int       		= int(items[1]						)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                  )
                  snake_case__    :       Optional[Any]       		= value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                  )
                  snake_case__    :       Optional[int]       		= value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                      " found."
                  )
                  snake_case__    :       List[Any]       		= value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                  )
                  snake_case__    :       Tuple       		= value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
      else:
            unused_weights.append(_lowerCAmelCase						)





def 		__snake_case(							_lowerCAmelCase						)						->					Tuple:
      snake_case__		,							snake_case__    :       List[Any]       		= emb.weight.shape
      snake_case__    :       List[Any]       		= nn.Linear(_lowerCAmelCase	,  _lowerCAmelCase	,  bias=_lowerCAmelCase						)
      snake_case__    :       List[Any]       		= emb.weight.data
      return lin_layer





def 		__snake_case(							_lowerCAmelCase						)						->					str:
      with open(_lowerCAmelCase	,  """r"""	,  encoding="""utf-8"""						) as f:
            snake_case__    :       Union[str, Any]       		= f.readlines()
            snake_case__    :       Union[str, Any]       		= [line.split(""" """						)[0] for line in lines]

      snake_case__    :       Any       		= len(_lowerCAmelCase						)

      snake_case__    :       Optional[Any]       		= {
          """<s>""": 0,
          """<pad>""": 1,
          """</s>""": 2,
          """<unk>""": 3,
      }

      vocab_dict.update(dict(zip(_lowerCAmelCase	,  range(4	,  num_words + 4						)						)						)						)
      return vocab_dict





@torch.no_grad()
def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  )						->					Optional[Any]:
      snake_case__    :       List[str]       		= WavaVecaConfig.from_pretrained(_lowerCAmelCase						)
      snake_case__    :       List[Any]       		= SpeechaTextaConfig.from_pretrained(
          _lowerCAmelCase	,  vocab_size=_lowerCAmelCase	,  decoder_layers=_lowerCAmelCase	,  do_stable_layer_norm=_lowerCAmelCase						)

      snake_case__    :       int       		= WavaVecaFeatureExtractor(
          feature_size=1	,  sampling_rate=16_000	,  padding_value=0	,  do_normalize=_lowerCAmelCase	,  return_attention_mask=_lowerCAmelCase	,  )

      snake_case__		,							snake_case__		,							snake_case__    :       Optional[Any]       		= fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]	,  arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						)}						)
      snake_case__    :       Optional[Any]       		= model[0].eval()

      # set weights for wav2vec2 encoder
      snake_case__    :       str       		= WavaVecaModel(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= recursively_load_weights_wavaveca(model.encoder	,  _lowerCAmelCase						)

      snake_case__    :       List[Any]       		= SpeechaTextaForCausalLM(_lowerCAmelCase						)
      snake_case__		,							snake_case__    :       Union[str, Any]       		= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,  strict=_lowerCAmelCase						)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""						)
      snake_case__    :       Optional[int]       		= nn.Parameter(model.decoder.embed_out.detach()						)

      # layer norm is init to identity matrix so leaving it is fine
      logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}"						)
      logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"						)

      snake_case__    :       Optional[int]       		= SpeechEncoderDecoderModel(encoder=_lowerCAmelCase	,  decoder=_lowerCAmelCase						)
      snake_case__    :       str       		= False

      # add projection layer
      snake_case__    :       List[str]       		= nn.Parameter(projection_layer.weight						)
      snake_case__    :       Optional[Any]       		= nn.Parameter(projection_layer.bias						)

      snake_case__    :       Union[str, Any]       		= create_vocab_dict(_lowerCAmelCase						)

      with open(os.path.join(_lowerCAmelCase	,  """vocab.json"""						)	,  """w"""						) as fp:
            json.dump(_lowerCAmelCase	,  _lowerCAmelCase						)

      snake_case__    :       Union[str, Any]       		= SpeechaTextaTokenizer(os.path.join(_lowerCAmelCase	,  """vocab.json"""						)						)
      tokenizer.save_pretrained(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= hf_wavavec.config.to_dict()
      snake_case__    :       Tuple       		= tokenizer.pad_token_id
      snake_case__    :       Tuple       		= tokenizer.bos_token_id
      snake_case__    :       List[str]       		= tokenizer.eos_token_id
      snake_case__    :       Union[str, Any]       		= """speech_to_text_2"""
      snake_case__    :       int       		= """wav2vec2"""

      snake_case__    :       List[str]       		= SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase						)

      hf_wavavec.save_pretrained(_lowerCAmelCase						)
      feature_extractor.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
       parser.add_argument(
           "--encoder_config_path",
           default="facebook/wav2vec2-large-lv60",
           type=str,
           help="Path to hf encoder wav2vec2 checkpoint config",
       )
       parser.add_argument(
           "--decoder_config_path",
           default="facebook/s2t-small-mustc-en-fr-st",
           type=str,
           help="Path to hf decoder s2t checkpoint config",
       )
       parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
       parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

       __a  			=						parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           vocab_size=args.vocab_size,
           num_decoder_layers=args.num_decoder_layers,
       )





'''simple docstring'''





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
__a  			=						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")

__a  			=						list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__a  			=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







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




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       str       		= {}
          if self.train_dir is not None:
                snake_case__    :       Optional[Any]       		= self.train_dir
          if self.validation_dir is not None:
                snake_case__    :       Union[str, Any]       		= self.validation_dir
          snake_case__    :       Union[str, Any]       		= data_files if data_files else None







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						field(
        default=_a						,						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."
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_a      )}						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
    lowercase   =						field(
        default=_a						,						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"
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}						,						)
    lowercase   =						field(
        default="main"						,						metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}						,						)
    lowercase   =						field(default=_a						,						metadata={"help": "Name or path of preprocessor config."}      )
    lowercase   =						field(
        default=_a						,						metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={
            "help": (
                "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={
            "help": (
                "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
            )
        }						,						)
    lowercase   =						field(
        default=_a						,						metadata={"help": "Stride to use for the encoder."}						,						)







class        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Tuple					,	snake_case_   :	str=192					,	snake_case_   :	Tuple=32					,	snake_case_   :	List[str]=4					,	snake_case_   :	Tuple=0.6		):
          snake_case__    :       int       		= input_size
          snake_case__    :       int       		= mask_patch_size
          snake_case__    :       List[Any]       		= model_patch_size
          snake_case__    :       int       		= 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"""		)

          snake_case__    :       List[Any]       		= self.input_size // self.mask_patch_size
          snake_case__    :       int       		= self.mask_patch_size // self.model_patch_size

          snake_case__    :       List[str]       		= self.rand_size**2
          snake_case__    :       int       		= int(np.ceil(self.token_count * self.mask_ratio		)		)




    def __call__(       self   :	str		):
          snake_case__    :       Optional[int]       		= np.random.permutation(self.token_count		)[: self.mask_count]
          snake_case__    :       str       		= np.zeros(self.token_count					,	dtype=snake_case_		)
          snake_case__    :       Union[str, Any]       		= 1

          snake_case__    :       List[str]       		= mask.reshape((self.rand_size, self.rand_size)		)
          snake_case__    :       str       		= mask.repeat(self.scale					,	axis=0		).repeat(self.scale					,	axis=1		)

          return torch.tensor(mask.flatten()		)





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      snake_case__    :       List[str]       		= torch.stack([example["""pixel_values"""] for example in examples]						)
      snake_case__    :       Optional[int]       		= torch.stack([example["""mask"""] for example in examples]						)
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}





def 		__snake_case(							)						->					Optional[Any]:
      # 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.

      snake_case__    :       Optional[int]       		= 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.
            snake_case__		,							snake_case__		,							snake_case__    :       Optional[int]       		= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
      else:
            snake_case__		,							snake_case__		,							snake_case__    :       str       		= 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"""	,  _lowerCAmelCase	,  _lowerCAmelCase						)

      # 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()

      snake_case__    :       int       		= training_args.get_process_log_level()
      logger.setLevel(_lowerCAmelCase						)
      transformers.utils.logging.set_verbosity(_lowerCAmelCase						)
      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.
      snake_case__    :       Optional[Any]       		= None
      if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
            snake_case__    :       Optional[Any]       		= 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.
      snake_case__    :       Dict       		= 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.
      snake_case__    :       Tuple       		= None if """validation""" in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split	,  _lowerCAmelCase						) and data_args.train_val_split > 0.0:
            snake_case__    :       Optional[Any]       		= ds["""train"""].train_test_split(data_args.train_val_split						)
            snake_case__    :       Optional[int]       		= split["""train"""]
            snake_case__    :       Any       		= split["""test"""]

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      snake_case__    :       List[str]       		= {
          """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:
            snake_case__    :       str       		= AutoConfig.from_pretrained(model_args.config_name_or_path	,  **_lowerCAmelCase						)
      elif model_args.model_name_or_path:
            snake_case__    :       Tuple       		= AutoConfig.from_pretrained(model_args.model_name_or_path	,  **_lowerCAmelCase						)
      else:
            snake_case__    :       List[Any]       		= 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(_lowerCAmelCase	,  """decoder_type"""						):
            snake_case__    :       Optional[int]       		= """simmim"""

      # adapt config
      snake_case__    :       Dict       		= model_args.image_size if model_args.image_size is not None else config.image_size
      snake_case__    :       Dict       		= model_args.patch_size if model_args.patch_size is not None else config.patch_size
      snake_case__    :       str       		= (
          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:
            snake_case__    :       str       		= AutoImageProcessor.from_pretrained(model_args.image_processor_name	,  **_lowerCAmelCase						)
      elif model_args.model_name_or_path:
            snake_case__    :       Union[str, Any]       		= AutoImageProcessor.from_pretrained(model_args.model_name_or_path	,  **_lowerCAmelCase						)
      else:
            snake_case__    :       Tuple       		= {
                conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
            }
            snake_case__    :       Optional[Any]       		= IMAGE_PROCESSOR_TYPES[model_args.model_type]()

      # create model
      if model_args.model_name_or_path:
            snake_case__    :       str       		= AutoModelForMaskedImageModeling.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	,  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"""						)
            snake_case__    :       Any       		= AutoModelForMaskedImageModeling.from_config(_lowerCAmelCase						)

      if training_args.do_train:
            snake_case__    :       Tuple       		= ds["""train"""].column_names
      else:
            snake_case__    :       Any       		= ds["""validation"""].column_names

      if data_args.image_column_name is not None:
            snake_case__    :       List[Any]       		= data_args.image_column_name
      elif "image" in column_names:
            snake_case__    :       int       		= """image"""
      elif "img" in column_names:
            snake_case__    :       Any       		= """img"""
      else:
            snake_case__    :       Dict       		= column_names[0]

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      snake_case__    :       Optional[int]       		= Compose(
          [
              Lambda(lambda _lowerCAmelCase						: 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
      snake_case__    :       List[str]       		= 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(_lowerCAmelCase						):
            snake_case__    :       List[Any]       		= [transforms(_lowerCAmelCase						) for image in examples[image_column_name]]
            snake_case__    :       List[str]       		= [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:
                  snake_case__    :       Optional[Any]       		= ds["""train"""].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
            # Set the training transforms
            ds["train"].set_transform(_lowerCAmelCase						)

      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:
                  snake_case__    :       Tuple       		= (
                      ds["""validation"""].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
                  )
            # Set the validation transforms
            ds["validation"].set_transform(_lowerCAmelCase						)

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

      # Training
      if training_args.do_train:
            snake_case__    :       str       		= None
            if training_args.resume_from_checkpoint is not None:
                  snake_case__    :       List[Any]       		= training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  snake_case__    :       List[str]       		= last_checkpoint
            snake_case__    :       Dict       		= trainer.train(resume_from_checkpoint=_lowerCAmelCase						)
            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:
            snake_case__    :       List[Any]       		= trainer.evaluate()
            trainer.log_metrics("""eval"""	,  _lowerCAmelCase						)
            trainer.save_metrics("""eval"""	,  _lowerCAmelCase						)

      # Write model card and (optionally) push to hub
      snake_case__    :       Optional[int]       		= {
          """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(**_lowerCAmelCase						)
      else:
            trainer.create_model_card(**_lowerCAmelCase						)


if __name__ == "__main__":
       main()
