



'''simple docstring'''





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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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__  :			List[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-classification/requirements.txt')

UpperCamelCase__  :			Union[str, Any]          =					list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCamelCase__  :			Optional[Any]          =					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def   UpperCAmelCase			(  a_						)					->    int:



   """simple docstring"""






   with open(__lowercase	,						"""rb"""						) as f:
      A_   :						List[str]		= Image.open(__lowercase						)
      return im.convert("""RGB"""						)


@dataclass
class 				_lowerCAmelCase     :


 """simple docstring"""

 lowerCamelCase		       =			field(
     default=__lowerCamelCase,				metadata={
         '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
     },				)
 lowerCamelCase		       =			field(
     default=__lowerCamelCase,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
 lowerCamelCase		       =			field(default=__lowerCamelCase,				metadata={'''help''': '''A folder containing the training data.'''}				)
 lowerCamelCase		       =			field(default=__lowerCamelCase,				metadata={'''help''': '''A folder containing the validation data.'''}				)
 lowerCamelCase		       =			field(
     default=0.1_5,				metadata={'''help''': '''Percent to split off of train for validation.'''}				)
 lowerCamelCase		       =			field(
     default=__lowerCamelCase,				metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     },				)
 lowerCamelCase		       =			field(
     default=__lowerCamelCase,				metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     },				)







 def 	UpperCAmelCase_    (	self   )  ->  Dict:
    if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
       raise ValueError(
           """You must specify either a dataset name from the hub or a train and/or validation directory."""   )


@dataclass
class 				_lowerCAmelCase     :


 """simple docstring"""

 lowerCamelCase		       =			field(
     default='''google/vit-base-patch16-224-in21k''',				metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''},				)
 lowerCamelCase		       =			field(
     default=__lowerCamelCase,				metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__lowerCamelCase				)},				)
 lowerCamelCase		       =			field(
     default=__lowerCamelCase,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
 lowerCamelCase		       =			field(
     default=__lowerCamelCase,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}				)
 lowerCamelCase		       =			field(
     default='''main''',				metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''},				)
 lowerCamelCase		       =			field(default=__lowerCamelCase,				metadata={'''help''': '''Name or path of preprocessor config.'''}				)
 lowerCamelCase		       =			field(
     default=__lowerCamelCase,				metadata={
         '''help''': (
             '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
             '''with private models).'''
         )
     },				)
 lowerCamelCase		       =			field(
     default=__lowerCamelCase,				metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''},				)



def   UpperCAmelCase			(  a_						)					->    str:



   """simple docstring"""






   A_   :						Union[str, Any]		= torch.stack([example["""pixel_values"""] for example in examples]						)
   A_   :						List[str]		= torch.tensor([example["""labels"""] for example in examples]						)
   return {"pixel_values": pixel_values, "labels": labels}



def   UpperCAmelCase			(  )					->    Optional[int]:



   """simple docstring"""






   A_   :						str		= 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_   :						Tuple		= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
   else:
      A_		,   A_		,   A_   :						Optional[Any]		= 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_image_classification"""	,						__lowercase	,						__lowercase						)

   # 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_   :						str		= training_args.get_process_log_level()
   logger.setLevel(__lowercase						)
   transformers.utils.logging.set_verbosity(__lowercase						)
   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_   :						Optional[Any]		= None
   if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
      A_   :						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."""						)

    # Set seed before initializing model.
   set_seed(training_args.seed						)

   # Initialize our dataset and prepare it for the 'image-classification' task.
   if data_args.dataset_name is not None:
      A_   :						Optional[Any]		= load_dataset(
          data_args.dataset_name	,						data_args.dataset_config_name	,						cache_dir=model_args.cache_dir	,						task="""image-classification"""	,						use_auth_token=True if model_args.use_auth_token else None	,						)
   else:
      A_   :						Optional[int]		= {}
      if data_args.train_dir is not None:
         A_   :						Optional[Any]		= os.path.join(data_args.train_dir	,						"""**"""						)
      if data_args.validation_dir is not None:
         A_   :						Any		= os.path.join(data_args.validation_dir	,						"""**"""						)
      A_   :						Any		= load_dataset(
          """imagefolder"""	,						data_files=__lowercase	,						cache_dir=model_args.cache_dir	,						task="""image-classification"""	,						)

   # If we don't have a validation split, split off a percentage of train as validation.
   A_   :						Optional[int]		= None if """validation""" in dataset.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split	,						__lowercase						) and data_args.train_val_split > 0.0:
      A_   :						Optional[int]		= dataset["""train"""].train_test_split(data_args.train_val_split						)
      A_   :						Union[str, Any]		= split["""train"""]
      A_   :						Union[str, Any]		= split["""test"""]

   # Prepare label mappings.
   # We'll include these in the model's config to get human readable labels in the Inference API.
   A_   :						Union[str, Any]		= dataset["""train"""].features["""labels"""].names
   A_		,   A_   :						List[Any]		= {}, {}
   for i, label in enumerate(__lowercase						):
      A_   :						Dict		= str(__lowercase						)
      A_   :						Optional[int]		= label

   # Load the accuracy metric from the datasets package
   A_   :						int		= evaluate.load("""accuracy"""						)

   # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
   # predictions and label_ids field) and has to return a dictionary string to float.
   def compute_metrics(a_						):
      return metric.compute(predictions=np.argmax(p.predictions	,						axis=1						)	,						references=p.label_ids						)

   A_   :						Optional[Any]		= AutoConfig.from_pretrained(
       model_args.config_name or model_args.model_name_or_path	,						num_labels=len(__lowercase						)	,						labelaid=__lowercase	,						idalabel=__lowercase	,						finetuning_task="""image-classification"""	,						cache_dir=model_args.cache_dir	,						revision=model_args.model_revision	,						use_auth_token=True if model_args.use_auth_token else None	,						)
   A_   :						Dict		= AutoModelForImageClassification.from_pretrained(
       model_args.model_name_or_path	,						from_tf=bool(""".ckpt""" in model_args.model_name_or_path						)	,						config=__lowercase	,						cache_dir=model_args.cache_dir	,						revision=model_args.model_revision	,						use_auth_token=True if model_args.use_auth_token else None	,						ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,						)
   A_   :						Union[str, Any]		= AutoImageProcessor.from_pretrained(
       model_args.image_processor_name or model_args.model_name_or_path	,						cache_dir=model_args.cache_dir	,						revision=model_args.model_revision	,						use_auth_token=True if model_args.use_auth_token else None	,						)

   # Define torchvision transforms to be applied to each image.
   if "shortest_edge" in image_processor.size:
      A_   :						Optional[int]		= image_processor.size["""shortest_edge"""]
   else:
      A_   :						Union[str, Any]		= (image_processor.size["""height"""], image_processor.size["""width"""])
   A_   :						Union[str, Any]		= Normalize(mean=image_processor.image_mean	,						std=image_processor.image_std						)
   A_   :						Optional[Any]		= Compose(
       [
           RandomResizedCrop(__lowercase						),
           RandomHorizontalFlip(),
           ToTensor(),
           normalize,
       ]						)
   A_   :						Any		= Compose(
       [
           Resize(__lowercase						),
           CenterCrop(__lowercase						),
           ToTensor(),
           normalize,
       ]						)

   def train_transforms(a_						):
      A_   :						Any		= [
          _train_transforms(pil_img.convert("""RGB"""						)						) for pil_img in example_batch["""image"""]
      ]
      return example_batch

   def val_transforms(a_						):
      A_   :						Any		= [_val_transforms(pil_img.convert("""RGB"""						)						) for pil_img in example_batch["""image"""]]
      return example_batch

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

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

   # Initalize our trainer
   A_   :						Tuple		= Trainer(
       model=__lowercase	,						args=__lowercase	,						train_dataset=dataset["""train"""] if training_args.do_train else None	,						eval_dataset=dataset["""validation"""] if training_args.do_eval else None	,						compute_metrics=__lowercase	,						tokenizer=__lowercase	,						data_collator=__lowercase	,						)

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

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


if __name__ == "__main__":
  main()







'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           '_float_tensor',
           'decoder.output_projection.weight',
       ]
       for k in ignore_keys:
              state_dict.pop(__lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       =  emb.weight.shape
       __UpperCamelCase       =  nn.Linear(__lowercase				,  __lowercase				,  bias=__lowercase     )
       __UpperCamelCase       =  emb.weight.data
       return lin_layer
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[str]="facebook/mbart-large-en-ro"				,  __lowercase						:  str=False				,  __lowercase						:  List[Any]=False     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )['model']
       remove_ignore_keys_(__lowercase     )
       __UpperCamelCase       =  state_dict['encoder.embed_tokens.weight'].shape[0]

       __UpperCamelCase       =  MBartConfig.from_pretrained(__lowercase				,  vocab_size=__lowercase     )
       if mbart_aa and finetuned:
              __UpperCamelCase       =  'relu'

       __UpperCamelCase       =  state_dict['decoder.embed_tokens.weight']
       __UpperCamelCase       =  MBartForConditionalGeneration(__lowercase     )
       model.model.load_state_dict(__lowercase     )

       if finetuned:
              __UpperCamelCase       =  make_linear_from_emb(model.model.shared     )

       return model


if __name__ == "__main__":
    a__							:							Dict =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
    )
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--hf_config''',
        default='''facebook/mbart-large-cc25''',
        type=str,
        help='''Which huggingface architecture to use: mbart-large''',
    )
    parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
    parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
    a__							:							Union[str, Any] =parser.parse_args()
    a__							:							str =convert_fairseq_mbart_checkpoint_from_disk(
        args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
    )
    model.save_pretrained(args.pytorch_dump_folder_path)
