



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
_lowerCAmelCase    : Any							=							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")

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Optional[int]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__						,  "rb"    ) as f:
          _UpperCAmelCase      :     Dict        =   Image.open(SCREAMING_SNAKE_CASE__    )
          return im.convert("RGB"    )






@dataclass
class      UpperCAmelCase_  :
      __SCREAMING_SNAKE_CASE						:       Optional[str]								=			field(
          default=UpperCAmelCase_	,      metadata={
              'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
          }	,      )
      __SCREAMING_SNAKE_CASE						:       Optional[str]								=			field(
          default=UpperCAmelCase_	,      metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}						)
      __SCREAMING_SNAKE_CASE						:       Optional[str]								=			field(default=UpperCAmelCase_	,      metadata={'help': 'A folder containing the training data.'}						)
      __SCREAMING_SNAKE_CASE						:       Optional[str]								=			field(default=UpperCAmelCase_	,      metadata={'help': 'A folder containing the validation data.'}						)
      __SCREAMING_SNAKE_CASE						:       Optional[float]								=			field(
          default=0.15	,      metadata={'help': 'Percent to split off of train for validation.'}						)
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			field(
          default=UpperCAmelCase_	,      metadata={
              'help': (
                  'For debugging purposes or quicker training, truncate the number of training examples to this '
                  'value if set.'
              )
          }	,      )
      __SCREAMING_SNAKE_CASE						:       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 						snake_case_ (					self					:     str						):
           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      UpperCAmelCase_  :
      __SCREAMING_SNAKE_CASE						:       str								=			field(
          default='google/vit-base-patch16-224-in21k'	,      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}	,      )
      __SCREAMING_SNAKE_CASE						:       Optional[str]								=			field(
          default=UpperCAmelCase_	,      metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCAmelCase_						)}	,      )
      __SCREAMING_SNAKE_CASE						:       Optional[str]								=			field(
          default=UpperCAmelCase_	,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
      __SCREAMING_SNAKE_CASE						:       Optional[str]								=			field(
          default=UpperCAmelCase_	,      metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}						)
      __SCREAMING_SNAKE_CASE						:       str								=			field(
          default='main'	,      metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,      )
      __SCREAMING_SNAKE_CASE						:       str								=			field(default=UpperCAmelCase_	,      metadata={'help': 'Name or path of preprocessor config.'}						)
      __SCREAMING_SNAKE_CASE						:       bool								=			field(
          default=UpperCAmelCase_	,      metadata={
              'help': (
                  'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                  'with private models).'
              )
          }	,      )
      __SCREAMING_SNAKE_CASE						:       bool								=			field(
          default=UpperCAmelCase_	,      metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}	,      )







def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple    )  ->       Tuple:





     '''simple docstring'''

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

def 						__snake_case						(				)  ->       Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   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.
          _UpperCAmelCase      :     str        =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
     else:
          _UpperCAmelCase      :     Dict        =   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"						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

     # 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()

     _UpperCAmelCase      :     int        =   training_args.get_process_log_level()
     logger.setLevel(SCREAMING_SNAKE_CASE__    )
     transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE__    )
     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.
     _UpperCAmelCase      :     Optional[Any]        =   None
     if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
          _UpperCAmelCase      :     Optional[int]        =   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:
          _UpperCAmelCase      :     List[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:
          _UpperCAmelCase      :     str        =   {}
          if data_args.train_dir is not None:
               _UpperCAmelCase      :     Tuple        =   os.path.join(data_args.train_dir						,  "**"    )
          if data_args.validation_dir is not None:
               _UpperCAmelCase      :     Dict        =   os.path.join(data_args.validation_dir						,  "**"    )
          _UpperCAmelCase      :     Any        =   load_dataset(
              "imagefolder"						,  data_files=SCREAMING_SNAKE_CASE__						,  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.
     _UpperCAmelCase      :     Optional[Any]        =   None if '''validation''' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split						,  SCREAMING_SNAKE_CASE__    ) and data_args.train_val_split > 0.0:
          _UpperCAmelCase      :     Union[str, Any]        =   dataset['''train'''].train_test_split(data_args.train_val_split    )
          _UpperCAmelCase      :     Tuple        =   split['''train''']
          _UpperCAmelCase      :     Dict        =   split['''test''']

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

     # Load the accuracy metric from the datasets package
     _UpperCAmelCase      :     Dict        =   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(SCREAMING_SNAKE_CASE__     :						Union[str, Any]    ):
          return metric.compute(predictions=np.argmax(p.predictions						,  axis=1    )						,  references=p.label_ids    )

     _UpperCAmelCase      :     Dict        =   AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path						,  num_labels=len(SCREAMING_SNAKE_CASE__    )						,  labelaid=SCREAMING_SNAKE_CASE__						,  idalabel=SCREAMING_SNAKE_CASE__						,  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						,  )
     _UpperCAmelCase      :     Tuple        =   AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path						,  from_tf=bool(".ckpt" in model_args.model_name_or_path    )						,  config=SCREAMING_SNAKE_CASE__						,  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						,  )
     _UpperCAmelCase      :     str        =   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:
          _UpperCAmelCase      :     List[Any]        =   image_processor.size['''shortest_edge''']
     else:
          _UpperCAmelCase      :     Optional[Any]        =   (image_processor.size['''height'''], image_processor.size['''width'''])
     _UpperCAmelCase      :     Optional[int]        =   Normalize(mean=image_processor.image_mean						,  std=image_processor.image_std    )
     _UpperCAmelCase      :     Optional[int]        =   Compose(
         [
             RandomResizedCrop(SCREAMING_SNAKE_CASE__    ),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]    )
     _UpperCAmelCase      :     Dict        =   Compose(
         [
             Resize(SCREAMING_SNAKE_CASE__    ),
             CenterCrop(SCREAMING_SNAKE_CASE__    ),
             ToTensor(),
             normalize,
         ]    )

     def train_transforms(SCREAMING_SNAKE_CASE__     :						List[str]    ):
          _UpperCAmelCase      :     Optional[Any]        =   [
              _train_transforms(pil_img.convert("RGB"    )    ) for pil_img in example_batch['''image''']
          ]
          return example_batch

     def val_transforms(SCREAMING_SNAKE_CASE__     :						Union[str, Any]    ):
          _UpperCAmelCase      :     Optional[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:
               _UpperCAmelCase      :     Any        =   (
                   dataset['''train'''].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
               )
          # Set the training transforms
          dataset["train"].set_transform(SCREAMING_SNAKE_CASE__    )

     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:
               _UpperCAmelCase      :     int        =   (
                   dataset['''validation'''].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
               )
          # Set the validation transforms
          dataset["validation"].set_transform(SCREAMING_SNAKE_CASE__    )

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

     # Training
     if training_args.do_train:
          _UpperCAmelCase      :     Tuple        =   None
          if training_args.resume_from_checkpoint is not None:
               _UpperCAmelCase      :     Optional[int]        =   training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _UpperCAmelCase      :     List[Any]        =   last_checkpoint
          _UpperCAmelCase      :     Optional[Any]        =   trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__    )
          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:
          _UpperCAmelCase      :     Optional[Any]        =   trainer.evaluate()
          trainer.log_metrics("eval"						,  SCREAMING_SNAKE_CASE__    )
          trainer.save_metrics("eval"						,  SCREAMING_SNAKE_CASE__    )

     # Write model card and (optionally) push to hub
     _UpperCAmelCase      :     Union[str, Any]        =   {
         '''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(**SCREAMING_SNAKE_CASE__    )
     else:
          trainer.create_model_card(**SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 main()





"""simple docstring"""




import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


_lowerCAmelCase    : List[str]							=							[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict    )  ->       List[str]:





     '''simple docstring'''

     for pegasus_name, hf_name in PATTERNS:
          _UpperCAmelCase      :     str        =   k.replace(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     return k

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						dict						,  SCREAMING_SNAKE_CASE__     :						dict    )  ->       PegasusForConditionalGeneration:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   DEFAULTS.copy()
     cfg_kwargs.update(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusConfig(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   torch_model.model.state_dict()
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     for k, v in tf_weights.items():
          _UpperCAmelCase      :     Union[str, Any]        =   rename_state_dict_key(SCREAMING_SNAKE_CASE__    )
          if new_k not in sd:
               raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'    )

          if "dense" in k or "proj" in new_k:
               _UpperCAmelCase      :     Any        =   v.T
          _UpperCAmelCase      :     str        =   torch.tensor(SCREAMING_SNAKE_CASE__						,  dtype=sd[new_k].dtype    )
          assert v.shape == sd[new_k].shape, f'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
     # make sure embedding.padding_idx is respected
     _UpperCAmelCase      :     Tuple        =   torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]    )
     _UpperCAmelCase      :     Any        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   mapping["shared.weight"]
     _UpperCAmelCase      :     Dict        =   {k: torch.zeros_like(SCREAMING_SNAKE_CASE__    ) for k, v in sd.items() if k.endswith("bias"    ) and k not in mapping}
     mapping.update(**SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE__						,  strict=SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     int        =   [
         k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
     ]
     assert unexpected_missing == [], f'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], f'no matches found for the following tf keys {extra}'
     return torch_model

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict="./ckpt/aeslc/model.ckpt-32000"    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   tf.train.list_variables(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Union[str, Any]        =   {}
     _UpperCAmelCase      :     Optional[Any]        =   ["Adafactor", "global_step"]
     for name, shape in tqdm(SCREAMING_SNAKE_CASE__						,  desc="converting tf checkpoint to dict"    ):
          _UpperCAmelCase      :     Union[str, Any]        =   any(pat in name for pat in ignore_name    )
          if skip_key:
               continue
          _UpperCAmelCase      :     int        =   tf.train.load_variable(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Dict        =   array
     return tf_weights

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   Path(SCREAMING_SNAKE_CASE__    ).parent.name
     _UpperCAmelCase      :     Tuple        =   task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
     _UpperCAmelCase      :     Dict        =   PegasusTokenizer.from_pretrained("sshleifer/pegasus"						,  model_max_length=SCREAMING_SNAKE_CASE__    )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(SCREAMING_SNAKE_CASE__    )

     # convert model
     _UpperCAmelCase      :     Union[str, Any]        =   get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   task_specific_params[f'summarization_{dataset}']
     if dataset == "large":
          _UpperCAmelCase      :     Optional[int]        =   task_specific_params
     _UpperCAmelCase      :     str        =   convert_pegasus(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
     torch_model.save_pretrained(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[Any]        =   torch_model.state_dict()
     sd.pop("model.decoder.embed_positions.weight"    )
     sd.pop("model.encoder.embed_positions.weight"    )
     torch.save(SCREAMING_SNAKE_CASE__						,  Path(SCREAMING_SNAKE_CASE__    ) / "pytorch_model.bin"    )


if __name__ == "__main__":
 _lowerCAmelCase    : Union[str, Any]							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
 parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
 _lowerCAmelCase    : Union[str, Any]							=							parser.parse_args()
 if args.save_dir is None:
  _lowerCAmelCase    : Tuple							=							Path(args.tf_ckpt_path).parent.name
  _lowerCAmelCase    : Dict							=							os.path.join("pegasus", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
