


'''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
A__		:					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""")

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str							)     ->		str:
     with open(UpperCAmelCase_      ,    'rb'							) as f:
          __lowerCamelCase   :	Optional[Any]								=		Image.open(UpperCAmelCase_							)
          return im.convert('RGB'							)





@dataclass
class    UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  :					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).'
        }					,		)
    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': '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  :					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       lowercase_				(							self	)				->     Tuple:
         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_  :





    """simple docstring"""






    lowerCamelCase  :					str							     =			field(
        default='google/vit-base-patch16-224-in21k'					,		metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					,		)
    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': 'Where do you want to store the pretrained models downloaded from s3'}  )
    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  :					bool							     =			field(
        default=_UpperCAmelCase					,		metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}					,		)







def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str							)     ->		Dict:
     __lowerCamelCase   :	Dict								=		torch.stack([example['pixel_values'] for example in examples]							)
     __lowerCamelCase   :	Optional[Any]								=		torch.tensor([example['labels'] for example in examples]							)
     return {"pixel_values": pixel_values, "labels": labels}



def 			UpperCAmelCase__					(	)     ->		int:
     # 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.

     __lowerCamelCase   :	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.
          __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	Optional[int]								=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
     else:
          __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	Optional[int]								=		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'      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)

     # 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()

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

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

     # Load the accuracy metric from the datasets package
     __lowerCamelCase   :	List[Any]								=		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(UpperCAmelCase_    :				Any							):
          return metric.compute(predictions=np.argmax(p.predictions      ,    axis=1							)      ,    references=p.label_ids							)

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

     def train_transforms(UpperCAmelCase_    :				List[Any]							):
          __lowerCamelCase   :	Any								=		[
              _train_transforms(pil_img.convert('RGB'							)							) for pil_img in example_batch['image']
          ]
          return example_batch

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

     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:
               __lowerCamelCase   :	str								=		(
                   dataset['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(UpperCAmelCase_							)

     # Initalize our trainer
     __lowerCamelCase   :	Dict								=		Trainer(
         model=UpperCAmelCase_      ,    args=UpperCAmelCase_      ,    train_dataset=dataset['train'] if training_args.do_train else None      ,    eval_dataset=dataset['validation'] if training_args.do_eval else None      ,    compute_metrics=UpperCAmelCase_      ,    tokenizer=UpperCAmelCase_      ,    data_collator=UpperCAmelCase_      ,    )

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

     # Write model card and (optionally) push to hub
     __lowerCamelCase   :	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(**UpperCAmelCase_							)
     else:
          trainer.create_model_card(**UpperCAmelCase_							)


if __name__ == "__main__":
      main()










'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


A__		:					str												=  [
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
A__		:					Dict												=  [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

A__		:					int												=  (
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  (
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  [
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any							)     ->		List[str]:
     for tf_name, hf_name in patterns:
          __lowerCamelCase   :	Optional[int]								=		k.replace(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return k



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				dict      ,    UpperCAmelCase_    :				dict							)     ->		BigBirdPegasusForConditionalGeneration:
     __lowerCamelCase   :	int								=		BigBirdPegasusConfig(**UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		BigBirdPegasusForConditionalGeneration(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		torch_model.state_dict()
     __lowerCamelCase   :	Tuple								=		{}

     # separating decoder weights
     __lowerCamelCase   :	Dict								=		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'							)}
     __lowerCamelCase   :	str								=		{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'							)}

     for k, v in tqdm(decoder_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Tuple								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Tuple								=		DECODER_PATTERNS
          __lowerCamelCase   :	Optional[int]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict:
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	Union[str, Any]								=		v.T
          __lowerCamelCase   :	str								=		torch.from_numpy(UpperCAmelCase_							)
          assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     for k, v in tqdm(remaining_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Optional[Any]								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Dict								=		REMAINING_PATTERNS
          __lowerCamelCase   :	List[str]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	List[str]								=		v.T
          __lowerCamelCase   :	List[str]								=		torch.from_numpy(UpperCAmelCase_							)
          if k != "pegasus/embeddings/position_embeddings":
               assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     __lowerCamelCase   :	Any								=		mapping['model.embed_positions.weight']
     __lowerCamelCase   :	Union[str, Any]								=		mapping.pop('model.embed_positions.weight'							)
     __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		torch_model.load_state_dict(UpperCAmelCase_      ,    strict=UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		[
         k
         for k in missing
         if k
         not in [
             'final_logits_bias',
             'model.encoder.embed_tokens.weight',
             'model.decoder.embed_tokens.weight',
             'lm_head.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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     __lowerCamelCase   :	int								=		tf.train.list_variables(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		{}
     __lowerCamelCase   :	List[Any]								=		['global_step']
     for name, shape in tqdm(UpperCAmelCase_      ,    desc='converting tf checkpoint to dict'							):
          __lowerCamelCase   :	Any								=		any(pat in name for pat in ignore_name							)
          if skip_key:
               continue
          __lowerCamelCase   :	Dict								=		tf.train.load_variable(UpperCAmelCase_      ,    UpperCAmelCase_							)
          __lowerCamelCase   :	List[Any]								=		array
     return tf_weights



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				dict							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		get_tf_weights_as_numpy(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		convert_bigbird_pegasus(UpperCAmelCase_      ,    UpperCAmelCase_							)
     torch_model.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					Any												=  argparse.ArgumentParser()
      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.""")
      A__		:					List[str]												=  parser.parse_args()
      A__		:					Optional[int]												=  {}
      convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






