



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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase = None     ,				):
    __a		 =       {}

    if train_file is not None:
        __a		 =       [train_file]
    if eval_file is not None:
        __a		 =       [eval_file]
    if test_file is not None:
        __a		 =       [test_file]

    __a		 =       datasets.load_dataset('''csv'''     ,				data_files=_UpperCAmelCase       )
    __a		 =       list(ds[list(files.keys()       )[0]].features.keys()       )
    __a		 =       features_name.pop(_UpperCAmelCase       )
    __a		 =       list(set(ds[list(files.keys()       )[0]][label_name]       )       )
    __a		 =       {label: i for i, label in enumerate(_UpperCAmelCase       )}
    __a		 =       tokenizer.model_input_names
    __a		 =       {}

    if len(_UpperCAmelCase       ) == 1:
        for k in files.keys():
            __a		 =       ds[k].map(
                lambda _UpperCAmelCase       : tokenizer.batch_encode_plus(
                    example[features_name[0]]     ,				truncation=_UpperCAmelCase     ,				max_length=_UpperCAmelCase     ,				padding='''max_length'''       )     ,				batched=_UpperCAmelCase     ,				)
    elif len(_UpperCAmelCase       ) == 2:
        for k in files.keys():
            __a		 =       ds[k].map(
                lambda _UpperCAmelCase       : tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]])     ,				truncation=_UpperCAmelCase     ,				max_length=_UpperCAmelCase     ,				padding='''max_length'''     ,				)     ,				batched=_UpperCAmelCase     ,				)

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            __a		 =       {k: v for k, v in ex.items() if k in input_names}
            __a		 =       labelaid[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            __a		 =       {k: v for k, v in ex.items() if k in input_names}
            __a		 =       labelaid[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            __a		 =       {k: v for k, v in ex.items() if k in input_names}
            __a		 =       labelaid[ex[label_name]]
            yield (d, label)

    __a		 =       (
        tf.data.Dataset.from_generator(
            _UpperCAmelCase     ,				({k: tf.intaa for k in input_names}, tf.intaa)     ,				({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))     ,				)
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        __a		 =       train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

    __a		 =       (
        tf.data.Dataset.from_generator(
            _UpperCAmelCase     ,				({k: tf.intaa for k in input_names}, tf.intaa)     ,				({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))     ,				)
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        __a		 =       val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

    __a		 =       (
        tf.data.Dataset.from_generator(
            _UpperCAmelCase     ,				({k: tf.intaa for k in input_names}, tf.intaa)     ,				({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))     ,				)
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        __a		 =       test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

    return train_ds, val_ds, test_ds, labelaid


__snake_case		:Optional[int]   	=  logging.getLogger(__name__)

@dataclass
class 					_A :
       UpperCamelCase__						:					int		      =		field(metadata={'''help''': '''Which column contains the label'''}					)
       UpperCamelCase__						:					str		      =		field(default=__UpperCAmelCase   ,metadata={'''help''': '''The path of the training file'''}					)
       UpperCamelCase__						:					Optional[str]		      =		field(default=__UpperCAmelCase   ,metadata={'''help''': '''The path of the development file'''}					)
       UpperCamelCase__						:					Optional[str]		      =		field(default=__UpperCAmelCase   ,metadata={'''help''': '''The path of the test file'''}					)
       UpperCamelCase__						:					int		      =		field(
           default=128   ,metadata={
               '''help''': (
                   '''The maximum total input sequence length after tokenization. Sequences longer '''
                   '''than this will be truncated, sequences shorter will be padded.'''
               )
           }   ,)
       UpperCamelCase__						:					bool		      =		field(
           default=__UpperCAmelCase   ,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)


@dataclass
class 					_A :
       UpperCamelCase__						:					str		      =		field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
       UpperCamelCase__						:					Optional[str]		      =		field(
           default=__UpperCAmelCase   ,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
       UpperCamelCase__						:					Optional[str]		      =		field(
           default=__UpperCAmelCase   ,metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
       UpperCamelCase__						:					bool		      =		field(default=__UpperCAmelCase   ,metadata={'''help''': '''Set this flag to use fast tokenization.'''}					)
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       UpperCamelCase__						:					Optional[str]		      =		field(
           default=__UpperCAmelCase   ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,)





def   __snake_case			(    ):
    # 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.
    __a		 =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
    __a ,     __a ,     __a		 =       parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir       )
        and os.listdir(training_args.output_dir       )
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
            ''' --overwrite_output_dir to overcome.'''       )

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,				datefmt='''%m/%d/%Y %H:%M:%S'''     ,				level=logging.INFO     ,				)
    logger.info(
        f'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1       )}, '
        f'16-bits training: {training_args.fpaa}'       )
    logger.info(f'Training/evaluation parameters {training_args}'       )

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    __a		 =       AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,				cache_dir=model_args.cache_dir     ,				)

    __a ,     __a ,     __a ,     __a		 =       get_tfds(
        train_file=data_args.train_file     ,				eval_file=data_args.dev_file     ,				test_file=data_args.test_file     ,				tokenizer=_UpperCAmelCase     ,				label_column_id=data_args.label_column_id     ,				max_seq_length=data_args.max_seq_length     ,				)

    __a		 =       AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,				num_labels=len(_UpperCAmelCase       )     ,				labelaid=_UpperCAmelCase     ,				idalabel={id: label for label, id in labelaid.items()}     ,				finetuning_task='''text-classification'''     ,				cache_dir=model_args.cache_dir     ,				)

    with training_args.strategy.scope():
        __a		 =       TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path     ,				from_pt=bool('''.bin''' in model_args.model_name_or_path       )     ,				config=_UpperCAmelCase     ,				cache_dir=model_args.cache_dir     ,				)

    def compute_metrics(_UpperCAmelCase       ) -> Dict:
        __a		 =       np.argmax(p.predictions     ,				axis=1       )

        return {"acc": (preds == p.label_ids).mean()}

    # Initialize our Trainer
    __a		 =       TFTrainer(
        model=_UpperCAmelCase     ,				args=_UpperCAmelCase     ,				train_dataset=_UpperCAmelCase     ,				eval_dataset=_UpperCAmelCase     ,				compute_metrics=_UpperCAmelCase     ,				)

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
    __a		 =       {}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''       )
        __a		 =       trainer.evaluate()
        __a		 =       os.path.join(training_args.output_dir     ,				'''eval_results.txt'''       )

        with open(_UpperCAmelCase     ,				'''w'''       ) as writer:
            logger.info('''***** Eval results *****'''       )

            for key, value in result.items():
                logger.info(f'  {key} = {value}'       )
                writer.write(f'{key} = {value}\n'       )

            results.update(_UpperCAmelCase       )

    return results


if __name__ == "__main__":
 main()










import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
            f'stage{idx}.patch_embed.proj.weight',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
            f'stage{idx}.patch_embed.proj.bias',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
            f'stage{idx}.patch_embed.norm.weight',
        )       )
    embed.append(
        (
            f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
            f'stage{idx}.patch_embed.norm.bias',
        )       )
    return embed





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       []
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
            f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
            f'stage{idx}.blocks.{cnt}.attn.proj.weight',
        )       )
    attention_weights.append(
        (
            f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
            f'stage{idx}.blocks.{cnt}.attn.proj.bias',
        )       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')       )
    attention_weights.append(
        (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')       )
    return attention_weights





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       []
    token.append((f'cvt.encoder.stages.{idx}.cls_token', '''stage2.cls_token''')       )
    return token





def   __snake_case			(    ):
    __a		 =       []
    head.append(('''layernorm.weight''', '''norm.weight''')       )
    head.append(('''layernorm.bias''', '''norm.bias''')       )
    head.append(('''classifier.weight''', '''head.weight''')       )
    head.append(('''classifier.bias''', '''head.bias''')       )
    return head





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       '''imagenet-1k-id2label.json'''
    __a		 =       1000

    __a		 =       '''huggingface/label-files'''
    __a		 =       num_labels
    __a		 =       json.load(open(cached_download(hf_hub_url(_UpperCAmelCase     ,				_UpperCAmelCase     ,				repo_type='''dataset'''       )       )     ,				'''r'''       )       )
    __a		 =       {int(_UpperCAmelCase       ): v for k, v in idalabel.items()}

    __a		 =       idalabel
    __a		 =       {v: k for k, v in idalabel.items()}

    __a		 =       __a		 =       CvtConfig(num_labels=_UpperCAmelCase     ,				idalabel=_UpperCAmelCase     ,				labelaid=_UpperCAmelCase       )

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('''/'''     ,				1       )[-1][4:6] == "13":
        __a		 =       [1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/'''     ,				1       )[-1][4:6] == "21":
        __a		 =       [1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        __a		 =       [2, 2, 20]
        __a		 =       [3, 12, 16]
        __a		 =       [192, 768, 1024]

    __a		 =       CvtForImageClassification(_UpperCAmelCase       )
    __a		 =       AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''       )
    __a		 =       image_size
    __a		 =       torch.load(_UpperCAmelCase     ,				map_location=torch.device('''cpu'''       )       )

    __a		 =       OrderedDict()
    __a		 =       []

    for idx in range(len(config.depth       )       ):
        if config.cls_token[idx]:
            __a		 =       list_of_state_dict + cls_token(_UpperCAmelCase       )
        __a		 =       list_of_state_dict + embeddings(_UpperCAmelCase       )
        for cnt in range(config.depth[idx]       ):
            __a		 =       list_of_state_dict + attention(_UpperCAmelCase     ,				_UpperCAmelCase       )

    __a		 =       list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase       )
    for i in range(len(_UpperCAmelCase       )       ):
        __a		 =       original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase       )
    model.save_pretrained(_UpperCAmelCase       )
    image_processor.save_pretrained(_UpperCAmelCase       )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
 __snake_case		:str   	=  argparse.ArgumentParser()
 parser.add_argument(
     '''--cvt_model''',
     default='''cvt-w24''',
     type=str,
     help='''Name of the cvt model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--image_size''',
     default=384,
     type=int,
     help='''Input Image Size''',
 )
 parser.add_argument(
     '''--cvt_file_name''',
     default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
     type=str,
     help='''Input Image Size''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 __snake_case		:Dict   	=  parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





