


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def  _a			(						__UpperCamelCase ):
       a_    :     Union[str, Any]										=    """huggingface/label-files"""
       a_    :     Tuple										=    """imagenet-1k-id2label.json"""
       a_    :     Tuple										=    json.load(open(hf_hub_download(__lowerCAmelCase  ,  __lowerCAmelCase  ,  repo_type="""dataset""" )  ,  """r""" ) )
       a_    :     str										=    {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
       a_    :     str										=    {v: k for k, v in idalabel.items()}

       a_    :     Optional[Any]										=    """std_conv""" if """bit""" in model_name else False

       # note that when using BiT as backbone for ViT-hybrid checkpoints,
       # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
       # config.conv_layer = "std_conv_same"
       a_    :     Dict										=    BitConfig(
           conv_layer=__lowerCAmelCase  ,  num_labels=1_0_0_0  ,  idalabel=__lowerCAmelCase  ,  labelaid=__lowerCAmelCase  ,  )

       return config



def  _a			(						__UpperCamelCase ):
       if "stem.conv" in name:
              a_    :     str										=    name.replace("""stem.conv"""  ,  """bit.embedder.convolution""" )
       if "blocks" in name:
              a_    :     str										=    name.replace("""blocks"""  ,  """layers""" )
       if "head.fc" in name:
              a_    :     int										=    name.replace("""head.fc"""  ,  """classifier.1""" )
       if name.startswith("""norm""" ):
              a_    :     str										=    """bit.""" + name
       if "bit" not in name and "classifier" not in name:
              a_    :     Optional[int]										=    """bit.encoder.""" + name

       return name



def  _a			(						):
       a_    :     Union[str, Any]										=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
       a_    :     Tuple										=    Image.open(requests.get(__lowerCAmelCase  ,  stream=__lowerCAmelCase ).raw )
       return im



@torch.no_grad()
def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase=False ):
       a_    :     List[Any]										=    get_config(__lowerCAmelCase )

       # load original model from timm
       a_    :     str										=    create_model(__lowerCAmelCase  ,  pretrained=__lowerCAmelCase )
       timm_model.eval()

       # load state_dict of original model
       a_    :     int										=    timm_model.state_dict()
       for key in state_dict.copy().keys():
              a_    :     int										=    state_dict.pop(__lowerCAmelCase )
              a_    :     Union[str, Any]										=    val.squeeze() if """head""" in key else val

       # load HuggingFace model
       a_    :     Any										=    BitForImageClassification(__lowerCAmelCase )
       model.eval()
       model.load_state_dict(__lowerCAmelCase )

       # create image processor
       a_    :     Dict										=    create_transform(**resolve_data_config({}  ,  model=__lowerCAmelCase ) )
       a_    :     Union[str, Any]										=    transform.transforms

       a_    :     Union[str, Any]										=    {
           """bilinear""": PILImageResampling.BILINEAR,
           """bicubic""": PILImageResampling.BICUBIC,
           """nearest""": PILImageResampling.NEAREST,
       }

       a_    :     Tuple										=    BitImageProcessor(
           do_resize=__lowerCAmelCase  ,  size={"""shortest_edge""": timm_transforms[0].size}  ,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,  do_center_crop=__lowerCAmelCase  ,  crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}  ,  do_normalize=__lowerCAmelCase  ,  image_mean=timm_transforms[-1].mean.tolist()  ,  image_std=timm_transforms[-1].std.tolist()  ,  )

       a_    :     Union[str, Any]										=    prepare_img()
       a_    :     Optional[int]										=    transform(__lowerCAmelCase ).unsqueeze(0 )
       a_    :     Any										=    processor(__lowerCAmelCase  ,  return_tensors="""pt""" ).pixel_values

       # verify pixel values
       assert torch.allclose(__lowerCAmelCase  ,  __lowerCAmelCase )

       # verify logits
       with torch.no_grad():
              a_    :     Optional[int]										=    model(__lowerCAmelCase )
              a_    :     List[Any]										=    outputs.logits

       print("""Logits:"""  ,  logits[0, :3] )
       print("""Predicted class:"""  ,  model.config.idalabel[logits.argmax(-1 ).item()] )
       a_    :     Optional[int]										=    timm_model(__lowerCAmelCase )
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(__lowerCAmelCase  ,  outputs.logits  ,  atol=1E-3 )
       print("""Looks ok!""" )

       if pytorch_dump_folder_path is not None:
              Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
              print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''' )
              model.save_pretrained(__lowerCAmelCase )
              processor.save_pretrained(__lowerCAmelCase )

       if push_to_hub:
              print(F'''Pushing model {model_name} and processor to the hub''' )
              model.push_to_hub(F'''ybelkada/{model_name}''' )
              processor.push_to_hub(F'''ybelkada/{model_name}''' )


if __name__ == "__main__":
       __lowerCamelCase        =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--model_name''',
           default='''resnetv2_50x1_bitm''',
           type=str,
           help='''Name of the BiT timm model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )
       parser.add_argument(
           '''--push_to_hub''',
           action='''store_true''',
           help='''Whether to push the model to the hub.''',
       )

       __lowerCamelCase        =     parser.parse_args()
       convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
       raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase        =     False
__lowerCamelCase        =     False



def  _a			(						__UpperCamelCase ):
     return TrainCommand(__UpperCamelCase )


class   a__	(						lowerCAmelCase_    ):

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     ArgumentParser    ):
            a_    :     List[Any]										=    parser.add_parser("""train"""      ,							help="""CLI tool to train a model on a task."""    )

            train_parser.add_argument(
                """--train_data"""      ,							type=lowerCamelCase_      ,							required=lowerCamelCase_      ,							help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""      ,							)
            train_parser.add_argument(
                """--column_label"""      ,							type=lowerCamelCase_      ,							default=0      ,							help="""Column of the dataset csv file with example labels."""    )
            train_parser.add_argument(
                """--column_text"""      ,							type=lowerCamelCase_      ,							default=1      ,							help="""Column of the dataset csv file with example texts."""    )
            train_parser.add_argument(
                """--column_id"""      ,							type=lowerCamelCase_      ,							default=2      ,							help="""Column of the dataset csv file with example ids."""    )
            train_parser.add_argument(
                """--skip_first_row"""      ,							action="""store_true"""      ,							help="""Skip the first row of the csv file (headers)."""    )

            train_parser.add_argument("""--validation_data"""      ,							type=lowerCamelCase_      ,							default=""""""      ,							help="""path to validation dataset."""    )
            train_parser.add_argument(
                """--validation_split"""      ,							type=lowerCamelCase_      ,							default=0.1      ,							help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""      ,							)

            train_parser.add_argument("""--output"""      ,							type=lowerCamelCase_      ,							default="""./"""      ,							help="""path to saved the trained model."""    )

            train_parser.add_argument(
                """--task"""      ,							type=lowerCamelCase_      ,							default="""text_classification"""      ,							help="""Task to train the model on."""    )
            train_parser.add_argument(
                """--model"""      ,							type=lowerCamelCase_      ,							default="""bert-base-uncased"""      ,							help="""Model's name or path to stored model."""    )
            train_parser.add_argument("""--train_batch_size"""      ,							type=lowerCamelCase_      ,							default=3_2      ,							help="""Batch size for training."""    )
            train_parser.add_argument("""--valid_batch_size"""      ,							type=lowerCamelCase_      ,							default=6_4      ,							help="""Batch size for validation."""    )
            train_parser.add_argument("""--learning_rate"""      ,							type=lowerCamelCase_      ,							default=3E-5      ,							help="""Learning rate."""    )
            train_parser.add_argument("""--adam_epsilon"""      ,							type=lowerCamelCase_      ,							default=1E-08      ,							help="""Epsilon for Adam optimizer."""    )
            train_parser.set_defaults(func=lowerCamelCase_    )

       def __init__(   self      :     int      ,							lowerCamelCase_      :     Namespace    ):
            a_    :     Optional[int]										=    logging.get_logger("""transformers-cli/training"""    )

            a_    :     List[str]										=    """tf""" if is_tf_available() else """torch"""

            os.makedirs(args.output      ,							exist_ok=lowerCamelCase_    )
            a_    :     List[str]										=    args.output

            a_    :     Optional[int]										=    args.column_label
            a_    :     Tuple										=    args.column_text
            a_    :     Optional[int]										=    args.column_id

            self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''    )
            if args.task == "text_classification":
                 a_    :     str										=    TextClassificationPipeline.from_pretrained(args.model    )
            elif args.task == "token_classification":
                 raise NotImplementedError
            elif args.task == "question_answering":
                 raise NotImplementedError

            self.logger.info(F'''Loading dataset from {args.train_data}'''    )
            a_    :     int										=    Processor.create_from_csv(
                args.train_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)
            a_    :     Union[str, Any]										=    None
            if args.validation_data:
                 self.logger.info(F'''Loading validation dataset from {args.validation_data}'''    )
                 a_    :     Tuple										=    Processor.create_from_csv(
                     args.validation_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)

            a_    :     str										=    args.validation_split
            a_    :     Tuple										=    args.train_batch_size
            a_    :     List[Any]										=    args.valid_batch_size
            a_    :     str										=    args.learning_rate
            a_    :     str										=    args.adam_epsilon

       def 						UpperCAmelCase(   self      :     List[str]    ):
            if self.framework == "tf":
                 return self.run_tf()
            return self.run_torch()

       def 						UpperCAmelCase(   self      :     Any    ):
            raise NotImplementedError

       def 						UpperCAmelCase(   self      :     List[str]    ):
            self.pipeline.fit(
                self.train_dataset      ,							validation_data=self.valid_dataset      ,							validation_split=self.validation_split      ,							learning_rate=self.learning_rate      ,							adam_epsilon=self.adam_epsilon      ,							train_batch_size=self.train_batch_size      ,							valid_batch_size=self.valid_batch_size      ,							)

            # Save trained pipeline
            self.pipeline.save_pretrained(self.output    )



