




import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      old_name

     if "patch_embed" in old_name:
          UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_      :							str       =      old_name.split('''.'''				)

          if layer == "0":
               UpperCAmelCase_      :							Optional[Any]       =      old_name.replace('''0'''			,						'''convolution1'''				)
          elif layer == "1":
               UpperCAmelCase_      :							int       =      old_name.replace('''1'''			,						'''batchnorm_before'''				)
          elif layer == "3":
               UpperCAmelCase_      :							int       =      old_name.replace('''3'''			,						'''convolution2'''				)
          else:
               UpperCAmelCase_      :							Union[str, Any]       =      old_name.replace('''4'''			,						'''batchnorm_after'''				)

     if "network" in old_name and re.search(r'''\d\.\d'''			,						_lowercase				):
          UpperCAmelCase_      :							List[Any]       =      r'''\b\d{2}\b'''
          if bool(re.search(_lowercase			,						_lowercase				)				):
               UpperCAmelCase_      :							Optional[int]       =      re.search(r'''\d\.\d\d.'''			,						_lowercase				).group()
          else:
               UpperCAmelCase_      :							Tuple       =      re.search(r'''\d\.\d.'''			,						_lowercase				).group()
          if int(match[0]				) < 6:
               UpperCAmelCase_      :							Union[str, Any]       =      old_name.replace(_lowercase			,						''''''				)
               UpperCAmelCase_      :							Union[str, Any]       =      trimmed_name.replace('''network'''			,						match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]				)
               UpperCAmelCase_      :							Dict       =      '''intermediate_stages.''' + trimmed_name
          else:
               UpperCAmelCase_      :							Optional[int]       =      old_name.replace(_lowercase			,						''''''				)
               if int(match[2]				) < num_meta4D_last_stage:
                    UpperCAmelCase_      :							Union[str, Any]       =      trimmed_name.replace('''network'''			,						'''meta4D_layers.blocks.''' + match[2]				)
               else:
                    UpperCAmelCase_      :							Optional[int]       =      str(int(match[2]				) - num_meta4D_last_stage				)
                    UpperCAmelCase_      :							Dict       =      trimmed_name.replace('''network'''			,						'''meta3D_layers.blocks.''' + layer_index				)
                    if "norm1" in old_name:
                         UpperCAmelCase_      :							int       =      trimmed_name.replace('''norm1'''			,						'''layernorm1'''				)
                    elif "norm2" in old_name:
                         UpperCAmelCase_      :							Optional[Any]       =      trimmed_name.replace('''norm2'''			,						'''layernorm2'''				)
                    elif "fc1" in old_name:
                         UpperCAmelCase_      :							int       =      trimmed_name.replace('''fc1'''			,						'''linear_in'''				)
                    elif "fc2" in old_name:
                         UpperCAmelCase_      :							List[Any]       =      trimmed_name.replace('''fc2'''			,						'''linear_out'''				)

               UpperCAmelCase_      :							str       =      '''last_stage.''' + trimmed_name

     elif "network" in old_name and re.search(r'''.\d.'''			,						_lowercase				):
          UpperCAmelCase_      :							Tuple       =      old_name.replace('''network'''			,						'''intermediate_stages'''				)

     if "fc" in new_name:
          UpperCAmelCase_      :							Any       =      new_name.replace('''fc'''			,						'''convolution'''				)
     elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          UpperCAmelCase_      :							Any       =      new_name.replace('''norm1'''			,						'''batchnorm_before'''				)
     elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          UpperCAmelCase_      :							List[Any]       =      new_name.replace('''norm2'''			,						'''batchnorm_after'''				)
     if "proj" in new_name:
          UpperCAmelCase_      :							Tuple       =      new_name.replace('''proj'''			,						'''projection'''				)
     if "dist_head" in new_name:
          UpperCAmelCase_      :							List[Any]       =      new_name.replace('''dist_head'''			,						'''distillation_classifier'''				)
     elif "head" in new_name:
          UpperCAmelCase_      :							Optional[Any]       =      new_name.replace('''head'''			,						'''classifier'''				)
     elif "patch_embed" in new_name:
          UpperCAmelCase_      :							Dict       =      '''efficientformer.''' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          UpperCAmelCase_      :							Any       =      new_name.replace('''norm'''			,						'''layernorm'''				)
          UpperCAmelCase_      :							List[str]       =      '''efficientformer.''' + new_name
     else:
          UpperCAmelCase_      :							Optional[int]       =      '''efficientformer.encoder.''' + new_name

     return new_name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in checkpoint.copy().keys():
          UpperCAmelCase_      :							Optional[Any]       =      checkpoint.pop(_lowercase				)
          UpperCAmelCase_      :							Optional[Any]       =      val

     return checkpoint







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCAmelCase_      :							int       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)

     return image







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      torch.load(_lowercase			,						map_location='''cpu'''				)['''model''']
     UpperCAmelCase_      :							int       =      EfficientFormerConfig.from_json_file(_lowercase				)
     UpperCAmelCase_      :							str       =      EfficientFormerForImageClassificationWithTeacher(_lowercase				)
     UpperCAmelCase_      :							List[Any]       =      '''_'''.join(checkpoint_path.split('''/'''				)[-1].split('''.'''				)[0].split('''_'''				)[:-1]				)

     UpperCAmelCase_      :							List[Any]       =      config.depths[-1] - config.num_metaad_blocks + 1
     UpperCAmelCase_      :							List[str]       =      convert_torch_checkpoint(_lowercase			,						_lowercase				)

     model.load_state_dict(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							int       =      {
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     # prepare image
     UpperCAmelCase_      :							int       =      prepare_img()
     UpperCAmelCase_      :							Union[str, Any]       =      256
     UpperCAmelCase_      :							Dict       =      224
     UpperCAmelCase_      :							Union[str, Any]       =      EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size}			,						crop_size={'''height''': crop_size, '''width''': crop_size}			,						resample=pillow_resamplings['''bicubic''']			,						)
     UpperCAmelCase_      :							List[str]       =      processor(images=_lowercase			,						return_tensors='''pt'''				).pixel_values

     # original processing pipeline
     UpperCAmelCase_      :							str       =      Compose(
         [
             Resize(_lowercase			,						interpolation=pillow_resamplings['''bicubic''']				),
             CenterCrop(_lowercase				),
             ToTensor(),
             Normalize(_lowercase			,						_lowercase				),
         ]				)
     UpperCAmelCase_      :							Any       =      image_transforms(_lowercase				).unsqueeze(0				)

     assert torch.allclose(_lowercase			,						_lowercase				)

     UpperCAmelCase_      :							Any       =      model(_lowercase				)
     UpperCAmelCase_      :							int       =      outputs.logits

     UpperCAmelCase_      :							int       =      (1, 1000)

     if "l1" in model_name:
          UpperCAmelCase_      :							Dict       =      torch.Tensor(
              [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]				)
          assert torch.allclose(logits[0, :10]			,						_lowercase			,						atol=1E-3				)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          UpperCAmelCase_      :							Optional[Any]       =      torch.Tensor(
              [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]				)
          assert torch.allclose(logits[0, :10]			,						_lowercase			,						atol=1E-3				)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          UpperCAmelCase_      :							Any       =      torch.Tensor(
              [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]				)
          assert logits.shape == expected_shape
     else:
          raise ValueError(
              f'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''				)

     # Save Checkpoints
     Path(_lowercase				).mkdir(exist_ok=_lowercase				)
     model.save_pretrained(_lowercase				)
     print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)
     processor.save_pretrained(_lowercase				)
     print(f'''Processor successfuly saved at {pytorch_dump_path}'''				)

     if push_to_hub:
          print('''Pushing model to the hub...'''				)

          model.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}'''			,						commit_message='''Add model'''			,						use_temp_dir=_lowercase			,						)
          processor.push_to_hub(
              repo_id=f'''Bearnardd/{pytorch_dump_path}'''			,						commit_message='''Add image processor'''			,						use_temp_dir=_lowercase			,						)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path',
         default=None,
         type=str,
         required=True,
         help='Path to EfficientFormer pytorch checkpoint.',
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for EfficientFormer model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )

     parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
     parser.add_argument(
         '--no-push_to_hub',
         dest='push_to_hub',
         action='store_false',
         help='Do not push model and image processor to the hub',
     )
     parser.set_defaults(push_to_hub=True)

     __a          =			parser.parse_args()
     convert_efficientformer_checkpoint(
         checkpoint_path=args.pytorch_model_path,
         efficientformer_config_file=args.config_file,
         pytorch_dump_path=args.pytorch_dump_path,
         push_to_hub=args.push_to_hub,
     )





from dataclasses import dataclass, field
from typing import Optional

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be trained.'''}       )
    lowerCAmelCase        =				field(
        default='''./'''    ,						metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-train'''    ,						metadata={'''help''': '''Name or path of training dataset.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-valid'''    ,						metadata={'''help''': '''Name or path of validation dataset.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size for training.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size for evaluation.'''}       )
    lowerCAmelCase        =				field(default=0.1    ,						metadata={'''help''': '''Value of weight decay.'''}       )
    lowerCAmelCase        =				field(
        default=1_0000    ,						metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}       )
    lowerCAmelCase        =				field(default=2E-4    ,						metadata={'''help''': '''Learning rate fo training.'''}       )
    lowerCAmelCase        =				field(default='''cosine'''    ,						metadata={'''help''': '''Learning rate.'''}       )
    lowerCAmelCase        =				field(
        default=750    ,						metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}       )
    lowerCAmelCase        =				field(
        default=16    ,						metadata={'''help''': '''Number of gradient accumulation steps.'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}       )
    lowerCAmelCase        =				field(default=5_0000    ,						metadata={'''help''': '''Maximum number of training steps.'''}       )
    lowerCAmelCase        =				field(
        default=-1    ,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}       )
    lowerCAmelCase        =				field(default=1024    ,						metadata={'''help''': '''Sequence lengths used for training.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Training seed.'''}       )
    lowerCAmelCase        =				field(
        default=1024    ,						metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''If True the data is pretokenized.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be evaluated.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-valid'''    ,						metadata={'''help''': '''Name or path of validation dataset.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default=-1    ,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}       )
    lowerCAmelCase        =				field(default=1024    ,						metadata={'''help''': '''Length of sequences to be evaluated.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be evaluated.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Number of workers used for code evaluation.'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Sample from the language model\'s output distribution.'''}       )
    lowerCAmelCase        =				field(default=0.2    ,						metadata={'''help''': '''Sampling temperature used for generation.'''}       )
    lowerCAmelCase        =				field(default=256    ,						metadata={'''help''': '''Maximum number of newly generated tokens.'''}       )
    lowerCAmelCase        =				field(default=0    ,						metadata={'''help''': '''Top-k parameter used for generation.'''}       )
    lowerCAmelCase        =				field(default=0.95    ,						metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}       )
    lowerCAmelCase        =				field(default=10    ,						metadata={'''help''': '''Number of generations to run in parallel.'''}       )
    lowerCAmelCase        =				field(
        default=200    ,						metadata={'''help''': '''Number of completions to generate for each sample.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default='''eval_results.json'''    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default='''0'''    ,						metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}       )
    lowerCAmelCase        =				field(
        default=-1    ,						metadata={
            '''help''': (
                '''Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'''
                ''' number corresponds to which GPU device id to run on.'''
            )
        }    ,						)


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
        }    ,						)
    lowerCAmelCase        =				field(
        default='''transformersbook/codeparrot'''    ,						metadata={'''help''': '''Folder or name of dataset to process.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot-clean'''    ,						metadata={'''help''': '''Folder to save processed processed dataset.'''}       )
    lowerCAmelCase        =				field(
        default=10_0000    ,						metadata={'''help''': '''Number of files to save per JSON output file.'''}       )
    lowerCAmelCase        =				field(default='''content'''    ,						metadata={'''help''': '''Column containing text data to process.'''}       )
    lowerCAmelCase        =				field(
        default=1000    ,						metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=100    ,						metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=0.25    ,						metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=1.5    ,						metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=0.7    ,						metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Name or path to the tokenizer.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''If True, near-duplicate samples are removed.'''}       )
    lowerCAmelCase        =				field(
        default=0.85    ,						metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''gpt2'''    ,						metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}       )
    lowerCAmelCase        =				field(
        default='''transformersbook/codeparrot-train'''    ,						metadata={'''help''': '''Dataset to train tokenizer on.'''}       )
    lowerCAmelCase        =				field(default='''content'''    ,						metadata={'''help''': '''Column containing text data to process.'''}       )
    lowerCAmelCase        =				field(default=20_0000    ,						metadata={'''help''': '''Number of examples to train tokenizer on.'''}       )
    lowerCAmelCase        =				field(
        default=3_2768    ,						metadata={'''help''': '''Number of examples to train the tokenizer on.'''}       )
    lowerCAmelCase        =				field(default='''codeparrot'''    ,						metadata={'''help''': '''Name of new tokenizer.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Name or path to the tokenizer.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-train'''    ,						metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}       )
    lowerCAmelCase        =				field(
        default='''tokenized-codeparrot-train'''    ,						metadata={'''help''': '''Repo name of the pretokenized data.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Number of workers used for code evaluation.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''gpt2-large'''    ,						metadata={'''help''': '''Configuration to use for model initialization.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Tokenizer attached to model.'''}       )
    lowerCAmelCase        =				field(default='''codeparrot'''    ,						metadata={'''help''': '''Name of the created model.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )