

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      a      (			A__	:   List[str]      ,   A__	:   int     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =old_name

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

            if layer == "0":
                  _lowercase	       =old_name.replace('0'      ,   'convolution1'     )
            elif layer == "1":
                  _lowercase	       =old_name.replace('1'      ,   'batchnorm_before'     )
            elif layer == "3":
                  _lowercase	       =old_name.replace('3'      ,   'convolution2'     )
            else:
                  _lowercase	       =old_name.replace('4'      ,   'batchnorm_after'     )

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

                  _lowercase	       ='last_stage.' + trimmed_name

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

      if "fc" in new_name:
            _lowercase	       =new_name.replace('fc'      ,   'convolution'     )
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
            _lowercase	       =new_name.replace('norm1'      ,   'batchnorm_before'     )
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
            _lowercase	       =new_name.replace('norm2'      ,   'batchnorm_after'     )
      if "proj" in new_name:
            _lowercase	       =new_name.replace('proj'      ,   'projection'     )
      if "dist_head" in new_name:
            _lowercase	       =new_name.replace('dist_head'      ,   'distillation_classifier'     )
      elif "head" in new_name:
            _lowercase	       =new_name.replace('head'      ,   'classifier'     )
      elif "patch_embed" in new_name:
            _lowercase	       ='efficientformer.' + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
            _lowercase	       =new_name.replace('norm'      ,   'layernorm'     )
            _lowercase	       ='efficientformer.' + new_name
      else:
            _lowercase	       ='efficientformer.encoder.' + new_name

      return new_name
def      a      (			A__	:   Optional[int]      ,   A__	:   List[Any]     )    ->      Union[str, Any]:





      """simple docstring"""



      for key in checkpoint.copy().keys():
            _lowercase	       =checkpoint.pop(A__     )
            _lowercase	       =val

      return checkpoint
def      a      (			)    ->      Union[str, Any]:





      """simple docstring"""



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

      return image
def      a      (			A__	:   Path      ,   A__	:   Path      ,   A__	:   Path      ,   A__	:   bool     )    ->      Union[str, Any]:





      """simple docstring"""



      _lowercase	       =torch.load(A__      ,   map_location='cpu'     )['model']
      _lowercase	       =EfficientFormerConfig.from_json_file(A__     )
      _lowercase	       =EfficientFormerForImageClassificationWithTeacher(A__     )
      _lowercase	       ='_'.join(checkpoint_path.split('/'     )[-1].split('.'     )[0].split('_'     )[:-1]     )

      _lowercase	       =config.depths[-1] - config.num_metaad_blocks + 1
      _lowercase	       =convert_torch_checkpoint(A__      ,   A__     )

      model.load_state_dict(A__     )
      model.eval()

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

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

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

      assert torch.allclose(A__      ,   A__     )

      _lowercase	       =model(A__     )
      _lowercase	       =outputs.logits

      _lowercase	       =(1, 1000)

      if "l1" in model_name:
            _lowercase	       =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]      ,   A__      ,   atol=1e-3     )
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            _lowercase	       =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]      ,   A__      ,   atol=1e-3     )
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            _lowercase	       =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(A__     ).mkdir(exist_ok=A__     )
      model.save_pretrained(A__     )
      print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''     )
      processor.save_pretrained(A__     )
      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=A__      ,   )
            processor.push_to_hub(
                repo_id=F'''Bearnardd/{pytorch_dump_path}'''      ,   commit_message='Add image processor'      ,   use_temp_dir=A__      ,   )


if __name__ == "__main__":
  lowercase_									=   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)

  lowercase_									=   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,
  )





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase_									=   data_utils.TransfoXLTokenizer
lowercase_									=   data_utils.TransfoXLCorpus
lowercase_									=   data_utils
lowercase_									=   data_utils
def      a      (			A__	:   int      ,   A__	:   Dict      ,   A__	:   Union[str, Any]      ,   A__	:   Union[str, Any]     )    ->      List[str]:





      """simple docstring"""



      if transfo_xl_dataset_file:
            # Convert a pre-processed corpus (see original TensorFlow repo)
            with open(A__      ,   'rb'     ) as fp:
                  _lowercase	       =pickle.load(A__      ,   encoding='latin1'     )
            # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
            _lowercase	       =pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
            print(F'''Save vocabulary to {pytorch_vocab_dump_path}'''     )
            _lowercase	       =corpus.vocab.__dict__
            torch.save(A__      ,   A__     )

            _lowercase	       =corpus.__dict__
            corpus_dict_no_vocab.pop('vocab'      ,   A__     )
            _lowercase	       =pytorch_dump_folder_path + '/' + CORPUS_NAME
            print(F'''Save dataset to {pytorch_dataset_dump_path}'''     )
            torch.save(A__      ,   A__     )

      if tf_checkpoint_path:
            # Convert a pre-trained TensorFlow model
            _lowercase	       =os.path.abspath(A__     )
            _lowercase	       =os.path.abspath(A__     )

            print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''     )
            # Initialise PyTorch model
            if transfo_xl_config_file == "":
                  _lowercase	       =TransfoXLConfig()
            else:
                  _lowercase	       =TransfoXLConfig.from_json_file(A__     )
            print(F'''Building PyTorch model from configuration: {config}'''     )
            _lowercase	       =TransfoXLLMHeadModel(A__     )

            _lowercase	       =load_tf_weights_in_transfo_xl(A__      ,   A__      ,   A__     )
            # Save pytorch-model
            _lowercase	       =os.path.join(A__      ,   A__     )
            _lowercase	       =os.path.join(A__      ,   A__     )
            print(F'''Save PyTorch model to {os.path.abspath(A__     )}'''     )
            torch.save(model.state_dict()      ,   A__     )
            print(F'''Save configuration file to {os.path.abspath(A__     )}'''     )
            with open(A__      ,   'w'      ,   encoding='utf-8'     ) as f:
                  f.write(config.to_json_string()     )


if __name__ == "__main__":
  lowercase_									=   argparse.ArgumentParser()
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=str,
      required=True,
      help='Path to the folder to store the PyTorch model or dataset/vocab.',
  )
  parser.add_argument(
      '--tf_checkpoint_path',
      default='',
      type=str,
      help='An optional path to a TensorFlow checkpoint path to be converted.',
  )
  parser.add_argument(
      '--transfo_xl_config_file',
      default='',
      type=str,
      help=(
          'An optional config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--transfo_xl_dataset_file',
      default='',
      type=str,
      help='An optional dataset file to be converted in a vocabulary.',
  )
  lowercase_									=   parser.parse_args()
  convert_transfo_xl_checkpoint_to_pytorch(
      args.tf_checkpoint_path,
      args.transfo_xl_config_file,
      args.pytorch_dump_folder_path,
      args.transfo_xl_dataset_file,
  )


