
"""simple docstring"""



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 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Any		)     ->					str:
  _snake_case					     =       old_name

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

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

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

      _snake_case					     =       '''last_stage.''' + trimmed_name

  elif "network" in old_name and re.search(R'''.\d.''' ,					lowerCAmelCase__		):
    _snake_case					     =       old_name.replace('''network''' ,					'''intermediate_stages'''		)

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

  return new_name
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Tuple		)     ->					Tuple:
  for key in checkpoint.copy().keys():
    _snake_case					     =       checkpoint.pop(lowerCAmelCase__		)
    _snake_case					     =       val

  return checkpoint
def 					_UpperCAmelCase      ( )     ->					Any:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(lowerCAmelCase__ ,					stream=lowerCAmelCase__		).raw		)

  return image
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Path ,					__lowerCamelCase   :       Path ,					__lowerCamelCase   :       Path ,					__lowerCamelCase   :       bool		)     ->					Any:
  _snake_case					     =       torch.load(lowerCAmelCase__ ,					map_location='''cpu'''		)['''model''']
  _snake_case					     =       EfficientFormerConfig.from_json_file(lowerCAmelCase__		)
  _snake_case					     =       EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__		)
  _snake_case					     =       '''_'''.join(checkpoint_path.split('''/'''		)[-1].split('''.'''		)[0].split('''_'''		)[:-1]		)

  _snake_case					     =       config.depths[-1] - config.num_metaad_blocks + 1
  _snake_case					     =       convert_torch_checkpoint(lowerCAmelCase__ ,					lowerCAmelCase__		)

  model.load_state_dict(lowerCAmelCase__		)
  model.eval()

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

  # prepare image
  _snake_case					     =       prepare_img()
  _snake_case					     =       2_56
  _snake_case					     =       2_24
  _snake_case					     =       EfficientFormerImageProcessor(
      size={'''shortest_edge''': image_size} ,					crop_size={'''height''': crop_size, '''width''': crop_size} ,					resample=pillow_resamplings['''bicubic'''] ,					)
  _snake_case					     =       processor(images=lowerCAmelCase__ ,					return_tensors='''pt'''		).pixel_values

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

  assert torch.allclose(lowerCAmelCase__ ,					lowerCAmelCase__		)

  _snake_case					     =       model(lowerCAmelCase__		)
  _snake_case					     =       outputs.logits

  _snake_case					     =       (1, 10_00)

  if "l1" in model_name:
    _snake_case					     =       torch.Tensor(
        [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]		)
    assert torch.allclose(logits[0, :10] ,					lowerCAmelCase__ ,					atol=1E-3		)
    assert logits.shape == expected_shape
  elif "l3" in model_name:
    _snake_case					     =       torch.Tensor(
        [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]		)
    assert torch.allclose(logits[0, :10] ,					lowerCAmelCase__ ,					atol=1E-3		)
    assert logits.shape == expected_shape
  elif "l7" in model_name:
    _snake_case					     =       torch.Tensor(
        [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]		)
    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(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
  model.save_pretrained(lowerCAmelCase__		)
  print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)
  processor.save_pretrained(lowerCAmelCase__		)
  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=lowerCAmelCase__ ,					)
    processor.push_to_hub(
        repo_id=f'''Bearnardd/{pytorch_dump_path}''' ,					commit_message='''Add image processor''' ,					use_temp_dir=lowerCAmelCase__ ,					)


if __name__ == "__main__":
 UpperCAmelCase__          = 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)

 UpperCAmelCase__          = 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,
 )







"""simple docstring"""




from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType
@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='Translation'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		def __call__(  self      ):
				return pa.struct({lang: pa.string() for lang in sorted(self.languages      )}      )





		def 			__UpperCAmelCase       (  self      ):
				from .features import Value

				return {k: Value('''string'''      ) for k in sorted(self.languages      )}







@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[List]										=				None
		__UpperCAmelCase		:			Optional[int]										=				None
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='TranslationVariableLanguages'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			sorted(set(self.languages      )      ) if self.languages else None
				__a           =			len(self.languages      ) if self.languages else None

		def __call__(  self      ):
				return pa.struct({'''language''': pa.list_(pa.string()      ), '''translation''': pa.list_(pa.string()      )}      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			set(self.languages      )
				if self.languages and set(_a      ) - lang_set:
						raise ValueError(
						    f'''Some languages in example ({', '.join(sorted(set(_a      ) - lang_set      )      )}) are not in valid set ({', '.join(_a      )}).'''      )

				# Convert dictionary into tuples, splitting out cases where there are
				# multiple translations for a single language.
				__a           =			[]
				for lang, text in translation_dict.items():
						if isinstance(_a							, _a      ):
								translation_tuples.append((lang, text)      )
						else:
								translation_tuples.extend([(lang, el) for el in text]      )

        # Ensure translations are in ascending order by language code.
				__a      ,     __a           =			zip(*sorted(_a      )      )

				return {"language": languages, "translation": translations}





		def 			__UpperCAmelCase       (  self      ):
				from .features import Sequence, Value

				return {
				    "language": Sequence(Value('''string'''      )      ),
				    "translation": Sequence(Value('''string'''      )      ),
				}






