



"""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    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      Optional[Any]			=			old_name

 if "patch_embed" in old_name:
  UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			old_name.split("""."""		)

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

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

   UpperCAmelCase			:      Dict			=			"""last_stage.""" + trimmed_name

 elif "network" in old_name and re.search(R""".\d."""    ,	UpperCamelCase		):
  UpperCAmelCase			:      List[str]			=			old_name.replace("""network"""    ,	"""intermediate_stages"""		)

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

 return new_name

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	Any		):
 for key in checkpoint.copy().keys():
  UpperCAmelCase			:      Any			=			checkpoint.pop(UpperCamelCase		)
  UpperCAmelCase			:      Dict			=			val

 return checkpoint

def    _snake_case		(		):
 UpperCAmelCase			:      Dict			=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase			:      Optional[Any]			=			Image.open(requests.get(UpperCamelCase    ,	stream=UpperCamelCase		).raw		)

 return image

def    _snake_case		(		UpperCamelCase		:	Path    ,	UpperCamelCase		:	Path    ,	UpperCamelCase		:	Path    ,	UpperCamelCase		:	bool		):
 UpperCAmelCase			:      List[Any]			=			torch.load(UpperCamelCase    ,	map_location="""cpu"""		)["""model"""]
 UpperCAmelCase			:      int			=			EfficientFormerConfig.from_json_file(UpperCamelCase		)
 UpperCAmelCase			:      Tuple			=			EfficientFormerForImageClassificationWithTeacher(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			"""_""".join(checkpoint_path.split("""/"""		)[-1].split("""."""		)[0].split("""_"""		)[:-1]		)

 UpperCAmelCase			:      Tuple			=			config.depths[-1] - config.num_metaad_blocks + 1
 UpperCAmelCase			:      Optional[Any]			=			convert_torch_checkpoint(UpperCamelCase    ,	UpperCamelCase		)

 model.load_state_dict(UpperCamelCase		)
 model.eval()

 UpperCAmelCase			:      Any			=			{
     """bilinear""": PILImageResampling.BILINEAR,
     """bicubic""": PILImageResampling.BICUBIC,
     """nearest""": PILImageResampling.NEAREST,
 }

 # prepare image
 UpperCAmelCase			:      str			=			prepare_img()
 UpperCAmelCase			:      Any			=			256
 UpperCAmelCase			:      List[Any]			=			224
 UpperCAmelCase			:      Optional[Any]			=			EfficientFormerImageProcessor(
     size={"""shortest_edge""": image_size}    ,	crop_size={"""height""": crop_size, """width""": crop_size}    ,	resample=pillow_resamplings["""bicubic"""]    ,	)
 UpperCAmelCase			:      Any			=			processor(images=UpperCamelCase    ,	return_tensors="""pt"""		).pixel_values

 # original processing pipeline
 UpperCAmelCase			:      Any			=			Compose(
     [
         Resize(UpperCamelCase    ,	interpolation=pillow_resamplings["""bicubic"""]		),
         CenterCrop(UpperCamelCase		),
         ToTensor(),
         Normalize(UpperCamelCase    ,	UpperCamelCase		),
     ]		)
 UpperCAmelCase			:      int			=			image_transforms(UpperCamelCase		).unsqueeze(0		)

 assert torch.allclose(UpperCamelCase    ,	UpperCamelCase		)

 UpperCAmelCase			:      Any			=			model(UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			outputs.logits

 UpperCAmelCase			:      Union[str, Any]			=			(1, 1000)

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


if __name__ == "__main__":
      A:     List[str]										=	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:     int										=	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"""
import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     List[Any]		=		TransfoXLTokenizer
   __lowerCAmelCase						:     Optional[int]		=		False
   __lowerCAmelCase						:     Optional[Any]		=		False

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    super().setUp()

    UpperCAmelCase			:      Optional[Any]			=			[
        """<unk>""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """unwanted""",
        """wa""",
        """un""",
        """running""",
        """,""",
        """low""",
        """l""",
    ]
    UpperCAmelCase			:      int			=			os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
    with open(self.vocab_file   ,  """w"""   ,  encoding="""utf-8"""		) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			True
    return TransfoXLTokenizer.from_pretrained(self.tmpdirname   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			"""<unk> UNwanted , running"""
    UpperCAmelCase			:      Dict			=			"""<unk> unwanted, running"""
    return input_text, output_text

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			TransfoXLTokenizer(vocab_file=self.vocab_file   ,  lower_case=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			tokenizer.tokenize("""<unk> UNwanted , running"""		)
    self.assertListEqual(_SCREAMING_SNAKE_CASE   ,  ["""<unk>""", """unwanted""", """,""", """running"""]		)

    self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)   ,  [0, 4, 8, 7]		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE		)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)   ,  ["""hello""", """!""", """how""", """are""", """you""", """?"""]		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE		)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)   ,  ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			"""Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
    UpperCAmelCase			:      Optional[Any]			=			[
        """Hello""",
        """(""",
        """bracket""",
        """)""",
        """and""",
        """side""",
        """@-@""",
        """scrolled""",
        """[""",
        """and""",
        """]""",
        """Henry""",
        """'s""",
        """$""",
        """5""",
        """@,@""",
        """000""",
        """with""",
        """3""",
        """@.@""",
        """34""",
        """m""",
        """.""",
        """What""",
        """'s""",
        """up""",
        """!""",
        """?""",
    ]

    self.assertListEqual(tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)

    self.assertEqual(tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			self.get_tokenizer()
    UpperCAmelCase			:      Tuple			=			len(_SCREAMING_SNAKE_CASE		)

    tokenizer.add_tokens(["""new1""", """new2"""]		)
    tokenizer.move_added_token("""new1"""   ,  1		)

    # Check that moved token is not copied (duplicate)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  original_len + 2		)
    # Check that token is moved to specified id
    self.assertEqual(tokenizer.encode("""new1"""		)   ,  [1]		)
    self.assertEqual(tokenizer.decode([1]		)   ,  """new1"""		)
