




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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Optional[Any]:
   snake_case				:    Dict           =	old_name

   if "patch_embed" in old_name:
      snake_case     , snake_case     , snake_case				:    Optional[int]           =	old_name.split("""."""					)

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

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

         snake_case				:    Tuple           =	"""last_stage.""" + trimmed_name

   elif "network" in old_name and re.search(R""".\d."""						,lowercase					):
      snake_case				:    Tuple           =	old_name.replace("""network"""						,"""intermediate_stages"""					)

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

   return new_name




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Optional[Any]:
   for key in checkpoint.copy().keys():
      snake_case				:    Optional[int]           =	checkpoint.pop(lowercase					)
      snake_case				:    List[Any]           =	val

   return checkpoint




def 						SCREAMING_SNAKE_CASE__ (   )  ->       Union[str, Any]:
   snake_case				:    Optional[Any]           =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case				:    str           =	Image.open(requests.get(lowercase						,stream=lowercase					).raw					)

   return image




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase						,lowercase					)  ->       List[Any]:
   snake_case				:    Union[str, Any]           =	torch.load(lowercase						,map_location="""cpu"""					)["""model"""]
   snake_case				:    Tuple           =	EfficientFormerConfig.from_json_file(lowercase					)
   snake_case				:    int           =	EfficientFormerForImageClassificationWithTeacher(lowercase					)
   snake_case				:    List[str]           =	"""_""".join(checkpoint_path.split("""/"""					)[-1].split("""."""					)[0].split("""_"""					)[:-1]					)

   snake_case				:    int           =	config.depths[-1] - config.num_metaad_blocks + 1
   snake_case				:    int           =	convert_torch_checkpoint(lowercase						,lowercase					)

   model.load_state_dict(lowercase					)
   model.eval()

   snake_case				:    Dict           =	{
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

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

   # original processing pipeline
   snake_case				:    str           =	Compose(
       [
           Resize(lowercase						,interpolation=pillow_resamplings["""bicubic"""]					),
           CenterCrop(lowercase					),
           ToTensor(),
           Normalize(lowercase						,lowercase					),
       ]					)
   snake_case				:    str           =	image_transforms(lowercase					).unsqueeze(0					)

   assert torch.allclose(lowercase						,lowercase					)

   snake_case				:    List[Any]           =	model(lowercase					)
   snake_case				:    Tuple           =	outputs.logits

   snake_case				:    int           =	(1, 1000)

   if "l1" in model_name:
      snake_case				:    List[str]           =	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:
      snake_case				:    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]						,lowercase						,atol=1E-3					)
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      snake_case				:    Tuple           =	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__":
       lowerCamelCase	:	Optional[Any]				=   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)

       lowerCamelCase	:	str				=   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 argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   return ConvertCommand(
       args.model_type						,args.tf_checkpoint						,args.pytorch_dump_output						,args.config						,args.finetuning_task_name					)


lowerCamelCase	:	Tuple				=   '\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      @staticmethod
      def      UpperCAmelCase				(				A     )		->    List[str]:
         snake_case				:    Union[str, Any]           =	parser.add_parser(
             """convert"""    ,				help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""    ,				)
         train_parser.add_argument("""--model_type"""    ,				type=A    ,				required=A    ,				help="""Model's type."""     )
         train_parser.add_argument(
             """--tf_checkpoint"""    ,				type=A    ,				required=A    ,				help="""TensorFlow checkpoint path or folder."""     )
         train_parser.add_argument(
             """--pytorch_dump_output"""    ,				type=A    ,				required=A    ,				help="""Path to the PyTorch saved model output."""     )
         train_parser.add_argument("""--config"""    ,				type=A    ,				default=""""""    ,				help="""Configuration file path or folder."""     )
         train_parser.add_argument(
             """--finetuning_task_name"""    ,				type=A    ,				default=A    ,				help="""Optional fine-tuning task name if the TF model was a finetuned model."""    ,				)
         train_parser.set_defaults(func=A     )

      def __init__(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				*A    ,				)		->    List[Any]:
         snake_case				:    Any           =	logging.get_logger("""transformers-cli/converting"""     )

         self._logger.info(f"""Loading model {model_type}"""     )
         snake_case				:    int           =	model_type
         snake_case				:    Any           =	tf_checkpoint
         snake_case				:    int           =	pytorch_dump_output
         snake_case				:    List[str]           =	config
         snake_case				:    Tuple           =	finetuning_task_name

      def      UpperCAmelCase				(				self     )		->    int:
         if self._model_type == "albert":
            try:
               from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                   convert_tf_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "bert":
            try:
               from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                   convert_tf_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "funnel":
            try:
               from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                   convert_tf_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "t5":
            try:
               from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "gpt":
            from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                convert_openai_checkpoint_to_pytorch,
            )

            convert_openai_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "transfo_xl":
            try:
               from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                   convert_transfo_xl_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            if "ckpt" in self._tf_checkpoint.lower():
               snake_case				:    List[Any]           =	self._tf_checkpoint
               snake_case				:    Tuple           =	""""""
            else:
               snake_case				:    Tuple           =	self._tf_checkpoint
               snake_case				:    Tuple           =	""""""
            convert_transfo_xl_checkpoint_to_pytorch(
                A    ,				self._config    ,				self._pytorch_dump_output    ,				A     )
         elif self._model_type == "gpt2":
            try:
               from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                   convert_gpta_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "xlnet":
            try:
               from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                   convert_xlnet_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_xlnet_checkpoint_to_pytorch(
                self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output    ,				self._finetuning_task_name     )
         elif self._model_type == "xlm":
            from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                convert_xlm_checkpoint_to_pytorch,
            )

            convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._pytorch_dump_output     )
         elif self._model_type == "lxmert":
            from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                convert_lxmert_checkpoint_to_pytorch,
            )

            convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._pytorch_dump_output     )
         elif self._model_type == "rembert":
            from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                convert_rembert_tf_checkpoint_to_pytorch,
            )

            convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         else:
            raise ValueError(
                """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""     )





