"""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__	:Optional[Any]   ,						lowerCAmelCase__	:Optional[int]						)					->       Optional[Any]:
      '''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"""   ,						lowerCAmelCase__						):
            lowercase								=      R"""\b\d{2}\b"""
            if bool(re.search(lowerCAmelCase__   ,						lowerCAmelCase__						)						):
                  lowercase								=      re.search(R"""\d\.\d\d."""   ,						lowerCAmelCase__						).group()
            else:
                  lowercase								=      re.search(R"""\d\.\d."""   ,						lowerCAmelCase__						).group()
            if int(match[0]						) < 6:
                  lowercase								=      old_name.replace(lowerCAmelCase__   ,						""""""						)
                  lowercase								=      trimmed_name.replace("""network"""   ,						match[0] + """.meta4D_layers.blocks.""" + match[2:-1]						)
                  lowercase								=      """intermediate_stages.""" + trimmed_name
            else:
                  lowercase								=      old_name.replace(lowerCAmelCase__   ,						""""""						)
                  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."""   ,						lowerCAmelCase__						):
            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      UpperCAmelCase__			(							lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:Tuple						)					->       Any:
      '''simple docstring'''


      for key in checkpoint.copy().keys():
            lowercase								=      checkpoint.pop(lowerCAmelCase__						)
            lowercase								=      val

      return checkpoint


def      UpperCAmelCase__			(							)					->       str:
      '''simple docstring'''


      lowercase								=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowercase								=      Image.open(requests.get(lowerCAmelCase__   ,						stream=lowerCAmelCase__						).raw						)

      return image


def      UpperCAmelCase__			(							lowerCAmelCase__	:Path   ,						lowerCAmelCase__	:Path   ,						lowerCAmelCase__	:Path   ,						lowerCAmelCase__	:bool						)					->       List[str]:
      '''simple docstring'''


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

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

      model.load_state_dict(lowerCAmelCase__						)
      model.eval()

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

      # prepare image
      lowercase								=      prepare_img()
      lowercase								=      2_5_6
      lowercase								=      2_2_4
      lowercase								=      EfficientFormerImageProcessor(
          size={"""shortest_edge""": image_size}   ,						crop_size={"""height""": crop_size, """width""": crop_size}   ,						resample=pillow_resamplings["""bicubic"""]   ,						)
      lowercase								=      processor(images=lowerCAmelCase__   ,						return_tensors="""pt"""						).pixel_values

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

      assert torch.allclose(lowerCAmelCase__   ,						lowerCAmelCase__						)

      lowercase								=      model(lowerCAmelCase__						)
      lowercase								=      outputs.logits

      lowercase								=      (1, 1_0_0_0)

      if "l1" in model_name:
            lowercase								=      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, :1_0]   ,						lowerCAmelCase__   ,						atol=1e-3						)
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            lowercase								=      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, :1_0]   ,						lowerCAmelCase__   ,						atol=1e-3						)
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            lowercase								=      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__":
       __lowerCAmelCase       :			Union[str, 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       :			Optional[Any]   	=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 ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__lowerCAmelCase       :			List[str]   	=[
    """cross_validation.py""",
    """gradient_accumulation.py""",
    """local_sgd.py""",
    """multi_process_metrics.py""",
    """memory.py""",
    """automatic_gradient_accumulation.py""",
    """fsdp_with_peak_mem_tracking.py""",
    """deepspeed_with_config_support.py""",
    """megatron_lm_gpt_pretraining.py""",
]





class 					_A		(			unittest.TestCase      ):

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      None
            lowercase								=      os.path.abspath(os.path.join("""examples"""						,			"""by_feature"""		)		)
            lowercase								=      os.path.abspath("""examples"""		)
            for item in os.listdir(__lowerCAmelCase		):
                  if item not in EXCLUDE_EXAMPLES:
                        lowercase								=      os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)
                        if os.path.isfile(__lowerCAmelCase		) and ".py" in item_path:
                              with self.subTest(
                                  tested_script=__lowerCAmelCase						,			feature_script=__lowerCAmelCase						,			tested_section="""main()""" if parser_only else """training_function()"""						,			):
                                    lowercase								=      compare_against_test(
                                        os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                                    lowercase								=      """\n""".join(__lowerCAmelCase		)
                                    if special_strings is not None:
                                          for string in special_strings:
                                                lowercase								=      diff.replace(__lowerCAmelCase						,			""""""		)
                                    self.assertEqual(__lowerCAmelCase						,			""""""		)

      def 							A__			(		self		):







            """simple docstring"""



            self.one_complete_example("""complete_nlp_example.py"""						,			__lowerCAmelCase		)
            self.one_complete_example("""complete_nlp_example.py"""						,			__lowerCAmelCase		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      os.path.abspath(os.path.join("""examples"""						,			"""cv_example.py"""		)		)
            lowercase								=      [
                """ """ * 16 + """{\n\n""",
                """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
                """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
                """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
                """ """ * 20 + """\"epoch\": epoch,\n\n""",
                """ """ * 16 + """},\n\n""",
                """ """ * 16 + """step=epoch,\n""",
                """ """ * 12,
                """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
            ]
            self.one_complete_example("""complete_cv_example.py"""						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
            self.one_complete_example("""complete_cv_example.py"""						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)






@mock.patch.dict(os.environ    ,       {'TESTING_MOCKED_DATALOADERS': '1'}      )
class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Any				    =      False

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            super().setUpClass()
            lowercase								=      tempfile.mkdtemp()
            lowercase								=      os.path.join(cls._tmpdir						,			"""default_config.yml"""		)

            write_basic_config(save_location=cls.configPath		)
            lowercase								=      ["""accelerate""", """launch""", """--config_file""", cls.configPath]

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            super().tearDownClass()
            shutil.rmtree(cls._tmpdir		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
            run_command(self._launch_args + testargs		)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,			"""epoch_0"""		)		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
            lowercase								=      run_command(self._launch_args + testargs		)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,			"""step_2"""		)		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,			"epoch_0"		)}\n        '.split()
            lowercase								=      run_command(self._launch_args + testargs						,			return_stdout=__lowerCAmelCase		)
            self.assertNotIn("""epoch 0:"""						,			__lowerCAmelCase		)
            self.assertIn("""epoch 1:"""						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,			"step_2"		)}\n        '.split()
            lowercase								=      run_command(self._launch_args + testargs						,			return_stdout=__lowerCAmelCase		)
            if torch.cuda.is_available():
                  lowercase								=      torch.cuda.device_count()
            else:
                  lowercase								=      1
            if num_processes > 1:
                  self.assertNotIn("""epoch 0:"""						,			__lowerCAmelCase		)
                  self.assertIn("""epoch 1:"""						,			__lowerCAmelCase		)
            else:
                  self.assertIn("""epoch 0:"""						,			__lowerCAmelCase		)
                  self.assertIn("""epoch 1:"""						,			__lowerCAmelCase		)

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
            with mock.patch.dict(os.environ						,			{"""TESTING_MOCKED_DATALOADERS""": """0"""}		):
                  lowercase								=      run_command(self._launch_args + testargs						,			return_stdout=__lowerCAmelCase		)
                  lowercase								=      re.findall("""({.+})"""						,			__lowerCAmelCase		)
                  lowercase								=      [r for r in results if """accuracy""" in r][-1]
                  lowercase								=      ast.literal_eval(__lowerCAmelCase		)
                  self.assertGreaterEqual(results["""accuracy"""]						,			0.7_5		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""examples/by_feature/multi_process_metrics.py"""]
            run_command(self._launch_args + testargs		)

      @require_trackers
      @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}		)
      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  lowercase								=      f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
                  run_command(self._launch_args + testargs		)
                  self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase						,			"""tracking"""		)		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""examples/by_feature/gradient_accumulation.py"""]
            run_command(self._launch_args + testargs		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""examples/by_feature/local_sgd.py"""]
            run_command(self._launch_args + testargs		)



