'''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	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   str						= old_name

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

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

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

   lowercase__    :   List[str]						= '''last_stage.''' + trimmed_name

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

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

 return new_name
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 for key in checkpoint.copy().keys():
  lowercase__    :   str						= checkpoint.pop(UpperCAmelCase					)
  lowercase__    :   List[str]						= val

 return checkpoint
def       __UpperCamelCase	(       ):
 lowercase__    :   Union[str, Any]						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 lowercase__    :   Union[str, Any]						= Image.open(requests.get(UpperCAmelCase		,					stream=UpperCAmelCase					).raw					)

 return image
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Optional[int]						= torch.load(UpperCAmelCase		,					map_location='''cpu'''					)['''model''']
 lowercase__    :   Any						= EfficientFormerConfig.from_json_file(UpperCAmelCase					)
 lowercase__    :   str						= EfficientFormerForImageClassificationWithTeacher(UpperCAmelCase					)
 lowercase__    :   str						= '''_'''.join(checkpoint_path.split('''/'''					)[-1].split('''.'''					)[0].split('''_'''					)[:-1]					)

 lowercase__    :   Any						= config.depths[-1] - config.num_metaad_blocks + 1
 lowercase__    :   Dict						= convert_torch_checkpoint(UpperCAmelCase		,					UpperCAmelCase					)

 model.load_state_dict(UpperCAmelCase					)
 model.eval()

 lowercase__    :   List[str]						= {
     '''bilinear''': PILImageResampling.BILINEAR,
     '''bicubic''': PILImageResampling.BICUBIC,
     '''nearest''': PILImageResampling.NEAREST,
 }

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

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

 assert torch.allclose(UpperCAmelCase		,					UpperCAmelCase					)

 lowercase__    :   Union[str, Any]						= model(UpperCAmelCase					)
 lowercase__    :   str						= outputs.logits

 lowercase__    :   str						= (1, 1000)

 if "l1" in model_name:
  lowercase__    :   Union[str, Any]						= torch.Tensor(
      [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]					)
  assert torch.allclose(logits[0, :10]		,					UpperCAmelCase		,					atol=1E-3					)
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  lowercase__    :   Union[str, Any]						= torch.Tensor(
      [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]					)
  assert torch.allclose(logits[0, :10]		,					UpperCAmelCase		,					atol=1E-3					)
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  lowercase__    :   Tuple						= torch.Tensor(
      [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]					)
  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:			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,
  )


'''simple docstring'''

import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__a:			Tuple	       =       datasets.utils.logging.get_logger(__name__)



@dataclass
class       UpperCAmelCase  (						datasets.BuilderConfig ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "utf-8"
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   True  # deprecated
       SCREAMING_SNAKE_CASE 		=   None  # deprecated
       SCREAMING_SNAKE_CASE 		=   1_0 << 2_0  # 10MB
       SCREAMING_SNAKE_CASE 		=   None



class       UpperCAmelCase  (						datasets.ArrowBasedBuilder ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   JsonConfig

       def _lowerCAmelCase(						self			)  ->  Any:
        if self.config.block_size is not None:
         logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''			)
         lowercase__    :   Tuple						= self.config.block_size
        if self.config.use_threads is not True:
         logger.warning(
             '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''			)
        if self.config.newlines_in_values is not None:
         raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''			)
        return datasets.DatasetInfo(features=self.config.features			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""			)
        lowercase__    :   List[str]						= dl_manager.download_and_extract(self.config.data_files			)
        if isinstance(__lowerCAmelCase     ,       (str, list, tuple)			):
         lowercase__    :   List[str]						= data_files
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Union[str, Any]						= [files]
         lowercase__    :   Dict						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,       gen_kwargs={'''files''': files}			)]
        lowercase__    :   str						= []
        for split_name, files in data_files.items():
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   List[str]						= [files]
         lowercase__    :   Optional[Any]						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         splits.append(datasets.SplitGenerator(name=__lowerCAmelCase     ,       gen_kwargs={'''files''': files}			)			)
        return splits

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  pa.Table:
        if self.config.features is not None:
         # adding missing columns
         for column_name in set(self.config.features			) - set(pa_table.column_names			):
          lowercase__    :   Optional[int]						= self.config.features.arrow_schema.field(__lowerCAmelCase			).type
          lowercase__    :   Union[str, Any]						= pa_table.append_column(__lowerCAmelCase     ,       pa.array([None] * len(__lowerCAmelCase			)     ,       type=__lowerCAmelCase			)			)
         # more expensive cast to support nested structures with keys in a different order
         # allows str <-> int/float or str to Audio for example
         lowercase__    :   Dict						= table_cast(__lowerCAmelCase     ,       self.config.features.arrow_schema			)
        return pa_table


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[int]:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase			)			):
         # If the file is one json object and if we need to look at the list of items in one specific field
         if self.config.field is not None:
          with open(__lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
           lowercase__    :   Dict						= json.load(__lowerCAmelCase			)

          # We keep only the field we are interested in
          lowercase__    :   Any						= dataset[self.config.field]

          # We accept two format: a list of dicts or a dict of lists
          if isinstance(__lowerCAmelCase     ,       (list, tuple)			):
           lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
           lowercase__    :   List[Any]						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
          else:
           lowercase__    :   Union[str, Any]						= dataset
          lowercase__    :   Optional[int]						= pa.Table.from_pydict(__lowerCAmelCase			)
          yield file_idx, self._cast_table(__lowerCAmelCase			)

         # If the file has one json object per line
         else:
          with open(__lowerCAmelCase     ,       '''rb'''			) as f:
           lowercase__    :   Optional[int]						= 0
           # Use block_size equal to the chunk size divided by 32 to leverage multithreading
           # Set a default minimum value of 16kB if the chunk size is really small
           lowercase__    :   List[str]						= max(self.config.chunksize // 32     ,       16 << 10			)
           lowercase__    :   Union[str, Any]						= (
               self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
           )
           while True:
            lowercase__    :   Tuple						= f.read(self.config.chunksize			)
            if not batch:
             break
            # Finish current line
            try:
             batch += f.readline()
            except (AttributeError, io.UnsupportedOperation):
             batch += readline(__lowerCAmelCase			)
            # PyArrow only accepts utf-8 encoded bytes
            if self.config.encoding != "utf-8":
             lowercase__    :   Dict						= batch.decode(self.config.encoding     ,       errors=__lowerCAmelCase			).encode('''utf-8'''			)
            try:
             while True:
              try:
               lowercase__    :   str						= paj.read_json(
                   io.BytesIO(__lowerCAmelCase			)     ,       read_options=paj.ReadOptions(block_size=__lowerCAmelCase			)			)
               break
              except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
               if (
                   isinstance(__lowerCAmelCase     ,       pa.ArrowInvalid			)
                   and "straddling" not in str(__lowerCAmelCase			)
                   or block_size > len(__lowerCAmelCase			)
               ):
                raise
               else:
                # Increase the block size in case it was too small.
                # The block size will be reset for the next file.
                logger.debug(
                    F"""Batch of {len(__lowerCAmelCase			)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""			)
                block_size *= 2
            except pa.ArrowInvalid as e:
             try:
              with open(
                  __lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
               lowercase__    :   Optional[int]						= json.load(__lowerCAmelCase			)
             except json.JSONDecodeError:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
              raise e
             # If possible, parse the file as a list of json objects and exit the loop
             if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):  # list is the only sequence type supported in JSON
              try:
               lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
               lowercase__    :   str						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
               lowercase__    :   Optional[int]						= pa.Table.from_pydict(__lowerCAmelCase			)
              except (pa.ArrowInvalid, AttributeError) as e:
               logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
               raise ValueError(F"""Not able to read records in the JSON file at {file}."""			) from None
              yield file_idx, self._cast_table(__lowerCAmelCase			)
              break
             else:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
              raise ValueError(
                  F"""Not able to read records in the JSON file at {file}. """
                  F"""You should probably indicate the field of the JSON file containing your records. """
                  F"""This JSON file contain the following fields: {str(list(dataset.keys()			)			)}. """
                  F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """			) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
            yield (file_idx, batch_idx), self._cast_table(__lowerCAmelCase			)
            batch_idx += 1

