

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__			(    SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    List[Any]			):



     '''simple docstring'''





     lowercase__    :   str      =       old_name

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

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

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

               lowercase__    :   str      =       'last_stage.' + trimmed_name

     elif "network" in old_name and re.search(R'.\d.'	,     SCREAMING_SNAKE_CASE_			):
          lowercase__    :   Optional[Any]      =       old_name.replace('network'	,     'intermediate_stages'			)

     if "fc" in new_name:
          lowercase__    :   Tuple      =       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[Any]      =       new_name.replace('norm2'	,     'batchnorm_after'			)
     if "proj" in new_name:
          lowercase__    :   Tuple      =       new_name.replace('proj'	,     'projection'			)
     if "dist_head" in new_name:
          lowercase__    :   Optional[int]      =       new_name.replace('dist_head'	,     'distillation_classifier'			)
     elif "head" in new_name:
          lowercase__    :   str      =       new_name.replace('head'	,     'classifier'			)
     elif "patch_embed" in new_name:
          lowercase__    :   Optional[Any]      =       'efficientformer.' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          lowercase__    :   Union[str, Any]      =       new_name.replace('norm'	,     'layernorm'			)
          lowercase__    :   int      =       'efficientformer.' + new_name
     else:
          lowercase__    :   Any      =       'efficientformer.encoder.' + new_name

     return new_name

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    List[str]			):



     '''simple docstring'''





     for key in checkpoint.copy().keys():
          lowercase__    :   Any      =       checkpoint.pop(SCREAMING_SNAKE_CASE_			)
          lowercase__    :   Any      =       val

     return checkpoint

def 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase__    :   int      =       Image.open(requests.get(SCREAMING_SNAKE_CASE_	,     stream=SCREAMING_SNAKE_CASE_			).raw			)

     return image

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Path	,     SCREAMING_SNAKE_CASE_		:    Path	,     SCREAMING_SNAKE_CASE_		:    Path	,     SCREAMING_SNAKE_CASE_		:    bool			):



     '''simple docstring'''





     lowercase__    :   int      =       torch.load(SCREAMING_SNAKE_CASE_	,     map_location='cpu'			)['model']
     lowercase__    :   str      =       EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Union[str, Any]      =       EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   List[str]      =       '_'.join(checkpoint_path.split('/'			)[-1].split('.'			)[0].split('_'			)[:-1]			)

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

     model.load_state_dict(SCREAMING_SNAKE_CASE_			)
     model.eval()

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

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

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

     assert torch.allclose(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)

     lowercase__    :   Any      =       model(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   List[str]      =       outputs.logits

     lowercase__    :   List[str]      =       (1, 1_000)

     if "l1" in model_name:
          lowercase__    :   Union[str, 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]	,     SCREAMING_SNAKE_CASE_	,     atol=1E-3			)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          lowercase__    :   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]	,     SCREAMING_SNAKE_CASE_	,     atol=1E-3			)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          lowercase__    :   List[str]      =       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(SCREAMING_SNAKE_CASE_			).mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
     model.save_pretrained(SCREAMING_SNAKE_CASE_			)
     print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""			)
     processor.save_pretrained(SCREAMING_SNAKE_CASE_			)
     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=SCREAMING_SNAKE_CASE_	,     )
          processor.push_to_hub(
              repo_id=f"""Bearnardd/{pytorch_dump_path}"""	,     commit_message='Add image processor'	,     use_temp_dir=SCREAMING_SNAKE_CASE_	,     )


if __name__ == "__main__":
       snake_case_			  = 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)

       snake_case_			  = 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,
       )




import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


snake_case_			  = datasets.utils.logging.get_logger(__name__)

snake_case_			  = ['''names''', '''prefix''']
snake_case_			  = ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
snake_case_			  = ['''encoding_errors''', '''on_bad_lines''']
snake_case_			  = ['''date_format''']


@dataclass
class       SCREAMING_SNAKE_CASE__					(datasets.BuilderConfig     ):
    __lowerCamelCase						:       str														=     ","
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[Union[int, List[int], str]]														=     "infer"
    __lowerCamelCase						:       Optional[List[str]]														=     None
    __lowerCamelCase						:       Optional[List[str]]														=     None
    __lowerCamelCase						:       Optional[Union[int, str, List[int], List[str]]]														=     None
    __lowerCamelCase						:       Optional[Union[List[int], List[str]]]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       Optional[Literal["c", "python", "pyarrow"]]														=     None
    __lowerCamelCase						:       Dict[Union[int, str], Callable[[Any], Any]]														=     None
    __lowerCamelCase						:       Optional[list]														=     None
    __lowerCamelCase						:       Optional[list]														=     None
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       Optional[Union[int, List[int]]]														=     None
    __lowerCamelCase						:       Optional[int]														=     None
    __lowerCamelCase						:       Optional[Union[str, List[str]]]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       str														=     "."
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       str														=     '"'
    __lowerCamelCase						:       int														=     0
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       int														=     0
    __lowerCamelCase						:       bool														=     True
    __lowerCamelCase						:       bool														=     False
    __lowerCamelCase						:       Optional[str]														=     None
    __lowerCamelCase						:       int														=     1_0000
    __lowerCamelCase						:       Optional[datasets.Features]														=     None
    __lowerCamelCase						:       Optional[str]														=     "strict"
    __lowerCamelCase						:       Literal["error", "warn", "skip"]														=     "error"
    __lowerCamelCase						:       Optional[str]														=     None





    def       snake_case_				(      self):
         if self.delimiter is not None:
              lowercase__    :   List[Any]      =       self.delimiter
         if self.column_names is not None:
              lowercase__    :   Optional[int]      =       self.column_names





    @property
    def       snake_case_				(      self):
         lowercase__    :   Dict      =       {
             'sep': self.sep,
             'header': self.header,
             'names': self.names,
             'index_col': self.index_col,
             'usecols': self.usecols,
             'prefix': self.prefix,
             'mangle_dupe_cols': self.mangle_dupe_cols,
             'engine': self.engine,
             'converters': self.converters,
             'true_values': self.true_values,
             'false_values': self.false_values,
             'skipinitialspace': self.skipinitialspace,
             'skiprows': self.skiprows,
             'nrows': self.nrows,
             'na_values': self.na_values,
             'keep_default_na': self.keep_default_na,
             'na_filter': self.na_filter,
             'verbose': self.verbose,
             'skip_blank_lines': self.skip_blank_lines,
             'thousands': self.thousands,
             'decimal': self.decimal,
             'lineterminator': self.lineterminator,
             'quotechar': self.quotechar,
             'quoting': self.quoting,
             'escapechar': self.escapechar,
             'comment': self.comment,
             'encoding': self.encoding,
             'dialect': self.dialect,
             'error_bad_lines': self.error_bad_lines,
             'warn_bad_lines': self.warn_bad_lines,
             'skipfooter': self.skipfooter,
             'doublequote': self.doublequote,
             'memory_map': self.memory_map,
             'float_precision': self.float_precision,
             'chunksize': self.chunksize,
             'encoding_errors': self.encoding_errors,
             'on_bad_lines': self.on_bad_lines,
             'date_format': self.date_format,
         }

         # some kwargs must not be passed if they don't have a default value
         # some others are deprecated and we can also not pass them if they are the default value
         for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
              if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()						,   a):
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
         if not (datasets.config.PANDAS_VERSION.major >= 2):
              for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
         if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
              for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

         return pd_read_csv_kwargs



class       SCREAMING_SNAKE_CASE__					(datasets.ArrowBasedBuilder     ):
    __lowerCamelCase						:       Optional[Any]														=     CsvConfig





    def       snake_case_				(      self):
         return datasets.DatasetInfo(features=self.config.features)





    def       snake_case_				(      self						,   a):
         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__    :   Any      =       dl_manager.download_and_extract(self.config.data_files)
         if isinstance(a						,   (str, list, tuple)):
              lowercase__    :   List[str]      =       data_files
              if isinstance(a						,   a):
                   lowercase__    :   Optional[Any]      =       [files]
              lowercase__    :   Optional[int]      =       [dl_manager.iter_files(a) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,   gen_kwargs={'files': files})]
         lowercase__    :   int      =       []
         for split_name, files in data_files.items():
              if isinstance(a						,   a):
                   lowercase__    :   Optional[int]      =       [files]
              lowercase__    :   Tuple      =       [dl_manager.iter_files(a) for file in files]
              splits.append(datasets.SplitGenerator(name=a						,   gen_kwargs={'files': files}))
         return splits





    def       snake_case_				(      self						,   a):
         if self.config.features is not None:
              lowercase__    :   Optional[int]      =       self.config.features.arrow_schema
              if all(not require_storage_cast(a) for feature in self.config.features.values()):
                   # cheaper cast
                   lowercase__    :   Dict      =       pa.Table.from_arrays([pa_table[field.name] for field in schema]						,   schema=a)
              else:
                   # more expensive cast; allows str <-> int/float or str to Audio for example
                   lowercase__    :   Optional[Any]      =       table_cast(a						,   a)
         return pa_table





    def       snake_case_				(      self						,   a):
         lowercase__    :   List[Any]      =       self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         lowercase__    :   Optional[int]      =       (
             {
                 name: dtype.to_pandas_dtype() if not require_storage_cast(a) else object
                 for name, dtype, feature in zip(schema.names						,   schema.types						,   self.config.features.values())
             }
             if schema is not None
             else None
         )
         for file_idx, file in enumerate(itertools.chain.from_iterable(a)):
              lowercase__    :   int      =       pd.read_csv(a						,   iterator=a						,   dtype=a						,   **self.config.pd_read_csv_kwargs)
              try:
                   for batch_idx, df in enumerate(a):
                        lowercase__    :   List[str]      =       pa.Table.from_pandas(a)
                        # 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(a)
              except ValueError as e:
                   logger.error(f"""Failed to read file '{file}' with error {type(a)}: {e}""")
                   raise

