"""simple docstring"""



import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class SCREAMING_SNAKE_CASE  (     _lowerCAmelCase						):







 """simple docstring"""


 def __init__( self		:   int       , _snake_case		:   Any       , _snake_case		:   List[str] = None       , _snake_case		:   int = None       , _snake_case		:   Dict = None       , _snake_case		:   Union[str, Any] = False       , _snake_case		:   List[Any] = False       , _snake_case		:   Optional[Any] = None       , _snake_case		:   Optional[int] = None       , **_snake_case		:   int       , )					->      Optional[Any]:




    '''simple docstring'''






    super().__init__(
        SCREAMING_SNAKE_CASE__       , split=SCREAMING_SNAKE_CASE__       , features=SCREAMING_SNAKE_CASE__       , cache_dir=SCREAMING_SNAKE_CASE__       , keep_in_memory=SCREAMING_SNAKE_CASE__       , streaming=SCREAMING_SNAKE_CASE__       , num_proc=SCREAMING_SNAKE_CASE__       , **SCREAMING_SNAKE_CASE__       , )
    a__  					=		field
    a__  					=		path_or_paths if isinstance(SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__					) else {self.split: path_or_paths}
    a__  					=		Json(
        cache_dir=SCREAMING_SNAKE_CASE__       , data_files=SCREAMING_SNAKE_CASE__       , features=SCREAMING_SNAKE_CASE__       , field=SCREAMING_SNAKE_CASE__       , **SCREAMING_SNAKE_CASE__       , )



 def 							_lowerCAmelCase					( self		:   str					)					->      Any:




    '''simple docstring'''






    if self.streaming:
       a__  					=		self.builder.as_streaming_dataset(split=self.split					)
    # Build regular (map-style) dataset
    else:
       a__  					=		None
       a__  					=		None
       a__  					=		None
       a__  					=		None

       self.builder.download_and_prepare(
           download_config=SCREAMING_SNAKE_CASE__       , download_mode=SCREAMING_SNAKE_CASE__       , verification_mode=SCREAMING_SNAKE_CASE__       , base_path=SCREAMING_SNAKE_CASE__       , num_proc=self.num_proc       , )
       a__  					=		self.builder.as_dataset(
           split=self.split       , verification_mode=SCREAMING_SNAKE_CASE__       , in_memory=self.keep_in_memory					)
    return dataset

class SCREAMING_SNAKE_CASE  :







 """simple docstring"""


 def __init__( self		:   int       , _snake_case		:   Any       , _snake_case		:   Tuple       , _snake_case		:   Optional[Any] = None       , _snake_case		:   Optional[int] = None       , **_snake_case		:   Tuple       , )					->      str:




    '''simple docstring'''






    if num_proc is not None and num_proc <= 0:
       raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''					)

    a__  					=		dataset
    a__  					=		path_or_buf
    a__  					=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    a__  					=		num_proc
    a__  					=		'utf-8'
    a__  					=		to_json_kwargs


 def 							_lowerCAmelCase					( self		:   Tuple					)					->      int:




    '''simple docstring'''






    a__  					=		self.to_json_kwargs.pop('path_or_buf'       , SCREAMING_SNAKE_CASE__					)
    a__  					=		self.to_json_kwargs.pop('orient'       , 'records'					)
    a__  					=		self.to_json_kwargs.pop('lines'       , True if orient == 'records' else False					)
    a__  					=		self.to_json_kwargs.pop('index'       , False if orient in ['split', 'table'] else True					)
    a__  					=		self.to_json_kwargs.pop('compression'       , SCREAMING_SNAKE_CASE__					)

    if compression not in [None, "infer", "gzip", "bz2", "xz"]:
       raise NotImplementedError(F'''`datasets` currently does not support {compression} compression'''					)

    if isinstance(self.path_or_buf       , (str, bytes, os.PathLike)					):
       with fsspec.open(self.path_or_buf       , 'wb'       , compression=SCREAMING_SNAKE_CASE__					) as buffer:
          a__  					=		self._write(file_obj=SCREAMING_SNAKE_CASE__       , orient=SCREAMING_SNAKE_CASE__       , lines=SCREAMING_SNAKE_CASE__       , index=SCREAMING_SNAKE_CASE__       , **self.to_json_kwargs					)
    else:
       if compression:
          raise NotImplementedError(
              F'''The compression parameter is not supported when writing to a buffer, but compression={compression}'''
              ' was passed. Please provide a local path instead.'					)
       a__  					=		self._write(
           file_obj=self.path_or_buf       , orient=SCREAMING_SNAKE_CASE__       , lines=SCREAMING_SNAKE_CASE__       , index=SCREAMING_SNAKE_CASE__       , **self.to_json_kwargs					)
    return written


 def 							_lowerCAmelCase					( self		:   Any       , _snake_case		:   List[str]					)					->      List[Any]:




    '''simple docstring'''






    a__					,							a__					,							a__					,							a__					,							a__  					=		args

    a__  					=		query_table(
        table=self.dataset.data       , key=slice(SCREAMING_SNAKE_CASE__       , offset + self.batch_size					)       , indices=self.dataset._indices       , )
    a__  					=		batch.to_pandas().to_json(
        path_or_buf=SCREAMING_SNAKE_CASE__       , orient=SCREAMING_SNAKE_CASE__       , lines=SCREAMING_SNAKE_CASE__       , index=SCREAMING_SNAKE_CASE__       , **SCREAMING_SNAKE_CASE__					)
    if not json_str.endswith('\n'					):
       json_str += "\n"
    return json_str.encode(self.encoding					)



 def 							_lowerCAmelCase					( self		:   Optional[int]       , _snake_case		:   Tuple       , _snake_case		:   Any       , _snake_case		:   List[Any]       , _snake_case		:   Tuple       , **_snake_case		:   str       , )					->      int:




    '''simple docstring'''






    a__  					=		0

    if self.num_proc is None or self.num_proc == 1:
       for offset in logging.tqdm(
           range(0       , len(self.dataset					)       , self.batch_size					)       , unit='ba'       , disable=not logging.is_progress_bar_enabled()       , desc='Creating json from Arrow format'       , ):
          a__  					=		self._batch_json((offset, orient, lines, index, to_json_kwargs)					)
          written += file_obj.write(SCREAMING_SNAKE_CASE__					)
    else:
       a__					,							a__  					=		len(self.dataset					), self.batch_size
       with multiprocessing.Pool(self.num_proc					) as pool:
          for json_str in logging.tqdm(
              pool.imap(
                  self._batch_json       , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0       , SCREAMING_SNAKE_CASE__       , SCREAMING_SNAKE_CASE__					)]       , )       , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size       , unit='ba'       , disable=not logging.is_progress_bar_enabled()       , desc='Creating json from Arrow format'       , ):
             written += file_obj.write(SCREAMING_SNAKE_CASE__					)

    return written







"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase     =							logging.get_logger()

def       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       LevitConfig,	UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       bool = True     )	-> int:






	"""simple docstring"""





	print(F"""Converting {name}..."""     )

	with torch.no_grad():
		if hidden_sizes == 128:
			if name[-1] == "S":
				A__											=					timm.create_model("levit_128s",	pretrained=UpperCAmelCase_     )
			else:
				A__											=					timm.create_model("levit_128",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 192:
			A__											=					timm.create_model("levit_192",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 256:
			A__											=					timm.create_model("levit_256",	pretrained=UpperCAmelCase_     )
		if hidden_sizes == 384:
			A__											=					timm.create_model("levit_384",	pretrained=UpperCAmelCase_     )

		from_model.eval()
		A__											=					LevitForImageClassificationWithTeacher(UpperCAmelCase_     ).eval()
		A__											=					OrderedDict()

		A__											=					from_model.state_dict()
		A__											=					list(from_model.state_dict().keys()     )
		A__											=					list(our_model.state_dict().keys()     )
		print(len(UpperCAmelCase_     ),	len(UpperCAmelCase_     )     )
		for i in range(len(UpperCAmelCase_     )     ):
			A__											=					weights[og_keys[i]]
		our_model.load_state_dict(UpperCAmelCase_     )

		A__											=					torch.randn((2, 3, 224, 224)     )
		A__											=					from_model(UpperCAmelCase_     )
		A__											=					our_model(UpperCAmelCase_     ).logits

	assert torch.allclose(UpperCAmelCase_,	UpperCAmelCase_     ), "The model logits don't match the original one."

	A__											=					name
	print(UpperCAmelCase_     )

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name     )
		A__											=					LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name     )

		print(F"""Pushed {checkpoint_name}"""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       Path,	UpperCAmelCase_ :       str = None,	UpperCAmelCase_ :       bool = True     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					"imagenet-1k-id2label.json"
	A__											=					1000
	A__											=					(1, num_labels)

	A__											=					"huggingface/label-files"
	A__											=					num_labels
	A__											=					json.load(open(hf_hub_download(UpperCAmelCase_,	UpperCAmelCase_,	repo_type="dataset"     ),	"r"     )     )
	A__											=					{int(UpperCAmelCase_     ): v for k, v in idalabel.items()}

	A__											=					idalabel
	A__											=					{v: k for k, v in idalabel.items()}

	A__											=					partial(UpperCAmelCase_,	num_labels=UpperCAmelCase_,	idalabel=UpperCAmelCase_,	labelaid=UpperCAmelCase_     )

	A__											=					{
	    "levit-128S": 128,
	    "levit-128": 128,
	    "levit-192": 192,
	    "levit-256": 256,
	    "levit-384": 384,
	}

	A__											=					{
	    "levit-128S": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 6, 8],	depths=[2, 3, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-128": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384],	num_attention_heads=[4, 8, 12],	depths=[4, 4, 4],	key_dim=[16, 16, 16],	drop_path_rate=0,	),
	    "levit-192": ImageNetPreTrainedConfig(
	        hidden_sizes=[192, 288, 384],	num_attention_heads=[3, 5, 6],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-256": ImageNetPreTrainedConfig(
	        hidden_sizes=[256, 384, 512],	num_attention_heads=[4, 6, 8],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0,	),
	    "levit-384": ImageNetPreTrainedConfig(
	        hidden_sizes=[384, 512, 768],	num_attention_heads=[6, 9, 12],	depths=[4, 4, 4],	key_dim=[32, 32, 32],	drop_path_rate=0.1,	),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name],	UpperCAmelCase_,	names_to_config[model_name],	UpperCAmelCase_,	UpperCAmelCase_     )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name],	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
	return config, expected_shape


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default=None,
			    type=str,
			    help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default="""levit-dump-folder/""",
			    type=Path,
			    required=False,
			    help="""Path to the output PyTorch model directory.""",
			)
			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""",
			)

			UpperCamelCase     =							parser.parse_args()
			UpperCamelCase     =							args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




