






"""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()
__lowerCamelCase							=  logging.get_logger()



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    LevitConfig      ,   __UpperCAmelCase      :    Path      ,   __UpperCAmelCase      :    bool = True  )    ->							Dict:
						print(f'Converting {name}...'  )

						with torch.no_grad():
												if hidden_sizes == 1_2_8:
																		if name[-1] == "S":
																								__magic_name__:	Dict							=       timm.create_model("""levit_128s"""      ,   pretrained=__UpperCAmelCase  )
																		else:
																								__magic_name__:	int							=       timm.create_model("""levit_128"""      ,   pretrained=__UpperCAmelCase  )
												if hidden_sizes == 1_9_2:
																		__magic_name__:	Union[str, Any]							=       timm.create_model("""levit_192"""      ,   pretrained=__UpperCAmelCase  )
												if hidden_sizes == 2_5_6:
																		__magic_name__:	List[Any]							=       timm.create_model("""levit_256"""      ,   pretrained=__UpperCAmelCase  )
												if hidden_sizes == 3_8_4:
																		__magic_name__:	Optional[int]							=       timm.create_model("""levit_384"""      ,   pretrained=__UpperCAmelCase  )

												from_model.eval()
												__magic_name__:	Dict							=       LevitForImageClassificationWithTeacher(__UpperCAmelCase  ).eval()
												__magic_name__:	str							=       OrderedDict()

												__magic_name__:	Any							=       from_model.state_dict()
												__magic_name__:	Dict							=       list(from_model.state_dict().keys()  )
												__magic_name__:	int							=       list(our_model.state_dict().keys()  )
												print(len(__UpperCAmelCase  )      ,   len(__UpperCAmelCase  )  )
												for i in range(len(__UpperCAmelCase  )  ):
																		__magic_name__:	int							=       weights[og_keys[i]]
												our_model.load_state_dict(__UpperCAmelCase  )

												__magic_name__:	List[str]							=       torch.randn((2, 3, 2_2_4, 2_2_4)  )
												__magic_name__:	Dict							=       from_model(__UpperCAmelCase  )
												__magic_name__:	List[str]							=       our_model(__UpperCAmelCase  ).logits

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

						__magic_name__:	List[Any]							=       name
						print(__UpperCAmelCase  )

						if push_to_hub:
												our_model.save_pretrained(save_directory / checkpoint_name  )
												__magic_name__:	Union[str, Any]							=       LevitImageProcessor()
												image_processor.save_pretrained(save_directory / checkpoint_name  )

												print(f'Pushed {checkpoint_name}'  )



def   a    (     __UpperCAmelCase      :    Path      ,   __UpperCAmelCase      :    str = None      ,   __UpperCAmelCase      :    bool = True  )    ->							List[Any]:
						__magic_name__:	str							=       """imagenet-1k-id2label.json"""
						__magic_name__:	Dict							=       1_0_0_0
						__magic_name__:	Optional[Any]							=       (1, num_labels)

						__magic_name__:	List[str]							=       """huggingface/label-files"""
						__magic_name__:	Union[str, Any]							=       num_labels
						__magic_name__:	Optional[int]							=       json.load(open(hf_hub_download(__UpperCAmelCase      ,   __UpperCAmelCase      ,   repo_type="""dataset"""  )      ,   """r"""  )  )
						__magic_name__:	List[Any]							=       {int(__UpperCAmelCase  ): v for k, v in idalabel.items()}

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

						__magic_name__:	List[Any]							=       partial(__UpperCAmelCase      ,   num_labels=__UpperCAmelCase      ,   idalabel=__UpperCAmelCase      ,   labelaid=__UpperCAmelCase  )

						__magic_name__:	List[Any]							=       {
						    """levit-128S""": 1_2_8,
						    """levit-128""": 1_2_8,
						    """levit-192""": 1_9_2,
						    """levit-256""": 2_5_6,
						    """levit-384""": 3_8_4,
						}

						__magic_name__:	List[Any]							=       {
						    """levit-128S""": ImageNetPreTrainedConfig(
						        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]      ,   num_attention_heads=[4, 6, 8]      ,   depths=[2, 3, 4]      ,   key_dim=[1_6, 1_6, 1_6]      ,   drop_path_rate=0      ,   ),
						    """levit-128""": ImageNetPreTrainedConfig(
						        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]      ,   num_attention_heads=[4, 8, 1_2]      ,   depths=[4, 4, 4]      ,   key_dim=[1_6, 1_6, 1_6]      ,   drop_path_rate=0      ,   ),
						    """levit-192""": ImageNetPreTrainedConfig(
						        hidden_sizes=[1_9_2, 2_8_8, 3_8_4]      ,   num_attention_heads=[3, 5, 6]      ,   depths=[4, 4, 4]      ,   key_dim=[3_2, 3_2, 3_2]      ,   drop_path_rate=0      ,   ),
						    """levit-256""": ImageNetPreTrainedConfig(
						        hidden_sizes=[2_5_6, 3_8_4, 5_1_2]      ,   num_attention_heads=[4, 6, 8]      ,   depths=[4, 4, 4]      ,   key_dim=[3_2, 3_2, 3_2]      ,   drop_path_rate=0      ,   ),
						    """levit-384""": ImageNetPreTrainedConfig(
						        hidden_sizes=[3_8_4, 5_1_2, 7_6_8]      ,   num_attention_heads=[6, 9, 1_2]      ,   depths=[4, 4, 4]      ,   key_dim=[3_2, 3_2, 3_2]      ,   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__":
							__lowerCamelCase							=  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',
							)

							__lowerCamelCase							=  parser.parse_args()
							__lowerCamelCase							=  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)











"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__lowerCamelCase							=  logging.get_logger(__name__)



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      int     ,		__snake_case  :      float     ,		**__snake_case  :      Optional[Any]					)			->							str:
											__magic_name__:	Optional[Any]							=       feature_size
											__magic_name__:	List[Any]							=       sampling_rate
											__magic_name__:	Tuple							=       padding_value

											__magic_name__:	int							=       kwargs.pop("""padding_side"""     ,		"""right"""					)
											__magic_name__:	Optional[Any]							=       kwargs.pop("""return_attention_mask"""     ,		__snake_case					)

											super().__init__(**__snake_case					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ]     ,		__snake_case  :      Union[bool, str, PaddingStrategy] = True     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      bool = False     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[bool] = None     ,		__snake_case  :      Optional[Union[str, TensorType]] = None     ,		)			->							BatchFeature:
											# If we have a list of dicts, let's convert it in a dict of lists
											# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
											if isinstance(__snake_case     ,		(list, tuple)					) and isinstance(processed_features[0]     ,		(dict, BatchFeature)					):
																	__magic_name__:	Union[str, Any]							=       {
																	    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																	}

											# The model's main input name, usually `input_values`, has be passed for padding
											if self.model_input_names[0] not in processed_features:
																	raise ValueError(
																	    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
																	    F' to this method that includes {self.model_input_names[0]}, but you provided'
																	    F' {list(processed_features.keys()					)}'					)

											__magic_name__:	Any							=       processed_features[self.model_input_names[0]]
											__magic_name__:	Tuple							=       (
											    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
											)

											if len(__snake_case					) == 0:
																	if return_attention_mask:
																							__magic_name__:	Optional[Any]							=       []
																	return processed_features

											# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
											# and rebuild them afterwards if no return_tensors is specified
											# Note that we lose the specific device the tensor may be on for PyTorch

											__magic_name__:	Tuple							=       required_input[0]
											if isinstance(__snake_case     ,		(list, tuple)					):
																	# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																	__magic_name__:	str							=       0
																	while len(required_input[index]					) == 0:
																							index += 1
																	if index < len(__snake_case					):
																							__magic_name__:	Optional[int]							=       required_input[index][0]

											if return_tensors is None:
																	if is_tf_tensor(__snake_case					):
																							__magic_name__:	List[str]							=       """tf"""
																	elif is_torch_tensor(__snake_case					):
																							__magic_name__:	Any							=       """pt"""
																	elif isinstance(__snake_case     ,		(int, float, list, tuple, np.ndarray)					):
																							__magic_name__:	int							=       """np"""
																	else:
																							raise ValueError(
																							    F'type of {first_element} unknown: {type(__snake_case					)}. '
																							    """Should be one of a python, numpy, pytorch or tensorflow object."""					)

											for key, value in processed_features.items():
																	if isinstance(value[0]     ,		(int, float)					):
																							__magic_name__:	List[str]							=       to_numpy(__snake_case					)
																	else:
																							__magic_name__:	Any							=       [to_numpy(__snake_case					) for v in value]

        # Convert padding_strategy in PaddingStrategy
											__magic_name__:	str							=       self._get_padding_strategies(padding=__snake_case     ,		max_length=__snake_case					)

											__magic_name__:	str							=       processed_features[self.model_input_names[0]]

											__magic_name__:	str							=       len(__snake_case					)
											if not all(len(__snake_case					) == batch_size for v in processed_features.values()					):
																	raise ValueError("""Some items in the output dictionary have a different batch size than others."""					)

											__magic_name__:	List[Any]							=       []
											for i in range(__snake_case					):
																	__magic_name__:	List[Any]							=       {k: v[i] for k, v in processed_features.items()}
																	# truncation
																	__magic_name__:	List[Any]							=       self._truncate(
																	    __snake_case     ,		max_length=__snake_case     ,		pad_to_multiple_of=__snake_case     ,		truncation=__snake_case     ,		)
																	truncated_inputs.append(__snake_case					)

											if padding_strategy == PaddingStrategy.LONGEST:
																	# make sure that `max_length` cannot be longer than the longest truncated length
																	__magic_name__:	Union[str, Any]							=       max(len(input_slice[self.model_input_names[0]]					) for input_slice in truncated_inputs					)
																	__magic_name__:	Union[str, Any]							=       PaddingStrategy.MAX_LENGTH

											__magic_name__:	List[str]							=       {}
											for i in range(__snake_case					):
																	# padding
																	__magic_name__:	str							=       self._pad(
																	    truncated_inputs[i]     ,		max_length=__snake_case     ,		padding_strategy=__snake_case     ,		pad_to_multiple_of=__snake_case     ,		return_attention_mask=__snake_case     ,		)

																	for key, value in outputs.items():
																							if key not in batch_outputs:
																													__magic_name__:	Optional[Any]							=       []
																							if value.dtype is np.dtype(np.floataa					):
																													__magic_name__:	Any							=       value.astype(np.floataa					)
																							batch_outputs[key].append(__snake_case					)

											return BatchFeature(__snake_case     ,		tensor_type=__snake_case					)

					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      Union[Dict[str, np.ndarray], BatchFeature]     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[bool] = None     ,		)			->							dict:
											__magic_name__:	List[str]							=       processed_features[self.model_input_names[0]]

											if padding_strategy == PaddingStrategy.LONGEST:
																	__magic_name__:	List[Any]							=       len(__snake_case					)

											if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	__magic_name__:	List[str]							=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

											__magic_name__:	str							=       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case					) < max_length

											if return_attention_mask and "attention_mask" not in processed_features:
																	__magic_name__:	int							=       np.ones(len(__snake_case					)     ,		dtype=np.intaa					)

											if needs_to_be_padded:
																	__magic_name__:	str							=       max_length - len(__snake_case					)
																	if self.padding_side == "right":
																							if return_attention_mask:
																													__magic_name__:	List[Any]							=       np.pad(
																													    processed_features["""attention_mask"""]     ,		(0, difference)					)
																							__magic_name__:	Union[str, Any]							=       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																							__magic_name__:	int							=       np.pad(
																							    __snake_case     ,		__snake_case     ,		"""constant"""     ,		constant_values=self.padding_value					)
																	elif self.padding_side == "left":
																							if return_attention_mask:
																													__magic_name__:	Dict							=       np.pad(
																													    processed_features["""attention_mask"""]     ,		(difference, 0)					)
																							__magic_name__:	Optional[Any]							=       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																							__magic_name__:	int							=       np.pad(
																							    __snake_case     ,		__snake_case     ,		"""constant"""     ,		constant_values=self.padding_value					)
																	else:
																							raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

											return processed_features

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Union[Dict[str, np.ndarray], BatchFeature]     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[bool] = None     ,		)			->							int:
											if not truncation:
																	return processed_features
											elif truncation and max_length is None:
																	raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""					)

											__magic_name__:	Dict							=       processed_features[self.model_input_names[0]]

											# find `max_length` that fits `pad_to_multiple_of`
											if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	__magic_name__:	Optional[Any]							=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

											__magic_name__:	Tuple							=       len(__snake_case					) > max_length

											if needs_to_be_truncated:
																	__magic_name__:	Any							=       processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																							__magic_name__:	List[Any]							=       processed_features["""attention_mask"""][:max_length]

											return processed_features






					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      int=False     ,		__snake_case  :      Tuple=None					)			->							Optional[Any]:

											# Get padding strategy
											if padding is not False:
																	if padding is True:
																							__magic_name__:	Optional[Any]							=       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(__snake_case     ,		__snake_case					):
																							__magic_name__:	Tuple							=       PaddingStrategy(__snake_case					)
																	elif isinstance(__snake_case     ,		__snake_case					):
																							__magic_name__:	Dict							=       padding
											else:
																	__magic_name__:	int							=       PaddingStrategy.DO_NOT_PAD

											# Set max length if needed
											if max_length is None:
																	if padding_strategy == PaddingStrategy.MAX_LENGTH:
																							raise ValueError(
																							    F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'					)

        # Test if we have a padding value
											if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																	raise ValueError(
																	    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
																	    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""					)

											return padding_strategy



