


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


snake_case							:       Optional[int]										=	logging.getLogger(__name__)







@dataclass
class 						_snake_case    :
 SCREAMING_SNAKE_CASE__ =			42
 SCREAMING_SNAKE_CASE__ =			42
 SCREAMING_SNAKE_CASE__ =			42







@dataclass
class 						_snake_case    :
 SCREAMING_SNAKE_CASE__ =			42
 SCREAMING_SNAKE_CASE__ =			42
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			None







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'train'
 SCREAMING_SNAKE_CASE__ =			'dev'
 SCREAMING_SNAKE_CASE__ =			'test'







class 						_snake_case    :




 @staticmethod
 def 			SCREAMING_SNAKE_CASE__							(						_lowerCamelCase   ,    _lowerCamelCase       ):
       raise NotImplementedError




 @staticmethod
 def 			SCREAMING_SNAKE_CASE__							(						_lowerCamelCase       ):
       raise NotImplementedError




 @staticmethod
 def 			SCREAMING_SNAKE_CASE__							(						_lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=False   ,    _lowerCamelCase="[CLS]"   ,    _lowerCamelCase=1   ,    _lowerCamelCase="[SEP]"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=False   ,    _lowerCamelCase=0   ,    _lowerCamelCase=0   ,    _lowerCamelCase=-100   ,    _lowerCamelCase=0   ,    _lowerCamelCase=True   ,    ):
       a		:Any									=			{label: i for i, label in enumerate(UpperCamelCase_       )}

       a		:List[str]									=			[]
       for ex_index, example in enumerate(UpperCamelCase_       ):
             if ex_index % 1_0000 == 0:
                   logger.info('''Writing example %d of %d'''   ,    UpperCamelCase_   ,    len(UpperCamelCase_       )       )

             a		:Tuple									=			[]
             a		:Dict									=			[]
             for word, label in zip(example.words   ,    example.labels       ):
                   a		:int									=			tokenizer.tokenize(UpperCamelCase_       )

                   # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                   if len(UpperCamelCase_       ) > 0:
                         tokens.extend(UpperCamelCase_       )
                         # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                         label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCamelCase_       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
             a		:Union[str, Any]									=			tokenizer.num_special_tokens_to_add()
             if len(UpperCamelCase_       ) > max_seq_length - special_tokens_count:
                   a		:Optional[int]									=			tokens[: (max_seq_length - special_tokens_count)]
                   a		:int									=			label_ids[: (max_seq_length - special_tokens_count)]

             # The convention in BERT is:
             # (a) For sequence pairs:
             #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
             #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
             # (b) For single sequences:
             #  tokens:   [CLS] the dog is hairy . [SEP]
             #  type_ids:   0   0   0   0  0     0   0
             #
             # Where "type_ids" are used to indicate whether this is the first
             # sequence or the second sequence. The embedding vectors for `type=0` and
             # `type=1` were learned during pre-training and are added to the wordpiece
             # embedding vector (and position vector). This is not *strictly* necessary
             # since the [SEP] token unambiguously separates the sequences, but it makes
             # it easier for the model to learn the concept of sequences.
             #
             # For classification tasks, the first vector (corresponding to [CLS]) is
             # used as the "sentence vector". Note that this only makes sense because
             # the entire model is fine-tuned.
             tokens += [sep_token]
             label_ids += [pad_token_label_id]
             if sep_token_extra:
                   # roberta uses an extra separator b/w pairs of sentences
                   tokens += [sep_token]
                   label_ids += [pad_token_label_id]
             a		:List[Any]									=			[sequence_a_segment_id] * len(UpperCamelCase_       )

             if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
             else:
                   a		:Any									=			[cls_token] + tokens
                   a		:List[str]									=			[pad_token_label_id] + label_ids
                   a		:List[Any]									=			[cls_token_segment_id] + segment_ids

             a		:Optional[Any]									=			tokenizer.convert_tokens_to_ids(UpperCamelCase_       )

             # The mask has 1 for real tokens and 0 for padding tokens. Only real
             # tokens are attended to.
             a		:Optional[Any]									=			[1 if mask_padding_with_zero else 0] * len(UpperCamelCase_       )

             # Zero-pad up to the sequence length.
             a		:Any									=			max_seq_length - len(UpperCamelCase_       )
             if pad_on_left:
                   a		:List[Any]									=			([pad_token] * padding_length) + input_ids
                   a		:Any									=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   a		:Optional[Any]									=			([pad_token_segment_id] * padding_length) + segment_ids
                   a		:List[str]									=			([pad_token_label_id] * padding_length) + label_ids
             else:
                   input_ids += [pad_token] * padding_length
                   input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                   segment_ids += [pad_token_segment_id] * padding_length
                   label_ids += [pad_token_label_id] * padding_length

             assert len(UpperCamelCase_       ) == max_seq_length
             assert len(UpperCamelCase_       ) == max_seq_length
             assert len(UpperCamelCase_       ) == max_seq_length
             assert len(UpperCamelCase_       ) == max_seq_length

             if ex_index < 5:
                   logger.info('''*** Example ***'''       )
                   logger.info('''guid: %s'''   ,    example.guid       )
                   logger.info('''tokens: %s'''   ,    ''' '''.join([str(UpperCamelCase_       ) for x in tokens]       )       )
                   logger.info('''input_ids: %s'''   ,    ''' '''.join([str(UpperCamelCase_       ) for x in input_ids]       )       )
                   logger.info('''input_mask: %s'''   ,    ''' '''.join([str(UpperCamelCase_       ) for x in input_mask]       )       )
                   logger.info('''segment_ids: %s'''   ,    ''' '''.join([str(UpperCamelCase_       ) for x in segment_ids]       )       )
                   logger.info('''label_ids: %s'''   ,    ''' '''.join([str(UpperCamelCase_       ) for x in label_ids]       )       )

             if "token_type_ids" not in tokenizer.model_input_names:
                   a		:str									=			None

             features.append(
                 InputFeatures(
                     input_ids=UpperCamelCase_   ,    attention_mask=UpperCamelCase_   ,    token_type_ids=UpperCamelCase_   ,    label_ids=UpperCamelCase_       )       )
       return features


if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data import Dataset







     class 						_snake_case    ( _snake_case				):
      SCREAMING_SNAKE_CASE__ =			42
      SCREAMING_SNAKE_CASE__ =			nn.CrossEntropyLoss().ignore_index




      def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase=False   ,    _lowerCamelCase = Split.train   ,    ):
            # Load data features from cache or dataset file
            a		:str									=			os.path.join(
                UpperCamelCase_   ,    '''cached_{}_{}_{}'''.format(mode.value   ,    tokenizer.__class__.__name__   ,    str(UpperCamelCase_       )       )   ,    )

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            a		:Dict									=			cached_features_file + '''.lock'''
            with FileLock(UpperCamelCase_       ):
                  if os.path.exists(UpperCamelCase_       ) and not overwrite_cache:
                        logger.info(F'''Loading features from cached file {cached_features_file}'''       )
                        a		:List[Any]									=			torch.load(UpperCamelCase_       )
                  else:
                        logger.info(F'''Creating features from dataset file at {data_dir}'''       )
                        a		:List[str]									=			token_classification_task.read_examples_from_file(UpperCamelCase_   ,    UpperCamelCase_       )
                        # TODO clean up all this to leverage built-in features of tokenizers
                        a		:Any									=			token_classification_task.convert_examples_to_features(
                            UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_   ,    cls_token_at_end=bool(model_type in ['''xlnet''']       )   ,    cls_token=tokenizer.cls_token   ,    cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0   ,    sep_token=tokenizer.sep_token   ,    sep_token_extra=UpperCamelCase_   ,    pad_on_left=bool(tokenizer.padding_side == '''left'''       )   ,    pad_token=tokenizer.pad_token_id   ,    pad_token_segment_id=tokenizer.pad_token_type_id   ,    pad_token_label_id=self.pad_token_label_id   ,    )
                        logger.info(F'''Saving features into cached file {cached_features_file}'''       )
                        torch.save(self.features   ,    UpperCamelCase_       )




      def __len__(						self       ):
            return len(self.features       )




      def __getitem__(						self   ,    _lowerCamelCase       ):
            return self.features[i]


if is_tf_available():
     import tensorflow as tf







     class 						_snake_case    :
      SCREAMING_SNAKE_CASE__ =			42
      SCREAMING_SNAKE_CASE__ =			-100




      def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase = None   ,    _lowerCamelCase=False   ,    _lowerCamelCase = Split.train   ,    ):
            a		:str									=			token_classification_task.read_examples_from_file(UpperCamelCase_   ,    UpperCamelCase_       )
            # TODO clean up all this to leverage built-in features of tokenizers
            a		:Tuple									=			token_classification_task.convert_examples_to_features(
                UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_   ,    cls_token_at_end=bool(model_type in ['''xlnet''']       )   ,    cls_token=tokenizer.cls_token   ,    cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0   ,    sep_token=tokenizer.sep_token   ,    sep_token_extra=UpperCamelCase_   ,    pad_on_left=bool(tokenizer.padding_side == '''left'''       )   ,    pad_token=tokenizer.pad_token_id   ,    pad_token_segment_id=tokenizer.pad_token_type_id   ,    pad_token_label_id=self.pad_token_label_id   ,    )

            def gen():
                  for ex in self.features:
                        if ex.token_type_ids is None:
                              yield (
                                  {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                  ex.label_ids,
                              )
                        else:
                              yield (
                                  {
                                      "input_ids": ex.input_ids,
                                      "attention_mask": ex.attention_mask,
                                      "token_type_ids": ex.token_type_ids,
                                  },
                                  ex.label_ids,
                              )

            if "token_type_ids" not in tokenizer.model_input_names:
                  a		:List[str]									=			tf.data.Dataset.from_generator(
                      UpperCamelCase_   ,    ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)   ,    (
                          {'''input_ids''': tf.TensorShape([None]       ), '''attention_mask''': tf.TensorShape([None]       )},
                          tf.TensorShape([None]       ),
                      )   ,    )
            else:
                  a		:Tuple									=			tf.data.Dataset.from_generator(
                      UpperCamelCase_   ,    ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)   ,    (
                          {
                              '''input_ids''': tf.TensorShape([None]       ),
                              '''attention_mask''': tf.TensorShape([None]       ),
                              '''token_type_ids''': tf.TensorShape([None]       ),
                          },
                          tf.TensorShape([None]       ),
                      )   ,    )




      def 			SCREAMING_SNAKE_CASE__							(						self       ):
            a		:List[str]									=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

            return self.dataset




      def __len__(						self       ):
            return len(self.features       )




      def __getitem__(						self   ,    _lowerCamelCase       ):
            return self.features[i]












'''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()
a__					:			Any          =     logging.get_logger()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = True							)   ->Dict:
	print(f'''Converting {name}...'''							)

	with torch.no_grad():
		if hidden_sizes == 1_28:
			if name[-1] == "S":
				snake_case__	       =   timm.create_model('levit_128s'					,				pretrained=UpperCAmelCase_							)
			else:
				snake_case__	       =   timm.create_model('levit_128'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 1_92:
			snake_case__	       =   timm.create_model('levit_192'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 2_56:
			snake_case__	       =   timm.create_model('levit_256'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 3_84:
			snake_case__	       =   timm.create_model('levit_384'					,				pretrained=UpperCAmelCase_							)

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

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

		snake_case__	       =   torch.randn((2, 3, 2_24, 2_24)							)
		snake_case__	       =   from_model(UpperCAmelCase_							)
		snake_case__	       =   our_model(UpperCAmelCase_							).logits

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

	snake_case__	       =   name
	print(UpperCAmelCase_							)

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

		print(f'''Pushed {checkpoint_name}'''							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = None					,				UpperCAmelCase_ = True							)   ->Any:
	snake_case__	       =   'imagenet-1k-id2label.json'
	snake_case__	       =   10_00
	snake_case__	       =   (1, num_labels)

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

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

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

	snake_case__	       =   {
	    'levit-128S': 1_28,
	    'levit-128': 1_28,
	    'levit-192': 1_92,
	    'levit-256': 2_56,
	    'levit-384': 3_84,
	}

	snake_case__	       =   {
	    'levit-128S': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 6, 8]					,				depths=[2, 3, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-128': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 8, 12]					,				depths=[4, 4, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-192': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_92, 2_88, 3_84]					,				num_attention_heads=[3, 5, 6]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-256': ImageNetPreTrainedConfig(
	        hidden_sizes=[2_56, 3_84, 5_12]					,				num_attention_heads=[4, 6, 8]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-384': ImageNetPreTrainedConfig(
	        hidden_sizes=[3_84, 5_12, 7_68]					,				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__":
			a__					:			Optional[int]          =     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''',
			)

			a__					:			Optional[Any]          =     parser.parse_args()
			a__					:			Path          =     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)



