





'''simple docstring'''




import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow






class 				__snake_case     :





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[Any]:
		return None






class 				__snake_case     :





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		str:
		return None






class 				__snake_case     (					unittest.TestCase							):
	__lowerCAmelCase       						=  [
	    # (model_name, model_kwargs)
	    ('''bert-base-cased''', {}),
	    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
	]





	@require_tf
	@slow
	def   _snake_case   (		self		)      ->		List[Any]:
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(UpperCamelCase_      ,       'tf'      ,       12      ,       **UpperCamelCase_		)





	@require_torch
	@slow
	def   _snake_case   (		self		)      ->		str:
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			self._test_export(UpperCamelCase_      ,       'pt'      ,       12      ,       **UpperCamelCase_		)





	@require_torch
	@slow
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		from transformers import BertModel

		snake_case__	       =   ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
		with NamedTemporaryFile(mode='w+t'		) as vocab_file:
			vocab_file.write('\n'.join(UpperCamelCase_		)		)
			vocab_file.flush()
			snake_case__	       =   BertTokenizerFast(vocab_file.name		)

		with TemporaryDirectory() as bert_save_dir:
			snake_case__	       =   BertModel(BertConfig(vocab_size=len(UpperCamelCase_		)		)		)
			model.save_pretrained(UpperCamelCase_		)
			self._test_export(UpperCamelCase_      ,       'pt'      ,       12      ,       UpperCamelCase_		)





	@require_tf
	@slow
	def   _snake_case   (		self		)      ->		str:
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			snake_case__	       =   self._test_export(UpperCamelCase_      ,       'tf'      ,       12      ,       **UpperCamelCase_		)
			snake_case__	       =   quantize(Path(UpperCamelCase_		)		)

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(UpperCamelCase_		).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'		)





	@require_torch
	@slow
	def   _snake_case   (		self		)      ->		List[Any]:
		for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
			snake_case__	       =   self._test_export(UpperCamelCase_      ,       'pt'      ,       12      ,       **UpperCamelCase_		)
			snake_case__	       =   quantize(UpperCamelCase_		)

			# Ensure the actual quantized model is not bigger than the original one
			if quantized_path.stat().st_size >= Path(UpperCamelCase_		).stat().st_size:
				self.fail('Quantized model is bigger than initial ONNX model'		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=None      ,       **UpperCamelCase_		)      ->		Any:
		try:
			# Compute path
			with TemporaryDirectory() as tempdir:
				snake_case__	       =   Path(UpperCamelCase_		).joinpath('model.onnx'		)

			# Remove folder if exists
			if path.parent.exists():
				path.parent.rmdir()

			# Export
			convert(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       **UpperCamelCase_		)

			return path
		except Exception as e:
			self.fail(UpperCamelCase_		)





	@require_torch
	@require_tokenizers
	@slow
	def   _snake_case   (		self		)      ->		str:
		from transformers import BertModel

		snake_case__	       =   BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'		)		)
		snake_case__	       =   BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'		)
		self._test_infer_dynamic_axis(UpperCamelCase_      ,       UpperCamelCase_      ,       'pt'		)





	@require_tf
	@require_tokenizers
	@slow
	def   _snake_case   (		self		)      ->		List[str]:
		from transformers import TFBertModel

		snake_case__	       =   TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'		)		)
		snake_case__	       =   BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'		)
		self._test_infer_dynamic_axis(UpperCamelCase_      ,       UpperCamelCase_      ,       'tf'		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[str]:
		snake_case__	       =   FeatureExtractionPipeline(UpperCamelCase_      ,       UpperCamelCase_		)

		snake_case__	       =   ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   infer_shapes(UpperCamelCase_      ,       UpperCamelCase_		)

		# Assert all variables are present
		self.assertEqual(len(UpperCamelCase_		)      ,       len(UpperCamelCase_		)		)
		self.assertTrue(all(var_name in shapes for var_name in variable_names		)		)
		self.assertSequenceEqual(variable_names[:3]      ,       UpperCamelCase_		)
		self.assertSequenceEqual(variable_names[3:]      ,       UpperCamelCase_		)

		# Assert inputs are {0: batch, 1: sequence}
		for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
			self.assertDictEqual(shapes[var_name]      ,       {0: 'batch', 1: 'sequence'}		)

		# Assert outputs are {0: batch, 1: sequence} and {0: batch}
		self.assertDictEqual(shapes['output_0']      ,       {0: 'batch', 1: 'sequence'}		)
		self.assertDictEqual(shapes['output_1']      ,       {0: 'batch'}		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   ['input_ids', 'attention_mask', 'token_type_ids']
		snake_case__	       =   {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
		snake_case__       ,  snake_case__	       =   ensure_valid_input(FuncContiguousArgs()      ,       UpperCamelCase_      ,       UpperCamelCase_		)

		# Should have exactly the same number of args (all are valid)
		self.assertEqual(len(UpperCamelCase_		)      ,       3		)

		# Should have exactly the same input names
		self.assertEqual(set(UpperCamelCase_		)      ,       set(UpperCamelCase_		)		)

		# Parameter should be reordered according to their respective place in the function:
		# (input_ids, token_type_ids, attention_mask)
		self.assertEqual(UpperCamelCase_      ,       (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])		)

		# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
		snake_case__       ,  snake_case__	       =   ensure_valid_input(FuncNonContiguousArgs()      ,       UpperCamelCase_      ,       UpperCamelCase_		)

		# Should have exactly the one arg (all before the one not provided "some_other_args")
		self.assertEqual(len(UpperCamelCase_		)      ,       1		)
		self.assertEqual(len(UpperCamelCase_		)      ,       1		)

		# Should have only "input_ids"
		self.assertEqual(inputs_args[0]      ,       tokens['input_ids']		)
		self.assertEqual(ordered_input_names[0]      ,       'input_ids'		)





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   generate_identified_filename(Path('/home/something/my_fake_model.onnx'		)      ,       '-test'		)
		self.assertEqual('/home/something/my_fake_model-test.onnx'      ,       generated.as_posix()		)










'''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)



