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		:       Tuple									=     logging.get_logger()
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase = True						):
						print(f"""Converting {name}..."""						)

						with torch.no_grad():
												if hidden_sizes == 128:
																		if name[-1] == "S":
																								SCREAMING_SNAKE_CASE							:    Any							=							timm.create_model('''levit_128s'''		,  pretrained=_lowercase						)
																		else:
																								SCREAMING_SNAKE_CASE							:    Dict							=							timm.create_model('''levit_128'''		,  pretrained=_lowercase						)
												if hidden_sizes == 192:
																		SCREAMING_SNAKE_CASE							:    str							=							timm.create_model('''levit_192'''		,  pretrained=_lowercase						)
												if hidden_sizes == 256:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							timm.create_model('''levit_256'''		,  pretrained=_lowercase						)
												if hidden_sizes == 384:
																		SCREAMING_SNAKE_CASE							:    Dict							=							timm.create_model('''levit_384'''		,  pretrained=_lowercase						)

												from_model.eval()
												SCREAMING_SNAKE_CASE							:    Tuple							=							LevitForImageClassificationWithTeacher(_lowercase						).eval()
												SCREAMING_SNAKE_CASE							:    int							=							OrderedDict()

												SCREAMING_SNAKE_CASE							:    Tuple							=							from_model.state_dict()
												SCREAMING_SNAKE_CASE							:    int							=							list(from_model.state_dict().keys()						)
												SCREAMING_SNAKE_CASE							:    int							=							list(our_model.state_dict().keys()						)
												print(len(_lowercase						)		,  len(_lowercase						)						)
												for i in range(len(_lowercase						)						):
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							weights[og_keys[i]]
												our_model.load_state_dict(_lowercase						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							torch.randn((2, 3, 224, 224)						)
												SCREAMING_SNAKE_CASE							:    Dict							=							from_model(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Any							=							our_model(_lowercase						).logits

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

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							name
						print(_lowercase						)

						if push_to_hub:
												our_model.save_pretrained(save_directory / checkpoint_name						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							LevitImageProcessor()
												image_processor.save_pretrained(save_directory / checkpoint_name						)

												print(f"""Pushed {checkpoint_name}"""						)
def       A	(    _lowercase		,  _lowercase = None		,  _lowercase = True						):
						SCREAMING_SNAKE_CASE							:    int							=							'''imagenet-1k-id2label.json'''
						SCREAMING_SNAKE_CASE							:    Tuple							=							1_000
						SCREAMING_SNAKE_CASE							:    List[Any]							=							(1, num_labels)

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

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							idalabel
						SCREAMING_SNAKE_CASE							:    str							=							{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:    int							=							partial(_lowercase		,  num_labels=_lowercase		,  idalabel=_lowercase		,  labelaid=_lowercase						)

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							{
						    '''levit-128S''': 128,
						    '''levit-128''': 128,
						    '''levit-192''': 192,
						    '''levit-256''': 256,
						    '''levit-384''': 384,
						}

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							{
						    '''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]		,  _lowercase		,  names_to_config[model_name]		,  _lowercase		,  _lowercase						)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(names_to_hidden_sizes[model_name]		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						)
						return config, expected_shape


if __name__ == "__main__":
					__UpperCamelCase		:       List[Any]									=     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		:       Union[str, Any]									=     parser.parse_args()
					__UpperCamelCase		:       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)






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf

					from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												for model_result in results.values():
																		for batch_size, sequence_length in zip(model_result['''bs''']    ,		model_result['''ss''']						):
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model_result['''result'''][batch_size][sequence_length]
																								self.assertIsNotNone(UpperCamelCase__						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    List[Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							'''sgugger/tiny-distilbert-classification'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		only_pretrain_model=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    int							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    str							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							'''patrickvonplaten/t5-tiny-random'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							TensorFlowBenchmark(UpperCamelCase__    ,		configs=[config]						)
												SCREAMING_SNAKE_CASE							:    Dict							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''						)						) == 0    ,		'''Cannot do xla on CPU.'''						)
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		use_xla=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Any							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''sshleifer/tiny-gpt2'''
												with tempfile.TemporaryDirectory() as tmp_dir:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]    ,		inference=UpperCamelCase__    ,		save_to_csv=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		inference_time_csv_file=os.path.join(UpperCamelCase__    ,		'''inf_time.csv'''						)    ,		inference_memory_csv_file=os.path.join(UpperCamelCase__    ,		'''inf_mem.csv'''						)    ,		env_info_csv_file=os.path.join(UpperCamelCase__    ,		'''env.csv'''						)    ,		multi_process=UpperCamelCase__    ,		)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
																		benchmark.run()
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''inf_time.csv'''						)						).exists()						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''inf_mem.csv'''						)						).exists()						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''env.csv'''						)						).exists()						)



						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							'''sshleifer/tiny-gpt2'''

												def _check_summary_is_not_empty(UpperCamelCase__	:				Dict						):
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''sequential'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''cumulative'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''current'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''total'''						)						)

												with tempfile.TemporaryDirectory() as tmp_dir:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		log_filename=os.path.join(UpperCamelCase__    ,		'''log.txt'''						)    ,		log_print=UpperCamelCase__    ,		trace_memory_line_by_line=UpperCamelCase__    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							benchmark.run()
																		_check_summary_is_not_empty(result.inference_summary						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''log.txt'''						)						).exists()						)





