




"""simple docstring"""






import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
				import os

				from flax.core.frozen_dict import unfreeze
				from flax.traverse_util import flatten_dict

				from transformers import FlaxBertModel

				UpperCamelCase__						=			'''0.12'''  # assumed parallelism: 8




@require_flax
@is_staging_test
class   a__			(       unittest.TestCase  ):





						@classmethod
						def       __UpperCamelCase     (							cls      :	str)						->       Dict:




									"""simple docstring"""







									_lowerCAmelCase:Dict									=  TOKEN
									HfFolder.save_token(a__)





						@classmethod
						def       __UpperCamelCase     (							cls      :	int)						->       Union[str, Any]:




									"""simple docstring"""







									try:
												delete_repo(token=cls._token     ,repo_id='''test-model-flax''')
									except HTTPError:
												pass

									try:
												delete_repo(token=cls._token     ,repo_id='''valid_org/test-model-flax-org''')
									except HTTPError:
												pass





						def       __UpperCamelCase     (							self      :	Dict)						->       Optional[int]:




									"""simple docstring"""







									_lowerCAmelCase:Optional[int]									=  BertConfig(
									    vocab_size=99     ,hidden_size=32     ,num_hidden_layers=5     ,num_attention_heads=4     ,intermediate_size=37)
									_lowerCAmelCase:Any									=  FlaxBertModel(a__)
									model.push_to_hub('''test-model-flax'''     ,use_auth_token=self._token)

									_lowerCAmelCase:Optional[Any]									=  FlaxBertModel.from_pretrained(F'{USER}/test-model-flax')

									_lowerCAmelCase:Any									=  flatten_dict(unfreeze(model.params))
									_lowerCAmelCase:int									=  flatten_dict(unfreeze(new_model.params))

									for key in base_params.keys():
												_lowerCAmelCase:Tuple									=  (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(a__     ,1E-3     ,msg=F'{key} not identical')

									# Reset repo
									delete_repo(token=self._token     ,repo_id='''test-model-flax''')

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(a__     ,repo_id='''test-model-flax'''     ,push_to_hub=a__     ,use_auth_token=self._token)

									_lowerCAmelCase:List[str]									=  FlaxBertModel.from_pretrained(F'{USER}/test-model-flax')

									_lowerCAmelCase:List[str]									=  flatten_dict(unfreeze(model.params))
									_lowerCAmelCase:Any									=  flatten_dict(unfreeze(new_model.params))

									for key in base_params.keys():
												_lowerCAmelCase:List[str]									=  (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(a__     ,1E-3     ,msg=F'{key} not identical')







						def       __UpperCamelCase     (							self      :	str)						->       Optional[int]:




									"""simple docstring"""







									_lowerCAmelCase:Optional[int]									=  BertConfig(
									    vocab_size=99     ,hidden_size=32     ,num_hidden_layers=5     ,num_attention_heads=4     ,intermediate_size=37)
									_lowerCAmelCase:List[Any]									=  FlaxBertModel(a__)
									model.push_to_hub('''valid_org/test-model-flax-org'''     ,use_auth_token=self._token)

									_lowerCAmelCase:List[str]									=  FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''')

									_lowerCAmelCase:str									=  flatten_dict(unfreeze(model.params))
									_lowerCAmelCase:int									=  flatten_dict(unfreeze(new_model.params))

									for key in base_params.keys():
												_lowerCAmelCase:Union[str, Any]									=  (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(a__     ,1E-3     ,msg=F'{key} not identical')

									# Reset repo
									delete_repo(token=self._token     ,repo_id='''valid_org/test-model-flax-org''')

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(
												    a__     ,repo_id='''valid_org/test-model-flax-org'''     ,push_to_hub=a__     ,use_auth_token=self._token)

									_lowerCAmelCase:Optional[Any]									=  FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''')

									_lowerCAmelCase:List[Any]									=  flatten_dict(unfreeze(model.params))
									_lowerCAmelCase:List[Any]									=  flatten_dict(unfreeze(new_model.params))

									for key in base_params.keys():
												_lowerCAmelCase:int									=  (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(a__     ,1E-3     ,msg=F'{key} not identical')





def 							UpperCAmelCase		(		snake_case			:					Union[str, Any]      ,       snake_case			:					int    ):
			_lowerCAmelCase:Tuple									=  True
			_lowerCAmelCase:List[str]									=  flatten_dict(modela.params    )
			_lowerCAmelCase:Optional[int]									=  flatten_dict(modela.params    )
			for key in flat_params_a.keys():
						if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]    )    ) > 1e-4:
									_lowerCAmelCase:Dict									=  False

			return models_are_equal





@require_flax
class   a__			(       unittest.TestCase  ):





						def       __UpperCamelCase     (							self      :	int)						->       str:




									"""simple docstring"""







									_lowerCAmelCase:Optional[Any]									=  BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')
									_lowerCAmelCase:Union[str, Any]									=  FlaxBertModel(a__)

									_lowerCAmelCase:Optional[Any]									=  '''bert'''
									with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(os.path.join(a__     ,a__))

												with self.assertRaises(a__):
															_lowerCAmelCase:str									=  FlaxBertModel.from_pretrained(a__)

												_lowerCAmelCase:Dict									=  FlaxBertModel.from_pretrained(a__     ,subfolder=a__)

									self.assertTrue(check_models_equal(a__     ,a__))





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Union[str, Any]:




									"""simple docstring"""







									_lowerCAmelCase:List[Any]									=  BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')
									_lowerCAmelCase:Optional[int]									=  FlaxBertModel(a__)

									_lowerCAmelCase:Tuple									=  '''bert'''
									with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(os.path.join(a__     ,a__)     ,max_shard_size='''10KB''')

												with self.assertRaises(a__):
															_lowerCAmelCase:int									=  FlaxBertModel.from_pretrained(a__)

												_lowerCAmelCase:Optional[int]									=  FlaxBertModel.from_pretrained(a__     ,subfolder=a__)

									self.assertTrue(check_models_equal(a__     ,a__))





						def       __UpperCamelCase     (							self      :	Any)						->       Optional[int]:




									"""simple docstring"""







									_lowerCAmelCase:int									=  '''bert'''
									_lowerCAmelCase:int									=  '''hf-internal-testing/tiny-random-bert-subfolder'''

									with self.assertRaises(a__):
												_lowerCAmelCase:List[Any]									=  FlaxBertModel.from_pretrained(a__)

									_lowerCAmelCase:str									=  FlaxBertModel.from_pretrained(a__     ,subfolder=a__)

									self.assertIsNotNone(a__)







						def       __UpperCamelCase     (							self      :	Optional[int])						->       int:




									"""simple docstring"""







									_lowerCAmelCase:Any									=  '''bert'''
									_lowerCAmelCase:Optional[Any]									=  '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
									with self.assertRaises(a__):
												_lowerCAmelCase:List[str]									=  FlaxBertModel.from_pretrained(a__)

									_lowerCAmelCase:Dict									=  FlaxBertModel.from_pretrained(a__     ,subfolder=a__)

									self.assertIsNotNone(a__)












"""simple docstring"""






import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__						=			logging.get_logger(__name__)





def 							UpperCAmelCase		(		snake_case			:					Optional[int]    ):
					_lowerCAmelCase:List[Any]									=  MobileNetVaConfig(layer_norm_eps=0.0_01    )

					if "_quant" in model_name:
										raise ValueError('''Quantized models are not supported.'''    )

					_lowerCAmelCase:List[Any]									=  re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''      ,       snake_case    )
					if matches:
										_lowerCAmelCase:str									=  float(matches[1]    )
										_lowerCAmelCase:str									=  int(matches[2]    )

					# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
					# the usual 1000. The first class (index 0) is "background".
					_lowerCAmelCase:int									=  1001
					_lowerCAmelCase:Union[str, Any]									=  '''imagenet-1k-id2label.json'''
					_lowerCAmelCase:Optional[int]									=  '''huggingface/label-files'''
					_lowerCAmelCase:List[str]									=  json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
					_lowerCAmelCase:Union[str, Any]									=  {int(snake_case    ) + 1: v for k, v in idalabel.items()}
					_lowerCAmelCase:Optional[Any]									=  '''background'''
					_lowerCAmelCase:Optional[Any]									=  idalabel
					_lowerCAmelCase:List[Any]									=  {v: k for k, v in idalabel.items()}

					return config





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Tuple									=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_lowerCAmelCase:Optional[Any]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    )
					return im





@torch.no_grad()
def 							UpperCAmelCase		(		snake_case			:					Any      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					List[Any]      ,       snake_case			:					Any=False    ):
					_lowerCAmelCase:List[str]									=  get_mobilenet_va_config(snake_case    )

					# Load 🤗 model
					_lowerCAmelCase:Any									=  MobileNetVaForImageClassification(snake_case    ).eval()

					# Load weights from TensorFlow checkpoint
					load_tf_weights_in_mobilenet_va(snake_case      ,       snake_case      ,       snake_case    )

					# Check outputs on an image, prepared by MobileNetV1ImageProcessor
					_lowerCAmelCase:Dict									=  MobileNetVaImageProcessor(
					    crop_size={'''width''': config.image_size, '''height''': config.image_size}      ,       size={'''shortest_edge''': config.image_size + 32}      ,       )
					_lowerCAmelCase:List[str]									=  image_processor(images=prepare_img()      ,       return_tensors='''pt'''    )
					_lowerCAmelCase:List[str]									=  model(**snake_case    )
					_lowerCAmelCase:int									=  outputs.logits

					assert logits.shape == (1, 1001)

					if model_name == "mobilenet_v1_1.0_224":
										_lowerCAmelCase:Union[str, Any]									=  torch.tensor([-4.17_39, -1.12_33, 3.12_05]    )
					elif model_name == "mobilenet_v1_0.75_192":
										_lowerCAmelCase:Any									=  torch.tensor([-3.94_40, -2.31_41, -0.33_33]    )
					else:
										_lowerCAmelCase:Dict									=  None

					if expected_logits is not None:
										assert torch.allclose(logits[0, :3]      ,       snake_case      ,       atol=1e-4    )

					Path(snake_case    ).mkdir(exist_ok=snake_case    )
					print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
					model.save_pretrained(snake_case    )
					print(F'Saving image processor to {pytorch_dump_folder_path}'    )
					image_processor.save_pretrained(snake_case    )

					if push_to_hub:
										print('''Pushing to the hub...'''    )
										_lowerCAmelCase:List[str]									=  '''google/''' + model_name
										image_processor.push_to_hub(snake_case    )
										model.push_to_hub(snake_case    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''mobilenet_v1_1.0_224''',
				    type=str,
				    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
				)
				parser.add_argument(
				    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
				)

				UpperCamelCase__						=			parser.parse_args()
				convert_movilevit_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






