





"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						# Initialise PyTorch model
						_UpperCamelCase      : List[Any]         =   MobileBertConfig.from_json_file(_lowercase       )
						print(F"""Building PyTorch model from configuration: {config}"""       )
						_UpperCamelCase      : List[str]         =   MobileBertForPreTraining(_lowercase       )
						# Load weights from tf checkpoint
						_UpperCamelCase      : Union[str, Any]         =   load_tf_weights_in_mobilebert(_lowercase	,						_lowercase	,						_lowercase       )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
						torch.save(model.state_dict()	,						_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
						)
						parser.add_argument(
						    """--mobilebert_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained MobileBERT model. \n"""
						        """This specifies the model architecture."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						UpperCamelCase_									=parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)










"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor




@require_vision
class 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : int         =   tempfile.mkdtemp()

										_UpperCamelCase      : List[str]         =   [
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''的''',
										    '''价''',
										    '''格''',
										    '''是''',
										    '''15''',
										    '''便''',
										    '''alex''',
										    '''##andra''',
										    '''，''',
										    '''。''',
										    '''-''',
										    '''t''',
										    '''shirt''',
										]
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['''vocab_file''']							)
										with open(self.vocab_file,			'''w''',			encoding='''utf-8'''							) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]							)							)

										_UpperCamelCase      : Dict         =   {
										    '''do_resize''': True,
										    '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
										    '''do_center_crop''': True,
										    '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
										    '''do_normalize''': True,
										    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
										    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
										    '''do_convert_rgb''': True,
										}
										_UpperCamelCase      : Optional[Any]         =   os.path.join(self.tmpdirname,			lowerCAmelCase__							)
										with open(self.image_processor_file,			'''w''',			encoding='''utf-8'''							) as fp:
																json.dump(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   str,			**lowerCAmelCase__		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										return BertTokenizer.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any],			**lowerCAmelCase__		:   Tuple							)     ->				str:




										'''simple docstring'''



										return BertTokenizerFast.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Any,			**lowerCAmelCase__		:   Optional[int]							)     ->				Optional[Any]:




										'''simple docstring'''



										return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






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




										'''simple docstring'''



										shutil.rmtree(self.tmpdirname							)






				def     snake_case     (			self		:   Any							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   [np.random.randint(2_5_5,			size=(3, 3_0, 4_0_0),			dtype=np.uinta							)]

										_UpperCamelCase      : List[Any]         =   [Image.fromarray(np.moveaxis(lowerCAmelCase__,			0,			-1							)							) for x in image_inputs]

										return image_inputs






				def     snake_case     (			self		:   str							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_tokenizer()
										_UpperCamelCase      : int         =   self.get_rust_tokenizer()
										_UpperCamelCase      : int         =   self.get_image_processor()

										_UpperCamelCase      : Tuple         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_slow.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname,			use_fast=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)
										processor_fast.save_pretrained(self.tmpdirname							)
										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname							)

										self.assertEqual(processor_slow.tokenizer.get_vocab(),			tokenizer_slow.get_vocab()							)
										self.assertEqual(processor_fast.tokenizer.get_vocab(),			tokenizer_fast.get_vocab()							)
										self.assertEqual(tokenizer_slow.get_vocab(),			tokenizer_fast.get_vocab()							)
										self.assertIsInstance(processor_slow.tokenizer,			lowerCAmelCase__							)
										self.assertIsInstance(processor_fast.tokenizer,			lowerCAmelCase__							)

										self.assertEqual(processor_slow.image_processor.to_json_string(),			image_processor.to_json_string()							)
										self.assertEqual(processor_fast.image_processor.to_json_string(),			image_processor.to_json_string()							)
										self.assertIsInstance(processor_slow.image_processor,			lowerCAmelCase__							)
										self.assertIsInstance(processor_fast.image_processor,			lowerCAmelCase__							)






				def     snake_case     (			self		:   int							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor()							)
										processor.save_pretrained(self.tmpdirname							)

										_UpperCamelCase      : Dict         =   self.get_tokenizer(cls_token='''(CLS)''',			sep_token='''(SEP)'''							)
										_UpperCamelCase      : List[str]         =   self.get_image_processor(do_normalize=lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor.from_pretrained(
										    self.tmpdirname,			cls_token='''(CLS)''',			sep_token='''(SEP)''',			do_normalize=lowerCAmelCase__							)

										self.assertEqual(processor.tokenizer.get_vocab(),			tokenizer_add_kwargs.get_vocab()							)
										self.assertIsInstance(processor.tokenizer,			lowerCAmelCase__							)

										self.assertEqual(processor.image_processor.to_json_string(),			image_processor_add_kwargs.to_json_string()							)
										self.assertIsInstance(processor.image_processor,			lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.get_image_processor()
										_UpperCamelCase      : str         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   self.prepare_image_inputs()

										_UpperCamelCase      : Any         =   image_processor(lowerCAmelCase__,			return_tensors='''np'''							)
										_UpperCamelCase      : Any         =   processor(images=lowerCAmelCase__,			return_tensors='''np'''							)

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum(),			input_processor[key].sum(),			delta=1e-2							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.get_image_processor()
										_UpperCamelCase      : Optional[Any]         =   self.get_tokenizer()

										_UpperCamelCase      : Any         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Tuple         =   '''Alexandra，T-shirt的价格是15便士。'''

										_UpperCamelCase      : List[str]         =   processor(text=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   tokenizer(lowerCAmelCase__							)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key],			encoded_processor[key]							)






				def     snake_case     (			self		:   Dict							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Tuple         =   self.get_image_processor()
										_UpperCamelCase      : Optional[Any]         =   self.get_tokenizer()

										_UpperCamelCase      : Dict         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : Union[str, Any]         =   self.prepare_image_inputs()

										_UpperCamelCase      : str         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

										self.assertListEqual(list(inputs.keys()							),			['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']							)

										# test if it raises when no input is passed
										with pytest.raises(lowerCAmelCase__							):
																processor()






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




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.get_image_processor()
										_UpperCamelCase      : int         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : List[Any]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										_UpperCamelCase      : List[Any]         =   processor.batch_decode(lowerCAmelCase__							)
										_UpperCamelCase      : Dict         =   tokenizer.batch_decode(lowerCAmelCase__							)

										self.assertListEqual(lowerCAmelCase__,			lowerCAmelCase__							)






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.get_image_processor()
										_UpperCamelCase      : Optional[int]         =   self.get_tokenizer()

										_UpperCamelCase      : Optional[Any]         =   ChineseCLIPProcessor(tokenizer=lowerCAmelCase__,			image_processor=lowerCAmelCase__							)

										_UpperCamelCase      : Any         =   '''Alexandra，T-shirt的价格是15便士。'''
										_UpperCamelCase      : int         =   self.prepare_image_inputs()

										_UpperCamelCase      : Dict         =   processor(text=lowerCAmelCase__,			images=lowerCAmelCase__							)

										self.assertListEqual(list(inputs.keys()							),			processor.model_input_names							)



