



"""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()
__lowerCAmelCase       :					List[Any]      					=					logging.get_logger(__name__)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			MobileNetVaConfig(layer_norm_eps=0.001    )

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

						snake_case_   : Union[str, Any]						=			re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""					, __UpperCamelCase    )
						if matches:
												snake_case_   : str						=			float(matches[1]    )
												snake_case_   : List[str]						=			int(matches[2]    )

						# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
						# the usual 1000. The first class (index 0) is "background".
						snake_case_   : str						=			1_0_0_1
						snake_case_   : Optional[int]						=			"""imagenet-1k-id2label.json"""
						snake_case_   : Optional[int]						=			"""huggingface/label-files"""
						snake_case_   : Optional[int]						=			json.load(open(hf_hub_download(__UpperCamelCase					, __UpperCamelCase					, repo_type="""dataset"""    )					, """r"""    )    )
						snake_case_   : Optional[Any]						=			{int(__UpperCamelCase    ) + 1: v for k, v in idalabel.items()}
						snake_case_   : Dict						=			"""background"""
						snake_case_   : Any						=			idalabel
						snake_case_   : Optional[Any]						=			{v: k for k, v in idalabel.items()}

						return config
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : Tuple						=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						snake_case_   : List[str]						=			Image.open(requests.get(__UpperCamelCase					, stream=__UpperCamelCase    ).raw    )
						return im
@torch.no_grad()
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			Dict					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			int=False    ):


						'''simple docstring'''






						snake_case_   : List[Any]						=			get_mobilenet_va_config(__UpperCamelCase    )

						# Load 🤗 model
						snake_case_   : int						=			MobileNetVaForImageClassification(__UpperCamelCase    ).eval()

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

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						snake_case_   : List[str]						=			MobileNetVaImageProcessor(
						    crop_size={"""width""": config.image_size, """height""": config.image_size}					, size={"""shortest_edge""": config.image_size + 3_2}					, )
						snake_case_   : Dict						=			image_processor(images=prepare_img()					, return_tensors="""pt"""    )
						snake_case_   : Dict						=			model(**__UpperCamelCase    )
						snake_case_   : List[Any]						=			outputs.logits

						assert logits.shape == (1, 1_0_0_1)

						if model_name == "mobilenet_v1_1.0_224":
												snake_case_   : Any						=			torch.tensor([-4.1_739, -1.1_233, 3.1_205]    )
						elif model_name == "mobilenet_v1_0.75_192":
												snake_case_   : Any						=			torch.tensor([-3.9_440, -2.3_141, -0.3_333]    )
						else:
												snake_case_   : Tuple						=			None

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

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

						if push_to_hub:
												print("""Pushing to the hub..."""    )
												snake_case_   : int						=			"""google/""" + model_name
												image_processor.push_to_hub(__UpperCamelCase    )
												model.push_to_hub(__UpperCamelCase    )


if __name__ == "__main__":
					__lowerCAmelCase       :					Any      					=					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.'''
					)

					__lowerCAmelCase       :					List[Any]      					=					parser.parse_args()
					convert_movilevit_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)






"""simple docstring"""





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       MgpstrTokenizer
		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       {}
		_lowerCamelCase			  =       False





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								super().setUp()

								# fmt: off
								snake_case_   : Optional[Any]						=			["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
								# fmt: on
								snake_case_   : str						=			dict(zip(_lowercase							,    range(len(_lowercase   )   )   )   )

								snake_case_   : Tuple						=			os.path.join(self.tmpdirname							,    VOCAB_FILES_NAMES["""vocab_file"""]   )
								with open(self.vocab_file							,    """w"""							,    encoding="""utf-8"""   ) as fp:
														fp.write(json.dumps(_lowercase   ) + """\n"""   )





		def    UpperCAmelCase__							(					self							,    **_lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								return MgpstrTokenizer.from_pretrained(self.tmpdirname							,    **_lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Tuple						=			"""tester"""
								snake_case_   : Tuple						=			"""tester"""
								return input_text, output_text





		@unittest.skip("""MGP-STR always lower cases letters."""   )
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								pass





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : Dict						=			self.get_tokenizers(do_lower_case=_lowercase   )
								for tokenizer in tokenizers:
														with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																				snake_case_   : Optional[Any]						=			"""[SPECIAL_TOKEN]"""

																				tokenizer.add_special_tokens({"""cls_token""": special_token}   )
																				snake_case_   : Union[str, Any]						=			tokenizer.encode([special_token]							,    add_special_tokens=_lowercase   )
																				self.assertEqual(len(_lowercase   )							,    1   )

																				snake_case_   : List[Any]						=			tokenizer.decode(_lowercase							,    skip_special_tokens=_lowercase   )
																				self.assertTrue(special_token not in decoded   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.get_tokenizers()
								for tokenizer in tokenizers:
														with self.subTest(f'{tokenizer.__class__.__name__}'   ):
																				snake_case_			,		snake_case_   : Union[str, Any]						=			self.get_input_output_texts(_lowercase   )

																				snake_case_   : List[Any]						=			tokenizer.tokenize(_lowercase   )
																				snake_case_   : Union[str, Any]						=			tokenizer.convert_tokens_to_ids(_lowercase   )
																				snake_case_   : Union[str, Any]						=			tokenizer.encode(_lowercase							,    add_special_tokens=_lowercase   )
																				self.assertListEqual(_lowercase							,    _lowercase   )

																				snake_case_   : List[str]						=			tokenizer.convert_ids_to_tokens(_lowercase   )
																				self.assertNotEqual(len(_lowercase   )							,    0   )
																				snake_case_   : str						=			tokenizer.decode(_lowercase   )
																				self.assertIsInstance(_lowercase							,    _lowercase   )

																				self.assertEqual(text_a.replace(""" """							,    """"""   )							,    _lowercase   )





		@unittest.skip("""MGP-STR tokenizer only handles one sequence."""   )
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								pass





		@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""   )
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								pass

