




'''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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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





def 						snake_case_							(_a     :							Union[str, Any]					):
    UpperCAmelCase      	=		MobileNetVaConfig(layer_norm_eps=0.001					)

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

    UpperCAmelCase      	=		re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''   ,       _a					)
    if matches:
        UpperCAmelCase      	=		float(matches[1]					)
        UpperCAmelCase      	=		int(matches[2]					)

    if model_name.startswith('''deeplabv3_'''					):
        UpperCAmelCase      	=		8
        UpperCAmelCase      	=		2_1
        UpperCAmelCase      	=		'''pascal-voc-id2label.json'''
    else:
        # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
        # of the usual 1000. The first class (index 0) is "background".
        UpperCAmelCase      	=		1_0_0_1
        UpperCAmelCase      	=		'''imagenet-1k-id2label.json'''

    UpperCAmelCase      	=		'''huggingface/label-files'''
    UpperCAmelCase      	=		json.load(open(hf_hub_download(_a   ,       _a   ,       repo_type='''dataset'''					)   ,       '''r'''					)					)

    if config.num_labels == 1_0_0_1:
        UpperCAmelCase      	=		{int(_a					) + 1: v for k, v in idalabel.items()}
        UpperCAmelCase      	=		'''background'''
    else:
        UpperCAmelCase      	=		{int(_a					): v for k, v in idalabel.items()}

    UpperCAmelCase      	=		idalabel
    UpperCAmelCase      	=		{v: k for k, v in idalabel.items()}

    return config





def 						snake_case_							():
    UpperCAmelCase      	=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase      	=		Image.open(requests.get(_a   ,       stream=_a					).raw					)
    return im





@torch.no_grad()
def 						snake_case_							(_a     :							Optional[Any]   ,       _a     :							str   ,       _a     :							str   ,       _a     :							Union[str, Any]=False					):
    UpperCAmelCase      	=		get_mobilenet_va_config(_a					)

    # Load 🤗 model
    if model_name.startswith('''deeplabv3_'''					):
        UpperCAmelCase      	=		MobileNetVaForSemanticSegmentation(_a					).eval()
    else:
        UpperCAmelCase      	=		MobileNetVaForImageClassification(_a					).eval()

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

    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
    UpperCAmelCase      	=		MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}   ,       size={'''shortest_edge''': config.image_size + 3_2}   ,       )
    UpperCAmelCase      	=		image_processor(images=prepare_img()   ,       return_tensors='''pt'''					)
    UpperCAmelCase      	=		model(**_a					)
    UpperCAmelCase      	=		outputs.logits

    if model_name.startswith('''deeplabv3_'''					):
        assert logits.shape == (1, 2_1, 6_5, 6_5)

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            UpperCAmelCase      	=		torch.tensor(
                [
                    [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                ]					)

        else:
            raise ValueError(F"Unknown model name: {model_name}"					)

        assert torch.allclose(logits[0, :3, :3, :3]   ,       _a   ,       atol=1E-4					)
    else:
        assert logits.shape == (1, 1_0_0_1)

        if model_name == "mobilenet_v2_1.4_224":
            UpperCAmelCase      	=		torch.tensor([0.0181, -1.0015, 0.4688]					)
        elif model_name == "mobilenet_v2_1.0_224":
            UpperCAmelCase      	=		torch.tensor([0.2445, -1.1993, 0.1905]					)
        elif model_name == "mobilenet_v2_0.75_160":
            UpperCAmelCase      	=		torch.tensor([0.2482, 0.4136, 0.6669]					)
        elif model_name == "mobilenet_v2_0.35_96":
            UpperCAmelCase      	=		torch.tensor([0.1451, -0.4624, 0.7192]					)
        else:
            UpperCAmelCase      	=		None

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

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

    if push_to_hub:
        print('''Pushing to the hub...'''					)
        UpperCAmelCase      	=		'''google/''' + model_name
        image_processor.push_to_hub(_a					)
        model.push_to_hub(_a					)


if __name__ == "__main__":
   A			   =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='mobilenet_v2_1.0_224',
       type=str,
       help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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.'
   )

   A			   =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 HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class       _a  (     __a							,							unittest.TestCase			):
       __a    :					str           = HerbertTokenizer
       __a    :					Optional[Any]           = HerbertTokenizerFast
       __a    :					Any           = True
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           super().setUp()

           # Use a simpler test file without japanese/chinese characters
           with open(f"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"       ,    encoding='''utf-8'''    ) as f_data:
               UpperCAmelCase      	=		f_data.read().replace('''\n\n'''       ,    '''\n'''    ).strip()

           UpperCAmelCase      	=		[
               '''<s>''',
               '''</s>''',
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''w</w>''',
               '''r</w>''',
               '''t</w>''',
               '''lo''',
               '''low''',
               '''er</w>''',
               '''low</w>''',
               '''lowest</w>''',
               '''newer</w>''',
               '''wider</w>''',
               ''',</w>''',
               '''<unk>''',
           ]
           UpperCAmelCase      	=		dict(zip(lowercase       ,    range(len(lowercase    )    )    )    )
           UpperCAmelCase      	=		['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''vocab_file''']    )
           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''merges_file''']    )
           with open(self.vocab_file       ,    '''w'''    ) as fp:
               fp.write(json.dumps(lowercase    )    )
           with open(self.merges_file       ,    '''w'''    ) as fp:
               fp.write('''\n'''.join(lowercase    )    )
       def    A				(     self						:     List[Any]       ,    lowercase						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''lower newer'''
           UpperCAmelCase      	=		'''lower newer'''
           return input_text, output_text
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(vocab_file=self.vocab_file       ,    merges_file=self.merges_file    )

           UpperCAmelCase      	=		'''lower'''
           UpperCAmelCase      	=		['''low''', '''er</w>''']
           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokens + ['''<unk>''']
           UpperCAmelCase      	=		[16, 17, 23]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )       ,    lowercase    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           if not self.test_rust_tokenizer:
               return

           UpperCAmelCase      	=		self.get_tokenizer()
           UpperCAmelCase      	=		self.get_rust_tokenizer()

           UpperCAmelCase      	=		'''lower,newer'''

           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		self.get_rust_tokenizer()
           UpperCAmelCase      	=		tokenizer.encode(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )







       @slow
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''    )

           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie sekwencji'''       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie wielu sekwencji'''       ,    add_special_tokens=lowercase    )

           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase    )
           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase       ,    lowercase    )

           assert encoded_sentence == [0] + text + [2]
           assert encoded_pair == [0] + text + [2] + text_a + [2]




