


'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


lowerCamelCase							:       List[str]  						=     logging.get_logger(__name__)

lowerCamelCase							:       List[str]  						=     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase							:       Dict  						=     {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase							:       Union[str, Any]  						=     {
    'allenai/longformer-base-4096': 4_096,
    'allenai/longformer-large-4096': 4_096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}
class   __lowerCAmelCase						(lowercase_					):





      '''simple docstring'''






      lowerCAmelCase__ :					List[str]   =							VOCAB_FILES_NAMES
      lowerCAmelCase__ :					List[str]   =							PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__ :					Tuple   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__ :					Tuple   =							["""input_ids""", """attention_mask"""]
      lowerCAmelCase__ :					Optional[Any]   =							LongformerTokenizer



      def __init__(self   :   Optional[Any]       ,						UpperCamelCase   :   Optional[int]=None       ,						UpperCamelCase   :   Optional[int]=None       ,						UpperCamelCase   :   Optional[Any]=None       ,						UpperCamelCase   :   Optional[int]="replace"       ,						UpperCamelCase   :   Optional[int]="<s>"       ,						UpperCamelCase   :   Optional[int]="</s>"       ,						UpperCamelCase   :   Optional[int]="</s>"       ,						UpperCamelCase   :   int="<s>"       ,						UpperCamelCase   :   str="<unk>"       ,						UpperCamelCase   :   Optional[int]="<pad>"       ,						UpperCamelCase   :   List[str]="<mask>"       ,						UpperCamelCase   :   Any=False       ,						UpperCamelCase   :   Tuple=True       ,						**UpperCamelCase   :   Tuple       ,						):

             '''simple docstring'''







             super().__init__(
                 UpperCamelCase       ,						UpperCamelCase       ,						tokenizer_file=UpperCamelCase       ,						errors=UpperCamelCase       ,						bos_token=UpperCamelCase       ,						eos_token=UpperCamelCase       ,						sep_token=UpperCamelCase       ,						cls_token=UpperCamelCase       ,						unk_token=UpperCamelCase       ,						pad_token=UpperCamelCase       ,						mask_token=UpperCamelCase       ,						add_prefix_space=UpperCamelCase       ,						trim_offsets=UpperCamelCase       ,						**UpperCamelCase       ,						)

             lowercase__				      =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
             if pre_tok_state.get('''add_prefix_space'''       ,						UpperCamelCase			) != add_prefix_space:
                    lowercase__				      =			getattr(UpperCamelCase       ,						pre_tok_state.pop('''type'''			)			)
                    lowercase__				      =			add_prefix_space
                    lowercase__				      =			pre_tok_class(**UpperCamelCase			)

             lowercase__				      =			add_prefix_space

             lowercase__				      =			'''post_processor'''
             lowercase__				      =			getattr(self.backend_tokenizer       ,						UpperCamelCase       ,						UpperCamelCase			)
             if tokenizer_component_instance:
                    lowercase__				      =			json.loads(tokenizer_component_instance.__getstate__()			)

                    # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                    if "sep" in state:
                           lowercase__				      =			tuple(state['''sep''']			)
                    if "cls" in state:
                           lowercase__				      =			tuple(state['''cls''']			)

                    lowercase__				      =			False

                    if state.get('''add_prefix_space'''       ,						UpperCamelCase			) != add_prefix_space:
                           lowercase__				      =			add_prefix_space
                           lowercase__				      =			True

                    if state.get('''trim_offsets'''       ,						UpperCamelCase			) != trim_offsets:
                           lowercase__				      =			trim_offsets
                           lowercase__				      =			True

                    if changes_to_apply:
                           lowercase__				      =			getattr(UpperCamelCase       ,						state.pop('''type'''			)			)
                           lowercase__				      =			component_class(**UpperCamelCase			)
                           setattr(self.backend_tokenizer       ,						UpperCamelCase       ,						UpperCamelCase			)



      @property
      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             if self._mask_token is None:
                    if self.verbose:
                           logger.error('''Using mask_token, but it is not set yet.'''			)
                    return None
             return str(self._mask_token			)



      @mask_token.setter
      def 				UpperCamelCase__		(self   :   Tuple       ,						UpperCamelCase   :   Optional[int]			):

             '''simple docstring'''







             lowercase__				      =			AddedToken(UpperCamelCase       ,						lstrip=UpperCamelCase       ,						rstrip=UpperCamelCase			) if isinstance(UpperCamelCase       ,						UpperCamelCase			) else value
             lowercase__				      =			value



      def 				UpperCamelCase__		(self   :   int       ,						*UpperCamelCase   :   str       ,						**UpperCamelCase   :   Optional[int]			):

             '''simple docstring'''







             lowercase__				      =			kwargs.get('''is_split_into_words'''       ,						UpperCamelCase			)
             assert self.add_prefix_space or not is_split_into_words, (
                 f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                 "to use it with pretokenized inputs."
             )

             return super()._batch_encode_plus(*UpperCamelCase       ,						**UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   int       ,						*UpperCamelCase   :   Dict       ,						**UpperCamelCase   :   List[Any]			):

             '''simple docstring'''







             lowercase__				      =			kwargs.get('''is_split_into_words'''       ,						UpperCamelCase			)

             assert self.add_prefix_space or not is_split_into_words, (
                 f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                 "to use it with pretokenized inputs."
             )

             return super()._encode_plus(*UpperCamelCase       ,						**UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Optional[int]       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   Optional[str] = None			):

             '''simple docstring'''







             lowercase__				      =			self._tokenizer.model.save(UpperCamelCase       ,						name=UpperCamelCase			)
             return tuple(UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Any       ,						UpperCamelCase   :   Tuple       ,						UpperCamelCase   :   str=None			):

             '''simple docstring'''







             lowercase__				      =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
             if token_ids_a is None:
                    return output

             return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






      def 				UpperCamelCase__		(self   :   Dict       ,						UpperCamelCase   :   List[int]       ,						UpperCamelCase   :   Optional[List[int]] = None			):

             '''simple docstring'''







             lowercase__				      =			[self.sep_token_id]
             lowercase__				      =			[self.cls_token_id]

             if token_ids_a is None:
                    return len(cls + token_ids_a + sep			) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]








'''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()
lowerCamelCase							:       Dict  						=     logging.get_logger(__name__)

def _SCREAMING_SNAKE_CASE				(A	)	->	Optional[Any]:






       """simple docstring"""







       lowercase__				      =			MobileNetVaConfig(layer_norm_eps=0.001	)

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

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

       if model_name.startswith('''deeplabv3_'''	):
              lowercase__				      =			8
              lowercase__				      =			21
              lowercase__				      =			'''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".
              lowercase__				      =			1_001
              lowercase__				      =			'''imagenet-1k-id2label.json'''

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

       if config.num_labels == 1_001:
              lowercase__				      =			{int(A	) + 1: v for k, v in idalabel.items()}
              lowercase__				      =			'''background'''
       else:
              lowercase__				      =			{int(A	): v for k, v in idalabel.items()}

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

       return config

def _SCREAMING_SNAKE_CASE				()	->	List[Any]:






       """simple docstring"""







       lowercase__				      =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowercase__				      =			Image.open(requests.get(A  ,			stream=A	).raw	)
       return im

@torch.no_grad()
def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A=False	)	->	Any:






       """simple docstring"""







       lowercase__				      =			get_mobilenet_va_config(A	)

       # Load 🤗 model
       if model_name.startswith('''deeplabv3_'''	):
              lowercase__				      =			MobileNetVaForSemanticSegmentation(A	).eval()
       else:
              lowercase__				      =			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
       lowercase__				      =			MobileNetVaImageProcessor(
           crop_size={'''width''': config.image_size, '''height''': config.image_size}  ,			size={'''shortest_edge''': config.image_size + 32}  ,			)
       lowercase__				      =			image_processor(images=prepare_img()  ,			return_tensors='''pt'''	)
       lowercase__				      =			model(**A	)
       lowercase__				      =			outputs.logits

       if model_name.startswith('''deeplabv3_'''	):
              assert logits.shape == (1, 21, 65, 65)

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     lowercase__				      =			torch.tensor(
                         [
                             [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
                             [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
                             [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
                         ]	)

              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_001)

              if model_name == "mobilenet_v2_1.4_224":
                     lowercase__				      =			torch.tensor([0.0_181, -1.0_015, 0.4_688]	)
              elif model_name == "mobilenet_v2_1.0_224":
                     lowercase__				      =			torch.tensor([0.2_445, -1.1_993, 0.1_905]	)
              elif model_name == "mobilenet_v2_0.75_160":
                     lowercase__				      =			torch.tensor([0.2_482, 0.4_136, 0.6_669]	)
              elif model_name == "mobilenet_v2_0.35_96":
                     lowercase__				      =			torch.tensor([0.1_451, -0.4_624, 0.7_192]	)
              else:
                     lowercase__				      =			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...'''	)
              lowercase__				      =			'''google/''' + model_name
              image_processor.push_to_hub(A	)
              model.push_to_hub(A	)


if __name__ == "__main__":
 lowerCamelCase							:       int  						=     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.'
 )

 lowerCamelCase							:       int  						=     parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )




