
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


lowercase  :					Optional[int]						    =							logging.get_logger(__name__)

lowercase  :					Union[str, Any]						    =							{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase  :					Optional[int]						    =							{
    """vocab_file""": {
        """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_char_small""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_chinese_char_base""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_discriminator""": (
            """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_generator""": (
            """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"""
        ),
    }
}

lowercase  :					Optional[int]						    =							{
    """junnyu/roformer_chinese_small""": 1536,
    """junnyu/roformer_chinese_base""": 1536,
    """junnyu/roformer_chinese_char_small""": 512,
    """junnyu/roformer_chinese_char_base""": 512,
    """junnyu/roformer_small_discriminator""": 128,
    """junnyu/roformer_small_generator""": 128,
}


lowercase  :					Tuple						    =							{
    """junnyu/roformer_chinese_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_base""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_base""": {"""do_lower_case""": True},
    """junnyu/roformer_small_discriminator""": {"""do_lower_case""": True},
    """junnyu/roformer_small_generator""": {"""do_lower_case""": True},
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				VOCAB_FILES_NAMES
    _a		:						List[str]=				PRETRAINED_VOCAB_FILES_MAP
    _a		:						List[str]=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						List[Any]=				PRETRAINED_INIT_CONFIGURATION
    _a		:						str=				RoFormerTokenizer
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case=True		,snake_case="[UNK]"		,snake_case="[SEP]"		,snake_case="[PAD]"		,snake_case="[CLS]"		,snake_case="[MASK]"		,snake_case=True		,snake_case=None		,**snake_case		,):



           '''simple docstring'''







           super().__init__(
               snake_case		,tokenizer_file=snake_case		,do_lower_case=snake_case		,unk_token=snake_case		,sep_token=snake_case		,pad_token=snake_case		,cls_token=snake_case		,mask_token=snake_case		,tokenize_chinese_chars=snake_case		,strip_accents=snake_case		,**snake_case		,)

           lowercase							:     Optional[int]       	=      json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
           if (
               pre_tok_state.get("""lowercase"""		,snake_case      ) != do_lower_case
               or pre_tok_state.get("""strip_accents"""		,snake_case      ) != strip_accents
           ):
                  lowercase							:     List[str]       	=      getattr(snake_case		,pre_tok_state.pop("""type"""      )      )
                  lowercase							:     Union[str, Any]       	=      do_lower_case
                  lowercase							:     int       	=      strip_accents
                  lowercase							:     Tuple       	=      pre_tok_class(**snake_case      )

           lowercase							:     List[str]       	=      do_lower_case
    def __getstate__(  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      self.__dict__.copy()
           lowercase							:     Tuple       	=      BertPreTokenizer()
           return state
    def __setstate__(  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      d
           lowercase							:     Dict       	=      self.__dict__["""_tokenizer"""].get_vocab()
           lowercase							:     Tuple       	=      PreTokenizer.custom(JiebaPreTokenizer(snake_case      )      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      [self.sep_token_id]
           lowercase							:     Tuple       	=      [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      ) * [0] + len(token_ids_a + sep      ) * [1]
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self._tokenizer.model.save(snake_case		,name=snake_case      )
           return tuple(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None		,snake_case=None		,snake_case=False		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      BertPreTokenizer()
           return super().save_pretrained(snake_case		,snake_case		,snake_case		,snake_case		,**snake_case      )


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor

class __snake_case		(      unittest.TestCase     ):
    def __init__(  self		,snake_case		,snake_case=7		,snake_case=3		,snake_case=18		,snake_case=30		,snake_case=400		,snake_case=True		,snake_case=None		,snake_case=True		,snake_case=None		,):



           '''simple docstring'''







           lowercase							:     Dict       	=      size if size is not None else {"""shortest_edge""": 20}
           lowercase							:     Union[str, Any]       	=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           lowercase							:     str       	=      parent
           lowercase							:     int       	=      batch_size
           lowercase							:     str       	=      num_channels
           lowercase							:     int       	=      image_size
           lowercase							:     List[str]       	=      min_resolution
           lowercase							:     str       	=      max_resolution
           lowercase							:     Dict       	=      do_resize
           lowercase							:     Dict       	=      size
           lowercase							:     Dict       	=      do_center_crop
           lowercase							:     str       	=      crop_size
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Any=				MobileNetVaImageProcessor if is_vision_available() else None
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      MobileNetVaImageProcessingTester(self      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.image_processing_class(**self.image_processor_dict      )
           self.assertTrue(hasattr(snake_case		,"""do_resize"""      )      )
           self.assertTrue(hasattr(snake_case		,"""size"""      )      )
           self.assertTrue(hasattr(snake_case		,"""do_center_crop"""      )      )
           self.assertTrue(hasattr(snake_case		,"""crop_size"""      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.image_processing_class.from_dict(self.image_processor_dict      )
           self.assertEqual(image_processor.size		,{"""shortest_edge""": 20}      )
           self.assertEqual(image_processor.crop_size		,{"""height""": 18, """width""": 18}      )

           lowercase							:     int       	=      self.image_processing_class.from_dict(self.image_processor_dict		,size=42		,crop_size=84      )
           self.assertEqual(image_processor.size		,{"""shortest_edge""": 42}      )
           self.assertEqual(image_processor.crop_size		,{"""height""": 84, """width""": 84}      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random PIL images
           lowercase							:     str       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,Image.Image      )

           # Test not batched input
           lowercase							:     Dict       	=      image_processing(image_inputs[0]		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)

           # Test batched
           lowercase							:     Tuple       	=      image_processing(snake_case		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random numpy tensors
           lowercase							:     Union[str, Any]       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case		,numpify=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,np.ndarray      )

           # Test not batched input
           lowercase							:     Optional[Any]       	=      image_processing(image_inputs[0]		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)

           # Test batched
           lowercase							:     List[str]       	=      image_processing(snake_case		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           lowercase							:     Tuple       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case		,torchify=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,torch.Tensor      )

           # Test not batched input
           lowercase							:     Optional[int]       	=      image_processing(image_inputs[0]		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)

           # Test batched
           lowercase							:     List[str]       	=      image_processing(snake_case		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)
