

'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__         =      {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__         =      {
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


lowercase__         =      {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}




class 	A_			(    UpperCamelCase__							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:     Union[str, Any]  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:     Any  		=  PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase_						:     Optional[Any]  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:     Optional[Any]  		=  SqueezeBertTokenizer


     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					Tuple=None					,      lowercase_			:					Dict=None					,      lowercase_			:					List[str]=True					,      lowercase_			:					Tuple="[UNK]"					,      lowercase_			:					Union[str, Any]="[SEP]"					,      lowercase_			:					Optional[int]="[PAD]"					,      lowercase_			:					int="[CLS]"					,      lowercase_			:					int="[MASK]"					,      lowercase_			:					Tuple=True					,      lowercase_			:					str=None					,      **lowercase_			:					List[str]					,      )						->     Any:
      super().__init__(
          __a					,      tokenizer_file=__a					,      do_lower_case=__a					,      unk_token=__a					,      sep_token=__a					,      pad_token=__a					,      cls_token=__a					,      mask_token=__a					,      tokenize_chinese_chars=__a					,      strip_accents=__a					,      **__a					,      )

      UpperCAmelCase	:     Optional[Any]						 =			json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
      if (
          normalizer_state.get('lowercase'					,      __a	) != do_lower_case
          or normalizer_state.get('strip_accents'					,      __a	) != strip_accents
          or normalizer_state.get('handle_chinese_chars'					,      __a	) != tokenize_chinese_chars
      ):
       UpperCAmelCase	:     str						 =			getattr(__a					,      normalizer_state.pop('type'	)	)
       UpperCAmelCase	:     str						 =			do_lower_case
       UpperCAmelCase	:     Any						 =			strip_accents
       UpperCAmelCase	:     Any						 =			tokenize_chinese_chars
       UpperCAmelCase	:     Optional[Any]						 =			normalizer_class(**__a	)

      UpperCAmelCase	:     Tuple						 =			do_lower_case


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Optional[int]					,      lowercase_			:					Dict=None	)						->     int:
      UpperCAmelCase	:     Any						 =			[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        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					List[int]					,      lowercase_			:					Optional[List[int]] = None	)						->     int:
      UpperCAmelCase	:     List[str]						 =			[self.sep_token_id]
      UpperCAmelCase	:     Optional[Any]						 =			[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        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					str					,      lowercase_			:					Optional[str] = None	)						->     int:
      UpperCAmelCase	:     Tuple						 =			self._tokenizer.model.save(__a					,      name=__a	)
      return tuple(__a	)









'''simple docstring'''

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 PoolFormerImageProcessor




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self			:					Any					,      lowercase_			:					str					,      lowercase_			:					Union[str, Any]=7					,      lowercase_			:					Union[str, Any]=3					,      lowercase_			:					int=30					,      lowercase_			:					Tuple=400					,      lowercase_			:					Tuple=True					,      lowercase_			:					Optional[int]=None					,      lowercase_			:					List[str]=0.9					,      lowercase_			:					Tuple=None					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					int=[0.5, 0.5, 0.5]					,      lowercase_			:					List[str]=[0.5, 0.5, 0.5]					,      )						->     Tuple:
      UpperCAmelCase	:     Optional[int]						 =			size if size is not None else {'shortest_edge': 30}
      UpperCAmelCase	:     int						 =			crop_size if crop_size is not None else {'height': 30, 'width': 30}
      UpperCAmelCase	:     Tuple						 =			parent
      UpperCAmelCase	:     Optional[Any]						 =			batch_size
      UpperCAmelCase	:     int						 =			num_channels
      UpperCAmelCase	:     int						 =			min_resolution
      UpperCAmelCase	:     Optional[int]						 =			max_resolution
      UpperCAmelCase	:     str						 =			do_resize_and_center_crop
      UpperCAmelCase	:     int						 =			size
      UpperCAmelCase	:     Dict						 =			crop_pct
      UpperCAmelCase	:     Union[str, Any]						 =			crop_size
      UpperCAmelCase	:     Optional[int]						 =			do_normalize
      UpperCAmelCase	:     Optional[Any]						 =			image_mean
      UpperCAmelCase	:     Optional[Any]						 =			image_std




     def        UpperCAmelCase_	(		self			:					str	)						->     int:
      return {
          "size": self.size,
          "do_resize_and_center_crop": self.do_resize_and_center_crop,
          "crop_pct": self.crop_pct,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }




@require_torch
@require_vision
class 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  PoolFormerImageProcessor if is_vision_available() else None


     def        UpperCAmelCase_	(		self			:					Dict	)						->     str:
      UpperCAmelCase	:     Any						 =			PoolFormerImageProcessingTester(self	)


     @property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      UpperCAmelCase	:     Any						 =			self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(lowercase_					,      'do_resize_and_center_crop'	)	)
      self.assertTrue(hasattr(lowercase_					,      'size'	)	)
      self.assertTrue(hasattr(lowercase_					,      'crop_pct'	)	)
      self.assertTrue(hasattr(lowercase_					,      'do_normalize'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_mean'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_std'	)	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Tuple:
      UpperCAmelCase	:     List[str]						 =			self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 30}	)
      self.assertEqual(image_processor.crop_size					,      {'height': 30, 'width': 30}	)

      UpperCAmelCase	:     Any						 =			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        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     str:
      pass


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     List[str]:
      # Initialize image_processing
      UpperCAmelCase	:     int						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      UpperCAmelCase	:     Any						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      Image.Image	)

      # Test not batched input
      UpperCAmelCase	:     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
      UpperCAmelCase	:     str						 =			image_processing(lowercase_					,      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        UpperCAmelCase_	(		self			:					List[Any]	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     int						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      UpperCAmelCase	:     Dict						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      numpify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      np.ndarray	)

      # Test not batched input
      UpperCAmelCase	:     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
      UpperCAmelCase	:     Optional[Any]						 =			image_processing(lowercase_					,      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        UpperCAmelCase_	(		self			:					str	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     Optional[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      UpperCAmelCase	:     Tuple						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      torch.Tensor	)

      # Test not batched input
      UpperCAmelCase	:     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
      UpperCAmelCase	:     Optional[int]						 =			image_processing(lowercase_					,      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'],
          )					,      )




