


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






     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Optional[Any]:
          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    (		_a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       PoolFormerImageProcessor if is_vision_available() else None

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      PoolFormerImageProcessingTester(self      )

     @property
     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Any:
          __UpperCAmelCase     :			List[Any]  							=      self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(snake_case							,			'''do_resize_and_center_crop'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''size'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''crop_pct'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''do_normalize'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''image_mean'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''image_std'''      )      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Tuple:
          __UpperCAmelCase     :			List[Any]  							=      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     :			Tuple  							=      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 			lowerCamelCase__			(    self					:		Optional[int]      )						->							List[str]:
          pass

     def 			lowerCamelCase__			(    self					:		Any      )						->							List[Any]:
          # Initialize image_processing
          __UpperCAmelCase     :			Optional[Any]  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          __UpperCAmelCase     :			Optional[int]  							=      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
          __UpperCAmelCase     :			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(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 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[int]:
          # Initialize image_processing
          __UpperCAmelCase     :			Any  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          __UpperCAmelCase     :			str  							=      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
          __UpperCAmelCase     :			List[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(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 			lowerCamelCase__			(    self					:		Any      )						->							Optional[int]:
          # Initialize image_processing
          __UpperCAmelCase     :			Optional[int]  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          __UpperCAmelCase     :			Union[str, Any]  							=      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
          __UpperCAmelCase     :			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
          __UpperCAmelCase     :			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'''],
              )							,			)




'''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_bert import BertTokenizer


__UpperCAmelCase      :Dict			    =       logging.get_logger(__name__)

__UpperCAmelCase      :str			    =       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase      :Optional[int]			    =       {
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCAmelCase      :Union[str, Any]			    =       {
    "bert-base-uncased": 5_1_2,
    "bert-large-uncased": 5_1_2,
    "bert-base-cased": 5_1_2,
    "bert-large-cased": 5_1_2,
    "bert-base-multilingual-uncased": 5_1_2,
    "bert-base-multilingual-cased": 5_1_2,
    "bert-base-chinese": 5_1_2,
    "bert-base-german-cased": 5_1_2,
    "bert-large-uncased-whole-word-masking": 5_1_2,
    "bert-large-cased-whole-word-masking": 5_1_2,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-large-cased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-base-cased-finetuned-mrpc": 5_1_2,
    "bert-base-german-dbmdz-cased": 5_1_2,
    "bert-base-german-dbmdz-uncased": 5_1_2,
    "TurkuNLP/bert-base-finnish-cased-v1": 5_1_2,
    "TurkuNLP/bert-base-finnish-uncased-v1": 5_1_2,
    "wietsedv/bert-base-dutch-cased": 5_1_2,
}

__UpperCAmelCase      :str			    =       {
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE	:			Tuple							       =       PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       BertTokenizer

     def __init__(    self					:		Optional[int]							,			snake_case					:		Union[str, Any]=None							,			snake_case					:		str=None							,			snake_case					:		Any=True							,			snake_case					:		Tuple="[UNK]"							,			snake_case					:		int="[SEP]"							,			snake_case					:		Optional[Any]="[PAD]"							,			snake_case					:		int="[CLS]"							,			snake_case					:		Optional[Any]="[MASK]"							,			snake_case					:		Union[str, Any]=True							,			snake_case					:		List[Any]=None							,			**snake_case					:		int							,			)						->							str:
          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							,			)

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

          __UpperCAmelCase     :			Optional[int]  							=      do_lower_case

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[str]=None      )						->							str:
          __UpperCAmelCase     :			Optional[int]  							=      [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 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[int]							,			snake_case					:		Optional[List[int]] = None      )						->							List[int]:
          __UpperCAmelCase     :			List[str]  							=      [self.sep_token_id]
          __UpperCAmelCase     :			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 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		str							,			snake_case					:		Optional[str] = None      )						->							Tuple[str]:
          __UpperCAmelCase     :			Optional[int]  							=      self._tokenizer.model.save(snake_case							,			name=snake_case      )
          return tuple(snake_case      )