
'''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_splinter import SplinterTokenizer


UpperCamelCase_            =    logging.get_logger(__name__)

UpperCamelCase_            =    {"""vocab_file""": """vocab.txt"""}

UpperCamelCase_            =    {
    """vocab_file""": {
        """tau/splinter-base""": """https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt""",
        """tau/splinter-base-qass""": """https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt""",
        """tau/splinter-large""": """https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt""",
        """tau/splinter-large-qass""": """https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt""",
    }
}

UpperCamelCase_            =    {
    """tau/splinter-base""": 5_12,
    """tau/splinter-base-qass""": 5_12,
    """tau/splinter-large""": 5_12,
    """tau/splinter-large-qass""": 5_12,
}

UpperCamelCase_            =    {
    """tau/splinter-base""": {"""do_lower_case""": False},
    """tau/splinter-base-qass""": {"""do_lower_case""": False},
    """tau/splinter-large""": {"""do_lower_case""": False},
    """tau/splinter-large-qass""": {"""do_lower_case""": False},
}


class 					a_					(_a      ):
       __lowerCAmelCase      :			List[Any]      = VOCAB_FILES_NAMES
       __lowerCAmelCase      :			Optional[int]      = PRETRAINED_VOCAB_FILES_MAP
       __lowerCAmelCase      :			str      = PRETRAINED_INIT_CONFIGURATION
       __lowerCAmelCase      :			List[str]      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCAmelCase      :			int      = SplinterTokenizer




       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_="[QUESTION]"				,				snake_case_=True				,				snake_case_=None				,				**snake_case_				,				):
         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_				,				additional_special_tokens=(question_token,)				,				**snake_case_				,				)

         _lowerCAmelCase    :					Tuple							=   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
         ):
           _lowerCAmelCase    :					Union[str, Any]							=   getattr(snake_case_				,				pre_tok_state.pop("""type"""					)					)
           _lowerCAmelCase    :					Any							=   do_lower_case
           _lowerCAmelCase    :					Optional[int]							=   strip_accents
           _lowerCAmelCase    :					List[str]							=   pre_tok_class(**snake_case_					)

         _lowerCAmelCase    :					Optional[int]							=   do_lower_case




       @property
       def 							__UpperCamelCase    (       self					):
         return self.convert_tokens_to_ids(self.question_token					)




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_ = None					):
         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         _lowerCAmelCase    :					Dict							=   [self.cls_token_id]
         _lowerCAmelCase    :					str							=   [self.sep_token_id]
         _lowerCAmelCase    :					List[str]							=   [self.question_token_id] + [self.convert_tokens_to_ids("""."""					)]
         if self.padding_side == "right":
           # Input is question-then-context
           return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
         else:
           # Input is context-then-question
           return cls + token_ids_a + sep + token_ids_a + question_suffix + sep




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_ = None					):
         _lowerCAmelCase    :					List[Any]							=   [self.sep_token_id]
         _lowerCAmelCase    :					Dict							=   [self.cls_token_id]
         _lowerCAmelCase    :					List[str]							=   [self.question_token_id] + [self.convert_tokens_to_ids("""."""					)]
         if token_ids_a is None:
           return len(cls + token_ids_a + sep					) * [0]

         if self.padding_side == "right":
           # Input is question-then-context
           return len(cls + token_ids_a + question_suffix + sep					) * [0] + len(token_ids_a + sep					) * [1]
         else:
           # Input is context-then-question
           return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + question_suffix + sep					) * [1]







       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_ = None					):
         _lowerCAmelCase    :					Tuple							=   self._tokenizer.model.save(snake_case_				,				name=snake_case_					)
         return tuple(snake_case_					)




'''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 ConvNextImageProcessor


class 					a_					(unittest.TestCase      ):




       def __init__(       self				,				snake_case_				,				snake_case_=7				,				snake_case_=3				,				snake_case_=1_8				,				snake_case_=3_0				,				snake_case_=4_0_0				,				snake_case_=True				,				snake_case_=None				,				snake_case_=0.875				,				snake_case_=True				,				snake_case_=[0.5, 0.5, 0.5]				,				snake_case_=[0.5, 0.5, 0.5]				,				):
         _lowerCAmelCase    :					List[str]							=   size if size is not None else {"""shortest_edge""": 2_0}
         _lowerCAmelCase    :					Tuple							=   parent
         _lowerCAmelCase    :					List[str]							=   batch_size
         _lowerCAmelCase    :					List[str]							=   num_channels
         _lowerCAmelCase    :					Tuple							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   min_resolution
         _lowerCAmelCase    :					Tuple							=   max_resolution
         _lowerCAmelCase    :					List[Any]							=   do_resize
         _lowerCAmelCase    :					Dict							=   size
         _lowerCAmelCase    :					str							=   crop_pct
         _lowerCAmelCase    :					List[str]							=   do_normalize
         _lowerCAmelCase    :					Any							=   image_mean
         _lowerCAmelCase    :					Any							=   image_std







       def 							__UpperCamelCase    (       self					):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }



@require_torch
@require_vision
class 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Dict      = ConvNextImageProcessor if is_vision_available() else None




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   ConvNextImageProcessingTester(self					)




       @property
       def 							__UpperCamelCase    (       self					):
         return self.image_processor_tester.prepare_image_processor_dict()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   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_				,				"""crop_pct"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""do_normalize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_mean"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_std"""					)					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   self.image_processing_class.from_dict(self.image_processor_dict					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 2_0}					)

         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 4_2}					)




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         _lowerCAmelCase    :					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
         _lowerCAmelCase    :					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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					int							=   self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         _lowerCAmelCase    :					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
         _lowerCAmelCase    :					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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)







       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					Tuple							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         _lowerCAmelCase    :					List[str]							=   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
         _lowerCAmelCase    :					List[str]							=   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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[int]							=   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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)


