



'''simple docstring'''





import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
       __a          =		'''pt'''
elif is_tf_available():
       __a          =		'''tf'''
else:
       __a          =		'''jax'''







class     UpperCAmelCase_       (       _lowercase		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		ByTaTokenizer
    lowercase					=		False




    def lowerCamelCase       (    self					:     str						):
          super().setUp()
          snake_case__  :    str    						=      ByTaTokenizer()
          tokenizer.save_pretrained(self.tmpdirname						)




    @cached_property
    def lowerCamelCase       (    self					:     Optional[int]						):
          return ByTaTokenizer.from_pretrained("""google/byt5-small"""						)




    def lowerCamelCase       (    self					:     Any ,     **snake_case_					:     Tuple						):
          return self.tokenizer_class.from_pretrained(self.tmpdirname ,     **UpperCamelCase__						)




    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     str ,     snake_case_					:     Any=False ,     snake_case_					:     Union[str, Any]=20 ,     snake_case_					:     Optional[int]=5						):
          # XXX The default common tokenizer tests assume that every ID is decodable on its own.
          # This assumption is invalid for ByT5 because single bytes might not be
          # valid utf-8 (byte 128 for instance).
          # Here we're overriding the smallest possible method to provide
          # a clean sequence without making the same assumption.

          snake_case__  :    List[str]    						=      []
          for i in range(len(UpperCamelCase__						)						):
                try:
                      snake_case__  :    Tuple    						=      tokenizer.decode([i] ,     clean_up_tokenization_spaces=UpperCamelCase__						)
                except UnicodeDecodeError:
                      pass
                toks.append((i, tok)						)

          snake_case__  :    Union[str, Any]    						=      list(filter(lambda snake_case_						: re.match(r"""^[ a-zA-Z]+$""" ,     t[1]						) ,     UpperCamelCase__						)						)
          snake_case__  :    Tuple    						=      list(filter(lambda snake_case_						: [t[0]] == tokenizer.encode(t[1] ,     add_special_tokens=UpperCamelCase__						) ,     UpperCamelCase__						)						)
          if max_length is not None and len(UpperCamelCase__						) > max_length:
                snake_case__  :    Dict    						=      toks[:max_length]
          if min_length is not None and len(UpperCamelCase__						) < min_length and len(UpperCamelCase__						) > 0:
                while len(UpperCamelCase__						) < min_length:
                      snake_case__  :    Optional[Any]    						=      toks + toks
        # toks_str = [t[1] for t in toks]
          snake_case__  :    Union[str, Any]    						=      [t[0] for t in toks]

          # Ensure consistency
          snake_case__  :    Union[str, Any]    						=      tokenizer.decode(UpperCamelCase__ ,     clean_up_tokenization_spaces=UpperCamelCase__						)
          if " " not in output_txt and len(UpperCamelCase__						) > 1:
                snake_case__  :    Optional[int]    						=      (
                    tokenizer.decode([toks_ids[0]] ,     clean_up_tokenization_spaces=UpperCamelCase__						)
                    + """ """
                    + tokenizer.decode(toks_ids[1:] ,     clean_up_tokenization_spaces=UpperCamelCase__						)
                )
          if with_prefix_space:
                snake_case__  :    str    						=      """ """ + output_txt
          snake_case__  :    List[Any]    						=      tokenizer.encode(UpperCamelCase__ ,     add_special_tokens=UpperCamelCase__						)
          return output_txt, output_ids




    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    str    						=      self.ta_base_tokenizer
          snake_case__  :    Union[str, Any]    						=      tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]						)
          snake_case__  :    Optional[int]    						=      tokenizer(["""hi""", """I went to the gym""", """"""]						)
          self.assertListEqual(batch_with_eos_added["""input_ids"""] ,     batch_without_eos_added["""input_ids"""]						)




    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    Optional[Any]    						=      self.ta_base_tokenizer
          snake_case__  :    Dict    						=      """Unicode €."""
          snake_case__  :    List[Any]    						=      tokenizer(UpperCamelCase__						)
          snake_case__  :    List[str]    						=      [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
          self.assertEqual(encoded["""input_ids"""] ,     UpperCamelCase__						)

          # decoding
          snake_case__  :    Tuple    						=      tokenizer.decode(UpperCamelCase__						)
          self.assertEqual(UpperCamelCase__ ,     """Unicode €.</s>"""						)

          snake_case__  :    List[Any]    						=      tokenizer("""e è é ê ë"""						)
          snake_case__  :    List[Any]    						=      [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
          self.assertEqual(encoded["""input_ids"""] ,     UpperCamelCase__						)
          # decoding
          snake_case__  :    str    						=      tokenizer.decode(UpperCamelCase__						)
          self.assertEqual(UpperCamelCase__ ,     """e è é ê ë</s>"""						)

          # encode/decode, but with `encode` instead of `__call__`
          self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""						)						) ,     """e è é ê ë</s>"""						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    int    						=      self.ta_base_tokenizer
          snake_case__  :    Tuple    						=      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          # fmt: off
          snake_case__  :    List[str]    						=      [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
          # fmt: on
          snake_case__  :    int    						=      tokenizer(UpperCamelCase__ ,     padding=UpperCamelCase__ ,     return_tensors=UpperCamelCase__						)
          self.assertIsInstance(UpperCamelCase__ ,     UpperCamelCase__						)

          if FRAMEWORK != "jax":
                snake_case__  :    Any    						=      list(batch.input_ids.numpy()[0]						)
          else:
                snake_case__  :    str    						=      list(batch.input_ids.tolist()[0]						)

          self.assertListEqual(UpperCamelCase__ ,     UpperCamelCase__						)

          self.assertEqual((2, 37) ,     batch.input_ids.shape						)
          self.assertEqual((2, 37) ,     batch.attention_mask.shape						)




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    List[str]    						=      self.ta_base_tokenizer
          snake_case__  :    Tuple    						=      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          snake_case__  :    Tuple    						=      tokenizer(UpperCamelCase__ ,     padding=UpperCamelCase__ ,     return_tensors=UpperCamelCase__						)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn("""input_ids""" ,     UpperCamelCase__						)
          self.assertIn("""attention_mask""" ,     UpperCamelCase__						)
          self.assertNotIn("""decoder_input_ids""" ,     UpperCamelCase__						)
          self.assertNotIn("""decoder_attention_mask""" ,     UpperCamelCase__						)




    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    str    						=      self.ta_base_tokenizer
          snake_case__  :    List[Any]    						=      [
              """Summary of the text.""",
              """Another summary.""",
          ]
          snake_case__  :    Union[str, Any]    						=      tokenizer(
              text_target=UpperCamelCase__ ,     max_length=32 ,     padding="""max_length""" ,     truncation=UpperCamelCase__ ,     return_tensors=UpperCamelCase__						)
          self.assertEqual(32 ,     targets["""input_ids"""].shape[1]						)




    def lowerCamelCase       (    self					:     str						):
          snake_case__  :    Tuple    						=      self.ta_base_tokenizer
          snake_case__  :    str    						=      ["""A long paragraph for summarization. </s>"""]
          snake_case__  :    Optional[Any]    						=      ["""Summary of the text. </s>"""]
          # fmt: off
          snake_case__  :    Tuple    						=      [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
          snake_case__  :    Any    						=      [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
          # fmt: on

          snake_case__  :    Any    						=      tokenizer(UpperCamelCase__ ,     text_target=UpperCamelCase__						)

          self.assertEqual(UpperCamelCase__ ,     batch["""input_ids"""][0]						)
          self.assertEqual(UpperCamelCase__ ,     batch["""labels"""][0]						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          # safety check on max_len default value so we are sure the test works
          snake_case__  :    Any    						=      self.get_tokenizers()
          for tokenizer in tokenizers:
                with self.subTest(f"{tokenizer.__class__.__name__}"						):
                      self.assertNotEqual(tokenizer.model_max_length ,     42						)

        # Now let's start the test
          snake_case__  :    Any    						=      self.get_tokenizers()
          for tokenizer in tokenizers:
                with self.subTest(f"{tokenizer.__class__.__name__}"						):
                      # Isolate this from the other tests because we save additional tokens/etc
                      snake_case__  :    int    						=      tempfile.mkdtemp()

                      snake_case__  :    List[str]    						=      """ He is very happy, UNwant\u00E9d,running"""
                      snake_case__  :    List[Any]    						=      tokenizer.encode(UpperCamelCase__ ,     add_special_tokens=UpperCamelCase__						)
                      tokenizer.save_pretrained(UpperCamelCase__						)

                      snake_case__  :    List[Any]    						=      tokenizer.__class__.from_pretrained(UpperCamelCase__						)
                      snake_case__  :    Optional[int]    						=      after_tokenizer.encode(UpperCamelCase__ ,     add_special_tokens=UpperCamelCase__						)
                      self.assertListEqual(UpperCamelCase__ ,     UpperCamelCase__						)

                      shutil.rmtree(UpperCamelCase__						)

          snake_case__  :    Any    						=      self.get_tokenizers(model_max_length=42						)
          for tokenizer in tokenizers:
                with self.subTest(f"{tokenizer.__class__.__name__}"						):
                      # Isolate this from the other tests because we save additional tokens/etc
                      snake_case__  :    Any    						=      tempfile.mkdtemp()

                      snake_case__  :    Optional[Any]    						=      """ He is very happy, UNwant\u00E9d,running"""
                      tokenizer.add_tokens(["""bim""", """bambam"""]						)
                      snake_case__  :    Optional[int]    						=      tokenizer.additional_special_tokens
                      additional_special_tokens.append("""new_additional_special_token"""						)
                      tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}						)
                      snake_case__  :    List[Any]    						=      tokenizer.encode(UpperCamelCase__ ,     add_special_tokens=UpperCamelCase__						)
                      tokenizer.save_pretrained(UpperCamelCase__						)

                      snake_case__  :    Union[str, Any]    						=      tokenizer.__class__.from_pretrained(UpperCamelCase__						)
                      snake_case__  :    int    						=      after_tokenizer.encode(UpperCamelCase__ ,     add_special_tokens=UpperCamelCase__						)
                      self.assertListEqual(UpperCamelCase__ ,     UpperCamelCase__						)
                      self.assertIn("""new_additional_special_token""" ,     after_tokenizer.additional_special_tokens						)
                      self.assertEqual(after_tokenizer.model_max_length ,     42						)

                      snake_case__  :    Any    						=      tokenizer.__class__.from_pretrained(UpperCamelCase__ ,     model_max_length=43						)
                      self.assertEqual(tokenizer.model_max_length ,     43						)

                      shutil.rmtree(UpperCamelCase__						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    List[Any]    						=      []
          if self.test_slow_tokenizer:
                tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

          if self.test_rust_tokenizer:
                tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

          for tokenizer_class, tokenizer_utils in tokenizer_list:
                with tempfile.TemporaryDirectory() as tmp_dir:
                      tokenizer_utils.save_pretrained(UpperCamelCase__						)

                      with open(os.path.join(UpperCamelCase__ ,     """special_tokens_map.json"""						) ,     encoding="""utf-8"""						) as json_file:
                            snake_case__  :    Union[str, Any]    						=      json.load(UpperCamelCase__						)

                      with open(os.path.join(UpperCamelCase__ ,     """tokenizer_config.json"""						) ,     encoding="""utf-8"""						) as json_file:
                            snake_case__  :    Optional[Any]    						=      json.load(UpperCamelCase__						)

                      snake_case__  :    Optional[int]    						=      [f"<extra_id_{i}>" for i in range(125						)]

                      snake_case__  :    int    						=      added_tokens_extra_ids + [
                          """an_additional_special_token"""
                      ]
                      snake_case__  :    Optional[Any]    						=      added_tokens_extra_ids + [
                          """an_additional_special_token"""
                      ]

                      with open(os.path.join(UpperCamelCase__ ,     """special_tokens_map.json"""						) ,     """w""" ,     encoding="""utf-8"""						) as outfile:
                            json.dump(UpperCamelCase__ ,     UpperCamelCase__						)
                      with open(os.path.join(UpperCamelCase__ ,     """tokenizer_config.json"""						) ,     """w""" ,     encoding="""utf-8"""						) as outfile:
                            json.dump(UpperCamelCase__ ,     UpperCamelCase__						)

                      # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                      # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                      # "special_tokens_map.json" files
                      snake_case__  :    Dict    						=      tokenizer_class.from_pretrained(
                          UpperCamelCase__ ,     )
                      self.assertIn(
                          """an_additional_special_token""" ,     tokenizer_without_change_in_init.additional_special_tokens						)
                      # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                      self.assertEqual(
                          ["""an_additional_special_token"""] ,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                              tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]						)						) ,     )

                      # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                      snake_case__  :    Optional[Any]    						=      added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""" ,     lstrip=UpperCamelCase__						)]
                      snake_case__  :    Any    						=      tokenizer_class.from_pretrained(
                          UpperCamelCase__ ,     additional_special_tokens=UpperCamelCase__ ,     )

                      self.assertIn("""a_new_additional_special_token""" ,     tokenizer.additional_special_tokens						)
                      self.assertEqual(
                          ["""a_new_additional_special_token"""] ,     tokenizer.convert_ids_to_tokens(
                              tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]						)						) ,     )




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          snake_case__  :    Dict    						=      []
          if self.test_slow_tokenizer:
                tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

          if self.test_rust_tokenizer:
                tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

          for tokenizer_class, tokenizer_utils in tokenizer_list:
                with tempfile.TemporaryDirectory() as tmp_dir:
                      tokenizer_utils.save_pretrained(UpperCamelCase__						)

                      snake_case__  :    str    						=      tokenizer_class.from_pretrained(UpperCamelCase__						)

                      self.assertTrue(tokenizer.decode([255]						) == """"""						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          pass




    def lowerCamelCase       (    self					:     str						):
          pass




    def lowerCamelCase       (    self					:     List[str]						):
          pass




    def lowerCamelCase       (    self					:     Optional[int]						):
          pass




    def lowerCamelCase       (    self					:     int						):
          # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
          # and special added tokens as tokens
          snake_case__  :    Dict    						=      self.get_tokenizers(fast=UpperCamelCase__ ,     do_lower_case=UpperCamelCase__						)
          for tokenizer in tokenizers:
                with self.subTest(f"{tokenizer.__class__.__name__}"						):
                      snake_case__  :    Dict    						=      ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
                      snake_case__  :    Optional[int]    						=      tokenizer.convert_tokens_to_string(UpperCamelCase__						)

                      self.assertIsInstance(UpperCamelCase__ ,     UpperCamelCase__						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    str    						=      self.get_tokenizers()
          for tokenizer in tokenizers:
                with self.subTest(f"{tokenizer.__class__.__name__}"						):
                      snake_case__  :    str    						=      [
                          """bos_token""",
                          """eos_token""",
                          """unk_token""",
                          """sep_token""",
                          """pad_token""",
                          """cls_token""",
                          """mask_token""",
                      ]

                      snake_case__  :    str    						=      0
                      snake_case__  :    Any    						=      tokenizer.convert_ids_to_tokens(
                          UpperCamelCase__ ,     skip_special_tokens=UpperCamelCase__						)

                      for attr in attributes_list:
                            setattr(UpperCamelCase__ ,     attr + """_id""" ,     UpperCamelCase__						)
                            self.assertEqual(getattr(UpperCamelCase__ ,     UpperCamelCase__						) ,     UpperCamelCase__						)
                            self.assertEqual(getattr(UpperCamelCase__ ,     attr + """_id"""						) ,     UpperCamelCase__						)

                            setattr(UpperCamelCase__ ,     attr + """_id""" ,     UpperCamelCase__						)
                            self.assertEqual(getattr(UpperCamelCase__ ,     UpperCamelCase__						) ,     UpperCamelCase__						)
                            self.assertEqual(getattr(UpperCamelCase__ ,     attr + """_id"""						) ,     UpperCamelCase__						)

                      setattr(UpperCamelCase__ ,     """additional_special_tokens_ids""" ,     []						)
                      self.assertListEqual(getattr(UpperCamelCase__ ,     """additional_special_tokens"""						) ,     []						)
                      self.assertListEqual(getattr(UpperCamelCase__ ,     """additional_special_tokens_ids"""						) ,     []						)

                      setattr(UpperCamelCase__ ,     """additional_special_tokens_ids""" ,     [token_id_to_test_setters]						)
                      self.assertListEqual(getattr(UpperCamelCase__ ,     """additional_special_tokens"""						) ,     [token_to_test_setters]						)
                      self.assertListEqual(getattr(UpperCamelCase__ ,     """additional_special_tokens_ids"""						) ,     [token_id_to_test_setters]						)








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A	:			Dict         ={
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Swinv2ForImageClassification''',
			    '''Swinv2ForMaskedImageModeling''',
			    '''Swinv2Model''',
			    '''Swinv2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			_A	:			Optional[Any]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

