
'''simple docstring'''



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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
						_lowercase          =						"""pt"""
elif is_tf_available():
						_lowercase          =						"""tf"""
else:
						_lowercase          =						"""jax"""




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: str										=  PerceiverTokenizer
						_lowercase						: Optional[Any]										=  False




						def    _lowercase      (   self ):

												"""simple docstring"""



												super().setUp()
												_lowerCAmelCase    		=					PerceiverTokenizer()
												tokenizer.save_pretrained(self.tmpdirname )




						@cached_property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver""" )




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												return self.tokenizer_class.from_pretrained(self.tmpdirname       ,  **_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=False       ,  _lowercase=20       ,  _lowercase=5 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					[]
												for i in range(len(_lowercase ) ):
																		try:
																								_lowerCAmelCase    		=					tokenizer.decode([i]       ,  clean_up_tokenization_spaces=_lowercase )
																		except UnicodeDecodeError:
																								pass
																		toks.append((i, tok) )

												_lowerCAmelCase    		=					list(filter(lambda _lowercase : re.match(R"""^[ a-zA-Z]+$"""       ,  t[1] )       ,  _lowercase ) )
												_lowerCAmelCase    		=					list(filter(lambda _lowercase : [t[0]] == tokenizer.encode(t[1]       ,  add_special_tokens=_lowercase )       ,  _lowercase ) )
												if max_length is not None and len(_lowercase ) > max_length:
																		_lowerCAmelCase    		=					toks[:max_length]
												if min_length is not None and len(_lowercase ) < min_length and len(_lowercase ) > 0:
																		while len(_lowercase ) < min_length:
																								_lowerCAmelCase    		=					toks + toks
        # toks_str = [t[1] for t in toks]
												_lowerCAmelCase    		=					[t[0] for t in toks]

												# Ensure consistency
												_lowerCAmelCase    		=					tokenizer.decode(_lowercase       ,  clean_up_tokenization_spaces=_lowercase )
												if " " not in output_txt and len(_lowercase ) > 1:
																		_lowerCAmelCase    		=					(
																		    tokenizer.decode([toks_ids[0]]       ,  clean_up_tokenization_spaces=_lowercase )
																		    + """ """
																		    + tokenizer.decode(toks_ids[1:]       ,  clean_up_tokenization_spaces=_lowercase )
																		)
												if with_prefix_space:
																		_lowerCAmelCase    		=					""" """ + output_txt
												_lowerCAmelCase    		=					tokenizer.encode(_lowercase       ,  add_special_tokens=_lowercase )
												return output_txt, output_ids




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.perceiver_tokenizer
												_lowerCAmelCase    		=					"""Unicode €."""
												_lowerCAmelCase    		=					tokenizer(_lowercase )
												_lowerCAmelCase    		=					[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
												self.assertEqual(encoded["""input_ids"""]       ,  _lowercase )

												# decoding
												_lowerCAmelCase    		=					tokenizer.decode(_lowercase )
												self.assertEqual(_lowercase       ,  """[CLS]Unicode €.[SEP]""" )

												_lowerCAmelCase    		=					tokenizer("""e è é ê ë""" )
												_lowerCAmelCase    		=					[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
												self.assertEqual(encoded["""input_ids"""]       ,  _lowercase )
												# decoding
												_lowerCAmelCase    		=					tokenizer.decode(_lowercase )
												self.assertEqual(_lowercase       ,  """[CLS]e è é ê ë[SEP]""" )

												# encode/decode, but with `encode` instead of `__call__`
												self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë""" ) )       ,  """[CLS]e è é ê ë[SEP]""" )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.perceiver_tokenizer
												_lowerCAmelCase    		=					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												# fmt: off
												_lowerCAmelCase    		=					[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
												# fmt: on
												_lowerCAmelCase    		=					tokenizer(_lowercase       ,  padding=_lowercase       ,  return_tensors=_lowercase )
												self.assertIsInstance(_lowercase       ,  _lowercase )

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

												self.assertListEqual(_lowercase       ,  _lowercase )

												self.assertEqual((2, 38)       ,  batch.input_ids.shape )
												self.assertEqual((2, 38)       ,  batch.attention_mask.shape )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.perceiver_tokenizer
												_lowerCAmelCase    		=					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												_lowerCAmelCase    		=					tokenizer(_lowercase       ,  padding=_lowercase       ,  return_tensors=_lowercase )
												# check if input_ids are returned and no decoder_input_ids
												self.assertIn("""input_ids"""       ,  _lowercase )
												self.assertIn("""attention_mask"""       ,  _lowercase )
												self.assertNotIn("""decoder_input_ids"""       ,  _lowercase )
												self.assertNotIn("""decoder_attention_mask"""       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.perceiver_tokenizer
												_lowerCAmelCase    		=					[
												    """Summary of the text.""",
												    """Another summary.""",
												]
												_lowerCAmelCase    		=					tokenizer(
												    text_target=_lowercase       ,  max_length=32       ,  padding="""max_length"""       ,  truncation=_lowercase       ,  return_tensors=_lowercase )
												self.assertEqual(32       ,  targets["""input_ids"""].shape[1] )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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
												_lowerCAmelCase    		=					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
																								_lowerCAmelCase    		=					tempfile.mkdtemp()

																								_lowerCAmelCase    		=					""" He is very happy, UNwant\u00E9d,running"""
																								_lowerCAmelCase    		=					tokenizer.encode(_lowercase       ,  add_special_tokens=_lowercase )
																								tokenizer.save_pretrained(_lowercase )

																								_lowerCAmelCase    		=					tokenizer.__class__.from_pretrained(_lowercase )
																								_lowerCAmelCase    		=					after_tokenizer.encode(_lowercase       ,  add_special_tokens=_lowercase )
																								self.assertListEqual(_lowercase       ,  _lowercase )

																								shutil.rmtree(_lowercase )

												_lowerCAmelCase    		=					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
																								_lowerCAmelCase    		=					tempfile.mkdtemp()

																								_lowerCAmelCase    		=					""" He is very happy, UNwant\u00E9d,running"""
																								tokenizer.add_tokens(["""bim""", """bambam"""] )
																								_lowerCAmelCase    		=					tokenizer.additional_special_tokens
																								additional_special_tokens.append("""new_additional_special_token""" )
																								tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
																								_lowerCAmelCase    		=					tokenizer.encode(_lowercase       ,  add_special_tokens=_lowercase )
																								tokenizer.save_pretrained(_lowercase )

																								_lowerCAmelCase    		=					tokenizer.__class__.from_pretrained(_lowercase )
																								_lowerCAmelCase    		=					after_tokenizer.encode(_lowercase       ,  add_special_tokens=_lowercase )
																								self.assertListEqual(_lowercase       ,  _lowercase )
																								self.assertIn("""new_additional_special_token"""       ,  after_tokenizer.additional_special_tokens )
																								self.assertEqual(after_tokenizer.model_max_length       ,  42 )

																								_lowerCAmelCase    		=					tokenizer.__class__.from_pretrained(_lowercase       ,  model_max_length=43 )
																								self.assertEqual(tokenizer.model_max_length       ,  43 )

																								shutil.rmtree(_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					[]
												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(_lowercase )

																								with open(os.path.join(_lowercase       ,  """special_tokens_map.json""" )       ,  encoding="""utf-8""" ) as json_file:
																														_lowerCAmelCase    		=					json.load(_lowercase )

																								with open(os.path.join(_lowercase       ,  """tokenizer_config.json""" )       ,  encoding="""utf-8""" ) as json_file:
																														_lowerCAmelCase    		=					json.load(_lowercase )

																								_lowerCAmelCase    		=					[F'<extra_id_{i}>' for i in range(125 )]

																								_lowerCAmelCase    		=					added_tokens_extra_ids + [
																								    """an_additional_special_token"""
																								]
																								_lowerCAmelCase    		=					added_tokens_extra_ids + [
																								    """an_additional_special_token"""
																								]

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

																								# 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
																								_lowerCAmelCase    		=					tokenizer_class.from_pretrained(
																								    _lowercase       ,  )
																								self.assertIn(
																								    """an_additional_special_token"""       ,  tokenizer_without_change_in_init.additional_special_tokens )
																								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
																								_lowerCAmelCase    		=					added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""       ,  lstrip=_lowercase )]
																								_lowerCAmelCase    		=					tokenizer_class.from_pretrained(
																								    _lowercase       ,  additional_special_tokens=_lowercase       ,  )

																								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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.perceiver_tokenizer
												self.assertEqual(tokenizer.decode([178] )       ,  """�""" )




						def    _lowercase      (   self ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.get_tokenizers(fast=_lowercase       ,  do_lower_case=_lowercase )
												for tokenizer in tokenizers:
																		with self.subTest(F'{tokenizer.__class__.__name__}' ):
																								_lowerCAmelCase    		=					["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
																								_lowerCAmelCase    		=					tokenizer.convert_tokens_to_string(_lowercase )

																								self.assertIsInstance(_lowercase       ,  _lowercase )


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase          =						{"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ViTMAEForPreTraining""",
						    """ViTMAELayer""",
						    """ViTMAEModel""",
						    """ViTMAEPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """TFViTMAEForPreTraining""",
						    """TFViTMAEModel""",
						    """TFViTMAEPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						_lowercase          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
