



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():
		_lowercase		:      Optional[Any]					="pt"
elif is_tf_available():
		_lowercase		:      Union[str, Any]					="tf"
else:
		_lowercase		:      str					="jax"

class    snake_case__				(A__					,	unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase     :List[Any]						=     ByTaTokenizer
						__lowerCAmelCase     :int						=     False

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      str:
									"""simple docstring"""




									super().setUp()
									a__   :						Optional[int]						       =  ByTaTokenizer()
									tokenizer.save_pretrained(self.tmpdirname  )

						@cached_property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      str:
									"""simple docstring"""




									return ByTaTokenizer.from_pretrained("""google/byt5-small"""  )

						def 							SCREAMING_SNAKE_CASE__(   self					,   **__lowercase  )					->      Any:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(   self					,   __lowercase					,   __lowercase=False					,   __lowercase=2_0					,   __lowercase=5  )					->      int:
									"""simple docstring"""




									a__   :						Dict						       =  []
									for i in range(len(_snake_case  )  ):
												try:
															a__   :						List[Any]						       =  tokenizer.decode([i]					,   clean_up_tokenization_spaces=_snake_case  )
												except UnicodeDecodeError:
															pass
												toks.append((i, tok)  )

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

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

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									a__   :						Dict						       =  self.ta_base_tokenizer
									a__   :						str						       =  tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]  )
									a__   :						int						       =  tokenizer(["""hi""", """I went to the gym""", """"""]  )
									self.assertListEqual(batch_with_eos_added["""input_ids"""]					,   batch_without_eos_added["""input_ids"""]  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Union[str, Any]:
									"""simple docstring"""




									a__   :						Optional[Any]						       =  self.ta_base_tokenizer
									a__   :						str						       =  """Unicode €."""
									a__   :						Tuple						       =  tokenizer(_snake_case  )
									a__   :						str						       =  [8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
									self.assertEqual(encoded["""input_ids"""]					,   _snake_case  )

									# decoding
									a__   :						Dict						       =  tokenizer.decode(_snake_case  )
									self.assertEqual(_snake_case					,   """Unicode €.</s>"""  )

									a__   :						List[str]						       =  tokenizer("""e è é ê ë"""  )
									a__   :						Union[str, Any]						       =  [1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
									self.assertEqual(encoded["""input_ids"""]					,   _snake_case  )
									# decoding
									a__   :						int						       =  tokenizer.decode(_snake_case  )
									self.assertEqual(_snake_case					,   """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 							SCREAMING_SNAKE_CASE__(   self  )					->      List[Any]:
									"""simple docstring"""




									a__   :						Dict						       =  self.ta_base_tokenizer
									a__   :						str						       =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									# fmt: off
									a__   :						List[str]						       =  [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
									# fmt: on
									a__   :						str						       =  tokenizer(_snake_case					,   padding=_snake_case					,   return_tensors=_snake_case  )
									self.assertIsInstance(_snake_case					,   _snake_case  )

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

									self.assertListEqual(_snake_case					,   _snake_case  )

									self.assertEqual((2, 3_7)					,   batch.input_ids.shape  )
									self.assertEqual((2, 3_7)					,   batch.attention_mask.shape  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Optional[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      List[str]:
									"""simple docstring"""




									a__   :						str						       =  self.ta_base_tokenizer
									a__   :						Tuple						       =  [
									    """Summary of the text.""",
									    """Another summary.""",
									]
									a__   :						List[Any]						       =  tokenizer(
									    text_target=_snake_case					,   max_length=3_2					,   padding="""max_length"""					,   truncation=_snake_case					,   return_tensors=_snake_case  )
									self.assertEqual(3_2					,   targets["""input_ids"""].shape[1]  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									a__   :						Tuple						       =  self.ta_base_tokenizer
									a__   :						Tuple						       =  ["""A long paragraph for summarization. </s>"""]
									a__   :						Optional[int]						       =  ["""Summary of the text. </s>"""]
									# fmt: off
									a__   :						Optional[int]						       =  [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
									a__   :						Dict						       =  [8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
									# fmt: on

									a__   :						List[str]						       =  tokenizer(_snake_case					,   text_target=_snake_case  )

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

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      List[str]:
									"""simple docstring"""




									a__   :						Any						       =  self.get_tokenizers()
									for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
															self.assertNotEqual(tokenizer.model_max_length					,   4_2  )

        # Now let's start the test
									a__   :						str						       =  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
															a__   :						Dict						       =  tempfile.mkdtemp()

															a__   :						str						       =  """ He is very happy, UNwant\u00E9d,running"""
															a__   :						Any						       =  tokenizer.encode(_snake_case					,   add_special_tokens=_snake_case  )
															tokenizer.save_pretrained(_snake_case  )

															a__   :						Tuple						       =  tokenizer.__class__.from_pretrained(_snake_case  )
															a__   :						str						       =  after_tokenizer.encode(_snake_case					,   add_special_tokens=_snake_case  )
															self.assertListEqual(_snake_case					,   _snake_case  )

															shutil.rmtree(_snake_case  )

									a__   :						Any						       =  self.get_tokenizers(model_max_length=4_2  )
									for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
															# Isolate this from the other tests because we save additional tokens/etc
															a__   :						Optional[Any]						       =  tempfile.mkdtemp()

															a__   :						Tuple						       =  """ He is very happy, UNwant\u00E9d,running"""
															tokenizer.add_tokens(["""bim""", """bambam"""]  )
															a__   :						Union[str, Any]						       =  tokenizer.additional_special_tokens
															additional_special_tokens.append("""new_additional_special_token"""  )
															tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}  )
															a__   :						Any						       =  tokenizer.encode(_snake_case					,   add_special_tokens=_snake_case  )
															tokenizer.save_pretrained(_snake_case  )

															a__   :						List[Any]						       =  tokenizer.__class__.from_pretrained(_snake_case  )
															a__   :						str						       =  after_tokenizer.encode(_snake_case					,   add_special_tokens=_snake_case  )
															self.assertListEqual(_snake_case					,   _snake_case  )
															self.assertIn("""new_additional_special_token"""					,   after_tokenizer.additional_special_tokens  )
															self.assertEqual(after_tokenizer.model_max_length					,   4_2  )

															a__   :						Optional[Any]						       =  tokenizer.__class__.from_pretrained(_snake_case					,   model_max_length=4_3  )
															self.assertEqual(tokenizer.model_max_length					,   4_3  )

															shutil.rmtree(_snake_case  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Union[str, Any]:
									"""simple docstring"""




									a__   :						Optional[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(_snake_case  )

															with open(os.path.join(_snake_case					,   """special_tokens_map.json"""  )					,   encoding="""utf-8"""  ) as json_file:
																		a__   :						List[str]						       =  json.load(_snake_case  )

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

															a__   :						Dict						       =  [F'''<extra_id_{i}>''' for i in range(1_2_5  )]

															a__   :						List[Any]						       =  added_tokens_extra_ids + [
															    """an_additional_special_token"""
															]
															a__   :						int						       =  added_tokens_extra_ids + [
															    """an_additional_special_token"""
															]

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

															# 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
															a__   :						Optional[int]						       =  tokenizer_class.from_pretrained(
															    _snake_case					,   )
															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
															a__   :						List[Any]						       =  added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""					,   lstrip=_snake_case  )]
															a__   :						Optional[Any]						       =  tokenizer_class.from_pretrained(
															    _snake_case					,   additional_special_tokens=_snake_case					,   )

															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 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									a__   :						List[str]						       =  []
									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(_snake_case  )

															a__   :						int						       =  tokenizer_class.from_pretrained(_snake_case  )

															self.assertTrue(tokenizer.decode([2_5_5]  ) == """"""  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Tuple:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Optional[int]:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Tuple:
									"""simple docstring"""




									a__   :						Dict						       =  self.get_tokenizers(fast=_snake_case					,   do_lower_case=_snake_case  )
									for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
															a__   :						List[str]						       =  ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
															a__   :						Any						       =  tokenizer.convert_tokens_to_string(_snake_case  )

															self.assertIsInstance(_snake_case					,   _snake_case  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      List[str]:
									"""simple docstring"""




									a__   :						int						       =  self.get_tokenizers()
									for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
															a__   :						Optional[Any]						       =  [
															    """bos_token""",
															    """eos_token""",
															    """unk_token""",
															    """sep_token""",
															    """pad_token""",
															    """cls_token""",
															    """mask_token""",
															]

															a__   :						Union[str, Any]						       =  0
															a__   :						List[Any]						       =  tokenizer.convert_ids_to_tokens(
															    _snake_case					,   skip_special_tokens=_snake_case  )

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

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

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

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










from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class __snake_case			:
   pass






