
"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
				from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline













"""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():
		__magic_name__       :					Any						=			"""pt"""
elif is_tf_available():
		__magic_name__       :					Optional[Any]						=			"""tf"""
else:
		__magic_name__       :					int						=			"""jax"""






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				Any										=  PerceiverTokenizer
			__lowerCAmelCase     :				int										=  False







			def   _a      (      self    ):






									'''simple docstring'''







									super().setUp()
									UpperCamelCase				:		List[Any]				   =			PerceiverTokenizer()
									tokenizer.save_pretrained(self.tmpdirname    )







			@cached_property
			def   _a      (      self    ):






									'''simple docstring'''







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







			def   _a      (      self						,   **_A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A						,   _A=False						,   _A=2_0						,   _A=5    ):






									'''simple docstring'''







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

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

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.perceiver_tokenizer
									UpperCamelCase				:		str				   =			"""Unicode €."""
									UpperCamelCase				:		Dict				   =			tokenizer(_A    )
									UpperCamelCase				:		Optional[Any]				   =			[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
									self.assertEqual(encoded["""input_ids"""]						,   _A    )

									# decoding
									UpperCamelCase				:		Optional[int]				   =			tokenizer.decode(_A    )
									self.assertEqual(_A						,   """[CLS]Unicode €.[SEP]"""    )

									UpperCamelCase				:		str				   =			tokenizer("""e è é ê ë"""    )
									UpperCamelCase				:		str				   =			[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
									self.assertEqual(encoded["""input_ids"""]						,   _A    )
									# decoding
									UpperCamelCase				:		Any				   =			tokenizer.decode(_A    )
									self.assertEqual(_A						,   """[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   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			self.perceiver_tokenizer
									UpperCamelCase				:		Optional[Any]				   =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									# fmt: off
									UpperCamelCase				:		List[Any]				   =			[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
									# fmt: on
									UpperCamelCase				:		Dict				   =			tokenizer(_A						,   padding=_A						,   return_tensors=_A    )
									self.assertIsInstance(_A						,   _A    )

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

									self.assertListEqual(_A						,   _A    )

									self.assertEqual((2, 3_8)						,   batch.input_ids.shape    )
									self.assertEqual((2, 3_8)						,   batch.attention_mask.shape    )







			def   _a      (      self    ):






									'''simple docstring'''







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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			self.perceiver_tokenizer
									UpperCamelCase				:		int				   =			[
									    """Summary of the text.""",
									    """Another summary.""",
									]
									UpperCamelCase				:		int				   =			tokenizer(
									    text_target=_A						,   max_length=3_2						,   padding="""max_length"""						,   truncation=_A						,   return_tensors=_A    )
									self.assertEqual(3_2						,   targets["""input_ids"""].shape[1]    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		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
									UpperCamelCase				:		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
																					UpperCamelCase				:		int				   =			tempfile.mkdtemp()

																					UpperCamelCase				:		Tuple				   =			""" He is very happy, UNwant\u00E9d,running"""
																					UpperCamelCase				:		Dict				   =			tokenizer.encode(_A						,   add_special_tokens=_A    )
																					tokenizer.save_pretrained(_A    )

																					UpperCamelCase				:		Any				   =			tokenizer.__class__.from_pretrained(_A    )
																					UpperCamelCase				:		Tuple				   =			after_tokenizer.encode(_A						,   add_special_tokens=_A    )
																					self.assertListEqual(_A						,   _A    )

																					shutil.rmtree(_A    )

									UpperCamelCase				:		Union[str, 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
																					UpperCamelCase				:		List[Any]				   =			tempfile.mkdtemp()

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

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

																					UpperCamelCase				:		Any				   =			tokenizer.__class__.from_pretrained(_A						,   model_max_length=4_3    )
																					self.assertEqual(tokenizer.model_max_length						,   4_3    )

																					shutil.rmtree(_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			[]
									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(_A    )

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

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

																					UpperCamelCase				:		Optional[int]				   =			[f"""<extra_id_{i}>""" for i in range(1_2_5    )]

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

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

																					# 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
																					UpperCamelCase				:		Tuple				   =			tokenizer_class.from_pretrained(
																					    _A						,   )
																					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
																					UpperCamelCase				:		List[str]				   =			added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""						,   lstrip=_A    )]
																					UpperCamelCase				:		Optional[Any]				   =			tokenizer_class.from_pretrained(
																					    _A						,   additional_special_tokens=_A						,   )

																					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   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			self.perceiver_tokenizer
									self.assertEqual(tokenizer.decode([1_7_8]    )						,   """�"""    )







			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			self.get_tokenizers(fast=_A						,   do_lower_case=_A    )
									for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																					UpperCamelCase				:		Optional[Any]				   =			["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
																					UpperCamelCase				:		Dict				   =			tokenizer.convert_tokens_to_string(_A    )

																					self.assertIsInstance(_A						,   _A    )



