



import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				GPTaTokenizer
       _lowerCamelCase			    =				GPTaTokenizerFast
       _lowerCamelCase			    =				True
       _lowerCamelCase			    =				{"""add_prefix_space""": True}
       _lowerCamelCase			    =				False



       def  A_   (     self):

             """simple docstring"""





             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             UpperCAmelCase_  :	List[Any]				      =		[
                 "l",
                 "o",
                 "w",
                 "e",
                 "r",
                 "s",
                 "t",
                 "i",
                 "d",
                 "n",
                 "\u0120",
                 "\u0120l",
                 "\u0120n",
                 "\u0120lo",
                 "\u0120low",
                 "er",
                 "\u0120lowest",
                 "\u0120newer",
                 "\u0120wider",
                 "<unk>",
                 "<|endoftext|>",
             ]
             UpperCAmelCase_  :	int				      =		dict(zip(lowercase     ,range(len(lowercase))))
             UpperCAmelCase_  :	Dict				      =		["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
             UpperCAmelCase_  :	List[str]				      =		{"unk_token": "<unk>"}

             UpperCAmelCase_  :	Dict				      =		os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"])
             UpperCAmelCase_  :	Dict				      =		os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["merges_file"])
             with open(self.vocab_file     ,"w"     ,encoding="utf-8") as fp:
                   fp.write(json.dumps(lowercase) + "\n")
             with open(self.merges_file     ,"w"     ,encoding="utf-8") as fp:
                   fp.write("\n".join(lowercase))



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             kwargs.update(self.special_tokens_map)
             return GPTaTokenizer.from_pretrained(self.tmpdirname     ,**lowercase)



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             kwargs.update(self.special_tokens_map)
             return GPTaTokenizerFast.from_pretrained(self.tmpdirname     ,**lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		"lower newer"
             UpperCAmelCase_  :	str				      =		"lower newer"
             return input_text, output_text



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		GPTaTokenizer(self.vocab_file     ,self.merges_file     ,**self.special_tokens_map)
             UpperCAmelCase_  :	Optional[int]				      =		"lower newer"
             UpperCAmelCase_  :	List[str]				      =		["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
             UpperCAmelCase_  :	Union[str, Any]				      =		tokenizer.tokenize(lowercase     ,add_prefix_space=lowercase)
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	Any				      =		tokens + [tokenizer.unk_token]
             UpperCAmelCase_  :	List[str]				      =		[14, 15, 10, 9, 3, 2, 15, 19]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase)     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             if not self.test_rust_tokenizer:
                   return

             UpperCAmelCase_  :	List[Any]				      =		self.get_tokenizer()
             UpperCAmelCase_  :	int				      =		self.get_rust_tokenizer(add_prefix_space=lowercase)

             UpperCAmelCase_  :	Dict				      =		"lower newer"

             # Testing tokenization
             UpperCAmelCase_  :	Dict				      =		tokenizer.tokenize(lowercase     ,add_prefix_space=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		rust_tokenizer.tokenize(lowercase)
             self.assertListEqual(lowercase     ,lowercase)

             # Testing conversion to ids without special tokens
             UpperCAmelCase_  :	Any				      =		tokenizer.encode(lowercase     ,add_special_tokens=lowercase     ,add_prefix_space=lowercase)
             UpperCAmelCase_  :	str				      =		rust_tokenizer.encode(lowercase     ,add_special_tokens=lowercase)
             self.assertListEqual(lowercase     ,lowercase)

             # Testing conversion to ids with special tokens
             UpperCAmelCase_  :	Dict				      =		self.get_rust_tokenizer(add_prefix_space=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		tokenizer.encode(lowercase     ,add_prefix_space=lowercase)
             UpperCAmelCase_  :	List[Any]				      =		rust_tokenizer.encode(lowercase)
             self.assertListEqual(lowercase     ,lowercase)

             # Testing the unknown token
             UpperCAmelCase_  :	Tuple				      =		tokens + [rust_tokenizer.unk_token]
             UpperCAmelCase_  :	int				      =		[14, 15, 10, 9, 3, 2, 15, 19]
             self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowercase)     ,lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             pass



       def  A_   (     self     ,lowercase=15):

             """simple docstring"""





             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                         UpperCAmelCase_  :	Tuple				      =		self.rust_tokenizer_class.from_pretrained(lowercase     ,**lowercase)

                         # Simple input
                         UpperCAmelCase_  :	Union[str, Any]				      =		"This is a simple input"
                         UpperCAmelCase_  :	Dict				      =		["This is a simple input 1", "This is a simple input 2"]
                         UpperCAmelCase_  :	List[str]				      =		("This is a simple input", "This is a pair")
                         UpperCAmelCase_  :	Dict				      =		[
                             ("This is a simple input 1", "This is a simple input 2"),
                             ("This is a simple pair 1", "This is a simple pair 2"),
                         ]

                         # Simple input tests
                         self.assertRaises(lowercase     ,tokenizer_r.encode     ,lowercase     ,max_length=lowercase     ,padding="max_length")

                         # Simple input
                         self.assertRaises(lowercase     ,tokenizer_r.encode_plus     ,lowercase     ,max_length=lowercase     ,padding="max_length")

                         # Simple input
                         self.assertRaises(
                             lowercase     ,tokenizer_r.batch_encode_plus     ,lowercase     ,max_length=lowercase     ,padding="max_length"     ,)

                         # Pair input
                         self.assertRaises(lowercase     ,tokenizer_r.encode     ,lowercase     ,max_length=lowercase     ,padding="max_length")

                         # Pair input
                         self.assertRaises(lowercase     ,tokenizer_r.encode_plus     ,lowercase     ,max_length=lowercase     ,padding="max_length")

                         # Pair input
                         self.assertRaises(
                             lowercase     ,tokenizer_r.batch_encode_plus     ,lowercase     ,max_length=lowercase     ,padding="max_length"     ,)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		GPTaTokenizer.from_pretrained(self.tmpdirname     ,pad_token="<pad>")

             # Simple input
             UpperCAmelCase_  :	List[str]				      =		"This is a simple input"
             UpperCAmelCase_  :	str				      =		["This is a simple input looooooooong", "This is a simple input"]
             UpperCAmelCase_  :	Dict				      =		("This is a simple input", "This is a pair")
             UpperCAmelCase_  :	Tuple				      =		[
                 ("This is a simple input loooooong", "This is a simple input"),
                 ("This is a simple pair loooooong", "This is a simple pair"),
             ]

             UpperCAmelCase_  :	str				      =		tokenizer.pad_token_id

             UpperCAmelCase_  :	Optional[Any]				      =		tokenizer(lowercase     ,padding="max_length"     ,max_length=30     ,return_tensors="np")
             UpperCAmelCase_  :	Optional[Any]				      =		tokenizer(lowercase     ,padding=lowercase     ,truncate=lowercase     ,return_tensors="np")
             UpperCAmelCase_  :	int				      =		tokenizer(*lowercase     ,padding="max_length"     ,max_length=60     ,return_tensors="np")
             UpperCAmelCase_  :	Optional[Any]				      =		tokenizer(lowercase     ,padding=lowercase     ,truncate=lowercase     ,return_tensors="np")

             # s
             # test single string max_length padding
             self.assertEqual(out_s["input_ids"].shape[-1]     ,30)
             self.assertTrue(pad_token_id in out_s["input_ids"])
             self.assertTrue(0 in out_s["attention_mask"])

             # s2
             # test automatic padding
             self.assertEqual(out_sa["input_ids"].shape[-1]     ,33)
             # long slice doesn't have padding
             self.assertFalse(pad_token_id in out_sa["input_ids"][0])
             self.assertFalse(0 in out_sa["attention_mask"][0])
             # short slice does have padding
             self.assertTrue(pad_token_id in out_sa["input_ids"][1])
             self.assertTrue(0 in out_sa["attention_mask"][1])

             # p
             # test single pair max_length padding
             self.assertEqual(out_p["input_ids"].shape[-1]     ,60)
             self.assertTrue(pad_token_id in out_p["input_ids"])
             self.assertTrue(0 in out_p["attention_mask"])

             # p2
             # test automatic padding pair
             self.assertEqual(out_pa["input_ids"].shape[-1]     ,52)
             # long slice pair doesn't have padding
             self.assertFalse(pad_token_id in out_pa["input_ids"][0])
             self.assertFalse(0 in out_pa["attention_mask"][0])
             # short slice pair does have padding
             self.assertTrue(pad_token_id in out_pa["input_ids"][1])
             self.assertTrue(0 in out_pa["attention_mask"][1])



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		"$$$"
             UpperCAmelCase_  :	Any				      =		GPTaTokenizer.from_pretrained(self.tmpdirname     ,bos_token=lowercase     ,add_bos_token=lowercase)

             UpperCAmelCase_  :	int				      =		"This is a simple input"
             UpperCAmelCase_  :	Any				      =		["This is a simple input 1", "This is a simple input 2"]

             UpperCAmelCase_  :	List[str]				      =		tokenizer.bos_token_id

             UpperCAmelCase_  :	Tuple				      =		tokenizer(lowercase)
             UpperCAmelCase_  :	Dict				      =		tokenizer(lowercase)

             self.assertEqual(out_s.input_ids[0]     ,lowercase)
             self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

             UpperCAmelCase_  :	Optional[int]				      =		tokenizer.decode(out_s.input_ids)
             UpperCAmelCase_  :	Tuple				      =		tokenizer.batch_decode(out_sa.input_ids)

             self.assertEqual(decode_s.split()[0]     ,lowercase)
             self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))



       def  A_   (     self):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		[self.get_tokenizer(do_lower_case=lowercase     ,add_bos_token=lowercase)]
             for tokenizer in tokenizers:
                   with self.subTest(F"""{tokenizer.__class__.__name__}"""):
                         UpperCAmelCase_  :	List[str]				      =		"Encode this."
                         UpperCAmelCase_  :	str				      =		"This one too please."
                         UpperCAmelCase_  :	str				      =		tokenizer.encode(lowercase     ,add_special_tokens=lowercase)
                         encoded_sequence += tokenizer.encode(lowercase     ,add_special_tokens=lowercase)
                         UpperCAmelCase_  :	Optional[int]				      =		tokenizer.encode_plus(
                             lowercase     ,lowercase     ,add_special_tokens=lowercase     ,return_special_tokens_mask=lowercase     ,)
                         UpperCAmelCase_  :	str				      =		encoded_sequence_dict["input_ids"]
                         UpperCAmelCase_  :	Optional[Any]				      =		encoded_sequence_dict["special_tokens_mask"]
                         self.assertEqual(len(lowercase)     ,len(lowercase))

                         UpperCAmelCase_  :	Dict				      =		[
                             (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowercase)
                         ]
                         UpperCAmelCase_  :	List[str]				      =		[x for x in filtered_sequence if x is not None]
                         self.assertEqual(lowercase     ,lowercase)


@require_tokenizers
class   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		AutoTokenizer.from_pretrained("facebook/opt-350m"     ,from_slow=lowercase)
             UpperCAmelCase_  :	List[Any]				      =		"A photo of a cat"

             UpperCAmelCase_  :	Union[str, Any]				      =		tokenizer.encode(
                 lowercase     ,)
             self.assertEqual(lowercase     ,[2, 250, 1345, 9, 10, 4758])
             tokenizer.save_pretrained("test_opt")

             UpperCAmelCase_  :	List[Any]				      =		AutoTokenizer.from_pretrained("./test_opt")
             UpperCAmelCase_  :	Optional[int]				      =		tokenizer.encode(
                 lowercase     ,)
             self.assertEqual(lowercase     ,[2, 250, 1345, 9, 10, 4758])



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		AutoTokenizer.from_pretrained("facebook/opt-350m"     ,use_slow=lowercase)
             UpperCAmelCase_  :	str				      =		"A photo of a cat"

             UpperCAmelCase_  :	Dict				      =		tokenizer.encode(
                 lowercase     ,)
             # Same as above
             self.assertEqual(lowercase     ,[2, 250, 1345, 9, 10, 4758])



       @unittest.skip("This test is failing because of a bug in the fast tokenizer")
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		AutoTokenizer.from_pretrained("facebook/opt-350m"     ,from_slow=lowercase)

             UpperCAmelCase_  :	str				      =		"bos"
             UpperCAmelCase_  :	Any				      =		tokenizer.get_vocab()["bos"]

             UpperCAmelCase_  :	List[Any]				      =		"A photo of a cat"
             UpperCAmelCase_  :	int				      =		tokenizer.encode(
                 lowercase     ,)
             # We changed the bos token
             self.assertEqual(lowercase     ,[31957, 250, 1345, 9, 10, 4758])
             tokenizer.save_pretrained("./tok")
             UpperCAmelCase_  :	int				      =		AutoTokenizer.from_pretrained("./tok")
             self.assertTrue(tokenizer.is_fast)
             UpperCAmelCase_  :	List[Any]				      =		tokenizer.encode(
                 lowercase     ,)
             self.assertEqual(lowercase     ,[31957, 250, 1345, 9, 10, 4758])





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""ClapFeatureExtractor"""
       _lowerCamelCase			    =				("""RobertaTokenizer""", """RobertaTokenizerFast""")



       def __init__(     self     ,lowercase     ,lowercase):

             """simple docstring"""





             super().__init__(lowercase     ,lowercase)



       def __call__(     self     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		kwargs.pop("sampling_rate"     ,lowercase)

             if text is None and audios is None:
                   raise ValueError("You have to specify either text or audios. Both cannot be none.")

             if text is not None:
                   UpperCAmelCase_  :	List[str]				      =		self.tokenizer(lowercase     ,return_tensors=lowercase     ,**lowercase)

             if audios is not None:
                   UpperCAmelCase_  :	str				      =		self.feature_extractor(
                       lowercase     ,sampling_rate=lowercase     ,return_tensors=lowercase     ,**lowercase)

             if text is not None and audios is not None:
                   UpperCAmelCase_  :	Optional[int]				      =		audio_features.input_features
                   return encoding
             elif text is not None:
                   return encoding
             else:
                   return BatchEncoding(data=dict(**lowercase)     ,tensor_type=lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.batch_decode(*lowercase     ,**lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.decode(*lowercase     ,**lowercase)



       @property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.tokenizer.model_input_names
             UpperCAmelCase_  :	str				      =		self.feature_extractor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
