
'''simple docstring'''
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    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						GPTaTokenizer
 UpperCAmelCase_		    =						GPTaTokenizerFast
 UpperCAmelCase_		    =						True
 UpperCAmelCase_		    =						{"""add_prefix_space""": True}
 UpperCAmelCase_		    =						False





 def        snake_case__		(    self):
     super().setUp()

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

     snake_case_ :    Optional[Any]     			=						os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["vocab_file"])
     snake_case_ :    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        snake_case__		(    self ,       **lowercase_):
     kwargs.update(self.special_tokens_map)
     return GPTaTokenizer.from_pretrained(self.tmpdirname ,       **lowercase_)





 def        snake_case__		(    self ,       **lowercase_):
     kwargs.update(self.special_tokens_map)
     return GPTaTokenizerFast.from_pretrained(self.tmpdirname ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    int     			=						"lower newer"
     snake_case_ :    Any     			=						"lower newer"
     return input_text, output_text





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						GPTaTokenizer(self.vocab_file ,       self.merges_file ,       **self.special_tokens_map)
     snake_case_ :    List[Any]     			=						"lower newer"
     snake_case_ :    str     			=						["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
     snake_case_ :    str     			=						tokenizer.tokenize(lowercase_ ,       add_prefix_space=lowercase_)
     self.assertListEqual(lowercase_ ,       lowercase_)

     snake_case_ :    int     			=						tokens + [tokenizer.unk_token]
     snake_case_ :    Dict     			=						[14, 15, 10, 9, 3, 2, 15, 19]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_) ,       lowercase_)





 def        snake_case__		(    self):
     if not self.test_rust_tokenizer:
         return

     snake_case_ :    int     			=						self.get_tokenizer()
     snake_case_ :    List[Any]     			=						self.get_rust_tokenizer(add_prefix_space=lowercase_)

     snake_case_ :    Union[str, Any]     			=						"lower newer"

     # Testing tokenization
     snake_case_ :    List[str]     			=						tokenizer.tokenize(lowercase_ ,       add_prefix_space=lowercase_)
     snake_case_ :    int     			=						rust_tokenizer.tokenize(lowercase_)
     self.assertListEqual(lowercase_ ,       lowercase_)

     # Testing conversion to ids without special tokens
     snake_case_ :    int     			=						tokenizer.encode(lowercase_ ,       add_special_tokens=lowercase_ ,       add_prefix_space=lowercase_)
     snake_case_ :    int     			=						rust_tokenizer.encode(lowercase_ ,       add_special_tokens=lowercase_)
     self.assertListEqual(lowercase_ ,       lowercase_)

     # Testing conversion to ids with special tokens
     snake_case_ :    Optional[Any]     			=						self.get_rust_tokenizer(add_prefix_space=lowercase_)
     snake_case_ :    str     			=						tokenizer.encode(lowercase_ ,       add_prefix_space=lowercase_)
     snake_case_ :    Optional[int]     			=						rust_tokenizer.encode(lowercase_)
     self.assertListEqual(lowercase_ ,       lowercase_)

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





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     # It's very difficult to mix/test pretokenization with byte-level
     # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
     pass





 def        snake_case__		(    self ,       lowercase_=15):
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
             snake_case_ :    int     			=						self.rust_tokenizer_class.from_pretrained(lowercase_ ,       **lowercase_)

             # Simple input
             snake_case_ :    int     			=						"This is a simple input"
             snake_case_ :    List[str]     			=						["This is a simple input 1", "This is a simple input 2"]
             snake_case_ :    str     			=						("This is a simple input", "This is a pair")
             snake_case_ :    Union[str, Any]     			=						[
                 ("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        snake_case__		(    self):
     snake_case_ :    int     			=						GPTaTokenizer.from_pretrained(self.tmpdirname ,       pad_token="<pad>")

     # Simple input
     snake_case_ :    Dict     			=						"This is a simple input"
     snake_case_ :    Optional[Any]     			=						["This is a simple input looooooooong", "This is a simple input"]
     snake_case_ :    Optional[Any]     			=						("This is a simple input", "This is a pair")
     snake_case_ :    int     			=						[
         ("This is a simple input loooooong", "This is a simple input"),
         ("This is a simple pair loooooong", "This is a simple pair"),
     ]

     snake_case_ :    Optional[int]     			=						tokenizer.pad_token_id

     snake_case_ :    Optional[int]     			=						tokenizer(lowercase_ ,       padding="max_length" ,       max_length=30 ,       return_tensors="np")
     snake_case_ :    Optional[int]     			=						tokenizer(lowercase_ ,       padding=lowercase_ ,       truncate=lowercase_ ,       return_tensors="np")
     snake_case_ :    List[str]     			=						tokenizer(*lowercase_ ,       padding="max_length" ,       max_length=60 ,       return_tensors="np")
     snake_case_ :    Tuple     			=						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        snake_case__		(    self):
     snake_case_ :    str     			=						"$$$"
     snake_case_ :    Dict     			=						GPTaTokenizer.from_pretrained(self.tmpdirname ,       bos_token=lowercase_ ,       add_bos_token=lowercase_)

     snake_case_ :    Tuple     			=						"This is a simple input"
     snake_case_ :    Optional[int]     			=						["This is a simple input 1", "This is a simple input 2"]

     snake_case_ :    Tuple     			=						tokenizer.bos_token_id

     snake_case_ :    Optional[int]     			=						tokenizer(lowercase_)
     snake_case_ :    Optional[int]     			=						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))

     snake_case_ :    int     			=						tokenizer.decode(out_s.input_ids)
     snake_case_ :    Optional[int]     			=						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        snake_case__		(    self):
     pass





 def        snake_case__		(    self):
     # TODO: change to self.get_tokenizers() when the fast version is implemented
     snake_case_ :    Dict     			=						[self.get_tokenizer(do_lower_case=lowercase_ ,       add_bos_token=lowercase_)]
     for tokenizer in tokenizers:
         with self.subTest(F'{tokenizer.__class__.__name__}'):
             snake_case_ :    Dict     			=						"Encode this."
             snake_case_ :    Dict     			=						"This one too please."
             snake_case_ :    str     			=						tokenizer.encode(lowercase_ ,       add_special_tokens=lowercase_)
             encoded_sequence += tokenizer.encode(lowercase_ ,       add_special_tokens=lowercase_)
             snake_case_ :    Union[str, Any]     			=						tokenizer.encode_plus(
                 lowercase_ ,       lowercase_ ,       add_special_tokens=lowercase_ ,       return_special_tokens_mask=lowercase_ ,       )
             snake_case_ :    List[str]     			=						encoded_sequence_dict["input_ids"]
             snake_case_ :    Tuple     			=						encoded_sequence_dict["special_tokens_mask"]
             self.assertEqual(len(lowercase_) ,       len(lowercase_))

             snake_case_ :    Any     			=						[
                 (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowercase_)
             ]
             snake_case_ :    Tuple     			=						[x for x in filtered_sequence if x is not None]
             self.assertEqual(lowercase_ ,       lowercase_)






@require_tokenizers
class    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     # More context:
     # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
     # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
     # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

     snake_case_ :    Union[str, Any]     			=						AutoTokenizer.from_pretrained("facebook/opt-350m" ,       from_slow=lowercase_)
     snake_case_ :    str     			=						"A photo of a cat"

     snake_case_ :    int     			=						tokenizer.encode(
         lowercase_ ,       )
     self.assertEqual(lowercase_ ,       [2, 2_50, 13_45, 9, 10, 47_58])
     tokenizer.save_pretrained("test_opt")

     snake_case_ :    List[str]     			=						AutoTokenizer.from_pretrained("./test_opt")
     snake_case_ :    int     			=						tokenizer.encode(
         lowercase_ ,       )
     self.assertEqual(lowercase_ ,       [2, 2_50, 13_45, 9, 10, 47_58])





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						AutoTokenizer.from_pretrained("facebook/opt-350m" ,       use_slow=lowercase_)
     snake_case_ :    List[str]     			=						"A photo of a cat"

     snake_case_ :    Dict     			=						tokenizer.encode(
         lowercase_ ,       )
     # Same as above
     self.assertEqual(lowercase_ ,       [2, 2_50, 13_45, 9, 10, 47_58])





 @unittest.skip("This test is failing because of a bug in the fast tokenizer")
 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						AutoTokenizer.from_pretrained("facebook/opt-350m" ,       from_slow=lowercase_)

     snake_case_ :    int     			=						"bos"
     snake_case_ :    int     			=						tokenizer.get_vocab()["bos"]

     snake_case_ :    List[Any]     			=						"A photo of a cat"
     snake_case_ :    Optional[int]     			=						tokenizer.encode(
         lowercase_ ,       )
     # We changed the bos token
     self.assertEqual(lowercase_ ,       [3_19_57, 2_50, 13_45, 9, 10, 47_58])
     tokenizer.save_pretrained("./tok")
     snake_case_ :    Optional[int]     			=						AutoTokenizer.from_pretrained("./tok")
     self.assertTrue(tokenizer.is_fast)
     snake_case_ :    Any     			=						tokenizer.encode(
         lowercase_ ,       )
     self.assertEqual(lowercase_ ,       [3_19_57, 2_50, 13_45, 9, 10, 47_58])






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_											=						{"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "WavLMForAudioFrameClassification",
      "WavLMForCTC",
      "WavLMForSequenceClassification",
      "WavLMForXVector",
      "WavLMModel",
      "WavLMPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

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




