





'''simple docstring'''






import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     Tuple			=  CodeGenTokenizer
      _lowercase:     Optional[Any]			=  CodeGenTokenizerFast
      _lowercase:     Dict			=  True
      _lowercase:     int			=  {'''add_prefix_space''': True}
      _lowercase:     Optional[Any]			=  False




      def 							lowercase__       (     self     :					Tuple    )							-> Tuple:
            super().setUp()

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

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




      def 							lowercase__       (     self     :					Union[str, Any]     ,     **__snake_case     :					List[str]    )							-> Tuple:
            kwargs.update(self.special_tokens_map    )
            return CodeGenTokenizer.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					List[Any]     ,     **__snake_case     :					Optional[Any]    )							-> str:
            kwargs.update(self.special_tokens_map    )
            return CodeGenTokenizerFast.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					Optional[int]    )							-> Union[str, Any]:
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				"""lower newer"""
            return input_text, output_text




      def 							lowercase__       (     self     :					Dict    )							-> int:
            _lowerCAmelCase					=				CodeGenTokenizer(self.vocab_file     ,     self.merges_file     ,     **self.special_tokens_map    )
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            _lowerCAmelCase					=				tokenizer.tokenize(__snake_case     ,     add_prefix_space=__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            _lowerCAmelCase					=				tokens + [tokenizer.unk_token]
            _lowerCAmelCase					=				[14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					Any    )							-> List[str]:
            if not self.test_rust_tokenizer:
                  return

            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				self.get_rust_tokenizer(add_prefix_space=__snake_case    )

            _lowerCAmelCase					=				"""lower newer"""

            # Testing tokenization
            _lowerCAmelCase					=				tokenizer.tokenize(__snake_case     ,     add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				rust_tokenizer.tokenize(__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            # Testing conversion to ids without special tokens
            _lowerCAmelCase					=				tokenizer.encode(__snake_case     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				rust_tokenizer.encode(__snake_case     ,     add_special_tokens=__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            # Testing conversion to ids with special tokens
            _lowerCAmelCase					=				self.get_rust_tokenizer(add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode(__snake_case     ,     add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				rust_tokenizer.encode(__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            # Testing the unknown token
            _lowerCAmelCase					=				tokens + [rust_tokenizer.unk_token]
            _lowerCAmelCase					=				[14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					List[str]     ,     *__snake_case     :					Optional[int]     ,     **__snake_case     :					List[Any]    )							-> Optional[int]:
            # It's very difficult to mix/test pretokenization with byte-level
            # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
            pass




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					Union[str, Any]=15    )							-> Tuple:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"    ):
                        _lowerCAmelCase					=				self.rust_tokenizer_class.from_pretrained(__snake_case     ,     **__snake_case    )

                        # Simple input
                        _lowerCAmelCase					=				"""This is a simple input"""
                        _lowerCAmelCase					=				["""This is a simple input 1""", """This is a simple input 2"""]
                        _lowerCAmelCase					=				("""This is a simple input""", """This is a pair""")
                        _lowerCAmelCase					=				[
                            ("""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(__snake_case     ,     tokenizer_r.encode     ,     __snake_case     ,     max_length=__snake_case     ,     padding="""max_length"""    )

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

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

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

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

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




      def 							lowercase__       (     self     :					List[str]    )							-> Union[str, Any]:
            _lowerCAmelCase					=				CodeGenTokenizer.from_pretrained(self.tmpdirname     ,     pad_token="""<pad>"""    )

            # Simple input
            _lowerCAmelCase					=				"""This is a simple input"""
            _lowerCAmelCase					=				["""This is a simple input looooooooong""", """This is a simple input"""]
            _lowerCAmelCase					=				("""This is a simple input""", """This is a pair""")
            _lowerCAmelCase					=				[
                ("""This is a simple input loooooong""", """This is a simple input"""),
                ("""This is a simple pair loooooong""", """This is a simple pair"""),
            ]

            _lowerCAmelCase					=				tokenizer.pad_token_id

            _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding="""max_length"""     ,     max_length=30     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case     ,     truncate=__snake_case     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				tokenizer(*__snake_case     ,     padding="""max_length"""     ,     max_length=60     ,     return_tensors="""np"""    )
            _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case     ,     truncate=__snake_case     ,     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 							lowercase__       (     self     :					str    )							-> List[Any]:
            _lowerCAmelCase					=				"""$$$"""
            _lowerCAmelCase					=				CodeGenTokenizer.from_pretrained(self.tmpdirname     ,     bos_token=__snake_case     ,     add_bos_token=__snake_case    )

            _lowerCAmelCase					=				"""This is a simple input"""
            _lowerCAmelCase					=				["""This is a simple input 1""", """This is a simple input 2"""]

            _lowerCAmelCase					=				tokenizer.bos_token_id

            _lowerCAmelCase					=				tokenizer(__snake_case    )
            _lowerCAmelCase					=				tokenizer(__snake_case    )

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

            _lowerCAmelCase					=				tokenizer.decode(out_s.input_ids    )
            _lowerCAmelCase					=				tokenizer.batch_decode(out_sa.input_ids    )

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




      @slow
      def 							lowercase__       (     self     :					Optional[int]    )							-> List[Any]:
            _lowerCAmelCase					=				CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""    )

            _lowerCAmelCase					=				"""\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
            _lowerCAmelCase					=				"""\nif len_a > len_b:      result = a\nelse:      result = b"""

            _lowerCAmelCase					=				tokenizer.encode(__snake_case    )
            _lowerCAmelCase					=				["""^#""", re.escape("""<|endoftext|>"""    ), """^'''""", """^\"\"\"""", """\n\n\n"""]
            _lowerCAmelCase					=				tokenizer.decode(__snake_case     ,     truncate_before_pattern=__snake_case    )
            self.assertEqual(__snake_case     ,     __snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[str]:
            pass













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A__							:  Any    ={
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SpeechT5ForSpeechToText''',
          '''SpeechT5ForSpeechToSpeech''',
          '''SpeechT5ForTextToSpeech''',
          '''SpeechT5Model''',
          '''SpeechT5PreTrainedModel''',
          '''SpeechT5HifiGan''',
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






