
from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE     :   int							 =      {
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_SCREAMING_SNAKE_CASE     :   Union[str, Any]							 =      [
					    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Speech2Text2ForCausalLM',
					    'Speech2Text2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
					from .processing_speech_to_text_a import SpeechaTextaProcessor
					from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_speech_to_text_a import (
										    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SpeechaTextaForCausalLM,
										    SpeechaTextaPreTrainedModel,
										)

else:
					import sys

					_SCREAMING_SNAKE_CASE     :   Dict							 =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							SCREAMING_SNAKE_CASE__       			=				None


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       			=				{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__       			=				{
    """facebook/xglm-564M""": 2_0_4_8,
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				VOCAB_FILES_NAMES
							lowerCAmelCase__          =				PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__          =				["input_ids", "attention_mask"]
							lowerCAmelCase__          =				XGLMTokenizer




							def __init__(  self						,       UpperCAmelCase=None						,       UpperCAmelCase=None						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="<unk>"						,       UpperCAmelCase="<pad>"						,       **UpperCAmelCase						,       )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 7
								lowercase_			 = [F'<madeupword{i}>' for i in range(self.num_madeup_words  )]

								lowercase_			 = kwargs.get("additional_special_tokens"						,       []  )
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    UpperCAmelCase						,       tokenizer_file=UpperCAmelCase						,       bos_token=UpperCAmelCase						,       eos_token=UpperCAmelCase						,       sep_token=UpperCAmelCase						,       cls_token=UpperCAmelCase						,       unk_token=UpperCAmelCase						,       pad_token=UpperCAmelCase						,       **UpperCAmelCase						,       )

								lowercase_			 = vocab_file
								lowercase_			 = False if not self.vocab_file else True




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								lowercase_			 = [self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								lowercase_			 = [self.sep_token_id]

								if token_ids_a is None:
									return len(sep + token_ids_a  ) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a  ) * [0]




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       Tuple[str]:






								'''simple docstring'''






								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."  )

								if not os.path.isdir(UpperCAmelCase  ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory.'  )
									return
								lowercase_			 = os.path.join(
								    UpperCAmelCase						,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCAmelCase  ):
									copyfile(self.vocab_file						,       UpperCAmelCase  )

								return (out_vocab_file,)

