




import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
    if is_torch_available():
        import torch
    if is_tf_available():
        import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


snake_case_ : Optional[int]    		=       logging.get_logger(__name__)

snake_case_ : List[str]    		=       {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

snake_case_ : str    		=       {
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

snake_case_ : Tuple    		=       {
    "Salesforce/codegen-350M-mono": 2048,
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    Optional[int]          =  VOCAB_FILES_NAMES
   UpperCAmelCase__  :    Tuple          =  PRETRAINED_VOCAB_FILES_MAP
   UpperCAmelCase__  :    Dict          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCAmelCase__  :    int          =  ['''input_ids''', '''attention_mask''']
   UpperCAmelCase__  :    List[Any]          =  CodeGenTokenizer







   def __init__(      self		:						List[str]					,						_snake_case		:						int=None					,						_snake_case		:						List[Any]=None					,						_snake_case		:						Tuple=None					,						_snake_case		:						int="<|endoftext|>"					,						_snake_case		:						Dict="<|endoftext|>"					,						_snake_case		:						Dict="<|endoftext|>"					,						_snake_case		:						Optional[int]=False					,						**_snake_case		:						int					,						):

        """simple docstring"""


        super().__init__(
            _snake_case					,						_snake_case					,						tokenizer_file=_snake_case					,						unk_token=_snake_case					,						bos_token=_snake_case					,						eos_token=_snake_case					,						add_prefix_space=_snake_case					,						**_snake_case					,						)

        if kwargs.pop('''add_bos_token'''					,						_snake_case):
             UpperCAmelCase_          =							kwargs.pop('''name_or_path'''					,						'''''')
             raise ValueError(
                 '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
                 '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
                 F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
                 F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
                 '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
                 ''' so that the fast tokenizer works correctly.''')

        UpperCAmelCase_          =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space'''					,						_snake_case) != add_prefix_space:
             UpperCAmelCase_          =							getattr(_snake_case					,						pre_tok_state.pop('''type'''))
             UpperCAmelCase_          =							add_prefix_space
             UpperCAmelCase_          =							pre_tok_class(**_snake_case)

        UpperCAmelCase_          =							add_prefix_space







   def lowerCamelCase						(      self		:						Any					,						*_snake_case		:						Optional[int]					,						**_snake_case		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							kwargs.get('''is_split_into_words'''					,						_snake_case)
        assert self.add_prefix_space or not is_split_into_words, (
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						int					,						*_snake_case		:						Any					,						**_snake_case		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							kwargs.get('''is_split_into_words'''					,						_snake_case)

        assert self.add_prefix_space or not is_split_into_words, (
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						str					,						_snake_case		:						Optional[str] = None):

        """simple docstring"""


        UpperCAmelCase_          =							self._tokenizer.model.save(_snake_case					,						name=_snake_case)
        return tuple(_snake_case)







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]					,						_snake_case		:						bool = False					,						_snake_case		:						bool = None					,						_snake_case		:						Optional[List[str]] = None					,						**_snake_case		:						List[str]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							super().decode(
            token_ids=_snake_case					,						skip_special_tokens=_snake_case					,						clean_up_tokenization_spaces=_snake_case					,						**_snake_case					,						)

        if truncate_before_pattern is not None and len(_snake_case) > 0:
             UpperCAmelCase_          =							self.truncate(_snake_case					,						_snake_case)

        return decoded_text







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


        def find_re(_snake_case		:						Union[str, Any]					,						_snake_case		:						Dict					,						_snake_case		:						List[str]):
             UpperCAmelCase_          =							pattern.search(_snake_case					,						_snake_case)
             return m.start() if m else -1

        UpperCAmelCase_          =							[re.compile(_snake_case					,						re.MULTILINE) for pattern in truncate_before_pattern]

        UpperCAmelCase_          =							list(re.finditer('''^print'''					,						_snake_case					,						re.MULTILINE))

        if len(_snake_case) > 1:
             UpperCAmelCase_          =							completion[: prints[1].start()]

        UpperCAmelCase_          =							list(re.finditer('''^def'''					,						_snake_case					,						re.MULTILINE))

        if len(_snake_case) > 1:
             UpperCAmelCase_          =							completion[: defs[1].start()]

        UpperCAmelCase_          =							0

        UpperCAmelCase_          =							[
            pos for pos in [find_re(_snake_case					,						_snake_case					,						_snake_case) for terminal in terminals] if pos != -1
        ]

        if len(_snake_case) > 0:
             return completion[: min(_snake_case)]
        else:
             return completion












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : Dict    		=       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Tuple    		=       ["MBartTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       ["MBartTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[Any]    		=       [
        "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MBartForCausalLM",
        "MBartForConditionalGeneration",
        "MBartForQuestionAnswering",
        "MBartForSequenceClassification",
        "MBartModel",
        "MBartPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TFMBartForConditionalGeneration",
        "TFMBartModel",
        "TFMBartPreTrainedModel",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "FlaxMBartForConditionalGeneration",
        "FlaxMBartForQuestionAnswering",
        "FlaxMBartForSequenceClassification",
        "FlaxMBartModel",
        "FlaxMBartPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart import MBartTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mbart_fast import MBartTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mbart import (
            MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
            MBartForCausalLM,
            MBartForConditionalGeneration,
            MBartForQuestionAnswering,
            MBartForSequenceClassification,
            MBartModel,
            MBartPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mbart import (
            FlaxMBartForConditionalGeneration,
            FlaxMBartForQuestionAnswering,
            FlaxMBartForSequenceClassification,
            FlaxMBartModel,
            FlaxMBartPreTrainedModel,
        )

else:
    import sys

    snake_case_ : Any    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






