'''simple docstring'''
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


lowerCamelCase			:				Union[str, Any] 			=					logging.get_logger(__name__)

lowerCamelCase			:				Dict 			=					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase			:				Dict 			=					{
    "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"
        ),
    },
}

lowerCamelCase			:				Union[str, Any] 			=					{
    "Salesforce/codegen-350M-mono": 2_0_4_8,
}







class 							A__   (	A__  ):
  A__					=    VOCAB_FILES_NAMES
  A__					=    PRETRAINED_VOCAB_FILES_MAP
  A__					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__					=    ['input_ids', 'attention_mask']
  A__					=    CodeGenTokenizer





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





       '''simple docstring'''






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

       if kwargs.pop('add_bos_token'							, _a			):
            _SCREAMING_SNAKE_CASE							   =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.'			)

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

       _SCREAMING_SNAKE_CASE							   =add_prefix_space





  def 						A  (		self				:     int							, *_a				:     List[Any]							, **_a				:     Union[str, Any]			)     ->   BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =kwargs.get('is_split_into_words'							, _a			)
       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(*_a							, **_a			)





  def 						A  (		self				:     Optional[Any]							, *_a				:     Tuple							, **_a				:     Optional[Any]			)     ->   BatchEncoding:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =kwargs.get('is_split_into_words'							, _a			)

       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(*_a							, **_a			)





  def 						A  (		self				:     int							, _a				:     str							, _a				:     Optional[str] = None			)     ->   Tuple[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self._tokenizer.model.save(_a							, name=_a			)
       return tuple(_a			)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =super().decode(
           token_ids=_a							, skip_special_tokens=_a							, clean_up_tokenization_spaces=_a							, **_a							, )

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

       return decoded_text





  def 						A  (		self				:     Optional[int]							, _a				:     List[str]							, _a				:     str			)     ->   Optional[Any]:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							   =[re.compile(_a							, re.MULTILINE			) for pattern in truncate_before_pattern]

       _SCREAMING_SNAKE_CASE							   =list(re.finditer('^print'							, _a							, re.MULTILINE			)			)

       if len(_a			) > 1:
            _SCREAMING_SNAKE_CASE							   =completion[: prints[1].start()]

       _SCREAMING_SNAKE_CASE							   =list(re.finditer('^def'							, _a							, re.MULTILINE			)			)

       if len(_a			) > 1:
            _SCREAMING_SNAKE_CASE							   =completion[: defs[1].start()]

       _SCREAMING_SNAKE_CASE							   =0

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

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



'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase			:				Optional[int] 			=					{
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					["EfficientNetImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "EfficientNetForImageClassification",
         "EfficientNetModel",
         "EfficientNetPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_efficientnet import (
         EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EfficientNetConfig,
         EfficientNetOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_efficientnet import EfficientNetImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_efficientnet import (
              EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              EfficientNetForImageClassification,
              EfficientNetModel,
              EfficientNetPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				int 			=					_LazyModule(__name__, globals()["__file__"], _import_structure)
