





"""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__						= logging.get_logger(__name__)

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

lowerCamelCase__						= {
    "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__						= {
    "Salesforce/codegen-350M-mono": 2048,
}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				:Union[str, Any]						  =       PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				:int						  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				:Dict						  =       ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__				:Dict						  =       CodeGenTokenizer

  def __init__(		self :							Optional[Any]       , __a :							Any=None       , __a :							Optional[int]=None       , __a :							str=None       , __a :							Union[str, Any]="<|endoftext|>"       , __a :							str="<|endoftext|>"       , __a :							Tuple="<|endoftext|>"       , __a :							int=False       , **__a :							Tuple       , )		->					Tuple:
        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						):
              _UpperCamelCase   :						str    =							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   :						int    =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get("add_prefix_space"       , __a						) != add_prefix_space:
              _UpperCamelCase   :						List[Any]    =							getattr(__a       , pre_tok_state.pop("type"						)						)
              _UpperCamelCase   :						Optional[int]    =							add_prefix_space
              _UpperCamelCase   :						str    =							pre_tok_class(**__a						)

        _UpperCamelCase   :						Union[str, Any]    =							add_prefix_space

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , *__a :							Tuple       , **__a :							List[str]						)		->					BatchEncoding:
        _UpperCamelCase   :						int    =							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  __SCREAMING_SNAKE_CASE					(		self :							int       , *__a :							List[Any]       , **__a :							Tuple						)		->					BatchEncoding:
        _UpperCamelCase   :						str    =							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  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]       , __a :							str       , __a :							Optional[str] = None						)		->					Tuple[str]:
        _UpperCamelCase   :						str    =							self._tokenizer.model.save(__a       , name=__a						)
        return tuple(__a						)

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

        return decoded_text


  def  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							Tuple       , __a :							List[Any]						)		->					Dict:
        def find_re(__a :							List[Any]       , __a :							Tuple       , __a :							Optional[int]						):
              _UpperCamelCase   :						Any    =							pattern.search(__a       , __a						)
              return m.start() if m else -1

        _UpperCamelCase   :						str    =							[re.compile(__a       , re.MULTILINE						) for pattern in truncate_before_pattern]

        _UpperCamelCase   :						Union[str, Any]    =							list(re.finditer("^print"       , __a       , re.MULTILINE						)						)

        if len(__a						) > 1:
              _UpperCamelCase   :						Optional[int]    =							completion[: prints[1].start()]

        _UpperCamelCase   :						Tuple    =							list(re.finditer("^def"       , __a       , re.MULTILINE						)						)

        if len(__a						) > 1:
              _UpperCamelCase   :						List[Any]    =							completion[: defs[1].start()]

        _UpperCamelCase   :						Tuple    =							0

        _UpperCamelCase   :						Optional[Any]    =							[
            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"""


from typing import TYPE_CHECKING

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


lowerCamelCase__						= {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ResNetForImageClassification",
      "ResNetModel",
      "ResNetPreTrainedModel",
      "ResNetBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFResNetForImageClassification",
      "TFResNetModel",
      "TFResNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "FlaxResNetForImageClassification",
      "FlaxResNetModel",
      "FlaxResNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_resnet import (
        RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        ResNetBackbone,
        ResNetForImageClassification,
        ResNetModel,
        ResNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_resnet import (
        TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFResNetForImageClassification,
        TFResNetModel,
        TFResNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
  import sys

  lowerCamelCase__						= _LazyModule(__name__, globals()["__file__"], _import_structure)
