






"""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


_UpperCamelCase					      =  logging.get_logger(__name__)

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

_UpperCamelCase					      =  {
    """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"""
        ),
    },
}

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







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']
    SCREAMING_SNAKE_CASE		=      CodeGenTokenizer






    def __init__(			self     ,A=None     ,A=None     ,A=None     ,A="<|endoftext|>"     ,A="<|endoftext|>"     ,A="<|endoftext|>"     ,A=False     ,**A     ,):
          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          =	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"""     ,A ) != add_prefix_space:
                UpperCAmelCase          =	getattr(A     ,pre_tok_state.pop("""type""" ) )
                UpperCAmelCase          =	add_prefix_space
                UpperCAmelCase          =	pre_tok_class(**A )

          UpperCAmelCase          =	add_prefix_space






    def     _UpperCamelCase							(			self     ,*A     ,**A ):
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,*A     ,**A ):
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	self._tokenizer.model.save(A     ,name=A )
          return tuple(A )






    def     _UpperCamelCase							(			self     ,A     ,A = False     ,A = None     ,A = None     ,**A     ,):
          UpperCAmelCase          =	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          =	self.truncate(A     ,A )

          return decoded_text






    def     _UpperCamelCase							(			self     ,A     ,A ):
          def find_re(A     ,A     ,A ):
                UpperCAmelCase          =	pattern.search(A     ,A )
                return m.start() if m else -1

          UpperCAmelCase          =	[re.compile(A     ,re.MULTILINE ) for pattern in truncate_before_pattern]

          UpperCAmelCase          =	list(re.finditer("""^print"""     ,A     ,re.MULTILINE ) )

          if len(A ) > 1:
                UpperCAmelCase          =	completion[: prints[1].start()]

          UpperCAmelCase          =	list(re.finditer("""^def"""     ,A     ,re.MULTILINE ) )

          if len(A ) > 1:
                UpperCAmelCase          =	completion[: defs[1].start()]

          UpperCAmelCase          =	0

          UpperCAmelCase          =	[
              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_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase					      =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""RoFormerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RoFormerForCausalLM""",
      """RoFormerForMaskedLM""",
      """RoFormerForMultipleChoice""",
      """RoFormerForQuestionAnswering""",
      """RoFormerForSequenceClassification""",
      """RoFormerForTokenClassification""",
      """RoFormerLayer""",
      """RoFormerModel""",
      """RoFormerPreTrainedModel""",
      """load_tf_weights_in_roformer""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRoFormerForCausalLM""",
      """TFRoFormerForMaskedLM""",
      """TFRoFormerForMultipleChoice""",
      """TFRoFormerForQuestionAnswering""",
      """TFRoFormerForSequenceClassification""",
      """TFRoFormerForTokenClassification""",
      """TFRoFormerLayer""",
      """TFRoFormerModel""",
      """TFRoFormerPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FlaxRoFormerForMaskedLM""",
      """FlaxRoFormerForMultipleChoice""",
      """FlaxRoFormerForQuestionAnswering""",
      """FlaxRoFormerForSequenceClassification""",
      """FlaxRoFormerForTokenClassification""",
      """FlaxRoFormerModel""",
      """FlaxRoFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



