
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": 2_0_4_8,
}




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     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						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE="<|endoftext|>"						,						__SCREAMING_SNAKE_CASE="<|endoftext|>"						,						__SCREAMING_SNAKE_CASE="<|endoftext|>"						,						__SCREAMING_SNAKE_CASE=False						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Any:




           """simple docstring"""







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

           if kwargs.pop('''add_bos_token'''						,						__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   int					=				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__    :   Union[str, Any]					=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
           if pre_tok_state.get('''add_prefix_space'''						,						__SCREAMING_SNAKE_CASE  ) != add_prefix_space:
                 UpperCamelCase__    :   Tuple					=				getattr(__SCREAMING_SNAKE_CASE						,						pre_tok_state.pop('''type'''  )  )
                 UpperCamelCase__    :   Tuple					=				add_prefix_space
                 UpperCamelCase__    :   Union[str, Any]					=				pre_tok_class(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Union[str, Any]					=				add_prefix_space






     def    __SCREAMING_SNAKE_CASE			(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   BatchEncoding:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				kwargs.get('''is_split_into_words'''						,						__SCREAMING_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(*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   BatchEncoding:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				kwargs.get('''is_split_into_words'''						,						__SCREAMING_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(*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				self._tokenizer.model.save(__SCREAMING_SNAKE_CASE						,						name=__SCREAMING_SNAKE_CASE  )
           return tuple(__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = False						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				super().decode(
               token_ids=__SCREAMING_SNAKE_CASE						,						skip_special_tokens=__SCREAMING_SNAKE_CASE						,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE						,						)

           if truncate_before_pattern is not None and len(__SCREAMING_SNAKE_CASE  ) > 0:
                 UpperCamelCase__    :   Optional[int]					=				self.truncate(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

           return decoded_text






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           def find_re(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   List[str]					=				pattern.search(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                 return m.start() if m else -1

           UpperCamelCase__    :   Any					=				[re.compile(__SCREAMING_SNAKE_CASE						,						re.MULTILINE  ) for pattern in truncate_before_pattern]

           UpperCamelCase__    :   Dict					=				list(re.finditer('''^print'''						,						__SCREAMING_SNAKE_CASE						,						re.MULTILINE  )  )

           if len(__SCREAMING_SNAKE_CASE  ) > 1:
                 UpperCamelCase__    :   Optional[Any]					=				completion[: prints[1].start()]

           UpperCamelCase__    :   str					=				list(re.finditer('''^def'''						,						__SCREAMING_SNAKE_CASE						,						re.MULTILINE  )  )

           if len(__SCREAMING_SNAKE_CASE  ) > 1:
                 UpperCamelCase__    :   Optional[int]					=				completion[: defs[1].start()]

           UpperCamelCase__    :   int					=				0

           UpperCamelCase__    :   Optional[int]					=				[
               pos for pos in [find_re(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) for terminal in terminals] if pos != -1
           ]

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase      ={"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SwinForImageClassification",
        "SwinForMaskedImageModeling",
        "SwinModel",
        "SwinPreTrainedModel",
        "SwinBackbone",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFSwinForImageClassification",
        "TFSwinForMaskedImageModeling",
        "TFSwinModel",
        "TFSwinPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swin import (
            SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
            SwinModel,
            SwinPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_swin import (
            TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
            TFSwinModel,
            TFSwinPreTrainedModel,
        )

else:
    import sys

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


