
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


__SCREAMING_SNAKE_CASE      : List[str]       =  logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE      : List[Any]       =  {
    """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"""
        ),
    },
}

__SCREAMING_SNAKE_CASE      : Optional[Any]       =  {
    """Salesforce/codegen-350M-mono""": 20_48,
}


class 		__magic_name__ (    a_     ):
		_lowerCAmelCase		=						VOCAB_FILES_NAMES
		_lowerCAmelCase		=						PRETRAINED_VOCAB_FILES_MAP
		_lowerCAmelCase		=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_lowerCAmelCase		=						["input_ids", "attention_mask"]
		_lowerCAmelCase		=						CodeGenTokenizer





		def __init__(				self     :						Tuple    ,	lowerCamelCase__     :						str=None    ,	lowerCamelCase__     :						Optional[int]=None    ,	lowerCamelCase__     :						Tuple=None    ,	lowerCamelCase__     :						Any="<|endoftext|>"    ,	lowerCamelCase__     :						Union[str, Any]="<|endoftext|>"    ,	lowerCamelCase__     :						str="<|endoftext|>"    ,	lowerCamelCase__     :						Optional[Any]=False    ,	**lowerCamelCase__     :						Optional[int]    ,	):
							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					):
												lowerCAmelCase		:       Optional[Any]							=  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.'''					)

							lowerCAmelCase		:       Union[str, Any]							=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
							if pre_tok_state.get('''add_prefix_space'''    ,	_a					) != add_prefix_space:
												lowerCAmelCase		:       Tuple							=  getattr(_a    ,	pre_tok_state.pop('''type'''					)					)
												lowerCAmelCase		:       Union[str, Any]							=  add_prefix_space
												lowerCAmelCase		:       Optional[Any]							=  pre_tok_class(**_a					)

							lowerCAmelCase		:       Any							=  add_prefix_space





		def    _A				(				self     :						Union[str, Any]    ,	*lowerCamelCase__     :						List[str]    ,	**lowerCamelCase__     :						Any					):
							lowerCAmelCase		:       Optional[Any]							=  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     :						Dict    ,	*lowerCamelCase__     :						Dict    ,	**lowerCamelCase__     :						Optional[Any]					):
							lowerCAmelCase		:       Optional[Any]							=  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     :						str    ,	lowerCamelCase__     :						List[str]    ,	lowerCamelCase__     :						List[Any] = None					):
							lowerCAmelCase		:       Optional[Any]							=  self._tokenizer.model.save(_a    ,	name=_a					)
							return tuple(_a					)





		def    _A				(				self     :						Optional[int]    ,	lowerCamelCase__     :						Optional[int]    ,	lowerCamelCase__     :						Tuple = False    ,	lowerCamelCase__     :						int = None    ,	lowerCamelCase__     :						List[Any] = None    ,	**lowerCamelCase__     :						Optional[Any]    ,	):
							lowerCAmelCase		:       List[str]							=  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:
												lowerCAmelCase		:       Union[str, Any]							=  self.truncate(_a    ,	_a					)

							return decoded_text





		def    _A				(				self     :						List[str]    ,	lowerCamelCase__     :						Optional[Any]    ,	lowerCamelCase__     :						List[Any]					):
							def find_re(lowerCamelCase__     :						List[Any]    ,	lowerCamelCase__     :						Any    ,	lowerCamelCase__     :						Optional[Any]					):
												lowerCAmelCase		:       List[Any]							=  pattern.search(_a    ,	_a					)
												return m.start() if m else -1

							lowerCAmelCase		:       List[Any]							=  [re.compile(_a    ,	re.MULTILINE					) for pattern in truncate_before_pattern]

							lowerCAmelCase		:       Tuple							=  list(re.finditer('''^print'''    ,	_a    ,	re.MULTILINE					)					)

							if len(_a					) > 1:
												lowerCAmelCase		:       List[Any]							=  completion[: prints[1].start()]

							lowerCAmelCase		:       Dict							=  list(re.finditer('''^def'''    ,	_a    ,	re.MULTILINE					)					)

							if len(_a					) > 1:
												lowerCAmelCase		:       Optional[Any]							=  completion[: defs[1].start()]

							lowerCAmelCase		:       Any							=  0

							lowerCAmelCase		:       int							=  [
							    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"""




import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


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

class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       **_a							):
          warnings.warn(
              "The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
              " use OwlViTImageProcessor instead."      ,       _a      ,       )
          super().__init__(*_a      ,       **_a							)


