






from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


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

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

a	: Optional[Any]      =				{
    "vocab_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json",
    },
    "merges_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "openai/clip-vit-base-patch32": (
            "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"
        ),
    },
}

a	: Tuple      =				{
    "openai/clip-vit-base-patch32": 77,
}
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				Dict							 =			VOCAB_FILES_NAMES
      a						:				int							 =			PRETRAINED_VOCAB_FILES_MAP
      a						:				List[str]							 =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						:				Optional[Any]							 =			['input_ids', 'attention_mask']
      a						:				List[str]							 =			CLIPTokenizer
      def __init__(   self      :    str	,   __lowercase      :    Any=None	,   __lowercase      :    Optional[int]=None	,   __lowercase      :    int=None	,   __lowercase      :    Any="<|endoftext|>"	,   __lowercase      :    Dict="<|startoftext|>"	,   __lowercase      :    Optional[int]="<|endoftext|>"	,   __lowercase      :    Any="<|endoftext|>"	,   **__lowercase      :    Optional[int]	,   )							->   Optional[int]:
        super().__init__(
            __lowercase	,   __lowercase	,   tokenizer_file=__lowercase	,   unk_token=__lowercase	,   bos_token=__lowercase	,   eos_token=__lowercase	,   pad_token=__lowercase	,   **__lowercase	,   )

        if not isinstance(self.backend_tokenizer.pre_tokenizer	,   pre_tokenizers.Sequence      ):
          raise ValueError(
              """The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"""
              """ heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"""
              """ to be compatible with this version.The easiest way to do so is"""
              """ `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"""
              """ to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"""
              """ transformers."""      )

        self._wrap_decode_method_backend_tokenizer()
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   Tuple:
        __UpperCAmelCase							:			Optional[int]     =						self.backend_tokenizer.decode

        def new_decode_method(*__lowercase      :    Optional[Any]	,   **__lowercase      :    Optional[Any]      ):
          __UpperCAmelCase							:			List[str]     =						orig_decode_method(*__lowercase	,   **__lowercase      )
          __UpperCAmelCase							:			Union[str, Any]     =						text.replace(self.backend_tokenizer.model.end_of_word_suffix	,   """ """      ).strip()
          return text

        __UpperCAmelCase							:			Optional[Any]     =						new_decode_method
      def 			UpperCAmelCase   (   self      :    int	,   __lowercase      :    List[int]	,   __lowercase      :    Optional[List[int]] = None      )							->   List[int]:
        __UpperCAmelCase							:			Optional[Any]     =						[self.bos_token_id]
        __UpperCAmelCase							:			Dict     =						[self.eos_token_id]

        if token_ids_a is None:
          return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
      def 			UpperCAmelCase   (   self      :    Optional[Any]	,   __lowercase      :    List[int]	,   __lowercase      :    Optional[List[int]] = None      )							->   List[int]:
        __UpperCAmelCase							:			Dict     =						[self.bos_token_id]
        __UpperCAmelCase							:			Union[str, Any]     =						[self.eos_token_id]

        if token_ids_a is None:
          return len(bos_token + token_ids_a + eos_token      ) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token      ) * [0]




      def 			UpperCAmelCase   (   self      :    int	,   __lowercase      :    str	,   __lowercase      :    Optional[str] = None      )							->   Tuple[str]:
        __UpperCAmelCase							:			Any     =						self._tokenizer.model.save(__lowercase	,   name=__lowercase      )
        return tuple(__lowercase      )










from typing import TYPE_CHECKING

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


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


