
'''simple docstring'''



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


UpperCamelCase__ :						Any    							=					logging.get_logger(__name__)

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

UpperCamelCase__ :						Optional[int]    							=					{
    "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"
        ),
    },
}

UpperCamelCase__ :						Optional[int]    							=					{
    "openai/clip-vit-base-patch32": 77,
}

class 				_a			(_lowerCamelCase):





       """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 =					CLIPTokenizer






       def __init__(							self    ,							A__=None    ,							A__=None    ,							A__=None    ,							A__="<|endoftext|>"    ,							A__="<|startoftext|>"    ,							A__="<|endoftext|>"    ,							A__="<|endoftext|>"    ,							**A__    ,							)      ->     int:
          super().__init__(
              A__    ,							A__    ,							tokenizer_file=A__    ,							unk_token=A__    ,							bos_token=A__    ,							eos_token=A__    ,							pad_token=A__    ,							**A__    ,							)

          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 )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self.backend_tokenizer.decode

          def new_decode_method(*A__    ,							**A__ ):
             _SCREAMING_SNAKE_CASE       	=			orig_decode_method(*A__    ,							**A__ )
             _SCREAMING_SNAKE_CASE       	=			text.replace(self.backend_tokenizer.model.end_of_word_suffix    ,							""" """ ).strip()
             return text

          _SCREAMING_SNAKE_CASE       	=			new_decode_method






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = None )      ->     List[int]:
          _SCREAMING_SNAKE_CASE       	=			[self.bos_token_id]
          _SCREAMING_SNAKE_CASE       	=			[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    ,							A__    ,							A__ = None )      ->     List[int]:
          _SCREAMING_SNAKE_CASE       	=			[self.bos_token_id]
          _SCREAMING_SNAKE_CASE       	=			[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    ,							A__    ,							A__ = None )      ->     Tuple[str]:
          _SCREAMING_SNAKE_CASE       	=			self._tokenizer.model.save(A__    ,							name=A__ )
          return tuple(A__ )








'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCamelCase__ :						List[str]    							=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						Optional[Any]    							=					[
         "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTJForCausalLM",
         "GPTJForQuestionAnswering",
         "GPTJForSequenceClassification",
         "GPTJModel",
         "GPTJPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[str]    							=					[
         "TFGPTJForCausalLM",
         "TFGPTJForQuestionAnswering",
         "TFGPTJForSequenceClassification",
         "TFGPTJModel",
         "TFGPTJPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__ :						List[Any]    							=					[
         "FlaxGPTJForCausalLM",
         "FlaxGPTJModel",
         "FlaxGPTJPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

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






