





import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
  from .testing_utils import (
      floats_tensor,
      load_hf_numpy,
      load_image,
      load_numpy,
      load_pt,
      nightly,
      parse_flag_from_env,
      print_tensor_test,
      require_torch_a,
      require_torch_gpu,
      skip_mps,
      slow,
      torch_all_close,
      torch_device,
  )
  from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_snake_case				= get_logger(__name__)


def _A   (    __magic_name__       ):
   if version.parse(lowerCAmelCase__       ) < version.parse(lowerCAmelCase__       ):
      if "dev" in min_version:
         lowercase__    =  (
             "This example requires a source install from HuggingFace diffusers (see "
             "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
         )
      else:
         lowercase__    =  f'''This example requires a minimum version of {min_version},'''
      error_message += f''' but the version found is {__version__}.\n'''
      raise ImportError(lowerCAmelCase__       )







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


lowerCAmelCase__							= logging.get_logger(__name__)

lowerCAmelCase__							= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__							= {
    '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'
        ),
    },
}

lowerCAmelCase__							= {
    'openai/clip-vit-base-patch32': 77,
}
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					VOCAB_FILES_NAMES
 UpperCAmelCase_	 =					PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_	 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_	 =					['input_ids', 'attention_mask']
 UpperCAmelCase_	 =					CLIPTokenizer




 def __init__(			self					:  Optional[int]			,    lowercase__					:  Optional[int]=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  Tuple=None			,    lowercase__					:  str="<|endoftext|>"			,    lowercase__					:  List[str]="<|startoftext|>"			,    lowercase__					:  Union[str, Any]="<|endoftext|>"			,    lowercase__					:  Any="<|endoftext|>"			,    **lowercase__					:  Any			,    ):







        '''simple docstring'''



        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 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.backend_tokenizer.decode

        def new_decode_method(*lowercase__					:  str			,    **lowercase__					:  int):
               lowerCAmelCase__     	=					orig_decode_method(*lowercase__			,    **lowercase__)
               lowerCAmelCase__     	=					text.replace(self.backend_tokenizer.model.end_of_word_suffix			,    ' ').strip()
               return text

        lowerCAmelCase__     	=					new_decode_method




 def 		__snake_case						(			self					:  Tuple			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[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 		__snake_case						(			self					:  Any			,    lowercase__					:  List[int]			,    lowercase__					:  Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					[self.bos_token_id]
        lowerCAmelCase__     	=					[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 		__snake_case						(			self					:  Dict			,    lowercase__					:  str			,    lowercase__					:  Optional[str] = None):







        '''simple docstring'''



        lowerCAmelCase__     	=					self._tokenizer.model.save(lowercase__			,    name=lowercase__)
        return tuple(lowercase__)
