


"""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


__a			 =   logging.get_logger(__name__)

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

__a			 =   {
    "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			 =   {
    "openai/clip-vit-base-patch32": 77,
}
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""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 :     Optional[int]		, lowerCamelCase_ :     str=None		, lowerCamelCase_ :     Tuple=None		, lowerCamelCase_ :     Tuple=None		, lowerCamelCase_ :     Dict="<|endoftext|>"		, lowerCamelCase_ :     List[str]="<|startoftext|>"		, lowerCamelCase_ :     Optional[int]="<|endoftext|>"		, lowerCamelCase_ :     int="<|endoftext|>"		, **lowerCamelCase_ :     Union[str, Any]		, ):
													super().__init__(
													    lowerCamelCase_		, lowerCamelCase_		, tokenizer_file=lowerCamelCase_		, unk_token=lowerCamelCase_		, bos_token=lowerCamelCase_		, eos_token=lowerCamelCase_		, pad_token=lowerCamelCase_		, **lowerCamelCase_		, )

													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 					lowerCAmelCase     (						self :     Dict				):
													UpperCAmelCase__		=    self.backend_tokenizer.decode

													def new_decode_method(*lowerCamelCase_ :     Union[str, Any]		, **lowerCamelCase_ :     Optional[Any]				):
																			UpperCAmelCase__		=    orig_decode_method(*lowerCamelCase_		, **lowerCamelCase_				)
																			UpperCAmelCase__		=    text.replace(self.backend_tokenizer.model.end_of_word_suffix		, ''' '''				).strip()
																			return text

													UpperCAmelCase__		=    new_decode_method






							def 					lowerCAmelCase     (						self :     Optional[Any]		, lowerCamelCase_ :     List[int]		, lowerCamelCase_ :     Optional[List[int]] = None				):
													UpperCAmelCase__		=    [self.bos_token_id]
													UpperCAmelCase__		=    [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 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     List[int]		, lowerCamelCase_ :     Optional[List[int]] = None				):
													UpperCAmelCase__		=    [self.bos_token_id]
													UpperCAmelCase__		=    [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 					lowerCAmelCase     (						self :     Dict		, lowerCamelCase_ :     str		, lowerCamelCase_ :     Optional[str] = None				):
													UpperCAmelCase__		=    self._tokenizer.model.save(lowerCamelCase_		, name=lowerCamelCase_				)
													return tuple(lowerCamelCase_				)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a			 =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CamembertForCausalLM",
						    "CamembertForMaskedLM",
						    "CamembertForMultipleChoice",
						    "CamembertForQuestionAnswering",
						    "CamembertForSequenceClassification",
						    "CamembertForTokenClassification",
						    "CamembertModel",
						    "CamembertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFCamembertForCausalLM",
						    "TFCamembertForMaskedLM",
						    "TFCamembertForMultipleChoice",
						    "TFCamembertForQuestionAnswering",
						    "TFCamembertForSequenceClassification",
						    "TFCamembertForTokenClassification",
						    "TFCamembertModel",
						    "TFCamembertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

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