
'''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": 7_7,
}



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''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	:			Dict						,			lowerCAmelCase_	:			Any=None						,			lowerCAmelCase_	:			List[Any]=None						,			lowerCAmelCase_	:			Dict=None						,			lowerCAmelCase_	:			Union[str, Any]="<|endoftext|>"						,			lowerCAmelCase_	:			Union[str, Any]="<|startoftext|>"						,			lowerCAmelCase_	:			Optional[int]="<|endoftext|>"						,			lowerCAmelCase_	:			int="<|endoftext|>"						,			**lowerCAmelCase_	:			Union[str, Any]						,			) ->							Tuple:
        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	:			Optional[int]     ) ->							Optional[Any]:
        snake_case_    							=				self.backend_tokenizer.decode

        def new_decode_method(*lowerCAmelCase_	:			Any						,			**lowerCAmelCase_	:			Any     ):
              snake_case_    							=				orig_decode_method(*lowerCAmelCase_						,			**lowerCAmelCase_     )
              snake_case_    							=				text.replace(self.backend_tokenizer.model.end_of_word_suffix						,			" "     ).strip()
              return text

        snake_case_    							=				new_decode_method

  def 		_lowerCamelCase						(self	:			List[Any]						,			lowerCAmelCase_	:			List[int]						,			lowerCAmelCase_	:			Optional[List[int]] = None     ) ->							List[int]:
        snake_case_    							=				[self.bos_token_id]
        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 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			List[int]						,			lowerCAmelCase_	:			Optional[List[int]] = None     ) ->							List[int]:
        snake_case_    							=				[self.bos_token_id]
        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 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Optional[str] = None     ) ->							Tuple[str]:
        snake_case_    							=				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_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["LlamaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			[
			    "OpenLlamaForCausalLM",
			    "OpenLlamaModel",
			    "OpenLlamaPreTrainedModel",
			    "OpenLlamaForSequenceClassification",
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Dict      	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




