
import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{"vocab_file": "vocab.txt"}

lowerCamelCase__	=							{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

lowerCamelCase__	=							{
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		with open(lowerCAmelCase						,  """r""" ) as f:
				_UpperCamelCase						    =	f.read().splitlines()
				return [l.strip() for l in lines]







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: str	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: Tuple	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: Any	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: List[Any]	=		["input_ids", "attention_mask"]

						def __init__(			self       ,							a       ,							a="<unk>"       ,							a="<cls>"       ,							a="<pad>"       ,							a="<mask>"       ,							a="<eos>"       ,							**a       ,							)					->      Optional[int]:


								'''simple docstring'''

								super().__init__(**a				)
								_UpperCamelCase						    =	load_vocab_file(a				)
								_UpperCamelCase						    =	dict(enumerate(self.all_tokens				)				)
								_UpperCamelCase						    =	{tok: ind for ind, tok in enumerate(self.all_tokens				)}
								_UpperCamelCase						    =	unk_token
								_UpperCamelCase						    =	cls_token
								_UpperCamelCase						    =	pad_token
								_UpperCamelCase						    =	mask_token
								_UpperCamelCase						    =	eos_token
								_UpperCamelCase						    =	self.all_tokens
								self._create_trie(self.unique_no_split_tokens				)

						def  A_				(			self       ,							a				)					->      str:


								'''simple docstring'''

								return self._id_to_token.get(a       ,							self.unk_token				)

						def  A_				(			self       ,							a				)					->      int:


								'''simple docstring'''

								return self._token_to_id.get(a       ,							self._token_to_id.get(self.unk_token				)				)

						def  A_				(			self       ,							a       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

								return text.split()

						def  A_				(			self       ,							a=False				)					->      Union[str, Any]:


								'''simple docstring'''

								return len(self._id_to_token				)

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								return {token: i for i, token in enumerate(self.all_tokens				)}

						def  A_				(			self       ,							a				)					->      int:


								'''simple docstring'''

								return self._token_to_id.get(a       ,							self._token_to_id.get(self.unk_token				)				)

						def  A_				(			self       ,							a				)					->      str:


								'''simple docstring'''

								return self._id_to_token.get(a       ,							self.unk_token				)

						def  A_				(			self       ,							a       ,							a = None				)					->      List[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.cls_token_id]
								_UpperCamelCase						    =	[self.eos_token_id]  # No sep token in ESM vocabulary
								if token_ids_a is None:
										if self.eos_token_id is None:
												return cls + token_ids_a
										else:
												return cls + token_ids_a + sep
								elif self.eos_token_id is None:
										raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""				)
								return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

						def  A_				(			self       ,							a       ,							a = None       ,							a = False				)					->      List[int]:


								'''simple docstring'''

								if already_has_special_tokens:
										if token_ids_a is not None:
												raise ValueError(
												    """You should not supply a second sequence if the provided sequence of """
												    """ids is already formatted with special tokens for the model."""				)

										return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
								_UpperCamelCase						    =	[1] + ([0] * len(a				)) + [1]
								if token_ids_a is not None:
										mask += [0] * len(a				) + [1]
								return mask

						def  A_				(			self       ,							a       ,							a				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	os.path.join(a       ,							(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""				)
								with open(a       ,							"""w"""				) as f:
										f.write("""\n""".join(self.all_tokens				)				)
								return (vocab_file,)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.get_vocab_size(with_added_tokens=a				)







						def  A_				(			self       ,							a       ,							a = False				)					->      int:


								'''simple docstring'''

								return super()._add_tokens(a       ,							special_tokens=a				)





from typing import TYPE_CHECKING

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


lowerCamelCase__	=							{"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							["YolosFeatureExtractor"]
		lowerCamelCase__	=							["YolosImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__	=							[
		    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "YolosForObjectDetection",
		    "YolosModel",
		    "YolosPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_yolos import YolosFeatureExtractor
				from .image_processing_yolos import YolosImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_yolos import (
				    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    YolosForObjectDetection,
				    YolosModel,
				    YolosPreTrainedModel,
				)


else:
		import sys

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



