

import os
from typing import List, Optional, Union

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


a__												=						logging.get_logger(__name__)

a__												=						{'''vocab_file''': '''vocab.txt'''}

a__												=						{
    '''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''',
    },
}

a__												=						{
    '''facebook/esm2_t6_8M_UR50D''': 1024,
    '''facebook/esm2_t12_35M_UR50D''': 1024,
}



def   __UpperCAmelCase					(      __a :     Tuple      )     ->	Tuple:

					"""simple docstring"""

					with open(__a						,'''r'''      ) as f:
										_a      :       Tuple   =						f.read().splitlines()
										return [l.strip() for l in lines]







class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       VOCAB_FILES_NAMES
							UpperCAmelCase__			:		Union[str, Any]             =       PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__			:		Optional[Any]             =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__			:		Dict             =       ["input_ids", "attention_mask"]


							def __init__(     self			,				_a			,				_a="<unk>"			,				_a="<cls>"			,				_a="<pad>"			,				_a="<mask>"			,				_a="<eos>"			,				**_a			,				)       ->       Tuple:
												super().__init__(**_a			)
												_a      :       List[str]   =						load_vocab_file(_a			)
												_a      :       Optional[Any]   =						dict(enumerate(self.all_tokens			)			)
												_a      :       List[Any]   =						{tok: ind for ind, tok in enumerate(self.all_tokens			)}
												_a      :       str   =						unk_token
												_a      :       Any   =						cls_token
												_a      :       int   =						pad_token
												_a      :       Optional[Any]   =						mask_token
												_a      :       Optional[int]   =						eos_token
												_a      :       List[Any]   =						self.all_tokens
												self._create_trie(self.unique_no_split_tokens			)


							def 							__lowercase							(     self			,				_a			)       ->       str:
												return self._id_to_token.get(_a			,				self.unk_token			)


							def 							__lowercase							(     self			,				_a			)       ->       int:
												return self._token_to_id.get(_a			,				self._token_to_id.get(self.unk_token			)			)


							def 							__lowercase							(     self			,				_a			,				**_a			)       ->       List[Any]:
												return text.split()


							def 							__lowercase							(     self			,				_a=False			)       ->       List[Any]:
												return len(self._id_to_token			)


							def 							__lowercase							(     self			)       ->       Any:
												return {token: i for i, token in enumerate(self.all_tokens			)}


							def 							__lowercase							(     self			,				_a			)       ->       int:
												return self._token_to_id.get(_a			,				self._token_to_id.get(self.unk_token			)			)


							def 							__lowercase							(     self			,				_a			)       ->       str:
												return self._id_to_token.get(_a			,				self.unk_token			)


							def 							__lowercase							(     self			,				_a			,				_a = None			)       ->       List[int]:
												_a      :       Tuple   =						[self.cls_token_id]
												_a      :       List[str]   =						[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 							__lowercase							(     self			,				_a			,				_a = None			,				_a = False			)       ->       List[int]:
												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]
												_a      :       List[Any]   =						[1] + ([0] * len(_a			)) + [1]
												if token_ids_a is not None:
																	mask += [0] * len(_a			) + [1]
												return mask


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       str:
												_a      :       Optional[Any]   =						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 							__lowercase							(     self			)       ->       int:
												return self.get_vocab_size(with_added_tokens=_a			)




							def 							__lowercase							(     self			,				_a			,				_a = False			)       ->       int:
												return super()._add_tokens(_a			,				special_tokens=_a			)






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       "mobilenet_v1"


							def __init__(     self			,				_a=3			,				_a=2_2_4			,				_a=1.0			,				_a=8			,				_a="relu6"			,				_a=True			,				_a=0.999			,				_a=0.02			,				_a=0.001			,				**_a			,				)       ->       List[Any]:
												super().__init__(**_a			)

												if depth_multiplier <= 0:
																	raise ValueError('''depth_multiplier must be greater than zero.'''			)

												_a      :       Tuple   =						num_channels
												_a      :       str   =						image_size
												_a      :       Tuple   =						depth_multiplier
												_a      :       Any   =						min_depth
												_a      :       int   =						hidden_act
												_a      :       Optional[Any]   =						tf_padding
												_a      :       str   =						classifier_dropout_prob
												_a      :       Optional[int]   =						initializer_range
												_a      :       Any   =						layer_norm_eps



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       version.parse("1.11"  )


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												return OrderedDict([('''pixel_values''', {0: '''batch'''})]			)


							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																	return OrderedDict([('''logits''', {0: '''batch'''})]			)
												else:
																	return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]			)




							@property
							def 							__lowercase							(     self			)       ->       float:
												return 1e-4



