


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE      :			str      				=   {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

_SCREAMING_SNAKE_CASE      :			Tuple      				=   {
    "roberta-base": 5_12,
    "roberta-large": 5_12,
    "roberta-large-mnli": 5_12,
    "distilroberta-base": 5_12,
    "roberta-base-openai-detector": 5_12,
    "roberta-large-openai-detector": 5_12,
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		VOCAB_FILES_NAMES
    __magic_name__        =		PRETRAINED_VOCAB_FILES_MAP
    __magic_name__        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__        =		['input_ids', 'attention_mask']
    __magic_name__        =		RobertaTokenizer
    def __init__(	self      , __snake_case=None      , __snake_case=None      , __snake_case=None      , __snake_case="replace"      , __snake_case="<s>"      , __snake_case="</s>"      , __snake_case="</s>"      , __snake_case="<s>"      , __snake_case="<unk>"      , __snake_case="<pad>"      , __snake_case="<mask>"      , __snake_case=False      , __snake_case=True      , **__snake_case      , ):
         super().__init__(
             __snake_case      , __snake_case      , tokenizer_file=__snake_case      , errors=__snake_case      , bos_token=__snake_case      , eos_token=__snake_case      , sep_token=__snake_case      , cls_token=__snake_case      , unk_token=__snake_case      , pad_token=__snake_case      , mask_token=__snake_case      , add_prefix_space=__snake_case      , trim_offsets=__snake_case      , **__snake_case      , )

         snake_case					=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
         if pre_tok_state.get('''add_prefix_space'''      , __snake_case   ) != add_prefix_space:
              snake_case					=    getattr(__snake_case      , pre_tok_state.pop('''type'''   )   )
              snake_case					=    add_prefix_space
              snake_case					=    pre_tok_class(**__snake_case   )

         snake_case					=    add_prefix_space

         snake_case					=    '''post_processor'''
         snake_case					=    getattr(self.backend_tokenizer      , __snake_case      , __snake_case   )
         if tokenizer_component_instance:
              snake_case					=    json.loads(tokenizer_component_instance.__getstate__()   )

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                   snake_case					=    tuple(state['''sep''']   )
              if "cls" in state:
                   snake_case					=    tuple(state['''cls''']   )

              snake_case					=    False

              if state.get('''add_prefix_space'''      , __snake_case   ) != add_prefix_space:
                   snake_case					=    add_prefix_space
                   snake_case					=    True

              if state.get('''trim_offsets'''      , __snake_case   ) != trim_offsets:
                   snake_case					=    trim_offsets
                   snake_case					=    True

              if changes_to_apply:
                   snake_case					=    getattr(__snake_case      , state.pop('''type'''   )   )
                   snake_case					=    component_class(**__snake_case   )
                   setattr(self.backend_tokenizer      , __snake_case      , __snake_case   )
    @property
    def 		a_      (	self   ):
         if self._mask_token is None:
              if self.verbose:
                   logger.error('''Using mask_token, but it is not set yet.'''   )
              return None
         return str(self._mask_token   )
    @mask_token.setter
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    AddedToken(__snake_case      , lstrip=__snake_case      , rstrip=__snake_case   ) if isinstance(__snake_case      , __snake_case   ) else value
         snake_case					=    value
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         snake_case					=    kwargs.get('''is_split_into_words'''      , __snake_case   )
         assert self.add_prefix_space or not is_split_into_words, (
             F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__snake_case      , **__snake_case   )
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         snake_case					=    kwargs.get('''is_split_into_words'''      , __snake_case   )

         assert self.add_prefix_space or not is_split_into_words, (
             F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__snake_case      , **__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case = None   ):
         snake_case					=    self._tokenizer.model.save(__snake_case      , name=__snake_case   )
         return tuple(__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case=None   ):
         snake_case					=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




    def 		a_      (	self      , __snake_case      , __snake_case = None   ):
         snake_case					=    [self.sep_token_id]
         snake_case					=    [self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep   ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]








from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


_SCREAMING_SNAKE_CASE      :			List[str]      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'efficientnet'
    def __init__(	self      , __snake_case = 3      , __snake_case = 6_0_0      , __snake_case = 2.0      , __snake_case = 3.1      , __snake_case = 8      , __snake_case = [3, 3, 5, 3, 5, 5, 3]      , __snake_case = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]      , __snake_case = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]      , __snake_case = []      , __snake_case = [1, 2, 2, 2, 1, 2, 1]      , __snake_case = [1, 2, 2, 3, 3, 4, 1]      , __snake_case = [1, 6, 6, 6, 6, 6, 6]      , __snake_case = 0.25      , __snake_case = "swish"      , __snake_case = 2_5_6_0      , __snake_case = "mean"      , __snake_case = 0.02      , __snake_case = 0.001      , __snake_case = 0.99      , __snake_case = 0.5      , __snake_case = 0.2      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    num_channels
         snake_case					=    image_size
         snake_case					=    width_coefficient
         snake_case					=    depth_coefficient
         snake_case					=    depth_divisor
         snake_case					=    kernel_sizes
         snake_case					=    in_channels
         snake_case					=    out_channels
         snake_case					=    depthwise_padding
         snake_case					=    strides
         snake_case					=    num_block_repeats
         snake_case					=    expand_ratios
         snake_case					=    squeeze_expansion_ratio
         snake_case					=    hidden_act
         snake_case					=    hidden_dim
         snake_case					=    pooling_type
         snake_case					=    initializer_range
         snake_case					=    batch_norm_eps
         snake_case					=    batch_norm_momentum
         snake_case					=    dropout_rate
         snake_case					=    drop_connect_rate
         snake_case					=    sum(__snake_case   ) * 4






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]   )




    @property
    def 		a_      (	self   ):
         return 1E-5




