





'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


A__							:  int    =logging.get_logger(__name__)

A__							:  Any    ={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__							:  Union[str, Any]    ={
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

A__							:  Tuple    ={
    '''squeezebert/squeezebert-uncased''': 5_12,
    '''squeezebert/squeezebert-mnli''': 5_12,
    '''squeezebert/squeezebert-mnli-headless''': 5_12,
}


A__							:  List[Any]    ={
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Any			=  VOCAB_FILES_NAMES
      _lowercase:     Optional[int]			=  PRETRAINED_VOCAB_FILES_MAP
      _lowercase:     Tuple			=  PRETRAINED_INIT_CONFIGURATION
      _lowercase:     Any			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:     List[Any]			=  SqueezeBertTokenizer




      def __init__(     self     :					Tuple     ,     __snake_case     :					Optional[Any]=None     ,     __snake_case     :					Tuple=None     ,     __snake_case     :					Any=True     ,     __snake_case     :					Union[str, Any]="[UNK]"     ,     __snake_case     :					Optional[int]="[SEP]"     ,     __snake_case     :					List[Any]="[PAD]"     ,     __snake_case     :					Any="[CLS]"     ,     __snake_case     :					Optional[int]="[MASK]"     ,     __snake_case     :					Optional[Any]=True     ,     __snake_case     :					List[Any]=None     ,     **__snake_case     :					Any     ,     )							-> Dict:
            super().__init__(
                __snake_case     ,     tokenizer_file=__snake_case     ,     do_lower_case=__snake_case     ,     unk_token=__snake_case     ,     sep_token=__snake_case     ,     pad_token=__snake_case     ,     cls_token=__snake_case     ,     mask_token=__snake_case     ,     tokenize_chinese_chars=__snake_case     ,     strip_accents=__snake_case     ,     **__snake_case     ,     )

            _lowerCAmelCase					=				json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
            if (
                normalizer_state.get("""lowercase"""     ,     __snake_case    ) != do_lower_case
                or normalizer_state.get("""strip_accents"""     ,     __snake_case    ) != strip_accents
                or normalizer_state.get("""handle_chinese_chars"""     ,     __snake_case    ) != tokenize_chinese_chars
            ):
                  _lowerCAmelCase					=				getattr(__snake_case     ,     normalizer_state.pop("""type"""    )    )
                  _lowerCAmelCase					=				do_lower_case
                  _lowerCAmelCase					=				strip_accents
                  _lowerCAmelCase					=				tokenize_chinese_chars
                  _lowerCAmelCase					=				normalizer_class(**__snake_case    )

            _lowerCAmelCase					=				do_lower_case




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Tuple     ,     __snake_case     :					Optional[Any]=None    )							-> List[str]:
            _lowerCAmelCase					=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

            if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

            return output




      def 							lowercase__       (     self     :					Any     ,     __snake_case     :					List[int]     ,     __snake_case     :					Optional[List[int]] = None    )							-> List[int]:
            _lowerCAmelCase					=				[self.sep_token_id]
            _lowerCAmelCase					=				[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    ) * [0] + len(token_ids_a + sep    ) * [1]




      def 							lowercase__       (     self     :					int     ,     __snake_case     :					str     ,     __snake_case     :					Optional[str] = None    )							-> Tuple[str]:
            _lowerCAmelCase					=				self._tokenizer.model.save(__snake_case     ,     name=__snake_case    )
            return tuple(__snake_case    )













'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

A__							:  Tuple    =logging.get_logger(__name__)

A__							:  Any    ={
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Tuple			=  '''imagegpt'''
      _lowercase:     Optional[int]			=  ['''past_key_values''']
      _lowercase:     str			=  {
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(     self     :					Optional[int]     ,     __snake_case     :					Optional[Any]=5_12 + 1     ,     __snake_case     :					Union[str, Any]=32 * 32     ,     __snake_case     :					List[Any]=5_12     ,     __snake_case     :					Any=24     ,     __snake_case     :					Optional[Any]=8     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					str="quick_gelu"     ,     __snake_case     :					List[Any]=0.1     ,     __snake_case     :					str=0.1     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					Tuple=1E-5     ,     __snake_case     :					Optional[int]=0.02     ,     __snake_case     :					str=True     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					Dict=False     ,     __snake_case     :					Union[str, Any]=False     ,     **__snake_case     :					Union[str, Any]     ,     )							-> List[Any]:
            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				n_positions
            _lowerCAmelCase					=				n_embd
            _lowerCAmelCase					=				n_layer
            _lowerCAmelCase					=				n_head
            _lowerCAmelCase					=				n_inner
            _lowerCAmelCase					=				activation_function
            _lowerCAmelCase					=				resid_pdrop
            _lowerCAmelCase					=				embd_pdrop
            _lowerCAmelCase					=				attn_pdrop
            _lowerCAmelCase					=				layer_norm_epsilon
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				scale_attn_weights
            _lowerCAmelCase					=				use_cache
            _lowerCAmelCase					=				scale_attn_by_inverse_layer_idx
            _lowerCAmelCase					=				reorder_and_upcast_attn
            _lowerCAmelCase					=				tie_word_embeddings

            super().__init__(tie_word_embeddings=__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):




      @property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ]    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					"FeatureExtractionMixin"     ,     __snake_case     :					int = 1     ,     __snake_case     :					int = -1     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional["TensorType"] = None     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 32     ,     __snake_case     :					int = 32     ,     )							-> Mapping[str, Any]:
            _lowerCAmelCase					=				self._generate_dummy_images(__snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				dict(preprocessor(images=__snake_case     ,     return_tensors=__snake_case    )    )

            return inputs






