


from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  str				     =  """flaubert"""
       lowerCamelCase__:  str				     =  {
           """hidden_size""": """emb_dim""",
           """num_attention_heads""": """n_heads""",
           """num_hidden_layers""": """n_layers""",
           """n_words""": """vocab_size""",  # For backward compatibility
       }

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  str=False   ,				lowerCamelCase_   :  List[Any]=0.0   ,				lowerCamelCase_   :  int=3_0_1_4_5   ,				lowerCamelCase_   :  str=2_0_4_8   ,				lowerCamelCase_   :  Dict=1_2   ,				lowerCamelCase_   :  Any=1_6   ,				lowerCamelCase_   :  Optional[int]=0.1   ,				lowerCamelCase_   :  Tuple=0.1   ,				lowerCamelCase_   :  List[str]=True   ,				lowerCamelCase_   :  str=False   ,				lowerCamelCase_   :  int=False   ,				lowerCamelCase_   :  Optional[int]=False   ,				lowerCamelCase_   :  Tuple=1   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  str=5_1_2   ,				lowerCamelCase_   :  Optional[Any]=2_0_4_8**-0.5   ,				lowerCamelCase_   :  Optional[int]=1E-12   ,				lowerCamelCase_   :  int=0.0_2   ,				lowerCamelCase_   :  int=0   ,				lowerCamelCase_   :  Optional[int]=1   ,				lowerCamelCase_   :  Dict=2   ,				lowerCamelCase_   :  int=3   ,				lowerCamelCase_   :  str=5   ,				lowerCamelCase_   :  Optional[int]=True   ,				lowerCamelCase_   :  Union[str, Any]="first"   ,				lowerCamelCase_   :  int=True   ,				lowerCamelCase_   :  Dict=None   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  str=0.1   ,				lowerCamelCase_   :  str=5   ,				lowerCamelCase_   :  Any=5   ,				lowerCamelCase_   :  int=0   ,				lowerCamelCase_   :  Any=0   ,				lowerCamelCase_   :  Tuple=2   ,				lowerCamelCase_   :  Optional[Any]=0   ,				**lowerCamelCase_   :  int   ,				):
            a_     :  Dict				      =     pre_norm
            a_     :  List[str]				      =     layerdrop
            a_     :  Any				      =     vocab_size
            a_     :  Tuple				      =     emb_dim
            a_     :  str				      =     n_layers
            a_     :  Tuple				      =     n_heads
            a_     :  List[Any]				      =     dropout
            a_     :  str				      =     attention_dropout
            a_     :  Dict				      =     gelu_activation
            a_     :  Optional[Any]				      =     sinusoidal_embeddings
            a_     :  List[str]				      =     causal
            a_     :  Union[str, Any]				      =     asm
            a_     :  int				      =     n_langs
            a_     :  int				      =     use_lang_emb
            a_     :  int				      =     layer_norm_eps
            a_     :  Dict				      =     bos_index
            a_     :  int				      =     eos_index
            a_     :  Dict				      =     pad_index
            a_     :  Dict				      =     unk_index
            a_     :  Tuple				      =     mask_index
            a_     :  Tuple				      =     is_encoder
            a_     :  Dict				      =     max_position_embeddings
            a_     :  Union[str, Any]				      =     embed_init_std
            a_     :  List[str]				      =     init_std
            a_     :  Union[str, Any]				      =     summary_type
            a_     :  Tuple				      =     summary_use_proj
            a_     :  int				      =     summary_activation
            a_     :  Union[str, Any]				      =     summary_proj_to_labels
            a_     :  List[str]				      =     summary_first_dropout
            a_     :  Optional[int]				      =     start_n_top
            a_     :  List[Any]				      =     end_n_top
            a_     :  Dict				      =     mask_token_id
            a_     :  List[Any]				      =     lang_id

            if "n_words" in kwargs:
                 a_     :  Any				      =     kwargs["""n_words"""]

            super().__init__(pad_token_id=lowerCamelCase_   ,				bos_token_id=lowerCamelCase_   ,				**lowerCamelCase_     )


class 	a__						(    lowerCAmelCase_     ):

       @property
       def     UpperCAmelCase( self   :  Tuple     ):
            if self.task == "multiple-choice":
                 a_     :  Optional[Any]				      =     {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                 a_     :  List[Any]				      =     {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                ]     )






"""simple docstring"""




import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class      __UpperCamelCase					(  unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       EsmTokenizer


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().setUp()
							A     =   tempfile.mkdtemp()
							# fmt: off
							A     =   ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
							# fmt: on
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["vocab_file"])
							with open(self.vocab_file	,	"w"	,	encoding="utf-8") as vocab_writer:
												vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	**__SCREAMING_SNAKE_CASE					:						str):
							return [self.get_tokenizer(**__SCREAMING_SNAKE_CASE)]


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[Any]):
							return self.tokenizer_class.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   tokenizer.tokenize("LAGVS")
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	["L", "A", "G", "V", "S"])
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)	,	[4, 5, 6, 7, 8])


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   "LAGVS"
							self.assertListEqual(tokenizer.encode(__SCREAMING_SNAKE_CASE)	,	[0, 4, 5, 6, 7, 8, 2])


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   ["LAGVS", "WCB"]
							A     =   tokenizer(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE)["input_ids"]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]])


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   ["LAGVS", "WCB"]
							A     =   tokenizer(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE)["input_ids"]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]])


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE)
							for tokenizer in tokenizers:
												with self.subTest(F"""{tokenizer.__class__.__name__}"""):
																	A     =   "<unk>"
																	A     =   "<mask>"

																	A     =   tokenizer.tokenize(__SCREAMING_SNAKE_CASE)
																	A     =   tokenizer.tokenize(__SCREAMING_SNAKE_CASE)

																	self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,	1)
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,	1)
																	self.assertEqual(token_a[0]	,	__SCREAMING_SNAKE_CASE)
																	self.assertEqual(token_a[0]	,	__SCREAMING_SNAKE_CASE)





