



'''simple docstring'''




import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = CTRLTokenizer
	__SCREAMING_SNAKE_CASE       :		List[str]		      = False
	__SCREAMING_SNAKE_CASE       :		int		      = False


	def    _a     (self							):
				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				A_     :						Any     =					["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
				A_     :						Optional[int]     =					dict(zip(lowercase	,				range(len(lowercase							)							)							)							)
				A_     :						Optional[Any]     =					["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
				A_     :						List[str]     =					{"""unk_token""": """<unk>"""}

				A_     :						str     =					os.path.join(self.tmpdirname	,				VOCAB_FILES_NAMES["""vocab_file"""]							)
				A_     :						Any     =					os.path.join(self.tmpdirname	,				VOCAB_FILES_NAMES["""merges_file"""]							)
				with open(self.vocab_file	,				"""w"""	,				encoding="""utf-8"""							) as fp:
							fp.write(json.dumps(lowercase							) + """\n"""							)
				with open(self.merges_file	,				"""w"""	,				encoding="""utf-8"""							) as fp:
							fp.write("""\n""".join(lowercase							)							)


	def    _a     (self	,				**lowercase							):
				kwargs.update(self.special_tokens_map							)
				return CTRLTokenizer.from_pretrained(self.tmpdirname	,				**lowercase							)


	def    _a     (self	,				lowercase							):
				A_     :						Dict     =					"""adapt react readapt apt"""
				A_     :						Optional[int]     =					"""adapt react readapt apt"""
				return input_text, output_text


	def    _a     (self							):
				A_     :						Optional[Any]     =					CTRLTokenizer(self.vocab_file	,				self.merges_file	,				**self.special_tokens_map							)
				A_     :						str     =					"""adapt react readapt apt"""
				A_     :						Any     =					"""adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
				A_     :						Tuple     =					tokenizer.tokenize(lowercase							)
				self.assertListEqual(lowercase	,				lowercase							)

				A_     :						List[str]     =					tokens + [tokenizer.unk_token]

				A_     :						Dict     =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase							)	,				lowercase							)




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase							:Any       = logging.get_logger(__name__)

lowerCamelCase							:List[Any]       = {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 'camembert'


	def __init__(self	,				lowercase=30522	,				lowercase=768	,				lowercase=12	,				lowercase=12	,				lowercase=3072	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=512	,				lowercase=2	,				lowercase=0.02	,				lowercase=1E-12	,				lowercase=1	,				lowercase=0	,				lowercase=2	,				lowercase="absolute"	,				lowercase=True	,				lowercase=None	,				**lowercase	,				):
				super().__init__(pad_token_id=lowercase	,				bos_token_id=lowercase	,				eos_token_id=lowercase	,				**lowercase							)

				A_     :						List[Any]     =					vocab_size
				A_     :						int     =					hidden_size
				A_     :						Dict     =					num_hidden_layers
				A_     :						Dict     =					num_attention_heads
				A_     :						Optional[Any]     =					hidden_act
				A_     :						str     =					intermediate_size
				A_     :						int     =					hidden_dropout_prob
				A_     :						List[Any]     =					attention_probs_dropout_prob
				A_     :						Optional[Any]     =					max_position_embeddings
				A_     :						Optional[int]     =					type_vocab_size
				A_     :						int     =					initializer_range
				A_     :						str     =					layer_norm_eps
				A_     :						int     =					position_embedding_type
				A_     :						Dict     =					use_cache
				A_     :						Any     =					classifier_dropout







class      _lowerCAmelCase							(      __UpperCAmelCase			):


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