






"""simple docstring"""





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     Union[str, Any]								=   BlenderbotSmallTokenizer
      _a   :     Dict								=   False


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            super().setUp()

            __snake_case      :    int  			=['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
            __snake_case      :    Union[str, Any]  			=dict(zip(a							,   range(len(a						)						)						)						)

            __snake_case      :    Tuple  			=['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
            __snake_case      :    Tuple  			={'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

            __snake_case      :    List[str]  			=os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES['''vocab_file''']						)
            __snake_case      :    Optional[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(a						) + '''\n'''						)
            with open(self.merges_file							,   '''w'''							,   encoding='''utf-8'''						) as fp:
                  fp.write('''\n'''.join(a						)						)


      def _UpperCamelCase						(    self				:	int							,   **a				:	Union[str, Any]						):





            """simple docstring"""







            kwargs.update(self.special_tokens_map						)
            return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname							,   **a						)


      def _UpperCamelCase						(    self				:	Dict							,   a				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Any  			='''adapt act apte'''
            __snake_case      :    List[str]  			='''adapt act apte'''
            return input_text, output_text


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Tuple  			=BlenderbotSmallTokenizer(self.vocab_file							,   self.merges_file							,   **self.special_tokens_map						)
            __snake_case      :    List[Any]  			='''adapt act apte'''
            __snake_case      :    Dict  			=['''adapt''', '''act''', '''ap@@''', '''te''']
            __snake_case      :    Any  			=tokenizer.tokenize(a						)
            self.assertListEqual(a							,   a						)

            __snake_case      :    Union[str, Any]  			=[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

            __snake_case      :    str  			=[0, 1, 2, 3, 4, 5]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(a						)							,   a						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    int  			=BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''						)
            assert tok('''sam'''						).input_ids == [1_3_8_4]
            __snake_case      :    List[str]  			='''I am a small frog.'''
            __snake_case      :    List[str]  			=tok([src_text]							,   padding=a							,   truncation=a						)['''input_ids''']
            __snake_case      :    List[Any]  			=tok.batch_decode(a							,   skip_special_tokens=a							,   clean_up_tokenization_spaces=a						)[0]
            assert src_text != decoded  # I wish it did!
            assert decoded == "i am a small frog ."







      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''						)
            __snake_case      :    Dict  			='''I am a small frog .'''
            __snake_case      :    str  			='''.'''
            __snake_case      :    Optional[Any]  			=tok(a						)['''input_ids''']
            __snake_case      :    Optional[int]  			=tok(a						)['''input_ids''']

            assert encoded[-1] == encoded_dot[0]










"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_	:				Dict										= logging.get_logger(__name__)

UpperCamelCase_	:				Dict										= {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}


class      _lowercase (     lowerCAmelCase						):
      _a   :     Union[str, Any]								=   '''xmod'''


      def __init__(    self				:	Optional[int]							,   a				:	Dict=3_0_5_2_2							,   a				:	Any=7_6_8							,   a				:	str=1_2							,   a				:	Optional[int]=1_2							,   a				:	Any=3_0_7_2							,   a				:	int="gelu"							,   a				:	List[Any]=0.1							,   a				:	Union[str, Any]=0.1							,   a				:	Tuple=5_1_2							,   a				:	str=2							,   a				:	Any=0.0_2							,   a				:	Tuple=1e-12							,   a				:	Optional[int]=1							,   a				:	Any=0							,   a				:	str=2							,   a				:	str="absolute"							,   a				:	List[Any]=True							,   a				:	Optional[Any]=None							,   a				:	Tuple=False							,   a				:	Union[str, Any]=2							,   a				:	Any=False							,   a				:	Dict=True							,   a				:	int=True							,   a				:	str=("en_XX",)							,   a				:	Dict=None							,   **a				:	Tuple							,   ):





            """simple docstring"""







            super().__init__(pad_token_id=a							,   bos_token_id=a							,   eos_token_id=a							,   **a						)

            __snake_case      :    Union[str, Any]  			=vocab_size
            __snake_case      :    Union[str, Any]  			=hidden_size
            __snake_case      :    int  			=num_hidden_layers
            __snake_case      :    Union[str, Any]  			=num_attention_heads
            __snake_case      :    Any  			=hidden_act
            __snake_case      :    Tuple  			=intermediate_size
            __snake_case      :    int  			=hidden_dropout_prob
            __snake_case      :    str  			=attention_probs_dropout_prob
            __snake_case      :    int  			=max_position_embeddings
            __snake_case      :    Tuple  			=type_vocab_size
            __snake_case      :    Union[str, Any]  			=initializer_range
            __snake_case      :    Dict  			=layer_norm_eps
            __snake_case      :    Optional[int]  			=position_embedding_type
            __snake_case      :    int  			=use_cache
            __snake_case      :    int  			=classifier_dropout
            __snake_case      :    int  			=pre_norm
            __snake_case      :    Any  			=adapter_reduction_factor
            __snake_case      :    str  			=adapter_layer_norm
            __snake_case      :    Union[str, Any]  			=adapter_reuse_layer_norm
            __snake_case      :    List[str]  			=ln_before_adapter
            __snake_case      :    Optional[int]  			=list(a						)
            __snake_case      :    int  			=default_language



class      _lowercase (     lowerCAmelCase						):


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            if self.task == "multiple-choice":
                  __snake_case      :    Tuple  			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
            else:
                  __snake_case      :    Tuple  			={0: '''batch''', 1: '''sequence'''}
            return OrderedDict(
                [
                    ('''input_ids''', dynamic_axis),
                    ('''attention_mask''', dynamic_axis),
                ]						)


