


import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin

class   A__ (   __snake_case	, unittest.TestCase     ):







				"""simple docstring"""

				__magic_name__        =		CTRLTokenizer
				__magic_name__        =		False
				__magic_name__        =		False
				def 		a_      (	self   ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									snake_case					=    ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
									snake_case					=    dict(zip(__snake_case      , range(len(__snake_case   )   )   )   )
									snake_case					=    ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
									snake_case					=    {'''unk_token''': '''<unk>'''}

									snake_case					=    os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES['''vocab_file''']   )
									snake_case					=    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(__snake_case   ) + '''\n'''   )
									with open(self.merges_file      , '''w'''      , encoding='''utf-8'''   ) as fp:
														fp.write('''\n'''.join(__snake_case   )   )
				def 		a_      (	self      , **__snake_case   ):
									kwargs.update(self.special_tokens_map   )
									return CTRLTokenizer.from_pretrained(self.tmpdirname      , **__snake_case   )
				def 		a_      (	self      , __snake_case   ):
									snake_case					=    '''adapt react readapt apt'''
									snake_case					=    '''adapt react readapt apt'''
									return input_text, output_text




				def 		a_      (	self   ):
									snake_case					=    CTRLTokenizer(self.vocab_file      , self.merges_file      , **self.special_tokens_map   )
									snake_case					=    '''adapt react readapt apt'''
									snake_case					=    '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
									snake_case					=    tokenizer.tokenize(__snake_case   )
									self.assertListEqual(__snake_case      , __snake_case   )

									snake_case					=    tokens + [tokenizer.unk_token]

									snake_case					=    [0, 1, 2, 4, 5, 1, 0, 3, 6]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case   )      , __snake_case   )








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




