




'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""switch_transformers"""
					_UpperCamelCase							=		["""past_key_values"""]
					_UpperCamelCase							=		{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



					def __init__(	self     :						str      ,       _lowercase     :						Union[str, Any]=32_128      ,       _lowercase     :						Tuple=768      ,       _lowercase     :						List[Any]=64      ,       _lowercase     :						Optional[Any]=2_048      ,       _lowercase     :						List[Any]=64      ,       _lowercase     :						Dict=12      ,       _lowercase     :						Dict=3      ,       _lowercase     :						int=12      ,       _lowercase     :						int=3      ,       _lowercase     :						int=12      ,       _lowercase     :						Tuple=8      ,       _lowercase     :						Optional[int]=False      ,       _lowercase     :						Any=0.01      ,       _lowercase     :						Tuple="float32"      ,       _lowercase     :						Tuple=False      ,       _lowercase     :						Union[str, Any]=32      ,       _lowercase     :						List[Any]=128      ,       _lowercase     :						Optional[Any]=0.1      ,       _lowercase     :						Any=1E-6      ,       _lowercase     :						List[str]=0.0_01      ,       _lowercase     :						Optional[int]=0.0_01      ,       _lowercase     :						str=1.0      ,       _lowercase     :						Optional[Any]="relu"      ,       _lowercase     :						Any=True      ,       _lowercase     :						Optional[int]=False      ,       _lowercase     :						List[str]=True      ,       _lowercase     :						Tuple=0      ,       _lowercase     :						Dict=1      ,       **_lowercase     :						str      ,       )    ->							Tuple:
								A_									= vocab_size
								A_									= d_model
								A_									= d_kv
								A_									= d_ff

								A_									= num_sparse_encoder_layers

								A_									= num_layers
								A_									= (
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								A_									= num_sparse_decoder_layers

								# This tells us, each how many encoder layer we'll have to set a sparse layer.
								if self.num_sparse_encoder_layers > 0:
											A_									= self.num_layers // self.num_sparse_encoder_layers
								else:
											A_									= self.num_layers  # HACK: this will create 0 sparse layers

								# This tells us, each how many encoder layer we'll have to set a sparse layer.
								if self.num_sparse_decoder_layers > 0:
											A_									= self.num_decoder_layers // self.num_sparse_decoder_layers
								else:
											A_									= self.num_decoder_layers  # HACK: this will create 0 sparse layers

								A_									= num_heads
								A_									= num_experts
								A_									= expert_capacity
								A_									= router_bias
								A_									= router_jitter_noise
								if router_dtype not in ["float32", "float16", "bfloat16"]:
											raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}')
								A_									= router_dtype

								A_									= router_ignore_padding_tokens
								A_									= relative_attention_num_buckets
								A_									= relative_attention_max_distance

								A_									= dropout_rate
								A_									= layer_norm_epsilon
								A_									= initializer_factor
								A_									= feed_forward_proj
								A_									= use_cache
								A_									= add_router_probs

								A_									= router_z_loss_coef
								A_									= router_aux_loss_coef

								A_									= self.feed_forward_proj.split('-')
								A_									= act_info[-1]
								A_									= act_info[0] == 'gated'

								if len(_lowercase) > 1 and act_info[0] != "gated" or len(_lowercase) > 2:
											raise ValueError(
											    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\'')

								# for backwards compatibility
								if feed_forward_proj == "gated-gelu":
											A_									= 'gelu_new'

								super().__init__(
								    pad_token_id=_lowercase      ,       eos_token_id=_lowercase      ,       is_encoder_decoder=_lowercase      ,       **_lowercase      ,       )










'''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   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		CTRLTokenizer
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False



					def     __snake_case						(	self     :						str)    ->							List[Any]:
								super().setUp()

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

								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file'])
								A_									= 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     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								kwargs.update(self.special_tokens_map)
								return CTRLTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any])    ->							Dict:
								A_									= 'adapt react readapt apt'
								A_									= 'adapt react readapt apt'
								return input_text, output_text



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

								A_									= tokens + [tokenizer.unk_token]

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




