import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 							__UpperCamelCase			( __UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Optional[int]										=OpenAIGPTTokenizer
				__a							: Tuple										=OpenAIGPTTokenizerFast
				__a							: Any										=True
				__a							: Union[str, Any]										=False



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

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											lowerCAmelCase   =		[
											    '''l''',
											    '''o''',
											    '''w''',
											    '''e''',
											    '''r''',
											    '''s''',
											    '''t''',
											    '''i''',
											    '''d''',
											    '''n''',
											    '''w</w>''',
											    '''r</w>''',
											    '''t</w>''',
											    '''lo''',
											    '''low''',
											    '''er</w>''',
											    '''low</w>''',
											    '''lowest</w>''',
											    '''newer</w>''',
											    '''wider</w>''',
											    '''<unk>''',
											]
											lowerCAmelCase   =		dict(zip(UpperCAmelCase_       , range(len(UpperCAmelCase_						)						)						)						)
											lowerCAmelCase   =		['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

											lowerCAmelCase   =		os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES['''vocab_file''']						)
											lowerCAmelCase   =		os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES['''merges_file''']						)
											with open(self.vocab_file       , '''w'''						) as fp:
																		fp.write(json.dumps(UpperCAmelCase_						)						)
											with open(self.merges_file       , '''w'''						) as fp:
																		fp.write('''\n'''.join(UpperCAmelCase_						)						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											return "lower newer", "lower newer"



				def       __snake_case		( self						):
											lowerCAmelCase   =		OpenAIGPTTokenizer(self.vocab_file       , self.merges_file						)

											lowerCAmelCase   =		'''lower'''
											lowerCAmelCase   =		['''low''', '''er</w>''']
											lowerCAmelCase   =		tokenizer.tokenize(UpperCAmelCase_						)
											self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_						)

											lowerCAmelCase   =		tokens + ['''<unk>''']
											lowerCAmelCase   =		[14, 15, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_						)       , UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_=15						):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																									lowerCAmelCase   =		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       , **UpperCAmelCase_						)

																									# Simple input
																									lowerCAmelCase   =		'''This is a simple input'''
																									lowerCAmelCase   =		['''This is a simple input 1''', '''This is a simple input 2''']
																									lowerCAmelCase   =		('''This is a simple input''', '''This is a pair''')
																									lowerCAmelCase   =		[
																									    ('''This is a simple input 1''', '''This is a simple input 2'''),
																									    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																									]

																									# Simple input tests
																									self.assertRaises(UpperCAmelCase_       , tokenizer_r.encode       , UpperCAmelCase_       , max_length=UpperCAmelCase_       , padding='''max_length'''						)

																									# Simple input
																									self.assertRaises(UpperCAmelCase_       , tokenizer_r.encode_plus       , UpperCAmelCase_       , max_length=UpperCAmelCase_       , padding='''max_length'''						)

																									# Simple input
																									self.assertRaises(
																									    UpperCAmelCase_       , tokenizer_r.batch_encode_plus       , UpperCAmelCase_       , max_length=UpperCAmelCase_       , padding='''max_length'''       , )

																									# Pair input
																									self.assertRaises(UpperCAmelCase_       , tokenizer_r.encode       , UpperCAmelCase_       , max_length=UpperCAmelCase_       , padding='''max_length'''						)

																									# Pair input
																									self.assertRaises(UpperCAmelCase_       , tokenizer_r.encode_plus       , UpperCAmelCase_       , max_length=UpperCAmelCase_       , padding='''max_length'''						)

																									# Pair input
																									self.assertRaises(
																									    UpperCAmelCase_       , tokenizer_r.batch_encode_plus       , UpperCAmelCase_       , max_length=UpperCAmelCase_       , padding='''max_length'''       , )






				def       __snake_case		( self						):
											pass




@require_ftfy
@require_spacy
@require_tokenizers
class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				pass









from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						={
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										="""switch_transformers"""
				__a							: Union[str, Any]										=["""past_key_values"""]
				__a							: Dict										={"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



				def __init__( self       , UpperCAmelCase_=3_21_28       , UpperCAmelCase_=7_68       , UpperCAmelCase_=64       , UpperCAmelCase_=20_48       , UpperCAmelCase_=64       , UpperCAmelCase_=12       , UpperCAmelCase_=3       , UpperCAmelCase_=12       , UpperCAmelCase_=3       , UpperCAmelCase_=12       , UpperCAmelCase_=8       , UpperCAmelCase_=False       , UpperCAmelCase_=0.01       , UpperCAmelCase_="float32"       , UpperCAmelCase_=False       , UpperCAmelCase_=32       , UpperCAmelCase_=1_28       , UpperCAmelCase_=0.1       , UpperCAmelCase_=1E-6       , UpperCAmelCase_=0.001       , UpperCAmelCase_=0.001       , UpperCAmelCase_=1.0       , UpperCAmelCase_="relu"       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_=True       , UpperCAmelCase_=0       , UpperCAmelCase_=1       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		vocab_size
											lowerCAmelCase   =		d_model
											lowerCAmelCase   =		d_kv
											lowerCAmelCase   =		d_ff

											lowerCAmelCase   =		num_sparse_encoder_layers

											lowerCAmelCase   =		num_layers
											lowerCAmelCase   =		(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											lowerCAmelCase   =		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:
																		lowerCAmelCase   =		self.num_layers // self.num_sparse_encoder_layers
											else:
																		lowerCAmelCase   =		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:
																		lowerCAmelCase   =		self.num_decoder_layers // self.num_sparse_decoder_layers
											else:
																		lowerCAmelCase   =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

											lowerCAmelCase   =		num_heads
											lowerCAmelCase   =		num_experts
											lowerCAmelCase   =		expert_capacity
											lowerCAmelCase   =		router_bias
											lowerCAmelCase   =		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}"""						)
											lowerCAmelCase   =		router_dtype

											lowerCAmelCase   =		router_ignore_padding_tokens
											lowerCAmelCase   =		relative_attention_num_buckets
											lowerCAmelCase   =		relative_attention_max_distance

											lowerCAmelCase   =		dropout_rate
											lowerCAmelCase   =		layer_norm_epsilon
											lowerCAmelCase   =		initializer_factor
											lowerCAmelCase   =		feed_forward_proj
											lowerCAmelCase   =		use_cache
											lowerCAmelCase   =		add_router_probs

											lowerCAmelCase   =		router_z_loss_coef
											lowerCAmelCase   =		router_aux_loss_coef

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

											if len(UpperCAmelCase_						) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_						) > 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":
																		lowerCAmelCase   =		'''gelu_new'''

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



