

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    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					BlenderbotSmallTokenizer
			_UpperCAmelCase       :							Any      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									super().setUp()

									SCREAMING_SNAKE_CASE_:			Any  		=	["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	dict(zip(lowerCAmelCase__       ,						range(len(lowerCAmelCase__))))

									SCREAMING_SNAKE_CASE_:			Tuple  		=	["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

									SCREAMING_SNAKE_CASE_:			int  		=	os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES["vocab_file"])
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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(lowerCAmelCase__) + "\n")
									with open(self.merges_file       ,						"w"       ,						encoding="utf-8") as fp:
															fp.write("\n".join(lowerCAmelCase__))



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						**lowerCAmelCase__  :					Any):
									kwargs.update(self.special_tokens_map)
									return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"adapt act apte"
									SCREAMING_SNAKE_CASE_:			int  		=	"adapt act apte"
									return input_text, output_text



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"adapt act apte"
									SCREAMING_SNAKE_CASE_:			Dict  		=	["adapt", "act", "ap@@", "te"]
									SCREAMING_SNAKE_CASE_:			Dict  		=	tokenizer.tokenize(lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[0, 1, 2, 3, 4, 5]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__)       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
									assert tok("sam").input_ids == [1384]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"I am a small frog."
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tok([src_text]       ,						padding=lowerCAmelCase__       ,						truncation=lowerCAmelCase__)["input_ids"]
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tok.batch_decode(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__       ,						clean_up_tokenization_spaces=lowerCAmelCase__)[0]
									assert src_text != decoded  # I wish it did!
									assert decoded == "i am a small frog ."




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
									SCREAMING_SNAKE_CASE_:			Any  		=	"I am a small frog ."
									SCREAMING_SNAKE_CASE_:			Dict  		=	"."
									SCREAMING_SNAKE_CASE_:			str  		=	tok(lowerCAmelCase__)["input_ids"]
									SCREAMING_SNAKE_CASE_:			Dict  		=	tok(lowerCAmelCase__)["input_ids"]

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









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


lowerCAmelCase							:  Optional[int]            =						logging.get_logger(__name__)

lowerCAmelCase							:  int            =						{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					'''fnet'''



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[str]=3_2000       ,						lowerCAmelCase__  :					Union[str, Any]=768       ,						lowerCAmelCase__  :					Optional[Any]=12       ,						lowerCAmelCase__  :					Tuple=3072       ,						lowerCAmelCase__  :					Any="gelu_new"       ,						lowerCAmelCase__  :					Union[str, Any]=0.1       ,						lowerCAmelCase__  :					Optional[Any]=512       ,						lowerCAmelCase__  :					int=4       ,						lowerCAmelCase__  :					List[Any]=0.02       ,						lowerCAmelCase__  :					Union[str, Any]=1E-12       ,						lowerCAmelCase__  :					Optional[int]=False       ,						lowerCAmelCase__  :					Any=512       ,						lowerCAmelCase__  :					Optional[int]=3       ,						lowerCAmelCase__  :					int=1       ,						lowerCAmelCase__  :					List[Any]=2       ,						**lowerCAmelCase__  :					Optional[int]       ,						):
									super().__init__(pad_token_id=lowerCAmelCase__       ,						bos_token_id=lowerCAmelCase__       ,						eos_token_id=lowerCAmelCase__       ,						**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			Any  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			int  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	type_vocab_size
									SCREAMING_SNAKE_CASE_:			int  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	use_tpu_fourier_optimizations
									SCREAMING_SNAKE_CASE_:			int  		=	tpu_short_seq_length






