

"""simple docstring"""




import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
__A	:       Tuple        =						'stas/tiny-wmt19-en-ru'





class      __UpperCamelCase					(  _A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       FSMTTokenizer
		SCREAMING_SNAKE_CASE														=       False


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							A     =   [
							    "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>",
							]
							A     =   dict(zip(__SCREAMING_SNAKE_CASE	,	range(len(__SCREAMING_SNAKE_CASE))))
							A     =   ["l o 123", "lo w 1456", "e r</w> 1789", ""]

							A     =   ["en", "ru"]
							A     =   {
							    "langs": self.langs,
							    "src_vocab_size": 1_0,
							    "tgt_vocab_size": 2_0,
							}

							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["src_vocab_file"])
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["tgt_vocab_file"])
							A     =   os.path.join(self.tmpdirname	,	"tokenizer_config.json")
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["merges_file"])
							with open(self.src_vocab_file	,	"w") as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE))
							with open(self.tgt_vocab_file	,	"w") as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE))
							with open(self.merges_file	,	"w") as fp:
												fp.write("\n".join(__SCREAMING_SNAKE_CASE))
							with open(__SCREAMING_SNAKE_CASE	,	"w") as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE))


		@cached_property
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en")


		@cached_property
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru")


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
							self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]	,	["en", "ru"])
							self.assertEqual(tokenizer.src_vocab_size	,	2_1)
							self.assertEqual(tokenizer.tgt_vocab_size	,	2_1)


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   FSMTTokenizer(self.langs	,	self.src_vocab_file	,	self.tgt_vocab_file	,	self.merges_file)

							A     =   "lower"
							A     =   ["low", "er</w>"]
							A     =   tokenizer.tokenize(__SCREAMING_SNAKE_CASE)
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)

							A     =   tokens + ["<unk>"]
							A     =   [1_4, 1_5, 2_0]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)	,	__SCREAMING_SNAKE_CASE)


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   self.tokenizer_ru_en

							A     =   tokenizer.encode("sequence builders"	,	add_special_tokens=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer.encode("multi-sequence build"	,	add_special_tokens=__SCREAMING_SNAKE_CASE)

							A     =   tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE)
							A     =   tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)

							assert encoded_sentence == text + [2]
							assert encoded_pair == text + [2] + text_a + [2]


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							A     =   self.tokenizer_en_ru
							A     =   self.tokenizer_ru_en

							A     =   [
							    [
							        "Here's a little song I wrote. Don't worry, be happy.",
							        [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
							    ],
							    ["This is it. No more. I'm done!", [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 2]],
							]

							# if data needs to be recreated or added, run:
							# import torch
							# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
							# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

							for src_text, tgt_input_ids in targets:
												A     =   tokenizer_enc.encode(__SCREAMING_SNAKE_CASE	,	return_tensors=__SCREAMING_SNAKE_CASE)
												self.assertListEqual(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)

												# and decode backward, using the reversed languages model
												A     =   tokenizer_dec.decode(__SCREAMING_SNAKE_CASE	,	skip_special_tokens=__SCREAMING_SNAKE_CASE)
												self.assertEqual(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"	,	do_lower_case=__SCREAMING_SNAKE_CASE)
							A     =   tokenizer.tokenize("USA is United States of America")
							A     =   ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)


		@unittest.skip("FSMTConfig.__init__  requires non-optional args")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							pass


		@unittest.skip("FSMTConfig.__init__  requires non-optional args")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							pass






from .number_theory.prime_numbers import next_prime




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int | None = None							,      lowerCamelCase							:		float | None = None							,      )			->				None:
        _UpperCamelCase  :							Tuple	    =	size_table
        _UpperCamelCase  :							Tuple	    =	[None] * self.size_table
        _UpperCamelCase  :							int	    =	0.7_5 if lim_charge is None else lim_charge
        _UpperCamelCase  :							Tuple	    =	1 if charge_factor is None else charge_factor
        _UpperCamelCase  :							list	    =	[]
        _UpperCamelCase  :							dict	    =	{}

 def        __snake_case       (      self							:		List[Any]	)			->				Union[str, Any]:
        return self._keys

 def        __snake_case       (      self							:		List[Any]	)			->				Optional[int]:
        return sum(1 for slot in self.values if slot is not None	) / (
            self.size_table * self.charge_factor
        )

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		int	)			->				Any:
        return key % self.size_table

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				Optional[Any]:
        print(f"step {step_ord}"	)
        print(list(range(len(self.values	)	)	)	)
        print(self.values	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	1
        _UpperCamelCase  :							Union[str, Any]	    =	values
        for value in values:
               self.insert_data(lowerCamelCase	)
               self._step_by_step(lowerCamelCase	)
               i += 1

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	data
        _UpperCamelCase  :							Optional[int]	    =	data

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		str=None	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	self.hash_function(key + 1	)

        while self.values[new_key] is not None and self.values[new_key] != key:
               if self.values.count(lowerCamelCase	) > 0:
                      _UpperCamelCase  :							Optional[int]	    =	self.hash_function(new_key + 1	)
               else:
                      _UpperCamelCase  :							Tuple	    =	None
                      break

        return new_key

 def        __snake_case       (      self							:		Optional[Any]	)			->				Optional[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	[value for value in self.values if value is not None]
        _UpperCamelCase  :							Union[str, Any]	    =	next_prime(self.size_table							,      factor=2	)
        self._keys.clear()
        _UpperCamelCase  :							Tuple	    =	[None] * self.size_table  # hell's pointers D: don't DRY ;/
        for value in survivor_values:
               self.insert_data(lowerCamelCase	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		str	)			->				Union[str, Any]:
        _UpperCamelCase  :							Dict	    =	self.hash_function(lowerCamelCase	)

        if self.values[key] is None:
               self._set_value(lowerCamelCase							,      lowerCamelCase	)

        elif self.values[key] == data:
               pass

        else:
               _UpperCamelCase  :							Tuple	    =	self._collision_resolution(lowerCamelCase							,      lowerCamelCase	)
               if collision_resolution is not None:
                      self._set_value(lowerCamelCase							,      lowerCamelCase	)
               else:
                      self.rehashing()
                      self.insert_data(lowerCamelCase	)



