

"""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













import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types




class 	lowerCamelCase__				(							unittest.TestCase,   UpperCAmelCase ):






      def      UpperCAmelCase_     (self					:    Any		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    str							=     load_tool('translation'		)
       self.tool.setup()
       lowerCamelCase_							:    Optional[int]							=     load_tool('translation'		,		remote=_snake_case		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     self.tool('Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    Optional[int]		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     self.remote_tool('Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     self.tool(text='Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     self.remote_tool(text='Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Any:


       """simple docstring"""






       lowerCamelCase_							:    str							=     ['Hey, what\'s up?', 'English', 'Spanish']
       lowerCamelCase_							:    Any							=     self.tool(*_snake_case		)

       # There is a single output
       if len(self.tool.outputs		) == 1:
        lowerCamelCase_							:    Any							=     [outputs]

       self.assertListEqual(output_types(_snake_case		)		,		self.tool.outputs		)






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     ['Hey, what\'s up?', 'English', 'Spanish']
       lowerCamelCase_							:    Optional[Any]							=     self.tool(*_snake_case		)

       if not isinstance(_snake_case		,		_snake_case		):
        lowerCamelCase_							:    List[str]							=     [outputs]

       self.assertEqual(len(_snake_case		)		,		len(self.tool.outputs		)		)

       for output, output_type in zip(_snake_case		,		self.tool.outputs		):
        lowerCamelCase_							:    Optional[int]							=     AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(_snake_case		,		_snake_case		)		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     ['Hey, what\'s up?', 'English', 'Spanish']

       lowerCamelCase_							:    List[str]							=     []

       for _input, input_type in zip(_snake_case		,		self.tool.inputs		):
        if isinstance(_snake_case		,		_snake_case		):
         _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
        else:
         _inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
       lowerCamelCase_							:    Any							=     self.tool(*_snake_case		)

       if not isinstance(_snake_case		,		_snake_case		):
        lowerCamelCase_							:    Optional[Any]							=     [outputs]

       self.assertEqual(len(_snake_case		)		,		len(self.tool.outputs		)		)
