

"""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 typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


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



class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    ["input_values", "attention_mask"]

							def __init__(   self					:  List[str]				,						a					:  Dict=1				,						a					:  Tuple=1_60_00				,						a					:  Union[str, Any]=0.0				,						a					:  Dict=False				,						a					:  List[Any]=True				,						**a					:  Any				,						)						->Optional[int]:
													super().__init__(feature_size=a				,						sampling_rate=a				,						padding_value=a				,						**a     )
													SCREAMING_SNAKE_CASE__  :					int =       return_attention_mask
													SCREAMING_SNAKE_CASE__  :					str =       do_normalize

							@staticmethod
							def   A_	(   a					:  List[np.ndarray]				,						a					:  List[np.ndarray]				,						a					:  float = 0.0     )						->List[np.ndarray]:
													if attention_mask is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       np.array(a				,						np.intaa     )
																			SCREAMING_SNAKE_CASE__  :					List[str] =       []

																			for vector, length in zip(a				,						attention_mask.sum(-1     )     ):
																									SCREAMING_SNAKE_CASE__  :					List[str] =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7     )
																									if length < normed_slice.shape[0]:
																															SCREAMING_SNAKE_CASE__  :					Tuple =       padding_value

																									normed_input_values.append(a     )
													else:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [(x - x.mean()) / np.sqrt(x.var() + 1E-7     ) for x in input_values]

													return normed_input_values






							def __call__(   self					:  Union[str, Any]				,						a					:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,						a					:  Union[bool, str, PaddingStrategy] = False				,						a					:  Optional[int] = None				,						a					:  bool = False				,						a					:  Optional[int] = None				,						a					:  Optional[bool] = None				,						a					:  Optional[Union[str, TensorType]] = None				,						a					:  Optional[int] = None				,						**a					:  Optional[Any]				,						)						->BatchFeature:

													if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																									    f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
																									    f""" {self.sampling_rate} and not {sampling_rate}."""     )
													else:
																			logger.warning(
																			    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
																			    "Failing to do so can result in silent errors that might be hard to debug."     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       isinstance(a				,						np.ndarray     ) and len(raw_speech.shape     ) > 1
													if is_batched_numpy and len(raw_speech.shape     ) > 2:
																			raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       is_batched_numpy or (
													    isinstance(a				,						(list, tuple)     ) and (isinstance(raw_speech[0]				,						(np.ndarray, tuple, list)     ))
													)

													# always return batch
													if not is_batched:
																			SCREAMING_SNAKE_CASE__  :					Any =       [raw_speech]

													# convert into correct format for padding
													SCREAMING_SNAKE_CASE__  :					Any =       BatchFeature({"input_values": raw_speech}     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       self.pad(
													    a				,						padding=a				,						max_length=a				,						truncation=a				,						pad_to_multiple_of=a				,						return_attention_mask=a				,						)

													# convert input values to correct format
													SCREAMING_SNAKE_CASE__  :					Any =       padded_inputs["input_values"]
													if not isinstance(input_values[0]				,						np.ndarray     ):
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [np.asarray(a				,						dtype=np.floataa     ) for array in input_values]
													elif (
													    not isinstance(a				,						np.ndarray     )
													    and isinstance(input_values[0]				,						np.ndarray     )
													    and input_values[0].dtype is np.dtype(np.floataa     )
													):
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [array.astype(np.floataa     ) for array in input_values]
													elif isinstance(a				,						np.ndarray     ) and input_values.dtype is np.dtype(np.floataa     ):
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       input_values.astype(np.floataa     )

													# convert attention_mask to correct format
													SCREAMING_SNAKE_CASE__  :					Dict =       padded_inputs.get("attention_mask"     )
													if attention_mask is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       [np.asarray(a				,						dtype=np.intaa     ) for array in attention_mask]

													# zero-mean and unit-variance normalization
													if self.do_normalize:
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    attention_mask
																			    if self._get_padding_strategies(a				,						max_length=a     ) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.zero_mean_unit_var_norm(
																			    padded_inputs["input_values"]				,						attention_mask=a				,						padding_value=self.padding_value     )

													if return_tensors is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       padded_inputs.convert_to_tensors(a     )

													return padded_inputs