


"""simple docstring"""







import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__       :							Union[str, Any]        =  logging.get_logger(__name__)

a__       :							Optional[Any]        =  '''▁'''

a__       :							Tuple        =  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

a__       :							Dict        =  {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

a__       :							Optional[int]        =  {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

a__       :							Tuple        =  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

a__       :							Any        =  {'''mustc''': MUSTC_LANGS}




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Optional[int]					      =    VOCAB_FILES_NAMES
	snake_case__       :						List[Any]					      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__       :						List[str]					      =    MAX_MODEL_INPUT_SIZES
	snake_case__       :						Tuple					      =    ["input_ids", "attention_mask"]

	snake_case__       :						List[int]					      =    []




	def __init__(				self    :       Any    ,    UpperCAmelCase__    :       Union[str, Any]    ,    UpperCAmelCase__    :       Dict    ,    UpperCAmelCase__    :       List[Any]="<s>"    ,    UpperCAmelCase__    :       Optional[Any]="</s>"    ,    UpperCAmelCase__    :       List[Any]="<pad>"    ,    UpperCAmelCase__    :       Optional[int]="<unk>"    ,    UpperCAmelCase__    :       str=False    ,    UpperCAmelCase__    :       Optional[Any]=False    ,    UpperCAmelCase__    :       Optional[Any]=None    ,    UpperCAmelCase__    :       List[str]=None    ,    UpperCAmelCase__    :       Optional[Dict[str, Any]] = None    ,    **UpperCAmelCase__    :       Tuple    ,    ) ->			None:
						__SCREAMING_SNAKE_CASE		     =       {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=UpperCAmelCase__    ,    eos_token=UpperCAmelCase__    ,    unk_token=UpperCAmelCase__    ,    pad_token=UpperCAmelCase__    ,    do_upper_case=UpperCAmelCase__    ,    do_lower_case=UpperCAmelCase__    ,    tgt_lang=UpperCAmelCase__    ,    lang_codes=UpperCAmelCase__    ,    sp_model_kwargs=self.sp_model_kwargs    ,    **UpperCAmelCase__    ,    )
						__SCREAMING_SNAKE_CASE		     =       do_upper_case
						__SCREAMING_SNAKE_CASE		     =       do_lower_case

						__SCREAMING_SNAKE_CASE		     =       load_json(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       {v: k for k, v in self.encoder.items()}
						__SCREAMING_SNAKE_CASE		     =       spm_file
						__SCREAMING_SNAKE_CASE		     =       load_spm(UpperCAmelCase__    ,    self.sp_model_kwargs     )

						if lang_codes is not None:
											__SCREAMING_SNAKE_CASE		     =       lang_codes
											__SCREAMING_SNAKE_CASE		     =       LANGUAGES[lang_codes]
											__SCREAMING_SNAKE_CASE		     =       [F"""<lang:{lang}>""" for lang in self.langs]
											__SCREAMING_SNAKE_CASE		     =       {lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""     ) for lang in self.langs}

											__SCREAMING_SNAKE_CASE		     =       self.lang_tokens
											__SCREAMING_SNAKE_CASE		     =       tgt_lang if tgt_lang is not None else self.langs[0]

											self.set_tgt_lang_special_tokens(self._tgt_lang     )
						else:
											__SCREAMING_SNAKE_CASE		     =       {}




	@property
	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			int:
						return len(self.encoder     )




	@property
	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			str:
						return self._tgt_lang




	@tgt_lang.setter
	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Tuple     ) ->			None:
						__SCREAMING_SNAKE_CASE		     =       new_tgt_lang
						self.set_tgt_lang_special_tokens(UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       str     ) ->			None:
						__SCREAMING_SNAKE_CASE		     =       self.lang_code_to_id[tgt_lang]
						__SCREAMING_SNAKE_CASE		     =       [lang_code_id]




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       str     ) ->			List[str]:
						return self.sp_model.encode(UpperCAmelCase__    ,    out_type=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       List[Any]     ) ->			Union[str, Any]:
						return self.encoder.get(UpperCAmelCase__    ,    self.encoder[self.unk_token]     )




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       int     ) ->			str:
						return self.decoder.get(UpperCAmelCase__    ,    self.unk_token     )




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       List[str]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       []
						__SCREAMING_SNAKE_CASE		     =       ""
						for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
																__SCREAMING_SNAKE_CASE		     =       self.sp_model.decode(UpperCAmelCase__     )
																out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																__SCREAMING_SNAKE_CASE		     =       []
											else:
																current_sub_tokens.append(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.sp_model.decode(UpperCAmelCase__     )
						out_string += decoded.upper() if self.do_upper_case else decoded
						return out_string.strip()




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Optional[int]=None     ) ->			List[int]:
						if token_ids_a is None:
											return self.prefix_tokens + token_ids_a + [self.eos_token_id]
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]




	def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None    ,    UpperCAmelCase__    :       bool = False     ) ->			List[int]:

						if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=UpperCAmelCase__    ,    token_ids_a=UpperCAmelCase__    ,    already_has_special_tokens=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       [1] * len(self.prefix_tokens     )
						__SCREAMING_SNAKE_CASE		     =       [1]
						if token_ids_a is None:
											return prefix_ones + ([0] * len(UpperCAmelCase__     )) + suffix_ones
						return prefix_ones + ([0] * len(UpperCAmelCase__     )) + ([0] * len(UpperCAmelCase__     )) + suffix_ones




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       self.encoder.copy()
						vocab.update(self.added_tokens_encoder     )
						return vocab




	def __getstate__(				self    :       Dict     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       self.__dict__.copy()
						__SCREAMING_SNAKE_CASE		     =       None
						return state




	def __setstate__(				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       Dict     ) ->			None:
						__SCREAMING_SNAKE_CASE		     =       d

						# for backward compatibility
						if not hasattr(self    ,    "sp_model_kwargs"     ):
											__SCREAMING_SNAKE_CASE		     =       {}

						__SCREAMING_SNAKE_CASE		     =       load_spm(self.spm_file    ,    self.sp_model_kwargs     )




	def 						UpperCAmelCase_   (				self    :       int    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Optional[str] = None     ) ->			Tuple[str]:
						__SCREAMING_SNAKE_CASE		     =       Path(UpperCAmelCase__     )
						assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
						__SCREAMING_SNAKE_CASE		     =       save_dir / (
						    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
						)
						__SCREAMING_SNAKE_CASE		     =       save_dir / (
						    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
						)

						save_json(self.encoder    ,    UpperCAmelCase__     )

						if os.path.abspath(self.spm_file     ) != os.path.abspath(UpperCAmelCase__     ) and os.path.isfile(self.spm_file     ):
											copyfile(self.spm_file    ,    UpperCAmelCase__     )
						elif not os.path.isfile(self.spm_file     ):
											with open(UpperCAmelCase__    ,    "wb"     ) as fi:
																__SCREAMING_SNAKE_CASE		     =       self.sp_model.serialized_model_proto()
																fi.write(UpperCAmelCase__     )

						return (str(UpperCAmelCase__     ), str(UpperCAmelCase__     ))




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       sentencepiece.SentencePieceProcessor(**lowerCAmelCase_     )
					spm.Load(str(lowerCAmelCase_     )     )
					return spm




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					with open(lowerCAmelCase_						,		"r"     ) as f:
										return json.load(lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					with open(lowerCAmelCase_						,		"w"     ) as f:
										json.dump(lowerCAmelCase_						,		lowerCAmelCase_						,		indent=2     )




"""simple docstring"""







import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace




@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"    ,						"False")) is not True    ,						reason="Skipping test because should only be run when releasing minor transformers version"    ,						)
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Any:
						if self.framework == "pytorch":
											subprocess.run(
											    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()    ,    encoding="utf-8"    ,    check=UpperCAmelCase__    ,    )
						assert hasattr(self    ,    "env"     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       int     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
						# distributed data settings
						__SCREAMING_SNAKE_CASE		     =       {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

						# creates estimator
						return HuggingFace(
						    entry_point=self.script    ,    source_dir=self.env.test_path    ,    role=self.env.role    ,    image_uri=self.env.image_uri    ,    base_job_name=UpperCAmelCase__    ,    instance_count=UpperCAmelCase__    ,    instance_type=self.instance_type    ,    debugger_hook_config=UpperCAmelCase__    ,    hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}    ,    metric_definitions=self.env.metric_definitions    ,    distribution=UpperCAmelCase__    ,    py_version="py36"    ,    )




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       Optional[int]     ) ->			Optional[Any]:
						TrainingJobAnalytics(UpperCAmelCase__     ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""     )




	@parameterized.expand([(2,)]     )
	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Optional[Any]     ) ->			List[str]:
						# create estimator
						__SCREAMING_SNAKE_CASE		     =       self.create_estimator(UpperCAmelCase__     )

						# run training
						estimator.fit()

						# result dataframe
						__SCREAMING_SNAKE_CASE		     =       TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

						# extract kpis
						__SCREAMING_SNAKE_CASE		     =       list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
						__SCREAMING_SNAKE_CASE		     =       list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						__SCREAMING_SNAKE_CASE		     =       (
						    Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds"    ,    9_9_9_9_9_9     )
						)

						# assert kpis
						assert train_runtime <= self.results["train_runtime"]
						assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy     )
						assert all(t <= self.results["eval_loss"] for t in eval_loss     )

						# dump tests result into json file to share in PR
						with open(F"""{estimator.latest_training_job.name}.json"""    ,    "w"     ) as outfile:
											json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}    ,    UpperCAmelCase__     )
