


import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


A				:       Optional[Any]      =  logging.get_logger(__name__)

A				:       Any      =  '▁'

A				:       List[Any]      =  {'vocab_file': 'prophetnet.tokenizer'}

A				:       Any      =  {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

A				:       Any      =  {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

A				:       int      =  {
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						__snake_case				=		collections.OrderedDict()
						with open(_lowerCAmelCase   ,						"r"   ,						encoding="utf-8"			) as reader:
												__snake_case				=		reader.readlines()
						for index, token in enumerate(_lowerCAmelCase			):
												__snake_case				=		token.rstrip("\n"			)
												__snake_case				=		index
						return vocab






class      UpperCamelCase(	_a ):
					snake_case_		:					Tuple    					=						VOCAB_FILES_NAMES
					snake_case_		:					str    					=						PRETRAINED_VOCAB_FILES_MAP
					snake_case_		:					List[Any]    					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case_		:					Tuple    					=						["""input_ids""", """attention_mask"""]


					def __init__(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		Dict  ,     SCREAMING_SNAKE_CASE						:		int="[SEP]"  ,     SCREAMING_SNAKE_CASE						:		Dict="[SEP]"  ,     SCREAMING_SNAKE_CASE						:		List[str]="[SEP]"  ,     SCREAMING_SNAKE_CASE						:		str="[UNK]"  ,     SCREAMING_SNAKE_CASE						:		List[str]="[PAD]"  ,     SCREAMING_SNAKE_CASE						:		Tuple="[CLS]"  ,     SCREAMING_SNAKE_CASE						:		Tuple="[MASK]"  ,     SCREAMING_SNAKE_CASE						:		Optional[Dict[str, Any]] = None  ,     **SCREAMING_SNAKE_CASE						:		str  ,     )      ->						None:




											'''simple docstring'''

											__snake_case				=		{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=SCREAMING_SNAKE_CASE  ,     eos_token=SCREAMING_SNAKE_CASE  ,     sep_token=SCREAMING_SNAKE_CASE  ,     unk_token=SCREAMING_SNAKE_CASE  ,     pad_token=SCREAMING_SNAKE_CASE  ,     cls_token=SCREAMING_SNAKE_CASE  ,     mask_token=SCREAMING_SNAKE_CASE  ,     sp_model_kwargs=self.sp_model_kwargs  ,     **SCREAMING_SNAKE_CASE  ,     )

											try:
																	import sentencepiece as spm
											except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"    )
																	raise

											__snake_case				=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
											self.sp_model.Load(str(SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# put special tokens and [unused] tokens into the vocab
											__snake_case				=		{"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

											for i in range(1_0    ):
																	__snake_case				=		f'''[unused{i}]'''
																	__snake_case				=		5 + i

											# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
											__snake_case				=		1_2
											__snake_case				=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
											for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(SCREAMING_SNAKE_CASE    )


					def __getstate__(    self						:		List[Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.__dict__.copy()
											__snake_case				=		None
											return state


					def __setstate__(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						List[Any]:




											'''simple docstring'''

											__snake_case				=		d
											try:
																	import sentencepiece as spm
											except ImportError:
																	logger.warning(
																	    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
																	    " pip install sentencepiece"    )
																	raise

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

											__snake_case				=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
											self.sp_model.Load(self.vocab_file    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		List[int]  ,     SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None  ,     SCREAMING_SNAKE_CASE						:		bool = False    )      ->						List[int]:




											'''simple docstring'''

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

											if token_ids_a is None:
																	return ([0] * len(SCREAMING_SNAKE_CASE    )) + [1]
											return ([0] * len(SCREAMING_SNAKE_CASE    )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE    )) + [1]


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		List[int]  ,     SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None    )      ->						List[int]:




											'''simple docstring'''

											__snake_case				=		[self.sep_token_id]

											if token_ids_a is None:
																	return len(token_ids_a + sep    ) * [0]
											return len(token_ids_a + sep + sep + token_ids_a + sep    ) * [0]


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Dict:




											'''simple docstring'''

											return len(self.sp_model    ) + self.fairseq_offset


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




											'''simple docstring'''

											__snake_case				=		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
											vocab.update(self.added_tokens_encoder    )
											return vocab


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						str:




											'''simple docstring'''

											return self.sp_model.encode(SCREAMING_SNAKE_CASE  ,     out_type=SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Dict    )      ->						List[Any]:




											'''simple docstring'''

											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											__snake_case				=		self.sp_model.PieceToId(SCREAMING_SNAKE_CASE    )

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						Any:




											'''simple docstring'''

											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Dict    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		"".join(SCREAMING_SNAKE_CASE    ).replace(SCREAMING_SNAKE_CASE  ,     " "    ).strip()
											return out_string


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Optional[str] = None    )      ->						Tuple[str]:




											'''simple docstring'''

											if not os.path.isdir(SCREAMING_SNAKE_CASE    ):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
																	return
											__snake_case				=		os.path.join(
											    SCREAMING_SNAKE_CASE  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

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

											return (out_vocab_file,)






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		List[int]  ,     SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None    )      ->						List[int]:




											'''simple docstring'''

											if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
											__snake_case				=		[self.sep_token_id]
											return token_ids_a + sep + token_ids_a + sep









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

import pytest
from parameterized import 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.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ] )
class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Dict:




											'''simple docstring'''

											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=SCREAMING_SNAKE_CASE  ,     )
											assert hasattr(self  ,     "env"    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		Any=1    )      ->						List[str]:




											'''simple docstring'''

											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=f'''{self.env.base_job_name}-single'''  ,     instance_count=SCREAMING_SNAKE_CASE  ,     instance_type=self.instance_type  ,     debugger_hook_config=SCREAMING_SNAKE_CASE  ,     hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}  ,     metric_definitions=self.env.metric_definitions  ,     py_version="py36"  ,     )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		Any    )      ->						List[str]:




											'''simple docstring'''

											TrainingJobAnalytics(SCREAMING_SNAKE_CASE    ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.create_estimator()

											# run training
											estimator.fit()

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

											# extract kpis
											__snake_case				=		list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]    )
											__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
											__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}  ,     SCREAMING_SNAKE_CASE    )





