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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       """▁"""

__lowerCamelCase							=       {"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCamelCase							=       {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

__lowerCamelCase							=       {
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
__lowerCamelCase							=       ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]






class   UpperCAmelCase		(						A_ ):
   A__					:		Optional[Any]						     =     VOCAB_FILES_NAMES
   A__					:		Union[str, Any]						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		str						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		str						     =     ["input_ids", "attention_mask"]

   A__					:		List[int]						     =     []
   A__					:		List[int]						     =     []



   def __init__(self		:					List[Any]    ,						snake_case__		:					Tuple    ,						snake_case__		:					Dict=None    ,						snake_case__		:					Any=None    ,						snake_case__		:					int="</s>"    ,						snake_case__		:					List[Any]="</s>"    ,						snake_case__		:					Optional[int]="<s>"    ,						snake_case__		:					Tuple="<unk>"    ,						snake_case__		:					List[str]="<pad>"    ,						snake_case__		:					Dict="<mask>"    ,						snake_case__		:					Optional[Dict[str, Any]] = None    ,						**snake_case__		:					Tuple    ,						)		->	None:


          '''simple docstring'''


          snake_case       :   int							=      AddedToken(snake_case__    ,						lstrip=snake_case__    ,						rstrip=snake_case__ ) if isinstance(snake_case__    ,						snake_case__ ) else mask_token

          snake_case       :   Tuple							=      {} if sp_model_kwargs is None else sp_model_kwargs

          snake_case       :   Union[str, Any]							=      kwargs.get("additional_special_tokens"    ,						[] )
          kwargs["additional_special_tokens"] += [
              code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=snake_case__    ,						tgt_lang=snake_case__    ,						eos_token=snake_case__    ,						unk_token=snake_case__    ,						sep_token=snake_case__    ,						cls_token=snake_case__    ,						pad_token=snake_case__    ,						mask_token=snake_case__    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**snake_case__    ,						)

          snake_case       :   Tuple							=      spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(str(snake_case__ ) )
          snake_case       :   Tuple							=      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'

          # Mimic fairseq token-to-id alignment for the first 4 token
          snake_case       :   Optional[int]							=      {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          snake_case       :   List[str]							=      1

          snake_case       :   int							=      len(self.sp_model )
          snake_case       :   Any							=      {
              code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(snake_case__ )
          }
          snake_case       :   Union[str, Any]							=      {v: k for k, v in self.lang_code_to_id.items()}
          snake_case       :   List[Any]							=      len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset

          self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
          snake_case       :   Any							=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

          snake_case       :   str							=      src_lang if src_lang is not None else "en_XX"
          snake_case       :   Dict							=      self.lang_code_to_id[self._src_lang]
          snake_case       :   int							=      tgt_lang
          self.set_src_lang_special_tokens(self._src_lang )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	int:


          '''simple docstring'''


          return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1  # Plus 1 for the mask token



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	str:


          '''simple docstring'''


          return self._src_lang



   @src_lang.setter
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					str )		->	None:


          '''simple docstring'''


          snake_case       :   Dict							=      new_src_lang
          self.set_src_lang_special_tokens(self._src_lang )



   def __getstate__(self		:					str )		->	Dict:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self.__dict__.copy()
          snake_case       :   List[str]							=      None
          return state



   def __setstate__(self		:					str    ,						snake_case__		:					Dict )		->	None:


          '''simple docstring'''


          snake_case       :   List[Any]							=      d

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

          snake_case       :   int							=      spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(self.vocab_file )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Dict:


          '''simple docstring'''


          snake_case       :   Dict							=      {self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					str )		->	int:


          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
          snake_case       :   List[Any]							=      self.sp_model.PieceToId(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		:					Optional[int]    ,						snake_case__		:					int )		->	str:


          '''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		:					List[str]    ,						snake_case__		:					List[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   int							=      []
          snake_case       :   Union[str, Any]							=      ""
          snake_case       :   Tuple							=      False
          for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                        if not prev_is_special:
                               out_string += " "
                        out_string += self.sp_model.decode(snake_case__ ) + token
                        snake_case       :   List[Any]							=      True
                        snake_case       :   List[Any]							=      []
                 else:
                        current_sub_tokens.append(snake_case__ )
                        snake_case       :   Optional[Any]							=      False
          out_string += self.sp_model.decode(snake_case__ )
          return out_string.strip()



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


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

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

          return (out_vocab_file,)



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None    ,						snake_case__		:					bool = False )		->	List[int]:


          '''simple docstring'''


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

          snake_case       :   Union[str, Any]							=      [1] * len(self.prefix_tokens )
          snake_case       :   int							=      [1] * len(self.suffix_tokens )
          if token_ids_a is None:
                 return prefix_ones + ([0] * len(snake_case__ )) + suffix_ones
          return prefix_ones + ([0] * len(snake_case__ )) + ([0] * len(snake_case__ )) + suffix_ones



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # 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.suffix_tokens



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str]    ,						snake_case__		:					Optional[str]    ,						**snake_case__		:					Optional[int] )		->	Optional[int]:


          '''simple docstring'''


          if src_lang is None or tgt_lang is None:
                 raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
          snake_case       :   str							=      src_lang
          snake_case       :   List[str]							=      self(snake_case__    ,						add_special_tokens=snake_case__    ,						return_tensors=snake_case__    ,						**snake_case__ )
          snake_case       :   Optional[int]							=      self.convert_tokens_to_ids(snake_case__ )
          snake_case       :   Optional[int]							=      tgt_lang_id
          return inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					List[str]    ,						snake_case__		:					str = "en_XX"    ,						snake_case__		:					Optional[List[str]] = None    ,						snake_case__		:					str = "ro_RO"    ,						**snake_case__		:					Union[str, Any]    ,						)		->	BatchEncoding:


          '''simple docstring'''


          snake_case       :   str							=      src_lang
          snake_case       :   int							=      tgt_lang
          return super().prepare_seqaseq_batch(snake_case__    ,						snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Optional[Any]:


          '''simple docstring'''


          return self.set_src_lang_special_tokens(self.src_lang )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	str:


          '''simple docstring'''


          return self.set_tgt_lang_special_tokens(self.tgt_lang )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					str )		->	None:


          '''simple docstring'''


          snake_case       :   List[str]							=      self.lang_code_to_id[src_lang]
          snake_case       :   List[str]							=      [self.cur_lang_code_id]
          snake_case       :   Any							=      [self.eos_token_id]



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					str )		->	None:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.lang_code_to_id[tgt_lang]
          snake_case       :   Union[str, Any]							=      [self.cur_lang_code_id]
          snake_case       :   Tuple							=      [self.eos_token_id]








"""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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]   )
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[int]	):
    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 					_lowercase (			self							:			str,					UpperCAmelCase__							:			List[Any]	):
    # configuration for running training on smdistributed Model Parallel
    __lowercase								=						{
        "enabled": True,
        "processes_per_host": 8,
    }
    __lowercase								=						{
        "enabled": True,
        "parameters": {
            "microbatches": 4,
            "placement_strategy": "spread",
            "pipeline": "interleaved",
            "optimize": "speed",
            "partitions": 4,
            "ddp": True,
        },
    }

    __lowercase								=						{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

    __lowercase								=						"trainer" if self.script == "run_glue.py" else "smtrainer"
    # 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=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""",					instance_count=UpperCAmelCase__,					instance_type=self.instance_type,					debugger_hook_config=UpperCAmelCase__,					hyperparameters={
            **self.env.hyperparameters,
            "model_name_or_path": self.model_name_or_path,
            "max_steps": 5_0_0,
        },					metric_definitions=self.env.metric_definitions,					distribution=UpperCAmelCase__,					py_version="py36",					)


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			int	):
    TrainingJobAnalytics(UpperCAmelCase__	).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""	)



 @parameterized.expand([(1,)]	)
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, Any]	):
    # create estimator
    __lowercase								=						self.create_estimator(UpperCAmelCase__	)

    # run training
    estimator.fit()

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

    # extract kpis
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]	)
    __lowercase								=						list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]	)
    # get train time from SageMaker job, this includes starting, preprocessing, stopping
    __lowercase								=						(
        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__	)




