





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase        =				get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
							_UpperCamelCase				:					List[str]         =   XLMProphetNetTokenizer
							_UpperCamelCase				:					Dict         =   False
							_UpperCamelCase				:					Optional[int]         =   True

							def lowercase (		self: Any		)       -> str:




														"""simple docstring"""


														super().setUp()

														# We have a SentencePiece fixture for testing
														UpperCamelCase_          =  XLMProphetNetTokenizer(_SCREAMING_SNAKE_CASE					,   keep_accents=_SCREAMING_SNAKE_CASE		)
														tokenizer.save_pretrained(self.tmpdirname		)

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




														"""simple docstring"""


														UpperCamelCase_          =  "[PAD]"
														UpperCamelCase_          =  0

														self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
														self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)

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




														"""simple docstring"""


														UpperCamelCase_          =  list(self.get_tokenizer().get_vocab().keys()		)

														self.assertEqual(vocab_keys[0]					,   "[PAD]"		)
														self.assertEqual(vocab_keys[1]					,   "[CLS]"		)
														self.assertEqual(vocab_keys[-1]					,   "j"		)
														self.assertEqual(len(_SCREAMING_SNAKE_CASE		)					,   1012		)

							def lowercase (		self: str		)       -> List[Any]:




														"""simple docstring"""


														self.assertEqual(self.get_tokenizer().vocab_size					,   1012		)

							def lowercase (		self: List[Any]		)       -> str:




														"""simple docstring"""


														UpperCamelCase_          =  XLMProphetNetTokenizer(_SCREAMING_SNAKE_CASE					,   keep_accents=_SCREAMING_SNAKE_CASE		)

														UpperCamelCase_          =  tokenizer.tokenize("This is a test"		)
														self.assertListEqual(_SCREAMING_SNAKE_CASE					,   ["▁This", "▁is", "▁a", "▁t", "est"]		)

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)					,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					,   )

														UpperCamelCase_          =  tokenizer.tokenize("I was born in 92000, and this is falsé."		)
														self.assertListEqual(
														    _SCREAMING_SNAKE_CASE					,   [
														        SPIECE_UNDERLINE + "I",
														        SPIECE_UNDERLINE + "was",
														        SPIECE_UNDERLINE + "b",
														        "or",
														        "n",
														        SPIECE_UNDERLINE + "in",
														        SPIECE_UNDERLINE + "",
														        "9",
														        "2",
														        "0",
														        "0",
														        "0",
														        ",",
														        SPIECE_UNDERLINE + "and",
														        SPIECE_UNDERLINE + "this",
														        SPIECE_UNDERLINE + "is",
														        SPIECE_UNDERLINE + "f",
														        "al",
														        "s",
														        "é",
														        ".",
														    ]					,   )
														UpperCamelCase_          =  tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)
														self.assertListEqual(
														    _SCREAMING_SNAKE_CASE					,   [
														        value + tokenizer.fairseq_offset
														        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
														    ]					,   )

														UpperCamelCase_          =  tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		)
														self.assertListEqual(
														    _SCREAMING_SNAKE_CASE					,   [
														        SPIECE_UNDERLINE + "I",
														        SPIECE_UNDERLINE + "was",
														        SPIECE_UNDERLINE + "b",
														        "or",
														        "n",
														        SPIECE_UNDERLINE + "in",
														        SPIECE_UNDERLINE + "",
														        "[UNK]",
														        "2",
														        "0",
														        "0",
														        "0",
														        ",",
														        SPIECE_UNDERLINE + "and",
														        SPIECE_UNDERLINE + "this",
														        SPIECE_UNDERLINE + "is",
														        SPIECE_UNDERLINE + "f",
														        "al",
														        "s",
														        "[UNK]",
														        ".",
														    ]					,   )

							@cached_property
							def lowercase (		self: Dict		)       -> int:




														"""simple docstring"""


														return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"		)

							@slow
							def lowercase (		self: Union[str, Any]		)       -> int:




														"""simple docstring"""


														UpperCamelCase_          =  "Hello World!"
														UpperCamelCase_          =  [35389, 6672, 49, 2]
														self.assertListEqual(_SCREAMING_SNAKE_CASE					,   self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE		)		)



							@slow
							def lowercase (		self: Any		)       -> str:




														"""simple docstring"""


														UpperCamelCase_          =  {"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
														# fmt: on

														self.tokenizer_integration_test_util(
														    expected_encoding=_SCREAMING_SNAKE_CASE					,   model_name="microsoft/xprophetnet-large-wiki100-cased"					,   revision="1acad1643ddd54a44df6a1b797ada8373685d90e"					,   )








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''': 6_5_0, '''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''': 6_0_0, '''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''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class 							_UpperCamelCase					(	unittest.TestCase     ):

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




              """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 lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
              # distributed data settings
              UpperCamelCase_          =  {"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=_SCREAMING_SNAKE_CASE					,   instance_count=_SCREAMING_SNAKE_CASE					,   instance_type=self.instance_type					,   debugger_hook_config=_SCREAMING_SNAKE_CASE					,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}					,   metric_definitions=self.env.metric_definitions					,   distribution=_SCREAMING_SNAKE_CASE					,   py_version="py36"					,   )

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




              """simple docstring"""


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



       @parameterized.expand([(2,)]		)
       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.create_estimator(_SCREAMING_SNAKE_CASE		)

              # run training
              estimator.fit()

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

              # extract kpis
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
              # get train time from SageMaker job, this includes starting, preprocessing, stopping
              UpperCamelCase_          =  (
                  Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"					,   999999		)
              )

              # 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		)

