
'''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 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def       UpperCAmelCase				(			self   ):

      '''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=UpperCamelCase__ ,   )
      assert hasattr(self ,   '''env'''   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''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=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 ,   lowerCamelCase__   ):

      '''simple docstring'''







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







   @parameterized.expand([(2,)]   )
   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.create_estimator(UpperCamelCase__   )

      # 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''' ,   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__   )







'''simple docstring'''


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_ :  Tuple     =					get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							PegasusTokenizer
   _snake_case		   =							PegasusTokenizerFast
   _snake_case		   =							True
   _snake_case		   =							True







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      super().setUp()

      # We have a SentencePiece fixture for testing
      UpperCamelCase       =  PegasusTokenizer(lowerCamelCase__   )
      tokenizer.save_pretrained(self.tmpdirname   )







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained('''google/pegasus-large'''   )







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return ("This is a test", "This is a test")







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  '''</s>'''
      UpperCamelCase       =  1

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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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

      self.assertEqual(vocab_keys[0] ,   '''<pad>'''   )
      self.assertEqual(vocab_keys[1] ,   '''</s>'''   )
      self.assertEqual(vocab_keys[-1] ,   '''v'''   )
      self.assertEqual(len(lowerCamelCase__   ) ,   1_1_0_3   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self.assertEqual(self.get_tokenizer().vocab_size ,   1_1_0_3   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  self.tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  (
          '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
          ''' </s> <pad> <pad> <pad>'''
      )
      UpperCamelCase       =  rust_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      UpperCamelCase       =  py_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      UpperCamelCase       =  '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
      UpperCamelCase       =  [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      UpperCamelCase       =  tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 9_6_1_0_3
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 1_0_3
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1_0_2_4
      UpperCamelCase       =  '''To ensure a smooth flow of bank resolutions.'''
      UpperCamelCase       =  [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      UpperCamelCase       =  tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]   ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







   @require_torch
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ['''This is going to be way too long.''' * 1_5_0, '''short example''']
      UpperCamelCase       =  ['''not super long but more than 5 tokens''', '''tiny''']
      UpperCamelCase       =  self._large_tokenizer(lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )
      UpperCamelCase       =  self._large_tokenizer(
          text_target=lowerCamelCase__ ,   max_length=5 ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )

      assert batch.input_ids.shape == (2, 1_0_2_4)
      assert batch.attention_mask.shape == (2, 1_0_2_4)
      assert targets["input_ids"].shape == (2, 5)
      assert len(lowerCamelCase__   ) == 2  # input_ids, attention_mask.







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {'''input_ids''': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 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], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 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]], '''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, 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, 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=lowerCamelCase__ ,   model_name='''google/bigbird-pegasus-large-arxiv''' ,   revision='''ba85d0851d708441f91440d509690f1ab6353415''' ,   )




@require_sentencepiece
@require_tokenizers
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							PegasusTokenizer
   _snake_case		   =							PegasusTokenizerFast
   _snake_case		   =							True
   _snake_case		   =							True







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      super().setUp()

      # We have a SentencePiece fixture for testing
      UpperCamelCase       =  PegasusTokenizer(lowerCamelCase__ ,   offset=0 ,   mask_token_sent=lowerCamelCase__ ,   mask_token='''[MASK]'''   )
      tokenizer.save_pretrained(self.tmpdirname   )







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''   )







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return ("This is a test", "This is a test")







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  self.tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  (
          '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
          ''' <pad> <pad> <pad>'''
      )
      UpperCamelCase       =  rust_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      UpperCamelCase       =  py_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   @require_torch
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ['''This is going to be way too long.''' * 1_0_0_0, '''short example''']
      UpperCamelCase       =  ['''not super long but more than 5 tokens''', '''tiny''']
      UpperCamelCase       =  self._large_tokenizer(lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )
      UpperCamelCase       =  self._large_tokenizer(
          text_target=lowerCamelCase__ ,   max_length=5 ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )

      assert batch.input_ids.shape == (2, 4_0_9_6)
      assert batch.attention_mask.shape == (2, 4_0_9_6)
      assert targets["input_ids"].shape == (2, 5)
      assert len(lowerCamelCase__   ) == 2  # input_ids, attention_mask.







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  (
          '''This is an example string that is used to test the original TF implementation against the HF'''
          ''' implementation'''
      )

      UpperCamelCase       =  self._large_tokenizer(lowerCamelCase__   ).input_ids

      self.assertListEqual(
          lowerCamelCase__ ,   [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] ,   )





