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': 16_00, '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': 16_00, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]					)
class 					A							(    unittest.TestCase					):


   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[Any]:
         """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       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  {
             "enabled": True,
             "processes_per_host": 8,
         }
         UpperCAmelCase__								=  {
             "enabled": True,
             "parameters": {
                 "microbatches": 4,
                 "placement_strategy": "spread",
                 "pipeline": "interleaved",
                 "optimize": "speed",
                 "partitions": 4,
                 "ddp": True,
             },
         }

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

         UpperCAmelCase__								=  "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					:						str	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         TrainingJobAnalytics(__UpperCAmelCase		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)





   @parameterized.expand([(1,)]		)
   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						List[str]		)						->    Any:
         """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		)




import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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


UpperCamelCase__    							= '▁'

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





@require_sentencepiece
@require_tokenizers
class 					A							(    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			int              =      BigBirdTokenizer
   __UpperCAmelCase			:			Optional[int]              =      BigBirdTokenizerFast
   __UpperCAmelCase			:			Union[str, Any]              =      True
   __UpperCAmelCase			:			List[Any]              =      True


   def       lowercase_				(self					:						Dict		)						->    List[str]:
         """simple docstring"""



         super().setUp()

         UpperCAmelCase__								=  self.tokenizer_class(__UpperCAmelCase	,  keep_accents=__UpperCAmelCase		)
         tokenizer.save_pretrained(self.tmpdirname		)


   def       lowercase_				(self					:						int		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  "<s>"
         UpperCAmelCase__								=  1

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


   def       lowercase_				(self					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



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

         self.assertEqual(vocab_keys[0]	,  "<unk>"		)
         self.assertEqual(vocab_keys[1]	,  "<s>"		)
         self.assertEqual(vocab_keys[-1]	,  "[MASK]"		)
         self.assertEqual(len(__UpperCAmelCase		)	,  1_0_0_4		)


   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[Any]:
         """simple docstring"""



         self.assertEqual(self.get_tokenizer().vocab_size	,  1_0_0_0		)


   def       lowercase_				(self					:						Union[str, Any]		)						->    Any:
         """simple docstring"""



         if not self.test_rust_tokenizer:
               return

         UpperCAmelCase__								=  self.get_tokenizer()
         UpperCAmelCase__								=  self.get_rust_tokenizer()

         UpperCAmelCase__								=  "I was born in 92000, and this is falsé."

         UpperCAmelCase__								=  tokenizer.tokenize(__UpperCAmelCase		)
         UpperCAmelCase__								=  rust_tokenizer.tokenize(__UpperCAmelCase		)
         self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  tokenizer.encode(__UpperCAmelCase	,  add_special_tokens=__UpperCAmelCase		)
         UpperCAmelCase__								=  rust_tokenizer.encode(__UpperCAmelCase	,  add_special_tokens=__UpperCAmelCase		)
         self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  self.get_rust_tokenizer()
         UpperCAmelCase__								=  tokenizer.encode(__UpperCAmelCase		)
         UpperCAmelCase__								=  rust_tokenizer.encode(__UpperCAmelCase		)
         self.assertListEqual(__UpperCAmelCase	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						str		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  BigBirdTokenizer(__UpperCAmelCase	,  keep_accents=__UpperCAmelCase		)

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

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__UpperCAmelCase		)	,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]	,  )

         UpperCAmelCase__								=  tokenizer.tokenize("I was born in 92000, and this is falsé."		)
         self.assertListEqual(
             __UpperCAmelCase	,  [
                 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(__UpperCAmelCase		)
         self.assertListEqual(
             __UpperCAmelCase	,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]	,  )

         UpperCAmelCase__								=  tokenizer.convert_ids_to_tokens(__UpperCAmelCase		)
         self.assertListEqual(
             __UpperCAmelCase	,  [
                 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					:						Optional[int]		)						->    Union[str, Any]:
         """simple docstring"""



         return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"		)


   @slow
   def       lowercase_				(self					:						str		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  "Hello World!"
         UpperCAmelCase__								=  [6_5, 1_8_5_3_6, 2_2_6_0, 1_0_1, 6_6]

         self.assertListEqual(__UpperCAmelCase	,  self.big_tokenizer.encode(__UpperCAmelCase		)		)


   @slow
   def       lowercase_				(self					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  (
             "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
             " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
         )
         # fmt: off
         UpperCAmelCase__								=  [6_5, 8_7_1, 4_1_9, 3_5_8, 9_4_6, 9_9_1, 2_5_2_1, 4_5_2, 3_5_8, 1_3_5_7, 3_8_7, 7_7_5_1, 3_5_3_6, 1_1_2, 9_8_5, 4_5_6, 1_2_6, 8_6_5, 9_3_8, 5_4_0_0, 5_7_3_4, 4_5_8, 1_3_6_8, 4_6_7, 7_8_6, 2_4_6_2, 5_2_4_6, 1_1_5_9, 6_3_3, 8_6_5, 4_5_1_9, 4_5_7, 5_8_2, 8_5_2, 2_5_5_7, 4_2_7, 9_1_6, 5_0_8, 4_0_5, 3_4_3_2_4, 4_9_7, 3_9_1, 4_0_8, 1_1_3_4_2, 1_2_4_4, 3_8_5, 1_0_0, 9_3_8, 9_8_5, 4_5_6, 5_7_4, 3_6_2, 1_2_5_9_7, 3_2_0_0, 3_1_2_9, 1_1_7_2, 6_6]  # noqa: E231
         # fmt: on
         self.assertListEqual(__UpperCAmelCase	,  self.big_tokenizer.encode(__UpperCAmelCase		)		)


   @require_torch
   @slow
   def       lowercase_				(self					:						List[str]		)						->    int:
         """simple docstring"""



         import torch

         from transformers import BigBirdConfig, BigBirdModel

         # Build sequence
         UpperCAmelCase__								=  list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)
         UpperCAmelCase__								=  self.big_tokenizer.encode_plus(__UpperCAmelCase	,  return_tensors="pt"	,  return_token_type_ids=__UpperCAmelCase		)
         UpperCAmelCase__								=  self.big_tokenizer.batch_encode_plus(
             [sequence + " " + sequence]	,  return_tensors="pt"	,  return_token_type_ids=__UpperCAmelCase		)

         UpperCAmelCase__								=  BigBirdConfig(attention_type="original_full"		)
         UpperCAmelCase__								=  BigBirdModel(__UpperCAmelCase		)

         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
               model(**__UpperCAmelCase		)
               model(**__UpperCAmelCase		)


   @slow
   def       lowercase_				(self					:						str		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"		)
         UpperCAmelCase__								=  tokenizer.decode(tokenizer("Paris is the [MASK]."		).input_ids		)

         self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"		)





   @slow
   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  {"input_ids": [[6_5, 3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4, 6_6], [6_5, 4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [6_5, 4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 6_6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=__UpperCAmelCase	,  model_name="google/bigbird-roberta-base"	,  revision="215c99f1600e06f83acce68422f2035b2b5c3510"	,  )



