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 ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  List[str]:
        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=__lowerCAmelCase     ,       )
        assert hasattr(self     ,       '''env'''			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
        lowercase__    :   Any						= F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
        # distributed data settings
        lowercase__    :   Dict						= {'''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=__lowerCAmelCase     ,       instance_count=__lowerCAmelCase     ,       instance_type=self.instance_type     ,       debugger_hook_config=__lowerCAmelCase     ,       hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}     ,       metric_definitions=self.env.metric_definitions     ,       distribution=__lowerCAmelCase     ,       py_version='''py36'''     ,       )

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        TrainingJobAnalytics(__lowerCAmelCase			).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""			)


       @parameterized.expand([(2,)]			)
       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  int:
        # create estimator
        lowercase__    :   List[Any]						= self.create_estimator(__lowerCAmelCase			)

        # run training
        estimator.fit()

        # result dataframe
        lowercase__    :   List[Any]						= TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

        # extract kpis
        lowercase__    :   Any						= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']			)
        lowercase__    :   Tuple						= list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']			)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        lowercase__    :   List[Any]						= (
            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}     ,       __lowerCAmelCase			)


'''simple docstring'''

import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__a:			Tuple	       =       datasets.utils.logging.get_logger(__name__)



@dataclass
class       UpperCAmelCase  (						datasets.BuilderConfig ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   "utf-8"
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   True  # deprecated
       SCREAMING_SNAKE_CASE 		=   None  # deprecated
       SCREAMING_SNAKE_CASE 		=   1_0 << 2_0  # 10MB
       SCREAMING_SNAKE_CASE 		=   None



class       UpperCAmelCase  (						datasets.ArrowBasedBuilder ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   JsonConfig

       def _lowerCAmelCase(						self			)  ->  Any:
        if self.config.block_size is not None:
         logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''			)
         lowercase__    :   Tuple						= self.config.block_size
        if self.config.use_threads is not True:
         logger.warning(
             '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''			)
        if self.config.newlines_in_values is not None:
         raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''			)
        return datasets.DatasetInfo(features=self.config.features			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""			)
        lowercase__    :   List[str]						= dl_manager.download_and_extract(self.config.data_files			)
        if isinstance(__lowerCAmelCase     ,       (str, list, tuple)			):
         lowercase__    :   List[str]						= data_files
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Union[str, Any]						= [files]
         lowercase__    :   Dict						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,       gen_kwargs={'''files''': files}			)]
        lowercase__    :   str						= []
        for split_name, files in data_files.items():
         if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   List[str]						= [files]
         lowercase__    :   Optional[Any]						= [dl_manager.iter_files(__lowerCAmelCase			) for file in files]
         splits.append(datasets.SplitGenerator(name=__lowerCAmelCase     ,       gen_kwargs={'''files''': files}			)			)
        return splits

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  pa.Table:
        if self.config.features is not None:
         # adding missing columns
         for column_name in set(self.config.features			) - set(pa_table.column_names			):
          lowercase__    :   Optional[int]						= self.config.features.arrow_schema.field(__lowerCAmelCase			).type
          lowercase__    :   Union[str, Any]						= pa_table.append_column(__lowerCAmelCase     ,       pa.array([None] * len(__lowerCAmelCase			)     ,       type=__lowerCAmelCase			)			)
         # more expensive cast to support nested structures with keys in a different order
         # allows str <-> int/float or str to Audio for example
         lowercase__    :   Dict						= table_cast(__lowerCAmelCase     ,       self.config.features.arrow_schema			)
        return pa_table


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[int]:
        for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase			)			):
         # If the file is one json object and if we need to look at the list of items in one specific field
         if self.config.field is not None:
          with open(__lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
           lowercase__    :   Dict						= json.load(__lowerCAmelCase			)

          # We keep only the field we are interested in
          lowercase__    :   Any						= dataset[self.config.field]

          # We accept two format: a list of dicts or a dict of lists
          if isinstance(__lowerCAmelCase     ,       (list, tuple)			):
           lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
           lowercase__    :   List[Any]						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
          else:
           lowercase__    :   Union[str, Any]						= dataset
          lowercase__    :   Optional[int]						= pa.Table.from_pydict(__lowerCAmelCase			)
          yield file_idx, self._cast_table(__lowerCAmelCase			)

         # If the file has one json object per line
         else:
          with open(__lowerCAmelCase     ,       '''rb'''			) as f:
           lowercase__    :   Optional[int]						= 0
           # Use block_size equal to the chunk size divided by 32 to leverage multithreading
           # Set a default minimum value of 16kB if the chunk size is really small
           lowercase__    :   List[str]						= max(self.config.chunksize // 32     ,       16 << 10			)
           lowercase__    :   Union[str, Any]						= (
               self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
           )
           while True:
            lowercase__    :   Tuple						= f.read(self.config.chunksize			)
            if not batch:
             break
            # Finish current line
            try:
             batch += f.readline()
            except (AttributeError, io.UnsupportedOperation):
             batch += readline(__lowerCAmelCase			)
            # PyArrow only accepts utf-8 encoded bytes
            if self.config.encoding != "utf-8":
             lowercase__    :   Dict						= batch.decode(self.config.encoding     ,       errors=__lowerCAmelCase			).encode('''utf-8'''			)
            try:
             while True:
              try:
               lowercase__    :   str						= paj.read_json(
                   io.BytesIO(__lowerCAmelCase			)     ,       read_options=paj.ReadOptions(block_size=__lowerCAmelCase			)			)
               break
              except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
               if (
                   isinstance(__lowerCAmelCase     ,       pa.ArrowInvalid			)
                   and "straddling" not in str(__lowerCAmelCase			)
                   or block_size > len(__lowerCAmelCase			)
               ):
                raise
               else:
                # Increase the block size in case it was too small.
                # The block size will be reset for the next file.
                logger.debug(
                    F"""Batch of {len(__lowerCAmelCase			)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""			)
                block_size *= 2
            except pa.ArrowInvalid as e:
             try:
              with open(
                  __lowerCAmelCase     ,       encoding=self.config.encoding     ,       errors=self.config.encoding_errors			) as f:
               lowercase__    :   Optional[int]						= json.load(__lowerCAmelCase			)
             except json.JSONDecodeError:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
              raise e
             # If possible, parse the file as a list of json objects and exit the loop
             if isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):  # list is the only sequence type supported in JSON
              try:
               lowercase__    :   List[Any]						= set().union(*[row.keys() for row in dataset]			)
               lowercase__    :   str						= {col: [row.get(__lowerCAmelCase			) for row in dataset] for col in keys}
               lowercase__    :   Optional[int]						= pa.Table.from_pydict(__lowerCAmelCase			)
              except (pa.ArrowInvalid, AttributeError) as e:
               logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
               raise ValueError(F"""Not able to read records in the JSON file at {file}."""			) from None
              yield file_idx, self._cast_table(__lowerCAmelCase			)
              break
             else:
              logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase			)}: {e}"""			)
              raise ValueError(
                  F"""Not able to read records in the JSON file at {file}. """
                  F"""You should probably indicate the field of the JSON file containing your records. """
                  F"""This JSON file contain the following fields: {str(list(dataset.keys()			)			)}. """
                  F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """			) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
            yield (file_idx, batch_idx), self._cast_table(__lowerCAmelCase			)
            batch_idx += 1

