




import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




class 			a__       (					UpperCamelCase__      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A=False )  ->    Union[str, Any]:



          '''simple docstring'''



          a  ,      a  ,      a      =      (
              filename_skip_take["filename"],
              filename_skip_take["skip"] if "skip" in filename_skip_take else None,
              filename_skip_take["take"] if "take" in filename_skip_take else None,
          )
          open(os.path.join(A )  ,		"wb" ).close()
          a      =      pa.Table.from_pydict({"filename": [Path(A ).name] * 100} )
          if take == -1:
               a      =      len(A ) - skip
          if skip is not None and take is not None:
               a      =      pa_table.slice(A  ,		A )
          return pa_table







class 			a__       (					UpperCamelCase__      ):







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      "my_name"
          a      =      SplitInfo(name="train"  ,		num_examples=100 )
          a      =      SplitInfo(name="test"  ,		num_examples=100 )
          a      =      [train_info, test_info]
          a      =      SplitDict()
          split_dict.add(A )
          split_dict.add(A )
          a      =      DatasetInfo(splits=A )

          with tempfile.TemporaryDirectory() as tmp_dir:
               a      =      ReaderTest(A  ,		A )

               a      =      "test[:33%]"
               a      =      Dataset(**reader.read(A  ,		A  ,		A ) )
               self.assertEqual(dset["filename"][0]  ,		F'''{name}-test''' )
               self.assertEqual(dset.num_rows  ,		33 )
               self.assertEqual(dset.num_columns  ,		1 )

               a      =      ["train", "test[:33%]"]
               a      =      [Split.TRAIN, ReadInstruction.from_spec("test[:33%]" )]
               for instructions in [instructionsa, instructionsa]:
                    a      =      [reader.read(A  ,		A  ,		A ) for instr in instructions]
                    a  ,      a      =      (Dataset(**A ) for dataset_kwargs in datasets_kwargs)
                    self.assertEqual(train_dset["filename"][0]  ,		F'''{name}-train''' )
                    self.assertEqual(train_dset.num_rows  ,		100 )
                    self.assertEqual(train_dset.num_columns  ,		1 )
                    self.assertIsInstance(train_dset.split  ,		A )
                    self.assertEqual(str(train_dset.split )  ,		"train" )
                    self.assertEqual(test_dset["filename"][0]  ,		F'''{name}-test''' )
                    self.assertEqual(test_dset.num_rows  ,		33 )
                    self.assertEqual(test_dset.num_columns  ,		1 )
                    self.assertIsInstance(test_dset.split  ,		A )
                    self.assertEqual(str(test_dset.split )  ,		"test[:33%]" )
                    del train_dset, test_dset







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          a      =      "my_name"
          a      =      SplitInfo(name="train"  ,		num_examples=1000  ,		shard_lengths=[100] * 10 )
          a      =      [train_info]
          a      =      SplitDict()
          split_dict.add(A )
          a      =      DatasetInfo(splits=A )

          with tempfile.TemporaryDirectory() as tmp_dir:
               a      =      ReaderTest(A  ,		A )

               a      =      "train[:33%]"
               a      =      Dataset(**reader.read(A  ,		A  ,		A ) )
               self.assertEqual(dset["filename"][0]  ,		F'''{name}-train-00000-of-00010''' )
               self.assertEqual(dset["filename"][-1]  ,		F'''{name}-train-00003-of-00010''' )
               self.assertEqual(dset.num_rows  ,		330 )
               self.assertEqual(dset.num_columns  ,		1 )







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          a      =      SplitInfo(name="train"  ,		num_examples=100 )
          a      =      SplitInfo(name="test"  ,		num_examples=100 )
          a      =      SplitDict()
          split_dict.add(A )
          split_dict.add(A )
          a      =      DatasetInfo(splits=A )

          with tempfile.TemporaryDirectory() as tmp_dir:
               a      =      ReaderTest(A  ,		A )

               a      =      [
                   {"filename": os.path.join(A  ,		"train" )},
                   {"filename": os.path.join(A  ,		"test" ), "skip": 10, "take": 10},
               ]
               a      =      Dataset(**reader.read_files(A  ,		original_instructions="train+test[10:20]" ) )
               self.assertEqual(dset.num_rows  ,		110 )
               self.assertEqual(dset.num_columns  ,		1 )
               del dset







@pytest.mark.parametrize("in_memory"		,					[False, True])
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)			->     Dict:
     a      =      arrow_file
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          a      =      ArrowReader.read_table(__UpperCamelCase		,					in_memory=__UpperCamelCase)
     assert table.shape == dataset.data.shape
     assert set(table.column_names) == set(dataset.data.column_names)
     assert dict(table.to_pydict()) == dict(dataset.data.to_pydict())  # to_pydict returns OrderedDict

@pytest.mark.parametrize("in_memory"		,					[False, True])
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)			->     Optional[int]:
     a      =      arrow_file
     a      =      ArrowReader(""		,					__UpperCamelCase)
     with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
          a      =      reader.read_files([{"filename": filename}]		,					in_memory=__UpperCamelCase)
     assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
     a      =      dataset_kwargs["arrow_table"]
     assert table.shape == dataset.data.shape
     assert set(table.column_names) == set(dataset.data.column_names)
     assert dict(table.to_pydict()) == dict(dataset.data.to_pydict())  # to_pydict returns OrderedDict

def 					SCREAMING_SNAKE_CASE							(   )			->     Optional[int]:
     assert ReadInstruction("train"		,					to=10		,					unit="abs").to_spec() == "train[:10]"
     assert ReadInstruction("train"		,					from_=-80		,					to=10		,					unit="%").to_spec() == "train[-80%:10%]"

     a      =      "train+test"
     assert ReadInstruction.from_spec(__UpperCamelCase).to_spec() == spec_train_test

     a      =      "train[2:10]"
     assert ReadInstruction.from_spec(__UpperCamelCase).to_spec() == spec_train_abs

     a      =      "train[15%:-20%]"
     assert ReadInstruction.from_spec(__UpperCamelCase).to_spec() == spec_train_pct

     a      =      "train[:10%](closest)"
     assert ReadInstruction.from_spec(__UpperCamelCase).to_spec() == "train[:10%]"

     a      =      "train[:10%](pct1_dropremainder)"
     assert ReadInstruction.from_spec(__UpperCamelCase).to_spec() == spec_train_pct_rounding

     a      =      "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
     assert ReadInstruction.from_spec(__UpperCamelCase).to_spec() == spec_train_test_pct_rounding

def 					SCREAMING_SNAKE_CASE							(   )			->     List[Any]:
     a      =      "dummy"
     a      =      [SplitInfo(name="train"		,					num_examples=1_00)]
     a      =      "train[:33%]"
     a      =      "arrow"
     a      =      "prefix"

     a      =      make_file_instructions(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)
     assert isinstance(__UpperCamelCase		,					__UpperCamelCase)
     assert file_instructions.num_examples == 33
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(__UpperCamelCase		,					f'''{name}-train.arrow'''), "skip": 0, "take": 33}
     ]

     a      =      [SplitInfo(name="train"		,					num_examples=1_00		,					shard_lengths=[10] * 10)]
     a      =      make_file_instructions(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)
     assert isinstance(__UpperCamelCase		,					__UpperCamelCase)
     assert file_instructions.num_examples == 33
     assert file_instructions.file_instructions == [
         {"filename": os.path.join(__UpperCamelCase		,					f'''{name}-train-00000-of-00010.arrow'''), "skip": 0, "take": -1},
         {"filename": os.path.join(__UpperCamelCase		,					f'''{name}-train-00001-of-00010.arrow'''), "skip": 0, "take": -1},
         {"filename": os.path.join(__UpperCamelCase		,					f'''{name}-train-00002-of-00010.arrow'''), "skip": 0, "take": -1},
         {"filename": os.path.join(__UpperCamelCase		,					f'''{name}-train-00003-of-00010.arrow'''), "skip": 0, "take": 3},
     ]





@pytest.mark.parametrize("name, expected_exception"		,					[(None, TypeError), ("", ValueError)])
def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Union[str, Any]:
     a      =      [SplitInfo(name="train"		,					num_examples=1_00)]
     a      =      "train"
     a      =      "arrow"
     a      =      "prefix_path"
     with pytest.raises(__UpperCamelCase):
          a      =      make_file_instructions(__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase)







import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      filter(lambda __UpperCamelCase: p.requires_grad		,					model.parameters())
     a      =      sum([np.prod(p.size()) for p in model_parameters])
     return params


lowercase__      :			Optional[int]           =					logging.getLogger(__name__)




class 			a__       (					pl.Callback      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    str:



          '''simple docstring'''



          a      =      {F'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
          pl_module.logger.log_metrics(A )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A=True )  ->    None:



          '''simple docstring'''



          logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
          a      =      trainer.callback_metrics
          trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
          # Log results
          a      =      Path(pl_module.hparams.output_dir )
          if type_path == "test":
               a      =      od / "test_results.txt"
               a      =      od / "test_generations.txt"
          else:
               # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
               # If people want this it will be easy enough to add back.
               a      =      od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
               a      =      od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
               results_file.parent.mkdir(exist_ok=A )
               generations_file.parent.mkdir(exist_ok=A )
          with open(A  ,		"a+" ) as writer:
               for key in sorted(A ):
                    if key in ["log", "progress_bar", "preds"]:
                         continue
                    a      =      metrics[key]
                    if isinstance(A  ,		torch.Tensor ):
                         a      =      val.item()
                    a      =      F'''{key}: {val:.6f}\n'''
                    writer.write(A )

          if not save_generations:
               return

          if "preds" in metrics:
               a      =      "\n".join(metrics["preds"] )
               generations_file.open("w+" ).write(A )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Any:



          '''simple docstring'''



          try:
               a      =      pl_module.model.model.num_parameters()
          except AttributeError:
               a      =      pl_module.model.num_parameters()

          a      =      count_trainable_parameters(A )
          # mp stands for million parameters
          trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6} )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Optional[int]:



          '''simple docstring'''



          save_json(pl_module.metrics  ,		pl_module.metrics_save_path )
          return self._write_logs(A  ,		A  ,		"test" )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Dict:



          '''simple docstring'''



          save_json(pl_module.metrics  ,		pl_module.metrics_save_path )
          # Uncommenting this will save val generations
          # return self._write_logs(trainer, pl_module, "valid")







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase=1		,					__UpperCamelCase=False)			->     Optional[int]:
     if metric == "rouge2":
          a      =      "{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
          a      =      "{val_avg_bleu:.4f}-{step_count}"
     elif metric == "loss":
          a      =      "{val_avg_loss:.4f}-{step_count}"
     else:
          raise NotImplementedError(
              f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
              " this function.")

     a      =      ModelCheckpoint(
         dirpath=__UpperCamelCase		,					filename=__UpperCamelCase		,					monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					save_top_k=__UpperCamelCase		,					)
     return checkpoint_callback





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Union[str, Any]:
     return EarlyStopping(
         monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					patience=__UpperCamelCase		,					verbose=__UpperCamelCase		,					)

