'''simple docstring'''






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_rag import save_json





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str				):
     _A    		=  filter(lambda __snake_case				: p.requires_grad   ,		model.parameters()				)
     _A    		=  sum([np.prod(p.size()				) for p in model_parameters]				)
     return params


_UpperCAmelCase    :     Any         =       logging.getLogger(__name__)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			Optional[int]				):
     if metric == "rouge2":
          _A    		=  '{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          _A    		=  '{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          _A    		=  '{val_avg_em:.4f}-{step_count}'
     elif metric == "loss":
          _A    		=  '{val_avg_loss:.4f}-{step_count}'
     else:
          raise NotImplementedError(
              F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
              ' function.'				)

     _A    		=  ModelCheckpoint(
         dirpath=__snake_case   ,		filename=__snake_case   ,		monitor=F'val_{metric}'   ,		mode='max'   ,		save_top_k=1   ,		every_n_epochs=1   ,		)

     return checkpoint_callback





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any   ,		__snake_case							:			List[Any]				):
     return EarlyStopping(
         monitor=F'val_{metric}'   ,		mode='min' if 'loss' in metric else 'max'   ,		patience=__snake_case   ,		verbose=__snake_case   ,		)







class lowercase_       (		pl.Callback			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			List[str], UpperCamelCase__      :			Tuple     )  ->			Union[str, Any]:
        _A    		=  {f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
        pl_module.logger.log_metrics(UpperCamelCase__     )
   @rank_zero_only
   def 	__UpperCAmelCase   (      self      :			List[Any], UpperCamelCase__      :			pl.Trainer, UpperCamelCase__      :			pl.LightningModule, UpperCamelCase__      :			str, UpperCamelCase__      :			Dict=True     )  ->			None:
        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=UpperCamelCase__     )
             generations_file.parent.mkdir(exist_ok=UpperCamelCase__     )
        with open(UpperCamelCase__, 'a+'     ) as writer:
             for key in sorted(UpperCamelCase__     ):
                  if key in ["log", "progress_bar", "preds"]:
                       continue
                  _A    		=  metrics[key]
                  if isinstance(UpperCamelCase__, torch.Tensor     ):
                       _A    		=  val.item()
                  _A    		=  f'{key}: {val:.6f}\n'
                  writer.write(UpperCamelCase__     )

        if not save_generations:
             return

        if "preds" in metrics:
             _A    		=  '\n'.join(metrics['preds']     )
             generations_file.open('w+'     ).write(UpperCamelCase__     )
   @rank_zero_only
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			Dict, UpperCamelCase__      :			List[Any]     )  ->			Union[str, Any]:
        try:
             _A    		=  pl_module.model.model.num_parameters()
        except AttributeError:
             _A    		=  pl_module.model.num_parameters()

        _A    		=  count_trainable_parameters(UpperCamelCase__     )
        # 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 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			pl.Trainer, UpperCamelCase__      :			pl.LightningModule     )  ->			Any:
        save_json(pl_module.metrics, pl_module.metrics_save_path     )
        return self._write_logs(UpperCamelCase__, UpperCamelCase__, 'test'     )

   @rank_zero_only
   def 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			pl.Trainer, UpperCamelCase__      :			str     )  ->			Optional[Any]:
        save_json(pl_module.metrics, pl_module.metrics_save_path     )
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")




'''simple docstring'''






import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


_UpperCAmelCase    :     Optional[int]         =       '''\
    Text data.
    Second line of data.'''

_UpperCAmelCase    :     Optional[Any]         =       '''file'''





@pytest.fixture(scope='session'				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _A    		=  tmp_path_factory.mktemp('data'				) / (FILE_PATH + '.zstd')
     _A    		=  bytes(__snake_case   ,		'utf-8'				)
     with zstd.open(__snake_case   ,		'wb'				) as f:
          f.write(__snake_case				)
     return path





@pytest.fixture
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     with open(os.path.join(tmpfs.local_root_dir   ,		__snake_case				)   ,		'w'				) as f:
          f.write(__snake_case				)
     return FILE_PATH





@pytest.mark.parametrize('compression_format'   ,		['gzip', 'xz', 'zstd']				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict   ,		__snake_case							:			str   ,		__snake_case							:			Any   ,		__snake_case							:			Any   ,		__snake_case							:			List[str]   ,		__snake_case							:			List[str]				):
     _A    		=  {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
     _A    		=  input_paths[compression_format]
     _A    		=  tmp_path / 'cache'
     _A    		=  DownloadConfig(cache_dir=__snake_case   ,		extract_compressed_file=__snake_case				)
     _A    		=  cached_path(__snake_case   ,		download_config=__snake_case				)
     with open(__snake_case				) as f:
          _A    		=  f.read()
     with open(__snake_case				) as f:
          _A    		=  f.read()
     assert extracted_file_content == expected_file_content





@pytest.mark.parametrize('default_extracted'   ,		[True, False]				)
@pytest.mark.parametrize('default_cache_dir'   ,		[True, False]				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]   ,		__snake_case							:			List[str]   ,		__snake_case							:			Union[str, Any]   ,		__snake_case							:			int   ,		__snake_case							:			List[str]				):
     _A    		=  'custom_cache'
     _A    		=  'custom_extracted_dir'
     _A    		=  tmp_path / 'custom_extracted_path'
     if default_extracted:
          _A    		=  ('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
     else:
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'   ,		__snake_case				)
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,		str(__snake_case				)				)
          _A    		=  custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

     _A    		=  xz_file
     _A    		=  (
         DownloadConfig(extract_compressed_file=__snake_case				)
         if default_cache_dir
         else DownloadConfig(cache_dir=tmp_path / custom_cache_dir   ,		extract_compressed_file=__snake_case				)
     )
     _A    		=  cached_path(__snake_case   ,		download_config=__snake_case				)
     assert Path(__snake_case				).parent.parts[-2:] == expected





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str				):
     # absolute path
     _A    		=  str(Path(__snake_case				).resolve()				)
     assert cached_path(__snake_case				) == text_file
     # relative path
     _A    		=  str(Path(__snake_case				).resolve().relative_to(Path(os.getcwd()				)				)				)
     assert cached_path(__snake_case				) == text_file





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]				):
     # absolute path
     _A    		=  str(tmp_path.resolve() / '__missing_file__.txt'				)
     with pytest.raises(__snake_case				):
          cached_path(__snake_case				)
     # relative path
     _A    		=  './__missing_file__.txt'
     with pytest.raises(__snake_case				):
          cached_path(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  get_from_cache(F'tmp://{tmpfs_file}'				)
     with open(__snake_case				) as f:
          _A    		=  f.read()
     assert output_file_content == FILE_CONTENT





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      ):
     with pytest.raises(__snake_case				):
          cached_path('https://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          http_get('https://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          http_head('https://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          ftp_get('ftp://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          ftp_head('ftp://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          fsspec_get('s3://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          fsspec_head('s3://huggingface.co'				)



