
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__      ( __lowerCAmelCase							):
    snake_case__  =			filter(lambda __lowerCAmelCase							: p.requires_grad      ,						model.parameters()							)
    snake_case__  =			sum([np.prod(p.size()							) for p in model_parameters]							)
    return params


__magic_name__           =					logging.getLogger(__name__)




class    _SCREAMING_SNAKE_CASE					( pl.Callback	):
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			{F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
          pl_module.logger.log_metrics(lowerCamelCase	)
      @rank_zero_only
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase=True	):
          logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""	)
          snake_case__  =			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
          snake_case__  =			Path(pl_module.hparams.output_dir	)
          if type_path == "test":
              snake_case__  =			od / "test_results.txt"
              snake_case__  =			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.
              snake_case__  =			od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
              snake_case__  =			od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
              results_file.parent.mkdir(exist_ok=lowerCamelCase	)
              generations_file.parent.mkdir(exist_ok=lowerCamelCase	)
          with open(lowerCamelCase						,		"a+"	) as writer:
              for key in sorted(lowerCamelCase	):
                  if key in ["log", "progress_bar", "preds"]:
                      continue
                  snake_case__  =			metrics[key]
                  if isinstance(lowerCamelCase						,		torch.Tensor	):
                      snake_case__  =			val.item()
                  snake_case__  =			F"""{key}: {val:.6f}\n"""
                  writer.write(lowerCamelCase	)

          if not save_generations:
              return

          if "preds" in metrics:
              snake_case__  =			"\n".join(metrics["preds"]	)
              generations_file.open("w+"	).write(lowerCamelCase	)
      @rank_zero_only
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):
          try:
              snake_case__  =			pl_module.model.model.num_parameters()
          except AttributeError:
              snake_case__  =			pl_module.model.num_parameters()

          snake_case__  =			count_trainable_parameters(lowerCamelCase	)
          # 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 A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):
          save_json(pl_module.metrics						,		pl_module.metrics_save_path	)
          return self._write_logs(lowerCamelCase						,		lowerCamelCase						,		"test"	)



      @rank_zero_only
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):
          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__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase=1      ,						__lowerCAmelCase=False							):
    if metric == "rouge2":
        snake_case__  =			"{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        snake_case__  =			"{val_avg_bleu:.4f}-{step_count}"
    elif metric == "loss":
        snake_case__  =			"{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."							)

    snake_case__  =			ModelCheckpoint(
        dirpath=__lowerCAmelCase      ,						filename=__lowerCAmelCase      ,						monitor=F"""val_{metric}"""      ,						mode="min" if "loss" in metric else "max"      ,						save_top_k=__lowerCAmelCase      ,						)
    return checkpoint_callback





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return EarlyStopping(
        monitor=F"""val_{metric}"""      ,						mode="min" if "loss" in metric else "max"      ,						patience=__lowerCAmelCase      ,						verbose=__lowerCAmelCase      ,						)





import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image




class    _SCREAMING_SNAKE_CASE					( unittest.TestCase    ,   __UpperCamelCase	):
      def A_     (     self	):
          snake_case__  =			load_tool("image-captioning"	)
          self.tool.setup()
          snake_case__  =			load_tool("image-captioning"						,		remote=lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



