
'''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 import save_json


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]						)      ->  Dict:
  _lowerCAmelCase    :					List[str]							=   filter(lambda _lowerCamelCase						: p.requires_grad					, model.parameters()						)
  _lowerCAmelCase    :					str							=   sum([np.prod(p.size()						) for p in model_parameters]						)
  return params


UpperCamelCase_            =    logging.getLogger(__name__)


class 					a_					(pl.Callback      ):




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
         _lowerCAmelCase    :					Optional[int]							=   {f'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
         pl_module.logger.log_metrics(snake_case_					)




       @rank_zero_only
       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_=True					):
         logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'					)
         _lowerCAmelCase    :					Optional[int]							=   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
         _lowerCAmelCase    :					Optional[Any]							=   Path(pl_module.hparams.output_dir					)
         if type_path == "test":
           _lowerCAmelCase    :					Tuple							=   od / """test_results.txt"""
           _lowerCAmelCase    :					Any							=   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.
           _lowerCAmelCase    :					Tuple							=   od / f'{type_path}_results/{trainer.global_step:05d}.txt'
           _lowerCAmelCase    :					Dict							=   od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
           results_file.parent.mkdir(exist_ok=snake_case_					)
           generations_file.parent.mkdir(exist_ok=snake_case_					)
         with open(snake_case_				,				"""a+"""					) as writer:
           for key in sorted(snake_case_					):
             if key in ["log", "progress_bar", "preds"]:
               continue
             _lowerCAmelCase    :					List[Any]							=   metrics[key]
             if isinstance(snake_case_				,				torch.Tensor					):
               _lowerCAmelCase    :					str							=   val.item()
             _lowerCAmelCase    :					Tuple							=   f'{key}: {val:.6f}\n'
             writer.write(snake_case_					)

         if not save_generations:
           return

         if "preds" in metrics:
           _lowerCAmelCase    :					List[Any]							=   """\n""".join(metrics["""preds"""]					)
           generations_file.open("""w+"""					).write(snake_case_					)




       @rank_zero_only
       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
         try:
           _lowerCAmelCase    :					List[str]							=   pl_module.model.model.num_parameters()
         except AttributeError:
           _lowerCAmelCase    :					int							=   pl_module.model.num_parameters()

         _lowerCAmelCase    :					Dict							=   count_trainable_parameters(snake_case_					)
         # 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				,				snake_case_				,				snake_case_					):
         save_json(pl_module.metrics				,				pl_module.metrics_save_path					)
         return self._write_logs(snake_case_				,				snake_case_				,				"""test"""					)







       @rank_zero_only
       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
         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 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    str					, _lowerCamelCase  :    Union[str, Any]=1					, _lowerCamelCase  :    Tuple=False						)      ->  Tuple:
  if metric == "rouge2":
    _lowerCAmelCase    :					Union[str, Any]							=   """{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    _lowerCAmelCase    :					Optional[Any]							=   """{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "loss":
    _lowerCAmelCase    :					Tuple							=   """{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."""						)

  _lowerCAmelCase    :					Tuple							=   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 		_UpperCAmelCase			(					_lowerCamelCase  :    List[str]					, _lowerCamelCase  :    str						)      ->  str:
  return EarlyStopping(
      monitor=f'val_{metric}'					, mode="""min""" if """loss""" in metric else """max"""					, patience=_lowerCamelCase					, verbose=_lowerCamelCase					, )




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ConvNextImageProcessor


class 					a_					(unittest.TestCase      ):




       def __init__(       self				,				snake_case_				,				snake_case_=7				,				snake_case_=3				,				snake_case_=1_8				,				snake_case_=3_0				,				snake_case_=4_0_0				,				snake_case_=True				,				snake_case_=None				,				snake_case_=0.875				,				snake_case_=True				,				snake_case_=[0.5, 0.5, 0.5]				,				snake_case_=[0.5, 0.5, 0.5]				,				):
         _lowerCAmelCase    :					List[str]							=   size if size is not None else {"""shortest_edge""": 2_0}
         _lowerCAmelCase    :					Tuple							=   parent
         _lowerCAmelCase    :					List[str]							=   batch_size
         _lowerCAmelCase    :					List[str]							=   num_channels
         _lowerCAmelCase    :					Tuple							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   min_resolution
         _lowerCAmelCase    :					Tuple							=   max_resolution
         _lowerCAmelCase    :					List[Any]							=   do_resize
         _lowerCAmelCase    :					Dict							=   size
         _lowerCAmelCase    :					str							=   crop_pct
         _lowerCAmelCase    :					List[str]							=   do_normalize
         _lowerCAmelCase    :					Any							=   image_mean
         _lowerCAmelCase    :					Any							=   image_std







       def 							__UpperCamelCase    (       self					):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }



@require_torch
@require_vision
class 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Dict      = ConvNextImageProcessor if is_vision_available() else None




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   ConvNextImageProcessingTester(self					)




       @property
       def 							__UpperCamelCase    (       self					):
         return self.image_processor_tester.prepare_image_processor_dict()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(snake_case_				,				"""do_resize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""size"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""crop_pct"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""do_normalize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_mean"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_std"""					)					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   self.image_processing_class.from_dict(self.image_processor_dict					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 2_0}					)

         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 4_2}					)




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         _lowerCAmelCase    :					str							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				Image.Image					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[int]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					int							=   self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         _lowerCAmelCase    :					Any							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				numpify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				np.ndarray					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					List[str]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)







       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					Tuple							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         _lowerCAmelCase    :					List[str]							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				torchify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				torch.Tensor					)

         # Test not batched input
         _lowerCAmelCase    :					List[str]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[int]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)


