




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 lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[str]  =     filter(lambda _lowercase : p.requires_grad					,		model.parameters() )
     UpperCAmelCase_     :				str  =     sum([np.prod(p.size() ) for p in model_parameters] )
     return params


__a							     =					logging.getLogger(__name__)







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     if metric == "rouge2":
          UpperCAmelCase_     :				List[Any]  =     '''{val_avg_rouge2:.4f}-{step_count}'''
     elif metric == "bleu":
          UpperCAmelCase_     :				int  =     '''{val_avg_bleu:.4f}-{step_count}'''
     elif metric == "em":
          UpperCAmelCase_     :				Any  =     '''{val_avg_em:.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.''' )

     UpperCAmelCase_     :				List[Any]  =     ModelCheckpoint(
         dirpath=_lowercase					,		filename=_lowercase					,		monitor=f'''val_{metric}'''					,		mode='''max'''					,		save_top_k=3					,		every_n_epochs=1					,		)
     return checkpoint_callback







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     return EarlyStopping(
         monitor=f'''val_{metric}'''					,		mode='''min''' if '''loss''' in metric else '''max'''					,		patience=_lowercase					,		verbose=_lowercase					,		)



class     __a(	pl.Callback  ):






    """simple docstring"""





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Tuple:
         UpperCAmelCase_     :				Tuple  =     {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
         pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE    )





    @rank_zero_only
    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=True    )					->						None:
         logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''    )
         UpperCAmelCase_     :				List[Any]  =     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
         UpperCAmelCase_     :				str  =     Path(pl_module.hparams.output_dir    )
         if type_path == "test":
              UpperCAmelCase_     :				Dict  =     od / '''test_results.txt'''
              UpperCAmelCase_     :				Union[str, 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.
              UpperCAmelCase_     :				Tuple  =     od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
              UpperCAmelCase_     :				int  =     od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
              results_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
              generations_file.parent.mkdir(exist_ok=_SCREAMING_SNAKE_CASE    )
         with open(_SCREAMING_SNAKE_CASE  ,'''a+'''    ) as writer:
              for key in sorted(_SCREAMING_SNAKE_CASE    ):
                   if key in ["log", "progress_bar", "preds"]:
                        continue
                   UpperCAmelCase_     :				Dict  =     metrics[key]
                   if isinstance(_SCREAMING_SNAKE_CASE  ,torch.Tensor    ):
                        UpperCAmelCase_     :				Any  =     val.item()
                   UpperCAmelCase_     :				Any  =     f'''{key}: {val:.6f}\n'''
                   writer.write(_SCREAMING_SNAKE_CASE    )

         if not save_generations:
              return

         if "preds" in metrics:
              UpperCAmelCase_     :				Union[str, Any]  =     '''\n'''.join(metrics['''preds''']    )
              generations_file.open('''w+'''    ).write(_SCREAMING_SNAKE_CASE    )





    @rank_zero_only
    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         try:
              UpperCAmelCase_     :				Optional[Any]  =     pl_module.model.model.num_parameters()
         except AttributeError:
              UpperCAmelCase_     :				Tuple  =     pl_module.model.num_parameters()

         UpperCAmelCase_     :				Optional[int]  =     count_trainable_parameters(_SCREAMING_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 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         save_json(pl_module.metrics  ,pl_module.metrics_save_path    )
         return self._write_logs(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,'''test'''    )





    @rank_zero_only
    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						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")





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 PoolFormerImageProcessor

class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=30  ,_SCREAMING_SNAKE_CASE=400  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=0.9  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,)					->						Optional[int]:
         UpperCAmelCase_     :				int  =     size if size is not None else {'''shortest_edge''': 30}
         UpperCAmelCase_     :				List[str]  =     crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
         UpperCAmelCase_     :				Dict  =     parent
         UpperCAmelCase_     :				int  =     batch_size
         UpperCAmelCase_     :				int  =     num_channels
         UpperCAmelCase_     :				Any  =     min_resolution
         UpperCAmelCase_     :				Tuple  =     max_resolution
         UpperCAmelCase_     :				Optional[int]  =     do_resize_and_center_crop
         UpperCAmelCase_     :				Tuple  =     size
         UpperCAmelCase_     :				List[str]  =     crop_pct
         UpperCAmelCase_     :				List[str]  =     crop_size
         UpperCAmelCase_     :				Any  =     do_normalize
         UpperCAmelCase_     :				str  =     image_mean
         UpperCAmelCase_     :				List[Any]  =     image_std





    def 				a__ ( self    )					->						str:
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }


@require_torch
@require_vision
class     __a(	_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = PoolFormerImageProcessor if is_vision_available() else None





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				str  =     PoolFormerImageProcessingTester(self    )





    @property
    def 				a__ ( self    )					->						Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_resize_and_center_crop'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''size'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''crop_pct'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_normalize'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_mean'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_std'''    )    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Any  =     self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 30}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 30, '''width''': 30}    )

         UpperCAmelCase_     :				Optional[Any]  =     self.image_processing_class.from_dict(self.image_processor_dict  ,size=42  ,crop_size=84    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 84, '''width''': 84}    )





    def 				a__ ( self    )					->						Optional[int]:
         pass





    def 				a__ ( self    )					->						Dict:
         # Initialize image_processing
         UpperCAmelCase_     :				str  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_     :				int  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,Image.Image    )

         # Test not batched input
         UpperCAmelCase_     :				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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				Union[str, Any]  =     image_processing(_SCREAMING_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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)





    def 				a__ ( self    )					->						List[Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				Any  =     self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_     :				List[str]  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE  ,numpify=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,np.ndarray    )

         # Test not batched input
         UpperCAmelCase_     :				Tuple  =     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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				List[Any]  =     image_processing(_SCREAMING_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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)





    def 				a__ ( self    )					->						Union[str, Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_     :				Optional[Any]  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE  ,torchify=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,torch.Tensor    )

         # Test not batched input
         UpperCAmelCase_     :				Tuple  =     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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				List[Any]  =     image_processing(_SCREAMING_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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)