
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 		UpperCamelCase						(   lowercase_ )		->						List[str]:


  '''simple docstring'''






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


lowerCamelCase__     :		Dict        = logging.getLogger(__name__)

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  if metric == "rouge2":
    lowercase__	: Union[str, Any]     		=   """{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    lowercase__	: Tuple     		=   """{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "em":
    lowercase__	: Tuple     		=   """{val_avg_em:.4f}-{step_count}"""
  elif metric == "loss":
    lowercase__	: Any     		=   """{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.""" )

  lowercase__	: Dict     		=   ModelCheckpoint(
      dirpath=lowercase_			,			filename=lowercase_			,			monitor=F'val_{metric}'			,			mode="""max"""			,			save_top_k=1			,			every_n_epochs=1			,			)

  return checkpoint_callback

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Any:


  '''simple docstring'''






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



class    _snake_case (						pl.Callback				):


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   {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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=True):


   '''simple docstring'''




   logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****')
   lowercase__	: 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
   lowercase__	: Union[str, Any]     		=   Path(pl_module.hparams.output_dir)
   if type_path == "test":
     lowercase__	: List[Any]     		=   od / """test_results.txt"""
     lowercase__	: List[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.
     lowercase__	: Tuple     		=   od / f'{type_path}_results/{trainer.global_step:05d}.txt'
     lowercase__	: Any     		=   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
       lowercase__	: Dict     		=   metrics[key]
       if isinstance(SCREAMING_SNAKE_CASE_       ,	torch.Tensor):
         lowercase__	: Any     		=   val.item()
       lowercase__	: Optional[int]     		=   f'{key}: {val:.6f}\n'
       writer.write(SCREAMING_SNAKE_CASE_)

   if not save_generations:
     return

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


 @rank_zero_only
 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   try:
     lowercase__	: Tuple     		=   pl_module.model.model.num_parameters()
   except AttributeError:
     lowercase__	: Optional[Any]     		=   pl_module.model.num_parameters()

   lowercase__	: List[Any]     		=   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''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")








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 ViTImageProcessor



class    _snake_case (						unittest.TestCase				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	SCREAMING_SNAKE_CASE_=True       ,	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]       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   size if size is not None else {"""height""": 18, """width""": 18}
   lowercase__	: int     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: List[str]     		=   num_channels
   lowercase__	: str     		=   image_size
   lowercase__	: int     		=   min_resolution
   lowercase__	: Dict     		=   max_resolution
   lowercase__	: Tuple     		=   do_resize
   lowercase__	: Union[str, Any]     		=   size
   lowercase__	: Any     		=   do_normalize
   lowercase__	: Tuple     		=   image_mean
   lowercase__	: str     		=   image_std



 def   lowercase__  (       self):


   '''simple docstring'''




   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
   }



@require_torch
@require_vision
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      ViTImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   EfficientFormerImageProcessorTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_proc_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""size"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__	: List[Any]     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	Image.Image)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: str     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__	: str     		=   prepare_image_inputs(self.image_proc_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
   lowercase__	: Optional[int]     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Dict     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Dict     		=   prepare_image_inputs(self.image_proc_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
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Any     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)






