



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(						__UpperCamelCase							:	Dict				):
		lowerCAmelCase_    :  Union[str, Any]             =				filter(lambda __UpperCamelCase				: p.requires_grad  ,model.parameters()				)
		lowerCAmelCase_    :  Tuple             =				sum([np.prod(p.size()				) for p in model_parameters]				)
		return params


A__						:						Tuple          =      logging.getLogger(__name__)



class        __snake_case		(  pl.Callback		):




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : Dict       ,       A_  : str):
									lowerCAmelCase_    :  List[str]             =				{F"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
									pl_module.logger.log_metrics(A_)




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

									if not save_generations:
											return

									if "preds" in metrics:
											lowerCAmelCase_    :  Tuple             =				'''\n'''.join(metrics['''preds'''])
											generations_file.open('''w+''').write(A_)




							@rank_zero_only
							def      UpperCAmelCase__    ( self  : Dict       ,       A_  : Union[str, Any]       ,       A_  : Dict):
									try:
											lowerCAmelCase_    :  Dict             =				pl_module.model.model.num_parameters()
									except AttributeError:
											lowerCAmelCase_    :  List[str]             =				pl_module.model.num_parameters()

									lowerCAmelCase_    :  str             =				count_trainable_parameters(A_)
									# 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  : List[Any]       ,       A_  : pl.Trainer       ,       A_  : pl.LightningModule):
									save_json(pl_module.metrics       ,       pl_module.metrics_save_path)
									return self._write_logs(A_       ,       A_       ,       '''test''')







							@rank_zero_only
							def      UpperCAmelCase__    ( self  : Tuple       ,       A_  : pl.Trainer       ,       A_  : Optional[int]):
									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(						__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	Dict  ,__UpperCamelCase							:	int=1  ,__UpperCamelCase							:	Tuple=False				):
		if metric == "rouge2":
				lowerCAmelCase_    :  Any             =				'''{val_avg_rouge2:.4f}-{step_count}'''
		elif metric == "bleu":
				lowerCAmelCase_    :  Union[str, Any]             =				'''{val_avg_bleu:.4f}-{step_count}'''
		elif metric == "loss":
				lowerCAmelCase_    :  str             =				'''{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_    :  Optional[int]             =				ModelCheckpoint(
		    dirpath=__UpperCamelCase  ,filename=__UpperCamelCase  ,monitor=f"""val_{metric}"""  ,mode='''min''' if '''loss''' in metric else '''max'''  ,save_top_k=__UpperCamelCase  ,)
		return checkpoint_callback





def     UpperCamelCase(						__UpperCamelCase							:	List[Any]  ,__UpperCamelCase							:	Dict				):
		return EarlyStopping(
		    monitor=f"""val_{metric}"""  ,mode='''min''' if '''loss''' in metric else '''max'''  ,patience=__UpperCamelCase  ,verbose=__UpperCamelCase  ,)










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 DeiTImageProcessor



class        __snake_case		(  unittest.TestCase		):




							def __init__( self  : Union[str, Any]       ,       A_  : Optional[Any]       ,       A_  : Optional[Any]=7       ,       A_  : str=3       ,       A_  : Optional[Any]=1_8       ,       A_  : Union[str, Any]=3_0       ,       A_  : str=4_0_0       ,       A_  : List[str]=True       ,       A_  : str=None       ,       A_  : str=True       ,       A_  : List[Any]=None       ,       A_  : List[Any]=True       ,       A_  : Optional[Any]=[0.5, 0.5, 0.5]       ,       A_  : Optional[int]=[0.5, 0.5, 0.5]       ,       ):
									lowerCAmelCase_    :  List[str]             =				size if size is not None else {'''height''': 2_0, '''width''': 2_0}
									lowerCAmelCase_    :  List[Any]             =				crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

									lowerCAmelCase_    :  str             =				parent
									lowerCAmelCase_    :  Union[str, Any]             =				batch_size
									lowerCAmelCase_    :  Optional[Any]             =				num_channels
									lowerCAmelCase_    :  List[str]             =				image_size
									lowerCAmelCase_    :  List[str]             =				min_resolution
									lowerCAmelCase_    :  Union[str, Any]             =				max_resolution
									lowerCAmelCase_    :  Optional[int]             =				do_resize
									lowerCAmelCase_    :  Dict             =				size
									lowerCAmelCase_    :  Dict             =				do_center_crop
									lowerCAmelCase_    :  str             =				crop_size
									lowerCAmelCase_    :  List[Any]             =				do_normalize
									lowerCAmelCase_    :  str             =				image_mean
									lowerCAmelCase_    :  Dict             =				image_std







							def      UpperCAmelCase__    ( self  : int):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "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        __snake_case		(  UpperCamelCase_		,unittest.TestCase		):
							_a       =				DeiTImageProcessor if is_vision_available() else None
							_a       =				True




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  int             =				DeiTImageProcessingTester(self)




							@property
							def      UpperCAmelCase__    ( self  : Optional[Any]):
									return self.image_processor_tester.prepare_image_processor_dict()




							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  List[Any]             =				self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(A_       ,       '''do_resize'''))
									self.assertTrue(hasattr(A_       ,       '''size'''))
									self.assertTrue(hasattr(A_       ,       '''do_center_crop'''))
									self.assertTrue(hasattr(A_       ,       '''center_crop'''))
									self.assertTrue(hasattr(A_       ,       '''do_normalize'''))
									self.assertTrue(hasattr(A_       ,       '''image_mean'''))
									self.assertTrue(hasattr(A_       ,       '''image_std'''))




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  Tuple             =				self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size       ,       {'''height''': 2_0, '''width''': 2_0})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 1_8, '''width''': 1_8})

									lowerCAmelCase_    :  Any             =				self.image_processing_class.from_dict(self.image_processor_dict       ,       size=4_2       ,       crop_size=8_4)
									self.assertEqual(image_processor.size       ,       {'''height''': 4_2, '''width''': 4_2})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 8_4, '''width''': 8_4})




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									pass




							def      UpperCAmelCase__    ( self  : Any):
									# Initialize image_processing
									lowerCAmelCase_    :  List[str]             =				self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									lowerCAmelCase_    :  Any             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       Image.Image)

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

									# Test batched
									lowerCAmelCase_    :  Dict             =				image_processing(A_       ,       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      UpperCAmelCase__    ( self  : List[str]):
									# Initialize image_processing
									lowerCAmelCase_    :  Union[str, Any]             =				self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_    :  Union[str, Any]             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_       ,       numpify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       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.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )

									# Test batched
									lowerCAmelCase_    :  str             =				image_processing(A_       ,       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      UpperCAmelCase__    ( self  : Any):
									# Initialize image_processing
									lowerCAmelCase_    :  str             =				self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_    :  Union[str, Any]             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_       ,       torchify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       torch.Tensor)

									# Test not batched input
									lowerCAmelCase_    :  Dict             =				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
									lowerCAmelCase_    :  Tuple             =				image_processing(A_       ,       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'''],
									    )       ,       )





