


"""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_rag import save_json


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




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


A     :   List[str]			    =			logging.getLogger(__name__)


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if metric == "rouge2":
						__lowerCAmelCase				    =			"{val_avg_rouge2:.4f}-{step_count}"
			elif metric == "bleu":
						__lowerCAmelCase				    =			"{val_avg_bleu:.4f}-{step_count}"
			elif metric == "em":
						__lowerCAmelCase				    =			"{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."      )

			__lowerCAmelCase				    =			ModelCheckpoint(
			    dirpath=_UpperCamelCase    , filename=_UpperCamelCase    , monitor=f"val_{metric}"    , mode="max"    , save_top_k=3    , every_n_epochs=1    , )
			return checkpoint_callback


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


class   _UpperCamelCase  (   pl.Callback		):
							'''simple docstring'''







							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			{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 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a=True	):
										logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"	)
										__lowerCAmelCase				    =			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				    =			Path(pl_module.hparams.output_dir	)
										if type_path == "test":
													__lowerCAmelCase				    =			od / "test_results.txt"
													__lowerCAmelCase				    =			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				    =			od / f"{type_path}_results/{trainer.global_step:05d}.txt"
													__lowerCAmelCase				    =			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				    =			metrics[key]
																if isinstance(__a    ,   torch.Tensor	):
																			__lowerCAmelCase				    =			val.item()
																__lowerCAmelCase				    =			f"{key}: {val:.6f}\n"
																writer.write(__a	)

										if not save_generations:
													return

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

							@rank_zero_only
							def 			snake_case				(     self    ,   __a    ,   __a	):
										try:
													__lowerCAmelCase				    =			pl_module.model.model.num_parameters()
										except AttributeError:
													__lowerCAmelCase				    =			pl_module.model.num_parameters()

										__lowerCAmelCase				    =			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 			snake_case				(     self    ,   __a    ,   __a	):
										save_json(pl_module.metrics    ,   pl_module.metrics_save_path	)
										return self._write_logs(__a    ,   __a    ,   "test"	)



							@rank_zero_only
							def 			snake_case				(     self    ,   __a    ,   __a	):
										save_json(pl_module.metrics    ,   pl_module.metrics_save_path	)
										# Uncommenting this will save val generations
										# return self._write_logs(trainer, pl_module, "valid")





'''simple docstring'''




import unittest

import numpy as np

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

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 DPTImageProcessor






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=18       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  size if size is not None else {"""height""": 18, """width""": 18}
					lowerCAmelCase   					=  parent
					lowerCAmelCase   					=  batch_size
					lowerCAmelCase   					=  num_channels
					lowerCAmelCase   					=  image_size
					lowerCAmelCase   					=  min_resolution
					lowerCAmelCase   					=  max_resolution
					lowerCAmelCase   					=  do_resize
					lowerCAmelCase   					=  size
					lowerCAmelCase   					=  do_normalize
					lowerCAmelCase   					=  image_mean
					lowerCAmelCase   					=  image_std







			def 							a_       (   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 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       DPTImageProcessor if is_vision_available() else None



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  DPTImageProcessingTester(self)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """image_mean"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """image_std"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """do_normalize"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """do_resize"""))
					self.assertTrue(hasattr(__lowerCAmelCase       ,    """size"""))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size       ,    {"""height""": 18, """width""": 18})

					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42)
					self.assertEqual(image_processor.size       ,    {"""height""": 42, """width""": 42})



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    Image.Image)

					# Test not batched input
					lowerCAmelCase   					=  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["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    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["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase       ,    numpify=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    np.ndarray)

					# Test not batched input
					lowerCAmelCase   					=  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["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    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["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					lowerCAmelCase   					=  prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCAmelCase       ,    torchify=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase       ,    torch.Tensor)

					# Test not batched input
					lowerCAmelCase   					=  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["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  image_processing(__lowerCAmelCase       ,    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["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )




