





"""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    A__							(     UpperCamelCase			):
							A						=    filter(lambda UpperCamelCase			: p.requires_grad   ,    model.parameters()			)
							A						=    sum([np.prod(p.size()			) for p in model_parameters]			)
							return params


_snake_case	:						str					=					logging.getLogger(__name__)






class    _UpperCAmelCase  (      pl.Callback					):


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


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

												if not save_generations:
																			return

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


					@rank_zero_only
					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :List[Any]	,			__UpperCamelCase     :Tuple						):
												try:
																			A						=    pl_module.model.model.num_parameters()
												except AttributeError:
																			A						=    pl_module.model.num_parameters()

												A						=    count_trainable_parameters(__UpperCamelCase						)
												# 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       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :pl.Trainer	,			__UpperCamelCase     :pl.LightningModule						):
												save_json(pl_module.metrics	,			pl_module.metrics_save_path						)
												return self._write_logs(__UpperCamelCase	,			__UpperCamelCase	,			"test"						)


					@rank_zero_only
					def       lowerCamelCase			(   self     :List[Any]	,			__UpperCamelCase     :pl.Trainer	,			__UpperCamelCase     :List[str]						):
												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    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase=1   ,    UpperCamelCase=False			):
							if metric == "rouge2":
														A						=    "{val_avg_rouge2:.4f}-{step_count}"
							elif metric == "bleu":
														A						=    "{val_avg_bleu:.4f}-{step_count}"
							elif metric == "loss":
														A						=    "{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."			)

							A						=    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    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							return EarlyStopping(
							    monitor=F"val_{metric}"   ,    mode="min" if "loss" in metric else "max"   ,    patience=UpperCamelCase   ,    verbose=UpperCamelCase   ,    )









"""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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import BlipImageProcessor






class    _UpperCAmelCase  (      unittest.TestCase					):


					def __init__(   self     :Optional[Any]	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :Dict=7	,			__UpperCamelCase     :Optional[Any]=3	,			__UpperCamelCase     :Tuple=18	,			__UpperCamelCase     :List[Any]=30	,			__UpperCamelCase     :Optional[Any]=4_00	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :Tuple=None	,			__UpperCamelCase     :List[str]=True	,			__UpperCamelCase     :Any=False	,			__UpperCamelCase     :str=[0.48_145_466, 0.4_578_275, 0.40_821_073]	,			__UpperCamelCase     :Optional[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]	,			__UpperCamelCase     :Optional[Any]=True	,			):
												A						=    size if size is not None else {"height": 20, "width": 20}
												A						=    parent
												A						=    batch_size
												A						=    num_channels
												A						=    image_size
												A						=    min_resolution
												A						=    max_resolution
												A						=    do_resize
												A						=    size
												A						=    do_normalize
												A						=    image_mean
												A						=    image_std
												A						=    do_pad
												A						=    do_convert_rgb


					def       lowerCamelCase			(   self     :List[str]						):
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_convert_rgb": self.do_convert_rgb,
												    "do_pad": self.do_pad,
												}


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :Optional[int]=False	,			__UpperCamelCase     :Dict=False	,			__UpperCamelCase     :Dict=False						):
												assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

												if equal_resolution:
																			A						=    []
																			for i in range(self.batch_size						):
																										image_inputs.append(
																										    np.random.randint(
																										        2_55	,			size=(self.num_channels, self.max_resolution, self.max_resolution)	,			dtype=np.uinta						)						)
												else:
																			A						=    []
																			for i in range(self.batch_size						):
																										A,      A						=    np.random.choice(np.arange(self.min_resolution	,			self.max_resolution						)	,			2						)
																										image_inputs.append(np.random.randint(2_55	,			size=(self.num_channels, width, height)	,			dtype=np.uinta						)						)

												if not numpify and not torchify:
																			# PIL expects the channel dimension as last dimension
																			A						=    [Image.fromarray(np.moveaxis(__UpperCamelCase	,			0	,			-1						)						) for x in image_inputs]

												if torchify:
																			A						=    [torch.from_numpy(__UpperCamelCase						) for x in image_inputs]

												return image_inputs







@require_torch
@require_vision
class    _UpperCAmelCase  (      lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				BlipImageProcessor if is_vision_available() else None


					def       lowerCamelCase			(   self     :Optional[Any]						):
												A						=    BlipImageProcessingTester(self						)


					@property
					def       lowerCamelCase			(   self     :List[str]						):
												return self.image_processor_tester.prepare_image_processor_dict()


					def       lowerCamelCase			(   self     :Any						):
												A						=    self.image_processing_class(**self.image_processor_dict						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"do_resize"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"size"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"do_normalize"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"image_mean"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"image_std"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"do_convert_rgb"						)						)


					def       lowerCamelCase			(   self     :List[Any]						):
												pass


					def       lowerCamelCase			(   self     :Optional[Any]						):
												# Initialize image_processor
												A						=    self.image_processing_class(**self.image_processor_dict						)
												# create random PIL images
												A						=    self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase						)
												for image in image_inputs:
																			self.assertIsInstance(__UpperCamelCase	,			Image.Image						)

												# Test not batched input
												A						=    image_processor(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
												A						=    image_processor(__UpperCamelCase	,			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       lowerCamelCase			(   self     :Union[str, Any]						):
												# Initialize image_processor
												A						=    self.image_processing_class(**self.image_processor_dict						)
												# create random numpy tensors
												A						=    self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase	,			numpify=__UpperCamelCase						)
												for image in image_inputs:
																			self.assertIsInstance(__UpperCamelCase	,			np.ndarray						)

												# Test not batched input
												A						=    image_processor(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
												A						=    image_processor(__UpperCamelCase	,			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       lowerCamelCase			(   self     :Tuple						):
												# Initialize image_processor
												A						=    self.image_processing_class(**self.image_processor_dict						)
												# create random PyTorch tensors
												A						=    self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase	,			torchify=__UpperCamelCase						)
												for image in image_inputs:
																			self.assertIsInstance(__UpperCamelCase	,			torch.Tensor						)

												# Test not batched input
												A						=    image_processor(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
												A						=    image_processor(__UpperCamelCase	,			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"],
												    )	,			)







@require_torch
@require_vision
class    _UpperCAmelCase  (      lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				BlipImageProcessor if is_vision_available() else None


					def       lowerCamelCase			(   self     :int						):
												A						=    BlipImageProcessingTester(self	,			num_channels=4						)
												A						=    3


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


					def       lowerCamelCase			(   self     :int						):
												A						=    self.image_processing_class(**self.image_processor_dict						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"do_resize"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"size"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"do_normalize"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"image_mean"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"image_std"						)						)
												self.assertTrue(hasattr(__UpperCamelCase	,			"do_convert_rgb"						)						)


					def       lowerCamelCase			(   self     :Any						):
												pass


					def       lowerCamelCase			(   self     :Optional[Any]						):
												# Initialize image_processor
												A						=    self.image_processing_class(**self.image_processor_dict						)
												# create random PIL images
												A						=    self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase						)
												for image in image_inputs:
																			self.assertIsInstance(__UpperCamelCase	,			Image.Image						)

												# Test not batched input
												A						=    image_processor(image_inputs[0]	,			return_tensors="pt"						).pixel_values
												self.assertEqual(
												    encoded_images.shape	,			(
												        1,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,			)

												# Test batched
												A						=    image_processor(__UpperCamelCase	,			return_tensors="pt"						).pixel_values
												self.assertEqual(
												    encoded_images.shape	,			(
												        self.image_processor_tester.batch_size,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.size["height"],
												        self.image_processor_tester.size["width"],
												    )	,			)


