



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 OwlViTImageProcessor






class 				SCREAMING_SNAKE_CASE     (							unittest.TestCase   ):





       def __init__(				self						:						Optional[Any] ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[str]=7 ,			__lowercase						:						Union[str, Any]=3 ,			__lowercase						:						int=18 ,			__lowercase						:						Optional[Any]=30 ,			__lowercase						:						Tuple=400 ,			__lowercase						:						List[str]=True ,			__lowercase						:						Optional[int]=None ,			__lowercase						:						Dict=True ,			__lowercase						:						int=None ,			__lowercase						:						Optional[int]=True ,			__lowercase						:						str=[0.48145466, 0.4578275, 0.40821073] ,			__lowercase						:						Any=[0.26862954, 0.26130258, 0.27577711] ,			__lowercase						:						List[Any]=True ,			):

           '''simple docstring'''
           __a					       =						parent
           __a					       =						batch_size
           __a					       =						num_channels
           __a					       =						image_size
           __a					       =						min_resolution
           __a					       =						max_resolution
           __a					       =						do_resize
           __a					       =						size if size is not None else {"""height""": 18, """width""": 18}
           __a					       =						do_center_crop
           __a					       =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           __a					       =						do_normalize
           __a					       =						image_mean
           __a					       =						image_std
           __a					       =						do_convert_rgb







       def   UpperCamelCase_   (				self						:						int		):

           '''simple docstring'''
           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,
               "do_convert_rgb": self.do_convert_rgb,
           }






@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE     (							SCREAMING_SNAKE_CASE_     , unittest.TestCase   ):
       __lowerCamelCase		:		str      =OwlViTImageProcessor if is_vision_available() else None





       def   UpperCamelCase_   (				self						:						Union[str, Any]		):

           '''simple docstring'''
           __a					       =						OwlViTImageProcessingTester(self		)





       @property
       def   UpperCamelCase_   (				self						:						str		):

           '''simple docstring'''
           return self.image_processor_tester.prepare_image_processor_dict()





       def   UpperCamelCase_   (				self						:						Tuple		):

           '''simple docstring'''
           __a					       =						self.image_processing_class(**self.image_processor_dict		)
           self.assertTrue(hasattr(lowercase_ ,			"""do_resize"""		)		)
           self.assertTrue(hasattr(lowercase_ ,			"""size"""		)		)
           self.assertTrue(hasattr(lowercase_ ,			"""do_center_crop"""		)		)
           self.assertTrue(hasattr(lowercase_ ,			"""center_crop"""		)		)
           self.assertTrue(hasattr(lowercase_ ,			"""do_normalize"""		)		)
           self.assertTrue(hasattr(lowercase_ ,			"""image_mean"""		)		)
           self.assertTrue(hasattr(lowercase_ ,			"""image_std"""		)		)
           self.assertTrue(hasattr(lowercase_ ,			"""do_convert_rgb"""		)		)





       def   UpperCamelCase_   (				self						:						str		):

           '''simple docstring'''
           __a					       =						self.image_processing_class.from_dict(self.image_processor_dict		)
           self.assertEqual(image_processor.size ,			{"""height""": 18, """width""": 18}		)
           self.assertEqual(image_processor.crop_size ,			{"""height""": 18, """width""": 18}		)

           __a					       =						self.image_processing_class.from_dict(self.image_processor_dict ,			size=42 ,			crop_size=84		)
           self.assertEqual(image_processor.size ,			{"""height""": 42, """width""": 42}		)
           self.assertEqual(image_processor.crop_size ,			{"""height""": 84, """width""": 84}		)





       def   UpperCamelCase_   (				self						:						int		):

           '''simple docstring'''
           # Initialize image_processing
           __a					       =						self.image_processing_class(**self.image_processor_dict		)
           # create random PIL images
           __a					       =						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=lowercase_		)

           for image in image_inputs:
               self.assertIsInstance(lowercase_ ,			Image.Image		)

           # Test not batched input
           __a					       =						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
           __a					       =						image_processing(lowercase_ ,			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						:						Tuple		):

           '''simple docstring'''
           # Initialize image_processing
           __a					       =						self.image_processing_class(**self.image_processor_dict		)
           # create random numpy tensors
           __a					       =						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=lowercase_ ,			numpify=lowercase_		)
           for image in image_inputs:
               self.assertIsInstance(lowercase_ ,			np.ndarray		)

           # Test not batched input
           __a					       =						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
           __a					       =						image_processing(lowercase_ ,			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						:						Union[str, Any]		):

           '''simple docstring'''
           # Initialize image_processing
           __a					       =						self.image_processing_class(**self.image_processor_dict		)
           # create random PyTorch tensors
           __a					       =						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=lowercase_ ,			torchify=lowercase_		)
           for image in image_inputs:
               self.assertIsInstance(lowercase_ ,			torch.Tensor		)

           # Test not batched input
           __a					       =						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
           __a					       =						image_processing(lowercase_ ,			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"""],
               ) ,			)





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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict      ):
				"""simple docstring"""







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


lowerCamelCase__     =	logging.getLogger(__name__)






class 				SCREAMING_SNAKE_CASE     (							pl.Callback   ):





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Dict ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						{F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
											pl_module.logger.log_metrics(__lowercase		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule ,			__lowercase						:						str ,			__lowercase						:						Tuple=True		):

											'''simple docstring'''
											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=__lowercase		)
															generations_file.parent.mkdir(exist_ok=__lowercase		)
											with open(__lowercase ,			"""a+"""		) as writer:
															for key in sorted(__lowercase		):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__a					       =						metrics[key]
																			if isinstance(__lowercase ,			torch.Tensor		):
																							__a					       =						val.item()
																			__a					       =						F"{key}: {val:.6f}\n"
																			writer.write(__lowercase		)

											if not save_generations:
															return

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





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											try:
															__a					       =						pl_module.model.model.num_parameters()
											except AttributeError:
															__a					       =						pl_module.model.num_parameters()

											__a					       =						count_trainable_parameters(__lowercase		)
											# 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						:						Union[str, Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule		):

											'''simple docstring'''
											save_json(pl_module.metrics ,			pl_module.metrics_save_path		)
											return self._write_logs(__lowercase ,			__lowercase ,			"""test"""		)







							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						Dict		):

											'''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")



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple=1			,       _SCREAMING_SNAKE_CASE    :     List[str]=False      ):
				"""simple docstring"""







				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=_SCREAMING_SNAKE_CASE			,       filename=_SCREAMING_SNAKE_CASE			,       monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       save_top_k=_SCREAMING_SNAKE_CASE			,       )
				return checkpoint_callback

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







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