



"""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 	snake_case__  (						__lowerCamelCase							:       Optional[int]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Any						      =filter(lambda __lowerCamelCase      : p.requires_grad				,						model.parameters()      )
			lowerCamelCase__						:						List[Any]						      =sum([np.prod(p.size()      ) for p in model_parameters]      )
			return params


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

class 			__SCREAMING_SNAKE_CASE      ( pl.Callback  ):


					'''simple docstring'''





					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    List[str],    lowerCamelCase				:    Dict	)->   Union[str, Any]:
								lowerCamelCase__						:						Union[str, Any]						      ={F'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
								pl_module.logger.log_metrics(lowerCamelCase	)



					@rank_zero_only
					def 						snake_case			(			self				:    str,    lowerCamelCase				:    pl.Trainer,    lowerCamelCase				:    pl.LightningModule,    lowerCamelCase				:    str,    lowerCamelCase				:    int=True	)->   None:
								logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''	)
								lowerCamelCase__						:						Optional[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
								lowerCamelCase__						:						str						      =Path(pl_module.hparams.output_dir	)
								if type_path == "test":
											lowerCamelCase__						:						Optional[Any]						      =od / '''test_results.txt'''
											lowerCamelCase__						:						Dict						      =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__						:						int						      =od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
											lowerCamelCase__						:						Any						      =od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=lowerCamelCase	)
											generations_file.parent.mkdir(exist_ok=lowerCamelCase	)
								with open(lowerCamelCase,    '''a+'''	) as writer:
											for key in sorted(lowerCamelCase	):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														lowerCamelCase__						:						List[Any]						      =metrics[key]
														if isinstance(lowerCamelCase,    torch.Tensor	):
																	lowerCamelCase__						:						List[str]						      =val.item()
														lowerCamelCase__						:						Any						      =F'''{key}: {val:.6f}\n'''
														writer.write(lowerCamelCase	)

								if not save_generations:
											return

								if "preds" in metrics:
											lowerCamelCase__						:						Optional[Any]						      ='''\n'''.join(metrics['''preds''']	)
											generations_file.open('''w+'''	).write(lowerCamelCase	)



					@rank_zero_only
					def 						snake_case			(			self				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    Optional[Any]	)->   List[str]:
								try:
											lowerCamelCase__						:						Union[str, Any]						      =pl_module.model.model.num_parameters()
								except AttributeError:
											lowerCamelCase__						:						Optional[Any]						      =pl_module.model.num_parameters()

								lowerCamelCase__						:						List[str]						      =count_trainable_parameters(lowerCamelCase	)
								# 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				:    Any,    lowerCamelCase				:    pl.Trainer,    lowerCamelCase				:    pl.LightningModule	)->   int:
								save_json(pl_module.metrics,    pl_module.metrics_save_path	)
								return self._write_logs(lowerCamelCase,    lowerCamelCase,    '''test'''	)




					@rank_zero_only
					def 						snake_case			(			self				:    str,    lowerCamelCase				:    pl.Trainer,    lowerCamelCase				:    Dict	)->   Union[str, Any]:
								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 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Any=1				,						__lowerCamelCase							:       Optional[int]=False      ):
			"""simple docstring"""
			if metric == "rouge2":
						lowerCamelCase__						:						str						      ='''{val_avg_rouge2:.4f}-{step_count}'''
			elif metric == "bleu":
						lowerCamelCase__						:						Dict						      ='''{val_avg_bleu:.4f}-{step_count}'''
			elif metric == "loss":
						lowerCamelCase__						:						Dict						      ='''{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[Any]						      =ModelCheckpoint(
			    dirpath=__lowerCamelCase				,						filename=__lowerCamelCase				,						monitor=f'''val_{metric}'''				,						mode='''min''' if '''loss''' in metric else '''max'''				,						save_top_k=__lowerCamelCase				,						)
			return checkpoint_callback






def 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       str      ):
			"""simple docstring"""
			return EarlyStopping(
			    monitor=f'''val_{metric}'''				,						mode='''min''' if '''loss''' in metric else '''max'''				,						patience=__lowerCamelCase				,						verbose=__lowerCamelCase				,						)









import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  str=False		,						__lowerCamelCase :  Optional[Any]=False		,						__lowerCamelCase :  int=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_processor_tester.min_resolution
   if getattr(__lowerCamelCase		,						"size_divisor"		,						__lowerCamelCase  ):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    __SCREAMING_SNAKE_CASE							:     str							=						max(image_processor_tester.size_divisor		,						__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						np.random.choice(np.arange(__lowerCamelCase		,						image_processor_tester.max_resolution  )		,						2  )
  image_inputs.append(
      np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

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

 if torchify:
  __SCREAMING_SNAKE_CASE							:     Dict							=						[torch.from_numpy(__lowerCamelCase  ) for image in image_inputs]

 return image_inputs






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  List[Any]=10		,						__lowerCamelCase :  Any=10		,						__lowerCamelCase :  int=False		,						__lowerCamelCase :  List[str]=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						[]
 for i in range(image_processor_tester.num_frames  ):
  video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for frame in video]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						[torch.from_numpy(__lowerCamelCase  ) for frame in video]

 return video






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict=False		,						__lowerCamelCase :  Optional[int]=False		,						__lowerCamelCase :  Any=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Dict							=						np.random.choice(
       np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution  )		,						2  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_video(
       image_processor_tester=__lowerCamelCase		,						width=__lowerCamelCase		,						height=__lowerCamelCase		,						numpify=__lowerCamelCase		,						torchify=__lowerCamelCase		,						)
  video_inputs.append(__lowerCamelCase  )

 return video_inputs






class    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      None





     def 						__snake_case     (							self			:				Dict					)->int:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict					)
      __SCREAMING_SNAKE_CASE							:     str							=						json.loads(image_processor.to_json_string()					)
      for key, value in self.image_processor_dict.items():
       self.assertEqual(obj[key]  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Dict							=						os.path.join(UpperCamelCase  ,	"image_processor.json"					)
       image_processor_first.to_json_file(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class.from_json_file(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				Optional[int]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor_first.save_pretrained(UpperCamelCase					)[0]
       check_json_file_has_correct_format(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class.from_pretrained(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class()
      self.assertIsNotNone(UpperCamelCase					)






     @require_torch
     @require_vision
     def 						__snake_case     (							self			:				Any					)->List[Any]:
      if self.test_cast_dtype is not None:
       # Initialize image_processor
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE							:     int							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	torchify=UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       # for layoutLM compatiblity
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     int							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.floataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to("cpu"  ,	torch.bfloataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.bfloataa					)

       with self.assertRaises(UpperCamelCase					):
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.bfloataa  ,	"cpu"					)

       # Try with text + image feature
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]					)}					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						encoding.to(torch.floataa					)

       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)
       self.assertEqual(encoding.input_ids.dtype  ,	torch.long					)






