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  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		filter(lambda SCREAMING_SNAKE_CASE	: p.requires_grad  ,			model.parameters()	)
    __UpperCamelCase     :List[str]			  =		sum([np.prod(p.size()	) for p in model_parameters]	)
    return params


__lowercase							 =					logging.getLogger(__name__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    if metric == "rouge2":
        __UpperCamelCase     :Any			  =		'''{val_avg_rouge2:.4f}-{step_count}'''
    elif metric == "bleu":
        __UpperCamelCase     :List[Any]			  =		'''{val_avg_bleu:.4f}-{step_count}'''
    elif metric == "em":
        __UpperCamelCase     :Union[str, Any]			  =		'''{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.'''	)

    __UpperCamelCase     :List[Any]			  =		ModelCheckpoint(
        dirpath=SCREAMING_SNAKE_CASE  ,			filename=SCREAMING_SNAKE_CASE  ,			monitor=f"""val_{metric}"""  ,			mode='''max'''  ,			save_top_k=3  ,			every_n_epochs=1  ,			)
    return checkpoint_callback



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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


class 	lowerCamelCase_     (   pl.Callback ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :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(__lowercase)







    @rank_zero_only
    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase=True)       ->	None:
        logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""")
        __UpperCamelCase     :Dict			  =		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
        __UpperCamelCase     :Tuple			  =		Path(pl_module.hparams.output_dir)
        if type_path == "test":
            __UpperCamelCase     :str			  =		od / '''test_results.txt'''
            __UpperCamelCase     :Optional[Any]			  =		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.
            __UpperCamelCase     :List[Any]			  =		od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
            __UpperCamelCase     :Optional[int]			  =		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
                __UpperCamelCase     :Union[str, Any]			  =		metrics[key]
                if isinstance(__lowercase , torch.Tensor):
                    __UpperCamelCase     :List[Any]			  =		val.item()
                __UpperCamelCase     :Optional[Any]			  =		f"""{key}: {val:.6f}\n"""
                writer.write(__lowercase)

        if not save_generations:
            return

        if "preds" in metrics:
            __UpperCamelCase     :Union[str, Any]			  =		'''\n'''.join(metrics['''preds'''])
            generations_file.open('''w+''').write(__lowercase)







    @rank_zero_only
    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	List[Any]:
        try:
            __UpperCamelCase     :int			  =		pl_module.model.model.num_parameters()
        except AttributeError:
            __UpperCamelCase     :Tuple			  =		pl_module.model.num_parameters()

        __UpperCamelCase     :Optional[Any]			  =		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 , __lowercase , __lowercase)       ->	Optional[int]:
        save_json(pl_module.metrics , pl_module.metrics_save_path)
        return self._write_logs(__lowercase , __lowercase , '''test''')







    @rank_zero_only
    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Tuple:
        save_json(pl_module.metrics , pl_module.metrics_save_path)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase							 =					logging.get_logger(__name__)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      ["""pixel_values"""]







    def __init__(	self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , )       ->	None:
        __UpperCamelCase     :Optional[int]			  =		do_resize
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :str			  =		size_divisor
        __UpperCamelCase     :Dict			  =		resample
        super().__init__(**__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		get_image_size(__lowercase)
        # Rounds the height and width down to the closest multiple of size_divisor
        __UpperCamelCase     :List[Any]			  =		height // size_divisor * size_divisor
        __UpperCamelCase     :List[str]			  =		width // size_divisor * size_divisor
        __UpperCamelCase     :str			  =		resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
        return image







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	BatchFeature:
        __UpperCamelCase     :Union[str, Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Tuple			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[str]			  =		size_divisor if size_divisor is not None else self.size_divisor
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError('''size_divisor is required for resizing''')

        __UpperCamelCase     :List[Any]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            raise ValueError('''Invalid image(s)''')

        # All transformations expect numpy arrays.
        __UpperCamelCase     :Optional[Any]			  =		[to_numpy_array(__lowercase) for img in images]

        if do_resize:
            __UpperCamelCase     :List[str]			  =		[self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Dict			  =		[self.rescale(__lowercase , scale=1 / 255) for image in images]

        __UpperCamelCase     :str			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :int			  =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase , tensor_type=__lowercase)





