
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 						lowercase_      (       A__		):
    """simple docstring"""

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


_A   =			logging.getLogger(__name__)



def 						lowercase_      (       A__			,		A__		):
    """simple docstring"""

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

    snake_case								= ModelCheckpoint(
        dirpath=A__			,		filename=A__			,		monitor=F'val_{metric}'			,		mode="max"			,		save_top_k=3			,		every_n_epochs=1			,		)
    return checkpoint_callback



def 						lowercase_      (       A__			,		A__		):
    """simple docstring"""

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







class 					lowerCamelCase		(  pl.Callback  ):





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   Optional[Any]    ,   _A				:   Dict       )						->						Dict:
         snake_case								= {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 		UpperCAmelCase(self				:   Any    ,   _A				:   pl.Trainer    ,   _A				:   pl.LightningModule    ,   _A				:   str    ,   _A				:   str=True       )						->						None:
         logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'       )
         snake_case								= 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
         snake_case								= Path(pl_module.hparams.output_dir       )
         if type_path == "test":
             snake_case								= od / "test_results.txt"
             snake_case								= 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.
             snake_case								= od / f'{type_path}_results/{trainer.global_step:05d}.txt'
             snake_case								= 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
                 snake_case								= metrics[key]
                 if isinstance(_A    ,   torch.Tensor       ):
                     snake_case								= val.item()
                 snake_case								= f'{key}: {val:.6f}\n'
                 writer.write(_A       )

         if not save_generations:
             return

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





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

         snake_case								= 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 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   pl.Trainer    ,   _A				:   pl.LightningModule       )						->						List[str]:
         save_json(pl_module.metrics    ,   pl_module.metrics_save_path       )
         return self._write_logs(_A    ,   _A    ,   "test"       )





     @rank_zero_only
     def 		UpperCAmelCase(self				:   Any    ,   _A				:   pl.Trainer    ,   _A				:   Union[str, Any]       )						->						Dict:
         save_json(pl_module.metrics    ,   pl_module.metrics_save_path       )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Dict									=		["image_processor", "tokenizer"]
     UpperCAmelCase__    :							Dict									=		"LayoutLMv2ImageProcessor"
     UpperCAmelCase__    :							Optional[Any]									=		("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")





     def __init__(self				:   str    ,   _A				:   Any=None    ,   _A				:   Tuple=None    ,   **_A				:   Optional[Any]       )						->						Optional[int]:
         if "feature_extractor" in kwargs:
             warnings.warn(
                 "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                 " instead."    ,   _A    ,   )
             snake_case								= kwargs.pop("feature_extractor"       )

         snake_case								= image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError("You need to specify an `image_processor`."       )
         if tokenizer is None:
             raise ValueError("You need to specify a `tokenizer`."       )

         super().__init__(_A    ,   _A       )





     def __call__(self				:   int    ,   _A				:   List[str]    ,   _A				:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,   _A				:   Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,   _A				:   Union[List[List[int]], List[List[List[int]]]] = None    ,   _A				:   Optional[Union[List[int], List[List[int]]]] = None    ,   _A				:   bool = True    ,   _A				:   Union[bool, str, PaddingStrategy] = False    ,   _A				:   Union[bool, str, TruncationStrategy] = None    ,   _A				:   Optional[int] = None    ,   _A				:   int = 0    ,   _A				:   Optional[int] = None    ,   _A				:   Optional[bool] = None    ,   _A				:   Optional[bool] = None    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = False    ,   _A				:   bool = True    ,   _A				:   Optional[Union[str, TensorType]] = None    ,   **_A				:   Dict    ,   )						->						BatchEncoding:
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
             raise ValueError(
                 "You cannot provide bounding boxes "
                 "if you initialized the image processor with apply_ocr set to True."       )

         if self.image_processor.apply_ocr and (word_labels is not None):
             raise ValueError(
                 "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."       )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
             raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."       )

         # first, apply the image processor
         snake_case								= self.image_processor(images=_A    ,   return_tensors=_A       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(_A    ,   _A       ):
                 snake_case								= [text]  # add batch dimension (as the image processor always adds a batch dimension)
             snake_case								= features["words"]

         snake_case								= self.tokenizer(
             text=text if text is not None else features["words"]    ,   text_pair=text_pair if text_pair is not None else None    ,   boxes=boxes if boxes is not None else features["boxes"]    ,   word_labels=_A    ,   add_special_tokens=_A    ,   padding=_A    ,   truncation=_A    ,   max_length=_A    ,   stride=_A    ,   pad_to_multiple_of=_A    ,   return_token_type_ids=_A    ,   return_attention_mask=_A    ,   return_overflowing_tokens=_A    ,   return_special_tokens_mask=_A    ,   return_offsets_mapping=_A    ,   return_length=_A    ,   verbose=_A    ,   return_tensors=_A    ,   **_A    ,   )

         # add pixel values
         snake_case								= features.pop("pixel_values"       )
         if return_overflowing_tokens is True:
             snake_case								= self.get_overflowing_images(_A    ,   encoded_inputs["overflow_to_sample_mapping"]       )
         snake_case								= images

         return encoded_inputs





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   Dict    ,   _A				:   List[str]       )						->						Optional[int]:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         snake_case								= []
         for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]       )

         if len(_A       ) != len(_A       ):
             raise ValueError(
                 "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                 f' {len(_A       )} and {len(_A       )}'       )

         return images_with_overflow





     def 		UpperCAmelCase(self				:   Tuple    ,   *_A				:   int    ,   **_A				:   Dict       )						->						str:
         return self.tokenizer.batch_decode(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   str    ,   *_A				:   List[Any]    ,   **_A				:   List[Any]       )						->						Optional[Any]:
         return self.tokenizer.decode(*_A    ,   **_A       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Optional[int]:
         return ["input_ids", "bbox", "attention_mask", "image"]





     @property
     def 		UpperCAmelCase(self				:   List[Any]       )						->						int:
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,   _A    ,   )
         return self.image_processor_class





     @property
     def 		UpperCAmelCase(self				:   Dict       )						->						Union[str, Any]:
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,   _A    ,   )
         return self.image_processor

