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     :Any			  =		filter(lambda SCREAMING_SNAKE_CASE	: p.requires_grad  ,			model.parameters()	)
    __UpperCamelCase     :Optional[int]			  =		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     :Union[str, Any]			  =		"{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        __UpperCamelCase     :Any			  =		"{val_avg_bleu:.4f}-{step_count}"
    elif metric == "em":
        __UpperCamelCase     :Optional[int]			  =		"{val_avg_em:.4f}-{step_count}"
    elif metric == "loss":
        __UpperCamelCase     :Optional[Any]			  =		"{val_avg_loss:.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     :Union[str, Any]			  =		ModelCheckpoint(
        dirpath=A_  ,			filename=A_  ,			monitor=f"""val_{metric}"""  ,			mode='''max'''  ,			save_top_k=1  ,			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=A_  ,			verbose=A_  ,			)


class 	lowerCamelCase_     (   pl.Callback ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :Optional[Any]			  =		{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 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase=True)       ->	int:
        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     :Optional[Any]			  =		Path(pl_module.hparams.output_dir)
        if type_path == "test":
            __UpperCamelCase     :str			  =		od / "test_results.txt"
            __UpperCamelCase     :str			  =		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     :str			  =		od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
            __UpperCamelCase     :List[Any]			  =		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
                __UpperCamelCase     :List[str]			  =		metrics[key]
                if isinstance(__UpperCAmelCase , torch.Tensor):
                    __UpperCamelCase     :str			  =		val.item()
                __UpperCamelCase     :Dict			  =		f"""{key}: {val:.6f}\n"""
                writer.write(__UpperCAmelCase)

        if not save_generations:
            return

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







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

        __UpperCamelCase     :Dict			  =		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 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	int:
        save_json(pl_module.metrics , pl_module.metrics_save_path)
        return self._write_logs(__UpperCAmelCase , __UpperCAmelCase , '''test''')







    @rank_zero_only
    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	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")









"""simple docstring"""







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       LayoutLMTokenizer
    UpperCAmelCase       :							int	=       LayoutLMTokenizerFast
    UpperCAmelCase       :							Union[str, Any]	=       True
    UpperCAmelCase       :							Optional[Any]	=       True






    def 	__snake_case							(					self				:    Optional[int]):
      super().setUp()

      a						:		Tuple      							=			[
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      a						:		Optional[Any]      							=			os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["vocab_file"])
      with open(self.vocab_file				, "w"				, encoding="utf-8") as vocab_writer:
        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))






    def 	__snake_case							(					self				:    Optional[int]				, **__UpperCAmelCase				:    Tuple):
      return LayoutLMTokenizer.from_pretrained(self.tmpdirname				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    str):
      a						:		Tuple      							=			"UNwant\u00E9d,running"
      a						:		Dict      							=			"unwanted, running"
      return input_text, output_text






    def 	__snake_case							(					self				:    Any):
      a						:		List[Any]      							=			self.tokenizer_class(self.vocab_file)

      a						:		str      							=			tokenizer.tokenize("UNwant\u00E9d,running")
      self.assertListEqual(__UpperCAmelCase				, ["un", "##want", "##ed", ",", "runn", "##ing"])
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase)				, [7, 4, 5, 10, 8, 9])






    def 	__snake_case							(					self				:    Dict):
      pass






