






'''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_rag import save_json
def 						A__			(				__lowerCAmelCase		:			str						):
      lowerCamelCase__         =  filter(lambda __lowerCAmelCase						: p.requires_grad   ,    model.parameters()						)
      lowerCamelCase__         =  sum([np.prod(p.size()						) for p in model_parameters]						)
      return params


UpperCamelCase    :      int  			=		logging.getLogger(__name__)
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Union[str, Any]						):
      if metric == "rouge2":
            lowerCamelCase__         =  """{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            lowerCamelCase__         =  """{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "em":
            lowerCamelCase__         =  """{val_avg_em:.4f}-{step_count}"""
      elif metric == "loss":
            lowerCamelCase__         =  """{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."""						)

      lowerCamelCase__         =  ModelCheckpoint(
          dirpath=__lowerCAmelCase   ,    filename=__lowerCAmelCase   ,    monitor=F'''val_{metric}'''   ,    mode="""max"""   ,    save_top_k=1   ,    every_n_epochs=1   ,    )

      return checkpoint_callback
def 						A__			(				__lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			Tuple						):
      return EarlyStopping(
          monitor=F'''val_{metric}'''   ,    mode="""min""" if """loss""" in metric else """max"""   ,    patience=__lowerCAmelCase   ,    verbose=__lowerCAmelCase   ,    )





class 		UpperCamelCase__    (pl.Callback			):




  '''simple docstring'''





  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        lowerCamelCase__         =  {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 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase=True       ):
        logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
        lowerCamelCase__         =  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__         =  Path(pl_module.hparams.output_dir       )
        if type_path == "test":
              lowerCamelCase__         =  od / """test_results.txt"""
              lowerCamelCase__         =  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__         =  od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
              lowerCamelCase__         =  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__         =  metrics[key]
                    if isinstance(_lowerCAmelCase   ,torch.Tensor       ):
                          lowerCamelCase__         =  val.item()
                    lowerCamelCase__         =  F'''{key}: {val:.6f}\n'''
                    writer.write(_lowerCAmelCase       )

        if not save_generations:
              return

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






  @rank_zero_only
  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        try:
              lowerCamelCase__         =  pl_module.model.model.num_parameters()
        except AttributeError:
              lowerCamelCase__         =  pl_module.model.num_parameters()

        lowerCamelCase__         =  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 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        save_json(pl_module.metrics   ,pl_module.metrics_save_path       )
        return self._write_logs(_lowerCAmelCase   ,_lowerCAmelCase   ,"""test"""       )






  @rank_zero_only
  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
        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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  tempfile.mkdtemp()

								lowerCamelCase__         =  BlipImageProcessor()
								lowerCamelCase__         =  BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""       )

								lowerCamelCase__         =  BlipProcessor(_lowerCAmelCase   ,_lowerCAmelCase       )

								processor.save_pretrained(self.tmpdirname       )






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).tokenizer






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).image_processor






		def 			UpperCamelCase_					(      self       ):
								shutil.rmtree(self.tmpdirname       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [np.random.randint(2_55   ,size=(3, 30, 4_00)   ,dtype=np.uinta       )]

								lowerCamelCase__         =  [Image.fromarray(np.moveaxis(_lowerCAmelCase   ,0   ,-1       )       ) for x in image_inputs]

								return image_inputs






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  BlipProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

								lowerCamelCase__         =  self.get_tokenizer(bos_token="""(BOS)"""   ,eos_token="""(EOS)"""       )
								lowerCamelCase__         =  self.get_image_processor(do_normalize=_lowerCAmelCase   ,padding_value=1.0       )

								lowerCamelCase__         =  BlipProcessor.from_pretrained(
								    self.tmpdirname   ,bos_token="""(BOS)"""   ,eos_token="""(EOS)"""   ,do_normalize=_lowerCAmelCase   ,padding_value=1.0       )

								self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()       )
								self.assertIsInstance(processor.tokenizer   ,_lowerCAmelCase       )

								self.assertEqual(processor.image_processor.to_json_string()   ,image_processor_add_kwargs.to_json_string()       )
								self.assertIsInstance(processor.image_processor   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  image_processor(_lowerCAmelCase   ,return_tensors="""np"""       )
								lowerCamelCase__         =  processor(images=_lowerCAmelCase   ,return_tensors="""np"""       )

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()   ,input_processor[key].sum()   ,delta=1E-2       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""

								lowerCamelCase__         =  processor(text=_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer(_lowerCAmelCase   ,return_token_type_ids=_lowerCAmelCase       )

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]   ,encoded_processor[key]       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

								self.assertListEqual(list(inputs.keys()       )   ,["""pixel_values""", """input_ids""", """attention_mask"""]       )

								# test if it raises when no input is passed
								with pytest.raises(_lowerCAmelCase       ):
														processor()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase__         =  processor.batch_decode(_lowerCAmelCase       )
								lowerCamelCase__         =  tokenizer.batch_decode(_lowerCAmelCase       )

								self.assertListEqual(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

								# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
								self.assertListEqual(list(inputs.keys()       )   ,["""pixel_values""", """input_ids""", """attention_mask"""]       )

