






"""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			(		_snake_case							):







      """simple docstring"""






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


_UpperCamelCase					      =  logging.getLogger(__name__)

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      if metric == "rouge2":
            UpperCAmelCase          =	"""{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            UpperCAmelCase          =	"""{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "em":
            UpperCAmelCase          =	"""{val_avg_em:.4f}-{step_count}"""
      elif metric == "loss":
            UpperCAmelCase          =	"""{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          =	ModelCheckpoint(
          dirpath=_snake_case				,	filename=_snake_case				,	monitor=F'''val_{metric}'''				,	mode="""max"""				,	save_top_k=1				,	every_n_epochs=1				,	)

      return checkpoint_callback

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






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







class  lowerCamelCase__ (	pl.Callback			):






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

          if not save_generations:
                return

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






    @rank_zero_only
    def     _UpperCamelCase							(			self     ,A     ,A ):
          try:
                UpperCAmelCase          =	pl_module.model.model.num_parameters()
          except AttributeError:
                UpperCAmelCase          =	pl_module.model.num_parameters()

          UpperCAmelCase          =	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     ,A     ,A ):
          save_json(pl_module.metrics     ,pl_module.metrics_save_path )
          return self._write_logs(A     ,A     ,"""test""" )






    @rank_zero_only
    def     _UpperCamelCase							(			self     ,A     ,A ):
          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 hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:







  class  lowerCamelCase__ :






      @staticmethod
      def     _UpperCamelCase							(			*A     ,**A ):
            pass

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	hashlib.mda(image.tobytes()							)
      return m.hexdigest()[:10]

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	np.array(_snake_case							)
      UpperCAmelCase          =	npimg.shape
      return {"hash": hashimage(_snake_case							), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      dict(
        (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
    SCREAMING_SNAKE_CASE		=      dict(
        (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	MaskGenerationPipeline(model=A     ,image_processor=A )
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]






    def     _UpperCamelCase							(			self     ,A     ,A ):
          pass






    @require_tf
    @unittest.skip("""Image segmentation not implemented in TF""" )
    def     _UpperCamelCase							(			self ):
          pass






    @slow
    @require_torch
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	pipeline("""mask-generation"""     ,model="""facebook/sam-vit-huge""" )

          UpperCAmelCase          =	image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""     ,points_per_batch=256 )

          # Shortening by hashing
          UpperCAmelCase          =	[]
          for i, o in enumerate(outputs["""masks"""] ):
                new_outupt += [{"mask": mask_to_test_readable(A ), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
                  {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
                  {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
                  {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
                  {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
                  {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9967},
                  {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
                  {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9909},
                  {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9879},
                  {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9834},
                  {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9716},
                  {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9612},
                  {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9599},
                  {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9552},
                  {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9532},
                  {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9516},
                  {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9499},
                  {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9483},
                  {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9464},
                  {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
                  {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
                  {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9408},
                  {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9335},
                  {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9326},
                  {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9262},
                  {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8999},
                  {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8986},
                  {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8984},
                  {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8873},
                  {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8871}
              ]     ,)
          # fmt: on






    @require_torch
    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""facebook/sam-vit-huge"""
          UpperCAmelCase          =	pipeline("""mask-generation"""     ,model=A )

          UpperCAmelCase          =	image_segmenter(
              """http://images.cocodataset.org/val2017/000000039769.jpg"""     ,pred_iou_thresh=1     ,points_per_batch=256 )

          # Shortening by hashing
          UpperCAmelCase          =	[]
          for i, o in enumerate(outputs["""masks"""] ):
                new_outupt += [{"mask": mask_to_test_readable(A ), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
                  {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0210},
                  {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
                  {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
                  {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
              ]     ,)



