
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 						__magic_name__			(   __a      :	Tuple						):



     '''simple docstring'''




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


lowerCamelCase_						     =					logging.getLogger(__name__)






def 						__magic_name__			(   __a      :	int	,		__a      :	List[str]						):



     '''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}"""
     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=__a	,		filename=__a	,		monitor=f"val_{metric}"	,		mode="""max"""	,		save_top_k=3	,		every_n_epochs=1	,		)
     return checkpoint_callback






def 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	str						):



     '''simple docstring'''




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




class      __A(    pl.Callback			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
       pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_						)







  @rank_zero_only
  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=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=SCREAMING_SNAKE_CASE_						)
            generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_						)
       with open(SCREAMING_SNAKE_CASE_	,      """a+"""						) as writer:
            for key in sorted(SCREAMING_SNAKE_CASE_						):
                 if key in ["log", "progress_bar", "preds"]:
                      continue
                 UpperCamelCase__				=    metrics[key]
                 if isinstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						):
                      UpperCamelCase__				=    val.item()
                 UpperCamelCase__				=    F"{key}: {val:.6f}\n"
                 writer.write(SCREAMING_SNAKE_CASE_						)

       if not save_generations:
            return

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







  @rank_zero_only
  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       try:
            UpperCamelCase__				=    pl_module.model.model.num_parameters()
       except AttributeError:
            UpperCamelCase__				=    pl_module.model.num_parameters()

       UpperCamelCase__				=    count_trainable_parameters(SCREAMING_SNAKE_CASE_						)
       # 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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       save_json(pl_module.metrics	,      pl_module.metrics_save_path						)
       return self._write_logs(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      """test"""						)







  @rank_zero_only
  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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 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      __A:

      """simple docstring"""







      @staticmethod
      def 	UpperCAmelCase_ (*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
           pass






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




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




@is_pipeline_test
@require_vision
@require_torch
class      __A(    unittest.TestCase			):

  """simple docstring"""
  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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    MaskGenerationPipeline(model=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_						)
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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=2_56						)

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

       # fmt: off
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE_	,      decimals=4						)	,      [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.021},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
               {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9967},
               {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.993},
               {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9909},
               {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9879},
               {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9834},
               {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9716},
               {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9612},
               {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9599},
               {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9552},
               {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9532},
               {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9516},
               {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9499},
               {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9483},
               {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9464},
               {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
               {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.943},
               {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9408},
               {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9335},
               {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9326},
               {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9262},
               {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8999},
               {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8986},
               {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8984},
               {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8873},
               {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8871}
           ]	,      )
       # fmt: on







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

       UpperCamelCase__				=    image_segmenter(
           """http://images.cocodataset.org/val2017/000000039769.jpg"""	,      pred_iou_thresh=1	,      points_per_batch=2_56						)

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

       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE_	,      decimals=4						)	,      [
               {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0444},
               {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0210},
               {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0167},
               {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0132},
               {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0053},
           ]	,      )





