import cva
import numpy as np



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[str]:

        if k in (0.04, 0.06):
               _A        =  k
               _A        =  window_size
        else:
               raise ValueError("""invalid k value"""							)



 def __str__(					self							)  ->					str:
        return str(self.k							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					tuple[cva.Mat, list[list[int]]]:
        _A        =  cva.imread(lowerCAmelCase_		,				0							)
        _A   ,  _A        =  img.shape
        _A        =  []
        _A        =  img.copy()
        _A        =  cva.cvtColor(lowerCAmelCase_		,				cva.COLOR_GRAY2RGB							)
        _A   ,  _A        =  np.gradient(lowerCAmelCase_							)
        _A        =  dx**2
        _A        =  dy**2
        _A        =  dx * dy
        _A        =  0.04
        _A        =  self.window_size // 2
        for y in range(lowerCAmelCase_		,				h - offset							):
               for x in range(lowerCAmelCase_		,				w - offset							):
                      _A        =  ixx[
                          y - offset : y + offset + 1, x - offset : x + offset + 1
                      ].sum()
                      _A        =  iyy[
                          y - offset : y + offset + 1, x - offset : x + offset + 1
                      ].sum()
                      _A        =  ixy[
                          y - offset : y + offset + 1, x - offset : x + offset + 1
                      ].sum()

                      _A        =  (wxx * wyy) - (wxy**2)
                      _A        =  wxx + wyy
                      _A        =  det - k * (trace**2)
                      # Can change the value
                      if r > 0.5:
                             corner_list.append([x, y, r]							)
                             color_img.itemset((y, x, 0)		,				0							)
                             color_img.itemset((y, x, 1)		,				0							)
                             color_img.itemset((y, x, 2)		,				2_55							)
        return color_img, corner_list


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						HarrisCorner(0.04, 3)
    _SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE										=						edge_detect.detect('path_to_image')
    cva.imwrite('detect.png', color_img)





import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


_SCREAMING_SNAKE_CASE										=						os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'''{bindir}/../../examples/pytorch/translation'''):
    from run_translation import main  # noqa


set_seed(42)
_SCREAMING_SNAKE_CASE										=						'sshleifer/student_marian_en_ro_6_1'
_SCREAMING_SNAKE_CASE										=						'sshleifer/tiny-mbart'



@require_torch
class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				)  ->					int:
        _A        =  self.run_trainer(
            eval_steps=1		,				max_len=12		,				model_name=lowerCAmelCase_		,				num_train_epochs=1		,				distributed=lowerCAmelCase_		,				extra_args_str=lowerCAmelCase_		,				predict_with_generate=lowerCAmelCase_		,				do_train=lowerCAmelCase_		,				do_eval=lowerCAmelCase_		,				do_predict=lowerCAmelCase_		,				)
        _A        =  TrainerState.load_from_json(os.path.join(lowerCAmelCase_		,				"""trainer_state.json"""							)							).log_history

        if not do_eval:
               return

        _A        =  [log for log in logs if """eval_loss""" in log.keys()]

        _A        =  eval_metrics[0]
        if predict_with_generate:
               assert "eval_bleu" in first_step_stats

               _A        =  eval_metrics[-1]
               assert isinstance(last_step_stats["""eval_bleu"""]		,				lowerCAmelCase_							)
               assert not math.isnan(float(last_step_stats["""eval_loss"""]							)							), "eval_loss must not be `nan`"



 @require_torch_non_multi_gpu
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        self.run_seqaseq_quick()



 @require_torch_multi_gpu
 def      UpperCAmelCase							(					self							)  ->					Dict:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_							)



 @require_torch_multi_gpu
 def      UpperCAmelCase							(					self							)  ->					Dict:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_							)



 @unittest.skip("""Requires an update of the env running those tests"""							)
 @require_torch_multi_gpu
 @require_fairscale
 def      UpperCAmelCase							(					self							)  ->					str:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_		,				extra_args_str="""--sharded_ddp simple"""							)



 @unittest.skip("""Requires an update of the env running those tests"""							)
 @require_torch_multi_gpu
 @require_fairscale
 def      UpperCAmelCase							(					self							)  ->					Dict:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_		,				extra_args_str="""--sharded_ddp simple --fp16"""							)



 @unittest.skip("""Requires an update of the env running those tests"""							)
 @require_torch_multi_gpu
 @require_fairscale
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        self.run_seqaseq_quick(distributed=lowerCAmelCase_		,				extra_args_str="""--sharded_ddp zero_dp_2"""		,				predict_with_generate=lowerCAmelCase_							)



 @unittest.skip("""Requires an update of the env running those tests"""							)
 @require_torch_multi_gpu
 @require_fairscale
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        self.run_seqaseq_quick(
            distributed=lowerCAmelCase_		,				extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""		,				predict_with_generate=lowerCAmelCase_							)



 @require_apex
 @require_torch_gpu
 def      UpperCAmelCase							(					self							)  ->					int:
        # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
        # program and it breaks other tests that run from the same pytest worker, therefore until this is
        # sorted out it must be run only in an external program, that is distributed=True in this
        # test and only under one or more gpus - if we want cpu will need to make a special test
        #
        # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
        # 2nd main() call it botches the future eval.
        #
        self.run_seqaseq_quick(distributed=lowerCAmelCase_		,				extra_args_str="""--fp16 --fp16_backend=apex"""							)
        # test 2nd time - was getting eval_loss': nan'
        # to reproduce the problem set distributed=False
        self.run_seqaseq_quick(distributed=lowerCAmelCase_		,				extra_args_str="""--fp16 --fp16_backend=apex"""							)



 @parameterized.expand(["""base""", """low""", """high""", """mixed"""]							)
 @require_torch_multi_gpu
 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					int:
        # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
        _A        =  {
            # test with the default log_level - should be info and thus log info once
            """base""": {"""extra_args_str""": """""", """n_matches""": 1},
            # test with low log_level and log_level_replica - should be noisy on all processes
            # now the info string should appear twice on 2 processes
            """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
            # test with high log_level and low log_level_replica
            # now the info string should appear once only on the replica
            """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
            # test with high log_level and log_level_replica - should be quiet on all processes
            """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
        }

        _A        =  experiments[experiment_id]
        _A        =  {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
        _A        =  """Running training"""
        with CaptureStderr() as cl:
               self.run_seqaseq_quick(**lowerCAmelCase_		,				extra_args_str=data["""extra_args_str"""]							)
        _A        =  len(re.findall(lowerCAmelCase_		,				cl.err							)							)
        self.assertEqual(lowerCAmelCase_		,				data["""n_matches"""]							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.run_trainer(
            eval_steps=2		,				max_len=1_28		,				model_name=lowerCAmelCase_		,				learning_rate=3E-4		,				num_train_epochs=10		,				distributed=lowerCAmelCase_		,				)

        # Check metrics
        _A        =  TrainerState.load_from_json(os.path.join(lowerCAmelCase_		,				"""trainer_state.json"""							)							).log_history
        _A        =  [log for log in logs if """eval_loss""" in log.keys()]
        _A        =  eval_metrics[0]
        _A        =  eval_metrics[-1]

        assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
        assert isinstance(last_step_stats["""eval_bleu"""]		,				lowerCAmelCase_							)

        # test if do_predict saves generations and metrics
        _A        =  os.listdir(lowerCAmelCase_							)
        _A        =  {os.path.basename(lowerCAmelCase_							) for p in contents}
        assert "generated_predictions.txt" in contents
        assert "predict_results.json" in contents



 @slow
 @require_bitsandbytes
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        from transformers.training_args import OptimizerNames

        def train_and_return_metrics(lowerCAmelCase_							) -> Tuple[int, float]:
               _A        =  """--skip_memory_metrics 0"""

               _A        =  self.run_trainer(
                   max_len=1_28		,				model_name=lowerCAmelCase_		,				learning_rate=3E-4		,				num_train_epochs=1		,				optim=lowerCAmelCase_		,				distributed=lowerCAmelCase_		,				extra_args_str=lowerCAmelCase_		,				do_eval=lowerCAmelCase_		,				do_predict=lowerCAmelCase_		,				n_gpus_to_use=1		,				)

               # Check metrics
               _A        =  TrainerState.load_from_json(Path(lowerCAmelCase_		,				"""trainer_state.json"""							)							).log_history
               _A        =  int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20							)
               _A        =  int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20							)

               _A        =  logs[0]["""train_loss"""]
               return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

        _A   ,  _A   ,  _A        =  train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
        _A   ,  _A   ,  _A        =  train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

        _A        =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

        _A        =  gpu_peak_mem_orig + gpu_alloc_mem_orig
        _A        =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
        _A        =  gpu_total_mem_orig - gpu_total_mem_bnb

        # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
        # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
        # in 2 bytes and the diff in optim memory usage is derived as so:
        #
        # - normal 25*8=~200MB (8 bytes per param)
        # - bnb    25*2= ~50MB (2 bytes per param)
        #
        # Thus we should expect ~150MB total memory saved.
        #
        # Peak memory should be the same - the total should be different by about that same margin
        #
        # After leaving a small margin to accommodate for differences between gpus let's check
        # that we have at least 120MB in savings
        _A        =  1_20

        # uncomment the following if this test starts failing - requires py38 for a new print feature
        # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
        # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
        # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
        # print(f"{gpu_alloc_mem_diff=}MB")
        # print(f"{gpu_peak_mem_diff=}MB")
        # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
        # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

        self.assertGreater(
            lowerCAmelCase_		,				lowerCAmelCase_		,				"""should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
            F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
            F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''		,				)

        self.assertGreater(
            lowerCAmelCase_		,				lowerCAmelCase_		,				"""should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
            F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
            F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''		,				)

        self.assertEqual(
            lowerCAmelCase_		,				lowerCAmelCase_		,				F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_ = 3E-3		,				lowerCAmelCase_ = "adafactor"		,				lowerCAmelCase_ = False		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = 0		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = None		,				)  ->					str:
        _A        =  self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
        _A        =  self.get_auto_remove_tmp_dir()
        _A        =  F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(lowerCAmelCase_							)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(lowerCAmelCase_							)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

        _A        =  F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(lowerCAmelCase_							)}
        '''.split()

        _A        =  """
            --do_predict
        """.split()

        _A        =  []
        if do_train:
               args += args_train

        if do_eval:
               args += args_eval

        if do_predict:
               args += args_predict

        if predict_with_generate:
               args += "--predict_with_generate".split()

        if do_train:
               if optim == "adafactor":
                      args += "--adafactor".split()
               else:
                      args += F'''--optim {optim}'''.split()

        if extra_args_str is not None:
               args += extra_args_str.split()

        if distributed:
               if n_gpus_to_use is None:
                      _A        =  get_gpu_count()
               _A        =  get_torch_dist_unique_port()
               _A        =  F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
               _A        =  [sys.executable] + distributed_args + args
               # keep for quick debug
               # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
               execute_subprocess_async(lowerCAmelCase_		,				env=self.get_env()							)
        else:
               _A        =  ["""run_translation.py"""] + args
               with patch.object(lowerCAmelCase_		,				"""argv"""		,				lowerCAmelCase_							):
                      main()

        return output_dir




