

'''simple docstring'''


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


lowerCAmelCase__    :							Optional[Any]					    =    os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
					from run_translation import main  # noqa


set_seed(42)
lowerCAmelCase__    :							Tuple					    =    '''sshleifer/student_marian_en_ro_6_1'''
lowerCAmelCase__    :							Tuple					    =    '''sshleifer/tiny-mbart'''




@require_torch
class      a      (					__a							):






						"""simple docstring"""




						def    __magic_name__				(				self       :   Union[str, Any]      ,      snake_case_       :   Union[str, Any]=False      ,      snake_case_       :   Optional[Any]=None      ,      snake_case_       :   int=True      ,      snake_case_       :   str=True      ,      snake_case_       :   List[str]=True      ,      snake_case_       :   List[str]=True      ,      ):



								'''simple docstring'''



								snake_case__    :							Optional[int]							    =	self.run_trainer(
								    eval_steps=1      ,      max_len=1_2      ,      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      ,      )
								snake_case__    :							Optional[int]							    =	TrainerState.load_from_json(os.path.join(_lowerCamelCase      ,      '''trainer_state.json'''	)	).log_history

								if not do_eval:
										return

								snake_case__    :							Dict							    =	[log for log in logs if '''eval_loss''' in log.keys()]

								snake_case__    :							List[str]							    =	eval_metrics[0]
								if predict_with_generate:
										assert "eval_bleu" in first_step_stats

										snake_case__    :							List[str]							    =	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    __magic_name__				(				self       :   Tuple	):



								'''simple docstring'''



								self.run_seqaseq_quick()







						@require_torch_multi_gpu
						def    __magic_name__				(				self       :   Tuple	):



								'''simple docstring'''



								self.run_seqaseq_quick(distributed=_lowerCamelCase	)







						@require_torch_multi_gpu
						def    __magic_name__				(				self       :   Union[str, Any]	):



								'''simple docstring'''



								self.run_seqaseq_quick(distributed=_lowerCamelCase	)







						@unittest.skip('''Requires an update of the env running those tests'''	)
						@require_torch_multi_gpu
						@require_fairscale
						def    __magic_name__				(				self       :   Tuple	):



								'''simple docstring'''



								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    __magic_name__				(				self       :   Union[str, Any]	):



								'''simple docstring'''



								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    __magic_name__				(				self       :   List[str]	):



								'''simple docstring'''



								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    __magic_name__				(				self       :   Optional[int]	):



								'''simple docstring'''



								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    __magic_name__				(				self       :   List[Any]	):



								'''simple docstring'''



								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    __magic_name__				(				self       :   int      ,      snake_case_       :   str	):



								'''simple docstring'''



								snake_case__    :							Optional[Any]							    =	{
								    # 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},
								}

								snake_case__    :							List[str]							    =	experiments[experiment_id]
								snake_case__    :							int							    =	{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
								snake_case__    :							Optional[int]							    =	'''Running training'''
								with CaptureStderr() as cl:
										self.run_seqaseq_quick(**_lowerCamelCase      ,      extra_args_str=data['''extra_args_str''']	)
								snake_case__    :							Union[str, Any]							    =	len(re.findall(_lowerCamelCase      ,      cl.err	)	)
								self.assertEqual(_lowerCamelCase      ,      data['''n_matches''']	)







						@slow
						def    __magic_name__				(				self       :   List[str]	):



								'''simple docstring'''



								snake_case__    :							List[Any]							    =	self.run_trainer(
								    eval_steps=2      ,      max_len=1_2_8      ,      model_name=_lowerCamelCase      ,      learning_rate=3e-4      ,      num_train_epochs=1_0      ,      distributed=_lowerCamelCase      ,      )

								# Check metrics
								snake_case__    :							Union[str, Any]							    =	TrainerState.load_from_json(os.path.join(_lowerCamelCase      ,      '''trainer_state.json'''	)	).log_history
								snake_case__    :							str							    =	[log for log in logs if '''eval_loss''' in log.keys()]
								snake_case__    :							Optional[int]							    =	eval_metrics[0]
								snake_case__    :							List[str]							    =	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
								snake_case__    :							Union[str, Any]							    =	os.listdir(_lowerCamelCase	)
								snake_case__    :							Union[str, Any]							    =	{os.path.basename(_lowerCamelCase	) for p in contents}
								assert "generated_predictions.txt" in contents
								assert "predict_results.json" in contents







						@slow
						@require_bitsandbytes
						def    __magic_name__				(				self       :   Dict	):



								'''simple docstring'''



								from transformers.training_args import OptimizerNames

								def train_and_return_metrics(snake_case_       :   Tuple	) -> Tuple[int, float]:
										snake_case__    :							Tuple							    =	'''--skip_memory_metrics 0'''

										snake_case__    :							Tuple							    =	self.run_trainer(
										    max_len=1_2_8      ,      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
										snake_case__    :							Any							    =	TrainerState.load_from_json(Path(_lowerCamelCase      ,      '''trainer_state.json'''	)	).log_history
										snake_case__    :							Optional[int]							    =	int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0	)
										snake_case__    :							Optional[int]							    =	int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0	)

										snake_case__    :							Any							    =	logs[0]['''train_loss''']
										return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

								snake_case__    ,							snake_case__    ,							snake_case__    :							Optional[int]							    =	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value	)
								snake_case__    ,							snake_case__    ,							snake_case__    :							Dict							    =	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value	)

								snake_case__    :							List[str]							    =	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

								snake_case__    :							List[str]							    =	gpu_peak_mem_orig + gpu_alloc_mem_orig
								snake_case__    :							Optional[int]							    =	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
								snake_case__    :							Dict							    =	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
								snake_case__    :							List[str]							    =	1_2_0

								# 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    __magic_name__				(				self       :   List[str]      ,      snake_case_       :   Any      ,      snake_case_       :   Union[str, Any]      ,      snake_case_       :   int      ,      snake_case_       :   Optional[int] = 3e-3      ,      snake_case_       :   Tuple = "adafactor"      ,      snake_case_       :   Dict = False      ,      snake_case_       :   Optional[int] = None      ,      snake_case_       :   Any = 0      ,      snake_case_       :   Union[str, Any] = True      ,      snake_case_       :   Tuple = True      ,      snake_case_       :   str = True      ,      snake_case_       :   List[str] = True      ,      snake_case_       :   Optional[int] = None      ,      ):



								'''simple docstring'''



								snake_case__    :							Dict							    =	self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
								snake_case__    :							Any							    =	self.get_auto_remove_tmp_dir()
								snake_case__    :							str							    =	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()

								snake_case__    :							int							    =	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()

								snake_case__    :							Any							    =	'''
            --do_predict
        '''.split()

								snake_case__    :							Optional[Any]							    =	[]
								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:
												snake_case__    :							Any							    =	get_gpu_count()
										snake_case__    :							str							    =	get_torch_dist_unique_port()
										snake_case__    :							Dict							    =	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()
										snake_case__    :							Union[str, Any]							    =	[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:
										snake_case__    :							List[Any]							    =	['''run_translation.py'''] + args
										with patch.object(_lowerCamelCase      ,      '''argv'''      ,      _lowerCamelCase	):
												main()

								return output_dir




'''simple docstring'''

import colorsys

from PIL import Image  # type: ignore



def snake_case_      (					__snake_case  :							float	,   __snake_case  :							float	,   __snake_case  :							int)			->					float:
   lowerCAmelCase_			=      x
   lowerCAmelCase_			=      y
   for step in range(__snake_case):  # noqa: B007
      lowerCAmelCase_			=      a * a - b * b + x
      lowerCAmelCase_			=      2 * a * b + y
      lowerCAmelCase_			=      a_new

      # divergence happens for all complex number with an absolute value
      # greater than 4
      if a * a + b * b > 4:
         break
   return step / (max_step - 1)



def snake_case_      (					__snake_case  :							float)			->					tuple:
   if distance == 1:
      return (0, 0, 0)
   else:
      return (255, 255, 255)



def snake_case_      (					__snake_case  :							float)			->					tuple:
   if distance == 1:
      return (0, 0, 0)
   else:
      return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(__snake_case	,   1	,   1))



def snake_case_      (					__snake_case  :							int = 800	,   __snake_case  :							int = 600	,   __snake_case  :							float = -0.6	,   __snake_case  :							float = 0	,   __snake_case  :							float = 3.2	,   __snake_case  :							int = 50	,   __snake_case  :							bool = True	,   )			->					Image.Image:
   lowerCAmelCase_			=      Image.new('''RGB'''	,   (image_width, image_height))
   lowerCAmelCase_			=      img.load()

   # loop through the image-coordinates
   for image_x in range(__snake_case):
      for image_y in range(__snake_case):
         # determine the figure-coordinates based on the image-coordinates
         lowerCAmelCase_			=      figure_width / image_width * image_height
         lowerCAmelCase_			=      figure_center_x + (image_x / image_width - 0.5) * figure_width
         lowerCAmelCase_			=      figure_center_y + (image_y / image_height - 0.5) * figure_height

         lowerCAmelCase_			=      get_distance(__snake_case	,   __snake_case	,   __snake_case)

         # color the corresponding pixel based on the selected coloring-function
         if use_distance_color_coding:
            lowerCAmelCase_			=      get_color_coded_rgb(__snake_case)
         else:
            lowerCAmelCase_			=      get_black_and_white_rgb(__snake_case)

   return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 A_     :						List[str]    							=get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()





