



"""simple docstring"""

import sys
from pathlib import Path


lowercase__			   =  Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase__			   =  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowercase__			   =  "zero2"
lowercase__			   =  "zero3"
lowercase__			   =  [ZEROa, ZEROa]





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Tuple					):
							# customize the test name generator function as we want both params to appear in the sub-test
							# name, as by default it shows only the first param
							__a		:		str									=       parameterized.to_safe_name("""_""".join(str(_lowerCamelCase					) for x in param.args					)					)
							return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowercase__			   =  list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE__					(		__snake_case	):



						@parameterized.expand(_lowercase						,     name_func=_lowercase							)
						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													self.run_and_check(
													    stage=_lowercase						,     model=_lowercase						,     distributed=_lowercase						,     fpaa=_lowercase						,     )



						@require_torch_multi_gpu
						@parameterized.expand(_lowercase						,     name_func=_lowercase							)
						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													self.run_and_check(
													    stage=_lowercase						,     model=_lowercase						,     distributed=_lowercase						,     fpaa=_lowercase						,     )



						@parameterized.expand(_lowercase						,     name_func=_lowercase							)
						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													self.run_and_check(
													    stage=_lowercase						,     model=_lowercase						,     distributed=_lowercase						,     fpaa=_lowercase						,     )



						@require_torch_multi_gpu
						@parameterized.expand(_lowercase						,     name_func=_lowercase							)
						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													self.run_and_check(
													    stage=_lowercase						,     model=_lowercase						,     distributed=_lowercase						,     fpaa=_lowercase						,     )



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													pass



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase = 10						,     _lowercase = True						,     _lowercase = True						,     _lowercase = True						,     ):





													'''simple docstring'''

													__a		:		Dict									=       models[model]

													__a		:		List[str]									=       self.run_trainer(
													    stage=_lowercase						,     model_name=_lowercase						,     eval_steps=_lowercase						,     num_train_epochs=1						,     distributed=_lowercase						,     fpaa=_lowercase						,     )

													self.do_checks(_lowercase							)

													return output_dir



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase = 10						,     _lowercase = 1						,     _lowercase = True						,     _lowercase = True						,     ):





													'''simple docstring'''

													__a		:		int									=       self.get_auto_remove_tmp_dir("""./xxx"""						,     after=_lowercase							)
													__a		:		List[Any]									=       F'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_lowercase							)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

													if fpaa:
																				args.extend(["""--fp16"""]							)

													# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
													# hence the separate config files
													__a		:		List[Any]									=       F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
													__a		:		Any									=       [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
													__a		:		Dict									=       self.get_launcher(_lowercase							)

													__a		:		Tuple									=       launcher + script + args + ds_args
													# keep for quick debug
													# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
													execute_subprocess_async(_lowercase						,     env=self.get_env()							)

													return output_dir





						def     lowerCAmelCase__(self						,     _lowercase=False							):





													'''simple docstring'''

													__a		:		Tuple									=       min(2						,     get_gpu_count()							) if distributed else 1
													return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()






"""simple docstring"""

import math
import sys

import cva
import numpy as np





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float					):
							# For applying gaussian function for each element in matrix.
							__a		:		int									=       math.sqrt(_lowerCamelCase					)
							__a		:		Any									=       1 / (sigma * math.sqrt(2 * math.pi					))
							return cons * np.exp(-((img / sigma) ** 2) * 0.5					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							__a		:		Any									=       kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float					):
							# Creates a gaussian kernel of given dimension.
							__a		:		int									=       np.zeros((kernel_size, kernel_size)					)
							for i in range(0  ,       _lowerCamelCase					):
														for j in range(0  ,       _lowerCamelCase					):
																					__a		:		Any									=       math.sqrt(
																					    abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
							return vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: int  ,       ):
							__a		:		Tuple									=       np.zeros(img.shape					)
							__a		:		Optional[int]									=       get_gauss_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a							,     __a		:		int									=       img.shape
							for i in range(kernel_size // 2  ,       size_x - kernel_size // 2					):
														for j in range(kernel_size // 2  ,       size_y - kernel_size // 2					):
																					__a		:		List[str]									=       get_slice(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__a		:		Optional[Any]									=       vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Optional[Any]									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Tuple									=       np.sum(_lowerCamelCase					) / np.sum(_lowerCamelCase					)
																					__a		:		Optional[Any]									=       val
							return imga





def       __magic_name__    (   _lowerCamelCase				: list					):
							__a		:		Optional[Any]									=       args[1] if args[1:] else """../image_data/lena.jpg"""
							__a		:		Union[str, Any]									=       float(args[2]					) if args[2:] else 1.0
							__a		:		Optional[int]									=       float(args[3]					) if args[3:] else 1.0
							if args[4:]:
														__a		:		Any									=       int(args[4]					)
														__a		:		Any									=       kernel_size + abs(kernel_size % 2 - 1					)
							else:
														__a		:		Optional[int]									=       5
							return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
	lowercase__   ,   lowercase__   ,   lowercase__   ,   lowercase__			   =  parse_args(sys.argv)
	lowercase__			   =  cva.imread(filename, 0)
	cva.imshow("input image", img)

	lowercase__			   =  img / 255
	lowercase__			   =  out.astype("float32")
	lowercase__			   =  bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
	lowercase__			   =  out * 255
	lowercase__			   =  np.uinta(out)
	cva.imshow("output image", out)
	cva.waitKey(0)
	cva.destroyAllWindows()

