



import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

SCREAMING_SNAKE_CASE__       =      imread(r'''digital_image_processing/image_data/lena_small.jpg''')
SCREAMING_SNAKE_CASE__       =      cvtColor(img, COLOR_BGR2GRAY)


def A						(       )						->      List[str]:
			A__       =    cn.convert_to_negative(__UpperCamelCase					)
			# assert negative_img array for at least one True
			assert negative_img.any()


def A						(       )						->      Tuple:
			with Image.open('digital_image_processing/image_data/lena_small.jpg'					) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(__UpperCamelCase ,	110					)					).startswith(
						    '<PIL.Image.Image image mode=RGB size=100x100 at'					)


def A						(       )						->      Optional[int]:
			A__       =    canny.gen_gaussian_kernel(9 ,	sigma=1.4					)
			# Assert ambiguous array
			assert resp.all()


def A						(       )						->      Any:
			A__       =    imread('digital_image_processing/image_data/lena_small.jpg' ,	0					)
			# assert ambiguous array for all == True
			assert canny_img.all()
			A__       =    canny.canny(__UpperCamelCase					)
			# assert canny array for at least one True
			assert canny_array.any()


def A						(       )						->      List[str]:
			assert gg.gaussian_filter(__UpperCamelCase ,	5 ,	sigma=0.9					).all()


def A						(       )						->      Optional[int]:
			# laplace diagonals
			A__       =    array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]					)
			A__       =    conv.img_convolve(__UpperCamelCase ,	__UpperCamelCase					).astype(__UpperCamelCase					)
			assert res.any()


def A						(       )						->      Any:
			assert med.median_filter(__UpperCamelCase ,	3					).any()


def A						(       )						->      Optional[Any]:
			A__     , A__       =    sob.sobel_filter(__UpperCamelCase					)
			assert grad.any() and theta.any()


def A						(       )						->      Dict:
			A__       =    sp.make_sepia(__UpperCamelCase ,	20					)
			assert sepia.all()


def A						(       __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"					)						->      Optional[int]:
			A__       =    bs.Burkes(imread(__UpperCamelCase ,	1					) ,	120					)
			burkes.process()
			assert burkes.output_img.any()


def A						(       __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ,	)						->      Optional[int]:
			A__       =    rs.NearestNeighbour(imread(__UpperCamelCase ,	1					) ,	400 ,	200					)
			nn.process()
			assert nn.output.any()






def A						(       )						->      List[str]:
			A__       =    'digital_image_processing/image_data/lena.jpg'

			# Reading the image and converting it to grayscale.
			A__       =    imread(__UpperCamelCase ,	0					)

			# Test for get_neighbors_pixel function() return not None
			A__       =    0
			A__       =    0
			A__       =    image[x_coordinate][y_coordinate]

			A__       =    lbp.get_neighbors_pixel(
			    __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			A__       =    np.zeros((image.shape[0], image.shape[1])					)

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0 ,	image.shape[0]					):
						for j in range(0 ,	image.shape[1]					):
									A__       =    lbp.local_binary_value(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)

			assert lbp_image.any()









import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			A__       =    args.log_outputs
			A__       =    '_'.join(args.dataset.split('/'					) + [args.config, args.split]					)

			# load metric
			A__       =    load_metric('wer'					)
			A__       =    load_metric('cer'					)

			# compute metrics
			A__       =    wer.compute(references=result['target'] ,	predictions=result['prediction']					)
			A__       =    cer.compute(references=result['target'] ,	predictions=result['prediction']					)

			# print & log results
			A__       =    f'''WER: {wer_result}\nCER: {cer_result}'''
			print(__UpperCamelCase					)

			with open(f'''{dataset_id}_eval_results.txt''' ,	'w'					) as f:
						f.write(__UpperCamelCase					)

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						A__       =    f'''log_{dataset_id}_predictions.txt'''
						A__       =    f'''log_{dataset_id}_targets.txt'''

						with open(__UpperCamelCase ,	'w'					) as p, open(__UpperCamelCase ,	'w'					) as t:
									# mapping function to write output
									def write_to_file(__UpperCamelCase ,	__UpperCamelCase					):
												p.write(f'''{i}''' + '\n'					)
												p.write(batch['prediction'] + '\n'					)
												t.write(f'''{i}''' + '\n'					)
												t.write(batch['target'] + '\n'					)

									result.map(__UpperCamelCase ,	with_indices=__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      str:
			A__       =    '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			A__       =    re.sub(__UpperCamelCase ,	'' ,	text.lower()					)

			# In addition, we can normalize the target text, e.g. removing new lines characters etc...
			# note that order is important here!
			A__       =    ['\n\n', '\n', '   ', '  ']

			for t in token_sequences_to_ignore:
						A__       =    ' '.join(text.split(__UpperCamelCase					)					)

			return text


def A						(       __UpperCamelCase					)						->      Union[str, Any]:
			# load dataset
			A__       =    load_dataset(args.dataset ,	args.config ,	split=args.split ,	use_auth_token=__UpperCamelCase					)

			# for testing: only process the first two examples as a test
			# dataset = dataset.select(range(10))

			# load processor
			A__       =    AutoFeatureExtractor.from_pretrained(args.model_id					)
			A__       =    feature_extractor.sampling_rate

			# resample audio
			A__       =    dataset.cast_column('audio' ,	Audio(sampling_rate=__UpperCamelCase					)					)

			# load eval pipeline
			if args.device is None:
						A__       =    0 if torch.cuda.is_available() else -1
			A__       =    pipeline('automatic-speech-recognition' ,	model=args.model_id ,	device=args.device					)

			# map function to decode audio
			def map_to_pred(__UpperCamelCase					):
						A__       =    asr(
						    batch['audio']['array'] ,	chunk_length_s=args.chunk_length_s ,	stride_length_s=args.stride_length_s					)

						A__       =    prediction['text']
						A__       =    normalize_text(batch['sentence']					)
						return batch

			# run inference on all examples
			A__       =    dataset.map(__UpperCamelCase ,	remove_columns=dataset.column_names					)

			# compute and log_results
			# do not change function below
			log_results(__UpperCamelCase ,	__UpperCamelCase					)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
							)
							parser.add_argument(
							    '''--dataset''',
							    type=str,
							    required=True,
							    help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
							)
							parser.add_argument(
							    '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
							)
							parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
							parser.add_argument(
							    '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
							)
							parser.add_argument(
							    '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
							)
							parser.add_argument(
							    '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
							)
							parser.add_argument(
							    '''--device''',
							    type=int,
							    default=None,
							    help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
							)
							SCREAMING_SNAKE_CASE__       =      parser.parse_args()

							main(args)




