from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


_SCREAMING_SNAKE_CASE:						Optional[int]    	=       {
    '''id''': '''5733be284776f41900661182''',
    '''title''': '''University_of_Notre_Dame''',
    '''context''': '''Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.''',
    '''question''': '''To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?''',
    '''answers''': {'''text''': ['''Saint Bernadette Soubirous'''], '''answer_start''': [515]},
}






@pytest.mark.parametrize(
    "task_cls"							,	[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]							,	)
def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      task_cls()
			_snake_case              =      asdict(lowerCamelCase_  )
			_snake_case              =      task_template_from_dict(lowerCamelCase_  )
			assert task == reloaded

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def 				_a				(			self    :				int   ):






					"""simple docstring"""
					_snake_case              =      LanguageModeling(text_column="input_text"   )
					assert {"input_text": "text"} == task.column_mapping







		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					_snake_case              =      Features({"text": Value("string"   )}   )
					_snake_case              =      {"text_column": "input_text"}
					_snake_case              =      LanguageModeling.from_dict(__SCREAMING_SNAKE_CASE   )
					assert "language-modeling" == task.task
					assert input_schema == task.input_schema

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def 				_a				(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      sorted(["pos", "neg"]   )



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      TextClassification(text_column="input_text"  ,					label_column="input_label"   )
					self.assertDictEqual({"input_text": "text", "input_label": "labels"}  ,					task.column_mapping   )



		def 				_a				(			self    :				int   ):






					"""simple docstring"""
					_snake_case              =      Features({"text": Value("string"   )}   )
					# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
					_snake_case              =      Features({"labels": ClassLabel}   )
					_snake_case              =      {"text_column": "input_text", "label_column": "input_labels"}
					_snake_case              =      TextClassification.from_dict(__SCREAMING_SNAKE_CASE   )
					self.assertEqual("text-classification"  ,					task.task   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.input_schema   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.label_schema   )







		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      TextClassification(text_column="input_text"  ,					label_column="input_label"   )
					self.assertEqual(task.label_schema["labels"]  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      task.align_with_features(Features({"input_label": ClassLabel(names=self.labels   )}   )   )
					self.assertEqual(task.label_schema["labels"]  ,					ClassLabel(names=self.labels   )   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					_snake_case              =      QuestionAnsweringExtractive(
					    context_column="input_context"  ,					question_column="input_question"  ,					answers_column="input_answers"   )
					self.assertDictEqual(
					    {"input_context": "context", "input_question": "question", "input_answers": "answers"}  ,					task.column_mapping   )







		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					_snake_case              =      Features({"question": Value("string"   ), "context": Value("string"   )}   )
					_snake_case              =      Features(
					    {
					        "answers": Sequence(
					            {
					                "text": Value("string"   ),
					                "answer_start": Value("int32"   ),
					            }   )
					    }   )
					_snake_case              =      {
					    "context_column": "input_input_context",
					    "question_column": "input_question",
					    "answers_column": "input_answers",
					}
					_snake_case              =      QuestionAnsweringExtractive.from_dict(__SCREAMING_SNAKE_CASE   )
					self.assertEqual("question-answering-extractive"  ,					task.task   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.input_schema   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.label_schema   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      Summarization(text_column="input_text"  ,					summary_column="input_summary"   )
					self.assertDictEqual({"input_text": "text", "input_summary": "summary"}  ,					task.column_mapping   )







		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      Features({"text": Value("string"   )}   )
					_snake_case              =      Features({"summary": Value("string"   )}   )
					_snake_case              =      {"text_column": "input_text", "summary_column": "input_summary"}
					_snake_case              =      Summarization.from_dict(__SCREAMING_SNAKE_CASE   )
					self.assertEqual("summarization"  ,					task.task   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.input_schema   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.label_schema   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					_snake_case              =      AutomaticSpeechRecognition(audio_column="input_audio"  ,					transcription_column="input_transcription"   )
					self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}  ,					task.column_mapping   )







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      Features({"audio": Audio()}   )
					_snake_case              =      Features({"transcription": Value("string"   )}   )
					_snake_case              =      {
					    "audio_column": "input_audio",
					    "transcription_column": "input_transcription",
					}
					_snake_case              =      AutomaticSpeechRecognition.from_dict(__SCREAMING_SNAKE_CASE   )
					self.assertEqual("automatic-speech-recognition"  ,					task.task   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.input_schema   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.label_schema   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def 				_a				(			self    :				int   ):






					"""simple docstring"""
					_snake_case              =      sorted(["pos", "neg"]   )



		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					_snake_case              =      AudioClassification(audio_column="input_audio"  ,					label_column="input_label"   )
					self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}  ,					task.column_mapping   )



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      Features({"audio": Audio()}   )
					_snake_case              =      Features({"labels": ClassLabel}   )
					_snake_case              =      {
					    "audio_column": "input_image",
					    "label_column": "input_label",
					}
					_snake_case              =      AudioClassification.from_dict(__SCREAMING_SNAKE_CASE   )
					self.assertEqual("audio-classification"  ,					task.task   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.input_schema   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.label_schema   )







		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					_snake_case              =      AudioClassification(audio_column="input_audio"  ,					label_column="input_label"   )
					self.assertEqual(task.label_schema["labels"]  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      task.align_with_features(Features({"input_label": ClassLabel(names=self.labels   )}   )   )
					self.assertEqual(task.label_schema["labels"]  ,					ClassLabel(names=self.labels   )   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      sorted(["pos", "neg"]   )



		def 				_a				(			self    :				Any   ):






					"""simple docstring"""
					_snake_case              =      ImageClassification(image_column="input_image"  ,					label_column="input_label"   )
					self.assertDictEqual({"input_image": "image", "input_label": "labels"}  ,					task.column_mapping   )



		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					_snake_case              =      Features({"image": Image()}   )
					_snake_case              =      Features({"labels": ClassLabel}   )
					_snake_case              =      {
					    "image_column": "input_image",
					    "label_column": "input_label",
					}
					_snake_case              =      ImageClassification.from_dict(__SCREAMING_SNAKE_CASE   )
					self.assertEqual("image-classification"  ,					task.task   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.input_schema   )
					self.assertEqual(__SCREAMING_SNAKE_CASE  ,					task.label_schema   )







		def 				_a				(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      ImageClassification(image_column="input_image"  ,					label_column="input_label"   )
					self.assertEqual(task.label_schema["labels"]  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      task.align_with_features(Features({"input_label": ClassLabel(names=self.labels   )}   )   )
					self.assertEqual(task.label_schema["labels"]  ,					ClassLabel(names=self.labels   )   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      DatasetInfo(task_templates=QuestionAnsweringExtractive()   )
					_snake_case              =      Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}  ,					info=__SCREAMING_SNAKE_CASE   )
					assert isinstance(dataset.info.task_templates  ,					__SCREAMING_SNAKE_CASE   )
					assert len(dataset.info.task_templates   ) == 1

					def keep_task(__SCREAMING_SNAKE_CASE    :				Tuple   ):
								return x

					def dont_keep_task(__SCREAMING_SNAKE_CASE    :				Optional[Any]   ):
								_snake_case              =      deepcopy(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      0
								return out

					_snake_case              =      dataset.map(__SCREAMING_SNAKE_CASE   )
					assert mapped_dataset.info.task_templates == dataset.info.task_templates
					# reload from cache
					_snake_case              =      dataset.map(__SCREAMING_SNAKE_CASE   )
					assert mapped_dataset.info.task_templates == dataset.info.task_templates

					_snake_case              =      dataset.map(__SCREAMING_SNAKE_CASE   )
					assert mapped_dataset.info.task_templates == []
					# reload from cache
					_snake_case              =      dataset.map(__SCREAMING_SNAKE_CASE   )
					assert mapped_dataset.info.task_templates == []







		def 				_a				(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      Features({"text": Value("string"   ), "label": ClassLabel(names=("pos", "neg")   )}   )
					_snake_case              =      TextClassification(text_column="text"  ,					label_column="label"   )
					_snake_case              =      DatasetInfo(features=__SCREAMING_SNAKE_CASE  ,					task_templates=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}  ,					info=__SCREAMING_SNAKE_CASE   )

					def process(__SCREAMING_SNAKE_CASE    :				Dict   ):
								return example

					_snake_case              =      dataset.remove_columns("label"   )
					_snake_case              =      modified_dataset.map(__SCREAMING_SNAKE_CASE   )
					assert mapped_dataset.info.task_templates == []


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      0
					# max greyscale value for #FFFFFF
					_snake_case              =      int(self.get_greyscale(255  ,					255  ,					255   )   )

					if not self.min_threshold < threshold < self.max_threshold:
								_snake_case              =      F'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      input_img
					_snake_case              =      threshold
					_snake_case       , _snake_case              =      self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					_snake_case              =      [
					    [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
					]
					_snake_case              =      np.ones((self.width, self.height, 3)  ,					np.uinta   ) * 255



		@classmethod
		def 				_a				(			cls    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					for y in range(self.height   ):
								for x in range(self.width   ):
											_snake_case              =      int(self.get_greyscale(*self.input_img[y][x]   )   )
											if self.threshold > greyscale + self.error_table[y][x]:
														_snake_case              =      (0, 0, 0)
														_snake_case              =      greyscale + self.error_table[x][y]
											else:
														_snake_case              =      (255, 255, 255)
														_snake_case              =      greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error   )
											self.error_table[y][x + 2] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x] += int(8 / 32 * current_error   )
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error   )
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error   )


if __name__ == "__main__":
			# create Burke's instances with original images in greyscale
			_SCREAMING_SNAKE_CASE:						Tuple    	=       [
			    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
			    for threshold in (1, 126, 130, 140)
			]

			for burkes in burkes_instances:
						burkes.process()

			for burkes in burkes_instances:
						imshow(
						    F"Original image with dithering threshold: {burkes.threshold}",
						    burkes.output_img,
						)

			waitKey(0)
			destroyAllWindows()

