




'''simple docstring'''
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
  import numpy as np
  import torch

if is_vision_available():
  from PIL import Image
def     snake_case_  (       a__		:     List[Any]   ,a__		:     Union[str, Any]=False   ,a__		:     Tuple=False   ,a__		:     Union[str, Any]=False      ):



    """simple docstring"""




    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __lowercase      =						[]
    for i in range(image_processor_tester.batch_size      ):
        if equal_resolution:
            __lowercase      =						__lowercase      =						image_processor_tester.max_resolution
        else:
            # To avoid getting image width/height 0
            __lowercase      =						image_processor_tester.min_resolution
            if getattr(a__   ,"""size_divisor"""   ,a__      ):
                # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                __lowercase      =						max(image_processor_tester.size_divisor   ,a__      )
            __lowercase,__lowercase      =						np.random.choice(np.arange(a__   ,image_processor_tester.max_resolution      )   ,2      )
        image_inputs.append(
            np.random.randint(2_55   ,size=(image_processor_tester.num_channels, width, height)   ,dtype=np.uinta      )      )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __lowercase      =						[Image.fromarray(np.moveaxis(a__   ,0   ,-1      )      ) for image in image_inputs]

    if torchify:
        __lowercase      =						[torch.from_numpy(a__      ) for image in image_inputs]

    return image_inputs
def     snake_case_  (       a__		:     Tuple   ,a__		:     List[str]=10   ,a__		:     Optional[Any]=10   ,a__		:     Dict=False   ,a__		:     Dict=False      ):



    """simple docstring"""




    __lowercase      =						[]
    for i in range(image_processor_tester.num_frames      ):
        video.append(np.random.randint(2_55   ,size=(image_processor_tester.num_channels, width, height)   ,dtype=np.uinta      )      )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __lowercase      =						[Image.fromarray(np.moveaxis(a__   ,0   ,-1      )      ) for frame in video]

    if torchify:
        __lowercase      =						[torch.from_numpy(a__      ) for frame in video]

    return video
def     snake_case_  (       a__		:     Dict   ,a__		:     Optional[Any]=False   ,a__		:     Union[str, Any]=False   ,a__		:     Any=False      ):



    """simple docstring"""




    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __lowercase      =						[]
    for i in range(image_processor_tester.batch_size      ):
        if equal_resolution:
            __lowercase      =						__lowercase      =						image_processor_tester.max_resolution
        else:
            __lowercase,__lowercase      =						np.random.choice(
                np.arange(image_processor_tester.min_resolution   ,image_processor_tester.max_resolution      )   ,2      )
            __lowercase      =						prepare_video(
                image_processor_tester=a__   ,width=a__   ,height=a__   ,numpify=a__   ,torchify=a__   ,)
        video_inputs.append(a__      )

    return video_inputs






class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					Dict   = None

       def    snake_case__      (   self					)   -> List[str]:

           """simple docstring"""



           __lowercase      =						self.image_processing_class(**self.image_processor_dict					)
           __lowercase      =						json.loads(image_processor.to_json_string()					)
           for key, value in self.image_processor_dict.items():
               self.assertEqual(obj[key] ,		lowerCamelCase__					)

       def    snake_case__      (   self					)   -> str:

           """simple docstring"""



           __lowercase      =						self.image_processing_class(**self.image_processor_dict					)

           with tempfile.TemporaryDirectory() as tmpdirname:
               __lowercase      =						os.path.join(lowerCamelCase__ ,		"""image_processor.json"""					)
               image_processor_first.to_json_file(lowerCamelCase__					)
               __lowercase      =						self.image_processing_class.from_json_file(lowerCamelCase__					)

           self.assertEqual(image_processor_second.to_dict() ,		image_processor_first.to_dict()					)

       def    snake_case__      (   self					)   -> str:

           """simple docstring"""



           __lowercase      =						self.image_processing_class(**self.image_processor_dict					)

           with tempfile.TemporaryDirectory() as tmpdirname:
               __lowercase      =						image_processor_first.save_pretrained(lowerCamelCase__					)[0]
               check_json_file_has_correct_format(lowerCamelCase__					)
               __lowercase      =						self.image_processing_class.from_pretrained(lowerCamelCase__					)

           self.assertEqual(image_processor_second.to_dict() ,		image_processor_first.to_dict()					)

       def    snake_case__      (   self					)   -> Any:

           """simple docstring"""



           __lowercase      =						self.image_processing_class()
           self.assertIsNotNone(lowerCamelCase__					)




       @require_torch
       @require_vision
       def    snake_case__      (   self					)   -> int:

           """simple docstring"""



           if self.test_cast_dtype is not None:
               # Initialize image_processor
               __lowercase      =						self.image_processing_class(**self.image_processor_dict					)

               # create random PyTorch tensors
               __lowercase      =						prepare_image_inputs(self.image_processor_tester ,		equal_resolution=lowerCamelCase__ ,		torchify=lowerCamelCase__					)

               __lowercase      =						image_processor(lowerCamelCase__ ,		return_tensors="""pt"""					)
               # for layoutLM compatiblity
               self.assertEqual(encoding.pixel_values.device ,		torch.device("""cpu"""					)					)
               self.assertEqual(encoding.pixel_values.dtype ,		torch.floataa					)

               __lowercase      =						image_processor(lowerCamelCase__ ,		return_tensors="""pt"""					).to(torch.floataa					)
               self.assertEqual(encoding.pixel_values.device ,		torch.device("""cpu"""					)					)
               self.assertEqual(encoding.pixel_values.dtype ,		torch.floataa					)

               __lowercase      =						image_processor(lowerCamelCase__ ,		return_tensors="""pt"""					).to("""cpu""" ,		torch.bfloataa					)
               self.assertEqual(encoding.pixel_values.device ,		torch.device("""cpu"""					)					)
               self.assertEqual(encoding.pixel_values.dtype ,		torch.bfloataa					)

               with self.assertRaises(lowerCamelCase__					):
                   __lowercase      =						image_processor(lowerCamelCase__ ,		return_tensors="""pt"""					).to(torch.bfloataa ,		"""cpu"""					)

               # Try with text + image feature
               __lowercase      =						image_processor(lowerCamelCase__ ,		return_tensors="""pt"""					)
               encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]					)}					)
               __lowercase      =						encoding.to(torch.floataa					)

               self.assertEqual(encoding.pixel_values.device ,		torch.device("""cpu"""					)					)
               self.assertEqual(encoding.pixel_values.dtype ,		torch.floataa					)
               self.assertEqual(encoding.input_ids.dtype ,		torch.long					)










'''simple docstring'''
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


A							:				Dict			     =	{
    """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     snake_case_  (       a__		:     str      ):



    """simple docstring"""




    __lowercase          =						task_cls()
    __lowercase          =						asdict(a__      )
    __lowercase          =						task_template_from_dict(a__      )
    assert task == reloaded






class 	SCREAMING_SNAKE_CASE:

       def    snake_case__      (   self					)   -> Any:

           """simple docstring"""



           __lowercase          =						LanguageModeling(text_column="""input_text"""					)
           assert {"input_text": "text"} == task.column_mapping




       def    snake_case__      (   self					)   -> Any:

           """simple docstring"""



           __lowercase          =						Features({"""text""": Value("""string"""					)}					)
           __lowercase          =						{"""text_column""": """input_text"""}
           __lowercase          =						LanguageModeling.from_dict(lowerCamelCase__					)
           assert "language-modeling" == task.task
           assert input_schema == task.input_schema






class 	SCREAMING_SNAKE_CASE(    __A					):

       def    snake_case__      (   self					)   -> Dict:

           """simple docstring"""



           __lowercase          =						sorted(["""pos""", """neg"""]					)

       def    snake_case__      (   self					)   -> Tuple:

           """simple docstring"""



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

       def    snake_case__      (   self					)   -> Dict:

           """simple docstring"""



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




       def    snake_case__      (   self					)   -> str:

           """simple docstring"""



           __lowercase          =						TextClassification(text_column="""input_text""" ,		label_column="""input_label"""					)
           self.assertEqual(task.label_schema["""labels"""] ,		lowerCamelCase__					)
           __lowercase          =						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(    __A					):

       def    snake_case__      (   self					)   -> List[str]:

           """simple docstring"""



           __lowercase          =						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    snake_case__      (   self					)   -> Dict:

           """simple docstring"""



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






class 	SCREAMING_SNAKE_CASE(    __A					):

       def    snake_case__      (   self					)   -> int:

           """simple docstring"""



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




       def    snake_case__      (   self					)   -> Union[str, Any]:

           """simple docstring"""



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






class 	SCREAMING_SNAKE_CASE(    __A					):

       def    snake_case__      (   self					)   -> str:

           """simple docstring"""



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




       def    snake_case__      (   self					)   -> List[str]:

           """simple docstring"""



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






class 	SCREAMING_SNAKE_CASE(    __A					):

       def    snake_case__      (   self					)   -> List[Any]:

           """simple docstring"""



           __lowercase          =						sorted(["""pos""", """neg"""]					)

       def    snake_case__      (   self					)   -> Optional[Any]:

           """simple docstring"""



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

       def    snake_case__      (   self					)   -> Optional[Any]:

           """simple docstring"""



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




       def    snake_case__      (   self					)   -> str:

           """simple docstring"""



           __lowercase          =						AudioClassification(audio_column="""input_audio""" ,		label_column="""input_label"""					)
           self.assertEqual(task.label_schema["""labels"""] ,		lowerCamelCase__					)
           __lowercase          =						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(    __A					):

       def    snake_case__      (   self					)   -> Optional[Any]:

           """simple docstring"""



           __lowercase          =						sorted(["""pos""", """neg"""]					)

       def    snake_case__      (   self					)   -> List[str]:

           """simple docstring"""



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

       def    snake_case__      (   self					)   -> List[str]:

           """simple docstring"""



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




       def    snake_case__      (   self					)   -> Tuple:

           """simple docstring"""



           __lowercase          =						ImageClassification(image_column="""input_image""" ,		label_column="""input_label"""					)
           self.assertEqual(task.label_schema["""labels"""] ,		lowerCamelCase__					)
           __lowercase          =						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(    __A					):

       def    snake_case__      (   self					)   -> Tuple:

           """simple docstring"""



           __lowercase          =						DatasetInfo(task_templates=QuestionAnsweringExtractive()					)
           __lowercase          =						Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()} ,		info=lowerCamelCase__					)
           assert isinstance(dataset.info.task_templates ,		lowerCamelCase__					)
           assert len(dataset.info.task_templates					) == 1

           def keep_task(lowerCamelCase__					):
               return x

           def dont_keep_task(lowerCamelCase__					):
               __lowercase          =						deepcopy(lowerCamelCase__					)
               __lowercase          =						0
               return out

           __lowercase          =						dataset.map(lowerCamelCase__					)
           assert mapped_dataset.info.task_templates == dataset.info.task_templates
           # reload from cache
           __lowercase          =						dataset.map(lowerCamelCase__					)
           assert mapped_dataset.info.task_templates == dataset.info.task_templates

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




       def    snake_case__      (   self					)   -> Union[str, Any]:

           """simple docstring"""



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

           def process(lowerCamelCase__					):
               return example

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




