




'''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_   :	str							   = {
    """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""": [5_15]},
}





@pytest.mark.parametrize(
    '''task_cls'''     , [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]     , )
def   a_							(   __snake_case							:    str     )							->		Optional[int]:



       """simple docstring"""
       lowerCamelCase_							=task_cls()
       lowerCamelCase_							=asdict(_UpperCAmelCase     )
       lowerCamelCase_							=task_template_from_dict(_UpperCAmelCase     )
       assert task == reloaded


class 						__UpperCamelCase :






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







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





class 						__UpperCamelCase (				lowerCamelCase__       ):






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=sorted(['''pos''', '''neg''']						)






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=Features({'''text''': Value('''string'''						)}						)
              # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
              lowerCamelCase_							=Features({'''labels''': ClassLabel}						)
              lowerCamelCase_							={'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
              lowerCamelCase_							=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   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=TextClassification(text_column='''input_text''',	label_column='''input_label'''						)
              self.assertEqual(task.label_schema['''labels'''],	SCREAMING_SNAKE_CASE_						)
              lowerCamelCase_							=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels						)}						)						)
              self.assertEqual(task.label_schema['''labels'''],	ClassLabel(names=self.labels						)						)





class 						__UpperCamelCase (				lowerCamelCase__       ):






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=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   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=Features({'''question''': Value('''string'''						), '''context''': Value('''string'''						)}						)
              lowerCamelCase_							=Features(
                  {
                      '''answers''': Sequence(
                          {
                              '''text''': Value('''string'''						),
                              '''answer_start''': Value('''int32'''						),
                          }						)
                  }						)
              lowerCamelCase_							={
                  '''context_column''': '''input_input_context''',
                  '''question_column''': '''input_question''',
                  '''answers_column''': '''input_answers''',
              }
              lowerCamelCase_							=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 						__UpperCamelCase (				lowerCamelCase__       ):






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=Features({'''text''': Value('''string'''						)}						)
              lowerCamelCase_							=Features({'''summary''': Value('''string'''						)}						)
              lowerCamelCase_							={'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
              lowerCamelCase_							=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 						__UpperCamelCase (				lowerCamelCase__       ):






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=Features({'''audio''': Audio()}						)
              lowerCamelCase_							=Features({'''transcription''': Value('''string'''						)}						)
              lowerCamelCase_							={
                  '''audio_column''': '''input_audio''',
                  '''transcription_column''': '''input_transcription''',
              }
              lowerCamelCase_							=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 						__UpperCamelCase (				lowerCamelCase__       ):






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=sorted(['''pos''', '''neg''']						)






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=Features({'''audio''': Audio()}						)
              lowerCamelCase_							=Features({'''labels''': ClassLabel}						)
              lowerCamelCase_							={
                  '''audio_column''': '''input_image''',
                  '''label_column''': '''input_label''',
              }
              lowerCamelCase_							=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   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=AudioClassification(audio_column='''input_audio''',	label_column='''input_label'''						)
              self.assertEqual(task.label_schema['''labels'''],	SCREAMING_SNAKE_CASE_						)
              lowerCamelCase_							=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels						)}						)						)
              self.assertEqual(task.label_schema['''labels'''],	ClassLabel(names=self.labels						)						)





class 						__UpperCamelCase (				lowerCamelCase__       ):






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=sorted(['''pos''', '''neg''']						)






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=Features({'''image''': Image()}						)
              lowerCamelCase_							=Features({'''labels''': ClassLabel}						)
              lowerCamelCase_							={
                  '''image_column''': '''input_image''',
                  '''label_column''': '''input_label''',
              }
              lowerCamelCase_							=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   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=ImageClassification(image_column='''input_image''',	label_column='''input_label'''						)
              self.assertEqual(task.label_schema['''labels'''],	SCREAMING_SNAKE_CASE_						)
              lowerCamelCase_							=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels						)}						)						)
              self.assertEqual(task.label_schema['''labels'''],	ClassLabel(names=self.labels						)						)





class 						__UpperCamelCase (				lowerCamelCase__       ):






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=DatasetInfo(task_templates=QuestionAnsweringExtractive()						)
              lowerCamelCase_							=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(lowerCAmelCase						):
                     return x

              def dont_keep_task(lowerCAmelCase						):
                     lowerCamelCase_							=deepcopy(SCREAMING_SNAKE_CASE_						)
                     lowerCamelCase_							=0
                     return out

              lowerCamelCase_							=dataset.map(SCREAMING_SNAKE_CASE_						)
              assert mapped_dataset.info.task_templates == dataset.info.task_templates
              # reload from cache
              lowerCamelCase_							=dataset.map(SCREAMING_SNAKE_CASE_						)
              assert mapped_dataset.info.task_templates == dataset.info.task_templates

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






       def   lowercase__  (							self						):



              """simple docstring"""







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

              def process(lowerCAmelCase						):
                     return example

              lowerCamelCase_							=dataset.remove_columns('''label'''						)
              lowerCamelCase_							=modified_dataset.map(SCREAMING_SNAKE_CASE_						)
              assert mapped_dataset.info.task_templates == []










'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					Tuple   		=KandinskyPipeline
       lowercase			:					Optional[int]   		=[
           'prompt',
           'image_embeds',
           'negative_image_embeds',
       ]
       lowercase			:					Union[str, Any]   		=['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
       lowercase			:					Dict   		=[
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'negative_prompt',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       lowercase			:					Dict   		=False






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim * 4






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 100






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''						)
              return tokenizer






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=MCLIPConfig(
                  numDims=self.cross_attention_dim,	transformerDimensions=self.text_embedder_hidden_size,	hidden_size=self.text_embedder_hidden_size,	intermediate_size=37,	num_attention_heads=4,	num_hidden_layers=5,	vocab_size=1_005,	)

              lowerCamelCase_							=MultilingualCLIP(lowerCAmelCase						)
              lowerCamelCase_							=text_encoder.eval()

              return text_encoder






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)

              lowerCamelCase_							={
                  '''in_channels''': 4,
                  # Out channels is double in channels because predicts mean and variance
                  '''out_channels''': 8,
                  '''addition_embed_type''': '''text_image''',
                  '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
                  '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
                  '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
                  '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
                  '''layers_per_block''': 1,
                  '''encoder_hid_dim''': self.text_embedder_hidden_size,
                  '''encoder_hid_dim_type''': '''text_image_proj''',
                  '''cross_attention_dim''': self.cross_attention_dim,
                  '''attention_head_dim''': 4,
                  '''resnet_time_scale_shift''': '''scale_shift''',
                  '''class_embed_type''': None,
              }

              lowerCamelCase_							=UNetaDConditionModel(**lowerCAmelCase						)
              return model






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return {
                  "block_out_channels": [32, 64],
                  "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                  "in_channels": 3,
                  "latent_channels": 4,
                  "layers_per_block": 1,
                  "norm_num_groups": 8,
                  "norm_type": "spatial",
                  "num_vq_embeddings": 12,
                  "out_channels": 3,
                  "up_block_types": [
                      "AttnUpDecoderBlock2D",
                      "UpDecoderBlock2D",
                  ],
                  "vq_embed_dim": 4,
              }






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=VQModel(**self.dummy_movq_kwargs						)
              return model






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.dummy_text_encoder
              lowerCamelCase_							=self.dummy_tokenizer
              lowerCamelCase_							=self.dummy_unet
              lowerCamelCase_							=self.dummy_movq

              lowerCamelCase_							=DDIMScheduler(
                  num_train_timesteps=1_000,	beta_schedule='''linear''',	beta_start=0.0_0_0_8_5,	beta_end=0.0_1_2,	clip_sample=lowerCAmelCase,	set_alpha_to_one=lowerCAmelCase,	steps_offset=1,	prediction_type='''epsilon''',	thresholding=lowerCAmelCase,	)

              lowerCamelCase_							={
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''movq''': movq,
              }
              return components






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=0						):



              """simple docstring"""







              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(seed + 1						)						).to(lowerCAmelCase						)
              if str(lowerCAmelCase						).startswith('''mps'''						):
                     lowerCamelCase_							=torch.manual_seed(lowerCAmelCase						)
              else:
                     lowerCamelCase_							=torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
              lowerCamelCase_							={
                  '''prompt''': '''horse''',
                  '''image_embeds''': image_embeds,
                  '''negative_image_embeds''': negative_image_embeds,
                  '''generator''': generator,
                  '''height''': 64,
                  '''width''': 64,
                  '''guidance_scale''': 4.0,
                  '''num_inference_steps''': 2,
                  '''output_type''': '''np''',
              }
              return inputs






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cpu'''

              lowerCamelCase_							=self.get_dummy_components()

              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase						)
              lowerCamelCase_							=pipe.to(lowerCAmelCase						)

              pipe.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							=pipe(**self.get_dummy_inputs(lowerCAmelCase						)						)
              lowerCamelCase_							=output.images

              lowerCamelCase_							=pipe(
                  **self.get_dummy_inputs(lowerCAmelCase						),	return_dict=lowerCAmelCase,	)[0]

              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=image_from_tuple[0, -3:, -3:, -1]

              assert image.shape == (1, 64, 64, 3)

              lowerCamelCase_							=np.array(
                  [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]						)

              assert (
                  np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
              ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
              assert (
                  np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
              ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





@slow
@require_torch_gpu
class 						__UpperCamelCase (				unittest.TestCase       ):






       def   lowercase__  (							self						):



              """simple docstring"""







              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/kandinsky/kandinsky_text2img_cat_fp16.npy'''						)

              lowerCamelCase_							=KandinskyPriorPipeline.from_pretrained(
                  '''kandinsky-community/kandinsky-2-1-prior''',	torch_dtype=torch.floataa						)
              pipe_prior.to(lowerCAmelCase						)

              lowerCamelCase_							=KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1''',	torch_dtype=torch.floataa						)
              lowerCamelCase_							=pipeline.to(lowerCAmelCase						)
              pipeline.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							='''red cat, 4k photo'''

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_,   lowerCamelCase_							=pipe_prior(
                  lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=5,	negative_prompt='''''',	).to_tuple()

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_							=pipeline(
                  lowerCAmelCase,	image_embeds=lowerCAmelCase,	negative_image_embeds=lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=100,	output_type='''np''',	)

              lowerCamelCase_							=output.images[0]

              assert image.shape == (512, 512, 3)

              assert_mean_pixel_difference(lowerCAmelCase,	lowerCAmelCase						)




