


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      _lowercase					(				lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     KandinskyPipeline
       A__   		=     [
           "prompt",
           "image_embeds",
           "negative_image_embeds",
       ]
       A__   		=     ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
       A__   		=     [
           "generator",
           "height",
           "width",
           "latents",
           "guidance_scale",
           "negative_prompt",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       A__   		=     False





       @property
       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             return self.time_input_dim





       @property
       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             return self.time_input_dim * 4





       @property
       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             return 100





       @property
       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"			)
             return tokenizer





       @property
       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             torch.manual_seed(0			)
             lowerCamelCase__						:							Optional[Any]       =    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=1005   , )

             lowerCamelCase__						:							Any       =    MultilingualCLIP(__lowerCamelCase			)
             lowerCamelCase__						:							Tuple       =    text_encoder.eval()

             return text_encoder





       @property
       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             torch.manual_seed(0			)

             lowerCamelCase__						:							Tuple       =    {
                 "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__						:							Dict       =    UNetaDConditionModel(**__lowerCamelCase			)
             return model





       @property
       def 		lowerCAmelCase	(  self				:     str			):


             '''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 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             torch.manual_seed(0			)
             lowerCamelCase__						:							List[str]       =    VQModel(**self.dummy_movq_kwargs			)
             return model





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    self.dummy_text_encoder
             lowerCamelCase__						:							Optional[Any]       =    self.dummy_tokenizer
             lowerCamelCase__						:							Dict       =    self.dummy_unet
             lowerCamelCase__						:							Optional[int]       =    self.dummy_movq

             lowerCamelCase__						:							Any       =    DDIMScheduler(
                 num_train_timesteps=1000   , 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__						:							Union[str, Any]       =    {
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "unet": unet,
                 "scheduler": scheduler,
                 "movq": movq,
             }
             return components





       def 		lowerCAmelCase	(  self				:     Dict   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     str=0			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    floats_tensor((1, self.cross_attention_dim)   , rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
             lowerCamelCase__						:							Optional[int]       =    floats_tensor((1, self.cross_attention_dim)   , rng=random.Random(seed + 1			)			).to(__lowerCamelCase			)
             if str(__lowerCamelCase			).startswith("mps"			):
                   lowerCamelCase__						:							Any       =    torch.manual_seed(__lowerCamelCase			)
             else:
                   lowerCamelCase__						:							Tuple       =    torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
             lowerCamelCase__						:							Dict       =    {
                 "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 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    "cpu"

             lowerCamelCase__						:							Any       =    self.get_dummy_components()

             lowerCamelCase__						:							Union[str, Any]       =    self.pipeline_class(**__lowerCamelCase			)
             lowerCamelCase__						:							int       =    pipe.to(__lowerCamelCase			)

             pipe.set_progress_bar_config(disable=__lowerCamelCase			)

             lowerCamelCase__						:							Union[str, Any]       =    pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
             lowerCamelCase__						:							Any       =    output.images

             lowerCamelCase__						:							List[Any]       =    pipe(
                 **self.get_dummy_inputs(__lowerCamelCase			)   , return_dict=__lowerCamelCase   , )[0]

             lowerCamelCase__						:							List[Any]       =    image[0, -3:, -3:, -1]
             lowerCamelCase__						:							Any       =    image_from_tuple[0, -3:, -3:, -1]

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

             lowerCamelCase__						:							Optional[int]       =    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      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



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





       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



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

             lowerCamelCase__						:							Any       =    KandinskyPriorPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-1-prior"   , torch_dtype=torch.floataa			)
             pipe_prior.to(__lowerCamelCase			)

             lowerCamelCase__						:							Tuple       =    KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"   , torch_dtype=torch.floataa			)
             lowerCamelCase__						:							Union[str, Any]       =    pipeline.to(__lowerCamelCase			)
             pipeline.set_progress_bar_config(disable=__lowerCamelCase			)

             lowerCamelCase__						:							Union[str, Any]       =    "red cat, 4k photo"

             lowerCamelCase__						:							Any       =    torch.Generator(device="cuda"			).manual_seed(0			)
             lowerCamelCase__       ,  lowerCamelCase__						:							List[Any]       =    pipe_prior(
                 __lowerCamelCase   , generator=__lowerCamelCase   , num_inference_steps=5   , negative_prompt=""   , ).to_tuple()

             lowerCamelCase__						:							Optional[int]       =    torch.Generator(device="cuda"			).manual_seed(0			)
             lowerCamelCase__						:							List[Any]       =    pipeline(
                 __lowerCamelCase   , image_embeds=__lowerCamelCase   , negative_image_embeds=__lowerCamelCase   , generator=__lowerCamelCase   , num_inference_steps=100   , output_type="np"   , )

             lowerCamelCase__						:							str       =    output.images[0]

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

             assert_mean_pixel_difference(__lowerCamelCase   , __lowerCamelCase			)






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def  lowercase_						(						_A     :       str		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    min(re.search("^ +"       ,   _A		).end() if t.startswith(" "		) else 0 for t in string.splitlines()		)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A		)]		)


A :       Optional[int]						=   "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

A :       str						=   "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


A :       Optional[Any]						=   "\\n# Begin of markdown\n\nSome cool dataset card\n"







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             with tempfile.TemporaryDirectory() as tmp_dir:
                   lowerCamelCase__						:							List[str]       =    Path(__lowerCamelCase			) / "README.md"
                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Dict       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Optional[Any]       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							int       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertEqual(__lowerCamelCase   , {}			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Dict       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "			)
             with self.assertRaises(__lowerCamelCase			):
                   DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Optional[Any]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)


