import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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 				a							(			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:List[str]    			=		KandinskyInpaintPipeline
 lowerCamelCase	:Union[str, Any]    			=		['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 lowerCamelCase	:int    			=		[
     '''prompt''',
     '''negative_prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 lowerCamelCase	:Any    			=		[
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 lowerCamelCase	:Optional[Any]    			=		False



 @property
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        return 32



 @property
 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        return 32



 @property
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        return self.time_input_dim



 @property
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        return self.time_input_dim * 4



 @property
 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        return 1_00



 @property
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
        return tokenizer



 @property
 def      UpperCAmelCase							(					self							)  ->					Dict:
        torch.manual_seed(0							)
        _A        =  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=10_05		,				)

        _A        =  MultilingualCLIP(lowerCAmelCase_							)
        _A        =  text_encoder.eval()

        return text_encoder



 @property
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        torch.manual_seed(0							)

        _A        =  {
            """in_channels""": 9,
            # 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,
        }

        _A        =  UNetaDConditionModel(**lowerCAmelCase_							)
        return model



 @property
 def      UpperCAmelCase							(					self							)  ->					Any:
        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      UpperCAmelCase							(					self							)  ->					Tuple:
        torch.manual_seed(0							)
        _A        =  VQModel(**self.dummy_movq_kwargs							)
        return model



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.dummy_text_encoder
        _A        =  self.dummy_tokenizer
        _A        =  self.dummy_unet
        _A        =  self.dummy_movq

        _A        =  DDIMScheduler(
            num_train_timesteps=10_00		,				beta_schedule="""linear"""		,				beta_start=0.0_0085		,				beta_end=0.012		,				clip_sample=lowerCAmelCase_		,				set_alpha_to_one=lowerCAmelCase_		,				steps_offset=1		,				prediction_type="""epsilon"""		,				thresholding=lowerCAmelCase_		,				)

        _A        =  {
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=0							)  ->					Union[str, Any]:
        _A        =  floats_tensor((1, self.cross_attention_dim)		,				rng=random.Random(lowerCAmelCase_							)							).to(lowerCAmelCase_							)
        _A        =  floats_tensor((1, self.cross_attention_dim)		,				rng=random.Random(seed + 1							)							).to(lowerCAmelCase_							)
        # create init_image
        _A        =  floats_tensor((1, 3, 64, 64)		,				rng=random.Random(lowerCAmelCase_							)							).to(lowerCAmelCase_							)
        _A        =  image.cpu().permute(0		,				2		,				3		,				1							)[0]
        _A        =  Image.fromarray(np.uinta(lowerCAmelCase_							)							).convert("""RGB"""							).resize((2_56, 2_56)							)
        # create mask
        _A        =  np.ones((64, 64)		,				dtype=np.floataa							)
        _A        =  0

        if str(lowerCAmelCase_							).startswith("""mps"""							):
               _A        =  torch.manual_seed(lowerCAmelCase_							)
        else:
               _A        =  torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
        _A        =  {
            """prompt""": """horse""",
            """image""": init_image,
            """mask_image""": mask,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 2,
            """guidance_scale""": 4.0,
            """output_type""": """np""",
        }
        return inputs



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  """cpu"""

        _A        =  self.get_dummy_components()

        _A        =  self.pipeline_class(**lowerCAmelCase_							)
        _A        =  pipe.to(lowerCAmelCase_							)

        pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

        _A        =  pipe(**self.get_dummy_inputs(lowerCAmelCase_							)							)
        _A        =  output.images

        _A        =  pipe(
            **self.get_dummy_inputs(lowerCAmelCase_							)		,				return_dict=lowerCAmelCase_		,				)[0]

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

        print(F'''image.shape {image.shape}'''							)

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

        _A        =  np.array(
            [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]							)

        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()}'''



 def      UpperCAmelCase							(					self							)  ->					int:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3							)



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







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					int:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""							)

        _A        =  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
        _A        =  np.ones((7_68, 7_68)		,				dtype=np.floataa							)
        _A        =  0

        _A        =  """a hat"""

        _A        =  KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""		,				torch_dtype=torch.floataa							)
        pipe_prior.to(lowerCAmelCase_							)

        _A        =  KandinskyInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-inpaint"""		,				torch_dtype=torch.floataa							)
        _A        =  pipeline.to(lowerCAmelCase_							)
        pipeline.set_progress_bar_config(disable=lowerCAmelCase_							)

        _A        =  torch.Generator(device="""cpu"""							).manual_seed(0							)
        _A   ,  _A        =  pipe_prior(
            lowerCAmelCase_		,				generator=lowerCAmelCase_		,				num_inference_steps=5		,				negative_prompt=""""""		,				).to_tuple()

        _A        =  pipeline(
            lowerCAmelCase_		,				image=lowerCAmelCase_		,				mask_image=lowerCAmelCase_		,				image_embeds=lowerCAmelCase_		,				negative_image_embeds=lowerCAmelCase_		,				generator=lowerCAmelCase_		,				num_inference_steps=1_00		,				height=7_68		,				width=7_68		,				output_type="""np"""		,				)

        _A        =  output.images[0]

        assert image.shape == (7_68, 7_68, 3)

        assert_mean_pixel_difference(lowerCAmelCase_		,				lowerCAmelCase_							)





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)






def  snake_case  (       snake_case__			:Union[str, Any]   ,	snake_case__			:Dict)						->   Any:
       _A        =  []
       for part_id in partition_order:
              _A        =  df.where(F'''SPARK_PARTITION_ID() = {part_id}''').collect()
              for row_idx, row in enumerate(snake_case__):
                     expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()))
       return expected_row_ids_and_row_dicts






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Optional[Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
       # that each partition can hold 2 rows.
       spark_builder._repartition_df_if_needed(max_shard_size=16)
       # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
       assert spark_builder.df.rdd.getNumPartitions() == 50






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(2)
       _A        =  [1, 0]
       _A        =  _generate_iterable_examples(snake_case__   ,	snake_case__)  # Reverse the partitions.
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	snake_case__)

       for i, (row_id, row_dict) in enumerate(generate_fn()):
              _A   ,  _A        =  expected_row_ids_and_row_dicts[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   int:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(1)
       _A        =  SparkExamplesIterable(snake_case__)
       assert it.n_shards == 1
       for i, (row_id, row_dict) in enumerate(snake_case__):
              assert row_id == F'''0_{i}'''
              assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(30).repartition(3)
       # Mock the generator so that shuffle reverses the partition indices.
       with patch("""numpy.random.Generator""") as generator_mock:
              _A        =  lambda snake_case__: x.reverse()
              _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[2, 1, 0])

              _A        =  SparkExamplesIterable(snake_case__).shuffle_data_sources(snake_case__)
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(snake_case__):
                     _A   ,  _A        =  expected_row_ids_and_row_dicts[i]
                     assert row_id == expected_row_id
                     assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   List[str]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(20).repartition(4)

       # Partitions 0 and 2
       _A        =  SparkExamplesIterable(snake_case__).shard_data_sources(worker_id=0   ,	num_workers=2)
       assert shard_it_a.n_shards == 2
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[0, 2])
       for i, (row_id, row_dict) in enumerate(snake_case__):
              _A   ,  _A        =  expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict

       # Partitions 1 and 3
       _A        =  SparkExamplesIterable(snake_case__).shard_data_sources(worker_id=1   ,	num_workers=2)
       assert shard_it_a.n_shards == 2
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[1, 3])
       for i, (row_id, row_dict) in enumerate(snake_case__):
              _A   ,  _A        =  expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Tuple:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # Choose a small max_shard_size for maximum partitioning.
       spark_builder._repartition_df_if_needed(max_shard_size=1)
       # The new number of partitions should not be greater than the number of rows.
       assert spark_builder.df.rdd.getNumPartitions() == 100




