'''simple docstring'''




import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class        __magic_name__		(       _UpperCAmelCase,	unittest.TestCase):
			UpperCamelCase__							=			KandinskyVaaPriorEmbaEmbPipeline
			UpperCamelCase__							=			['''prompt''', '''image''']
			UpperCamelCase__							=			['''prompt''', '''image''']
			UpperCamelCase__							=			[
			    '''num_images_per_prompt''',
			    '''strength''',
			    '''generator''',
			    '''num_inference_steps''',
			    '''latents''',
			    '''negative_prompt''',
			    '''guidance_scale''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase__							=			False







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]       ):
										return 32







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										return 32







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										return self.time_input_dim







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										return self.time_input_dim * 4







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										return 100







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										lowercase_   :     str									=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
										return tokenizer







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										torch.manual_seed(0       )
										lowercase_   :     List[str]									=			CLIPTextConfig(
										    bos_token_id=0							,     eos_token_id=2							,     hidden_size=self.text_embedder_hidden_size							,     projection_dim=self.text_embedder_hidden_size							,     intermediate_size=37							,     layer_norm_eps=1E-05							,     num_attention_heads=4							,     num_hidden_layers=5							,     pad_token_id=1							,     vocab_size=1000							,     )
										return CLIPTextModelWithProjection(lowercase_       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										torch.manual_seed(0       )

										lowercase_   :     Optional[Any]									=			{
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 12,
										    """embedding_dim""": self.text_embedder_hidden_size,
										    """num_layers""": 1,
										}

										lowercase_   :     List[str]									=			PriorTransformer(**lowercase_       )
										# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
										lowercase_   :     Optional[int]									=			nn.Parameter(torch.ones(model.clip_std.shape       )       )
										return model







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										torch.manual_seed(0       )
										lowercase_   :     int									=			CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size							,     image_size=224							,     projection_dim=self.text_embedder_hidden_size							,     intermediate_size=37							,     num_attention_heads=4							,     num_channels=3							,     num_hidden_layers=5							,     patch_size=14							,     )

										lowercase_   :     Union[str, Any]									=			CLIPVisionModelWithProjection(lowercase_       )
										return model







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										lowercase_   :     Tuple									=			CLIPImageProcessor(
										    crop_size=224							,     do_center_crop=lowercase_							,     do_normalize=lowercase_							,     do_resize=lowercase_							,     image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]							,     image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]							,     resample=3							,     size=224							,     )

										return image_processor







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										lowercase_   :     int									=			self.dummy_prior
										lowercase_   :     Tuple									=			self.dummy_image_encoder
										lowercase_   :     Tuple									=			self.dummy_text_encoder
										lowercase_   :     Tuple									=			self.dummy_tokenizer
										lowercase_   :     Union[str, Any]									=			self.dummy_image_processor

										lowercase_   :     Optional[int]									=			UnCLIPScheduler(
										    variance_type="""fixed_small_log"""							,     prediction_type="""sample"""							,     num_train_timesteps=1000							,     clip_sample=lowercase_							,     clip_sample_range=10.0							,     )

										lowercase_   :     Tuple									=			{
										    """prior""": prior,
										    """image_encoder""": image_encoder,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """scheduler""": scheduler,
										    """image_processor""": image_processor,
										}

										return components







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    Union[str, Any]							,     lowercase_							:    int=0       ):
										if str(lowercase_       ).startswith("""mps"""       ):
																	lowercase_   :     int									=			torch.manual_seed(lowercase_       )
										else:
																	lowercase_   :     Tuple									=			torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

										lowercase_   :     Optional[Any]									=			floats_tensor((1, 3, 64, 64)							,     rng=random.Random(lowercase_       )       ).to(lowercase_       )
										lowercase_   :     Any									=			image.cpu().permute(0							,     2							,     3							,     1       )[0]
										lowercase_   :     str									=			Image.fromarray(np.uinta(lowercase_       )       ).convert("""RGB"""       ).resize((256, 256)       )

										lowercase_   :     str									=			{
										    """prompt""": """horse""",
										    """image""": init_image,
										    """strength""": 0.5,
										    """generator""": generator,
										    """guidance_scale""": 4.0,
										    """num_inference_steps""": 2,
										    """output_type""": """np""",
										}
										return inputs







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     Optional[int]									=			"""cpu"""

										lowercase_   :     List[str]									=			self.get_dummy_components()

										lowercase_   :     List[Any]									=			self.pipeline_class(**lowercase_       )
										lowercase_   :     Union[str, Any]									=			pipe.to(lowercase_       )

										pipe.set_progress_bar_config(disable=lowercase_       )

										lowercase_   :     Any									=			pipe(**self.get_dummy_inputs(lowercase_       )       )
										lowercase_   :     List[str]									=			output.image_embeds

										lowercase_   :     Tuple									=			pipe(
										    **self.get_dummy_inputs(lowercase_       )							,     return_dict=lowercase_							,     )[0]

										lowercase_   :     List[Any]									=			image[0, -10:]
										lowercase_   :     Tuple									=			image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										lowercase_   :     Optional[Any]									=			np.array(
										    [
										        0.1_07_12_84,
										        1.3_33_02_71,
										        0.61_26_02_23,
										        -0.6_69_10_65,
										        -0.3_84_68_52,
										        -1.0_30_36_61,
										        0.22_71_61_11,
										        0.03_34_89_01,
										        0.30_04_06_75,
										        -0.24_80_50_29,
										    ]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







			@skip_mps
			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										lowercase_   :     Any									=			torch_device == """cpu"""
										lowercase_   :     Optional[Any]									=			True
										lowercase_   :     Optional[int]									=			False

										self._test_inference_batch_single_identical(
										    test_max_difference=lowercase_							,     relax_max_difference=lowercase_							,     test_mean_pixel_difference=lowercase_							,     )







			@skip_mps
			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     int									=			torch_device == """cpu"""
										lowercase_   :     Optional[int]									=			False

										self._test_attention_slicing_forward_pass(
										    test_max_difference=lowercase_							,     test_mean_pixel_difference=lowercase_							,     )






'''simple docstring'''




import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class        __magic_name__		(       _UpperCAmelCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    Dict							,     lowercase_							:    List[str]=False       ):
										lowercase_		,		lowercase_		,		lowercase_   :     str									=			(
										    filename_skip_take["""filename"""],
										    filename_skip_take["""skip"""] if """skip""" in filename_skip_take else None,
										    filename_skip_take["""take"""] if """take""" in filename_skip_take else None,
										)
										open(os.path.join(lowercase_       )							,     """wb"""       ).close()
										lowercase_   :     Union[str, Any]									=			pa.Table.from_pydict({"""filename""": [Path(lowercase_       ).name] * 100}       )
										if take == -1:
																	lowercase_   :     Optional[int]									=			len(lowercase_       ) - skip
										if skip is not None and take is not None:
																	lowercase_   :     Any									=			pa_table.slice(lowercase_							,     lowercase_       )
										return pa_table





class        __magic_name__		(       _UpperCAmelCase):







			def       SCREAMING_SNAKE_CASE_	(      self							:    int       ):
										lowercase_   :     Union[str, Any]									=			"""my_name"""
										lowercase_   :     Optional[Any]									=			SplitInfo(name="""train"""							,     num_examples=100       )
										lowercase_   :     Dict									=			SplitInfo(name="""test"""							,     num_examples=100       )
										lowercase_   :     str									=			[train_info, test_info]
										lowercase_   :     Any									=			SplitDict()
										split_dict.add(lowercase_       )
										split_dict.add(lowercase_       )
										lowercase_   :     Union[str, Any]									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Tuple									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     Optional[Any]									=			"""test[:33%]"""
																	lowercase_   :     List[Any]									=			Dataset(**reader.read(lowercase_							,     lowercase_							,     lowercase_       )       )
																	self.assertEqual(dset["""filename"""][0]							,     f'''{name}-test'''       )
																	self.assertEqual(dset.num_rows							,     33       )
																	self.assertEqual(dset.num_columns							,     1       )

																	lowercase_   :     str									=			["""train""", """test[:33%]"""]
																	lowercase_   :     Any									=			[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""       )]
																	for instructions in [instructionsa, instructionsa]:
																								lowercase_   :     Dict									=			[reader.read(lowercase_							,     lowercase_							,     lowercase_       ) for instr in instructions]
																								lowercase_		,		lowercase_   :     List[Any]									=			(Dataset(**lowercase_       ) for dataset_kwargs in datasets_kwargs)
																								self.assertEqual(train_dset["""filename"""][0]							,     f'''{name}-train'''       )
																								self.assertEqual(train_dset.num_rows							,     100       )
																								self.assertEqual(train_dset.num_columns							,     1       )
																								self.assertIsInstance(train_dset.split							,     lowercase_       )
																								self.assertEqual(str(train_dset.split       )							,     """train"""       )
																								self.assertEqual(test_dset["""filename"""][0]							,     f'''{name}-test'''       )
																								self.assertEqual(test_dset.num_rows							,     33       )
																								self.assertEqual(test_dset.num_columns							,     1       )
																								self.assertIsInstance(test_dset.split							,     lowercase_       )
																								self.assertEqual(str(test_dset.split       )							,     """test[:33%]"""       )
																								del train_dset, test_dset







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										lowercase_   :     List[Any]									=			"""my_name"""
										lowercase_   :     int									=			SplitInfo(name="""train"""							,     num_examples=1000							,     shard_lengths=[100] * 10       )
										lowercase_   :     Tuple									=			[train_info]
										lowercase_   :     Any									=			SplitDict()
										split_dict.add(lowercase_       )
										lowercase_   :     Union[str, Any]									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Optional[int]									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     Optional[int]									=			"""train[:33%]"""
																	lowercase_   :     int									=			Dataset(**reader.read(lowercase_							,     lowercase_							,     lowercase_       )       )
																	self.assertEqual(dset["""filename"""][0]							,     f'''{name}-train-00000-of-00010'''       )
																	self.assertEqual(dset["""filename"""][-1]							,     f'''{name}-train-00003-of-00010'''       )
																	self.assertEqual(dset.num_rows							,     330       )
																	self.assertEqual(dset.num_columns							,     1       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										lowercase_   :     Any									=			SplitInfo(name="""train"""							,     num_examples=100       )
										lowercase_   :     Optional[int]									=			SplitInfo(name="""test"""							,     num_examples=100       )
										lowercase_   :     Union[str, Any]									=			SplitDict()
										split_dict.add(lowercase_       )
										split_dict.add(lowercase_       )
										lowercase_   :     Tuple									=			DatasetInfo(splits=lowercase_       )

										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_   :     Tuple									=			ReaderTest(lowercase_							,     lowercase_       )

																	lowercase_   :     List[str]									=			[
																	    {"""filename""": os.path.join(lowercase_							,     """train"""       )},
																	    {"""filename""": os.path.join(lowercase_							,     """test"""       ), """skip""": 10, """take""": 10},
																	]
																	lowercase_   :     int									=			Dataset(**reader.read_files(lowercase_							,     original_instructions="""train+test[10:20]"""       )       )
																	self.assertEqual(dset.num_rows							,     110       )
																	self.assertEqual(dset.num_columns							,     1       )
																	del dset




@pytest.mark.parametrize("""in_memory""" , [False, True]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Union[str, Any]					)     -> Union[str, Any]:
							lowercase_   :     str									=			arrow_file
							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
														lowercase_   :     Optional[Any]									=			ArrowReader.read_table(UpperCAmelCase__ , in_memory=UpperCAmelCase__					)
							assert table.shape == dataset.data.shape
							assert set(table.column_names					) == set(dataset.data.column_names					)
							assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict




@pytest.mark.parametrize("""in_memory""" , [False, True]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   Tuple , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Optional[int]					)     -> Any:
							lowercase_   :     Tuple									=			arrow_file
							lowercase_   :     Optional[Any]									=			ArrowReader("""""" , UpperCAmelCase__					)
							with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
														lowercase_   :     Dict									=			reader.read_files([{"""filename""": filename}] , in_memory=UpperCAmelCase__					)
							assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
							lowercase_   :     Optional[int]									=			dataset_kwargs["""arrow_table"""]
							assert table.shape == dataset.data.shape
							assert set(table.column_names					) == set(dataset.data.column_names					)
							assert dict(table.to_pydict()					) == dict(dataset.data.to_pydict()					)  # to_pydict returns OrderedDict




def 				lowerCamelCase      ( )     -> Optional[Any]:
							assert ReadInstruction("""train""" , to=10 , unit="""abs"""					).to_spec() == "train[:10]"
							assert ReadInstruction("""train""" , from_=-80 , to=10 , unit="""%"""					).to_spec() == "train[-80%:10%]"

							lowercase_   :     Tuple									=			"""train+test"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_test

							lowercase_   :     List[Any]									=			"""train[2:10]"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_abs

							lowercase_   :     Any									=			"""train[15%:-20%]"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_pct

							lowercase_   :     Optional[int]									=			"""train[:10%](closest)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == "train[:10%]"

							lowercase_   :     List[Any]									=			"""train[:10%](pct1_dropremainder)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_pct_rounding

							lowercase_   :     Union[str, Any]									=			"""train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"""
							assert ReadInstruction.from_spec(UpperCAmelCase__					).to_spec() == spec_train_test_pct_rounding




def 				lowerCamelCase      ( )     -> int:
							lowercase_   :     Optional[int]									=			"""dummy"""
							lowercase_   :     Union[str, Any]									=			[SplitInfo(name="""train""" , num_examples=100					)]
							lowercase_   :     Optional[int]									=			"""train[:33%]"""
							lowercase_   :     Dict									=			"""arrow"""
							lowercase_   :     int									=			"""prefix"""

							lowercase_   :     int									=			make_file_instructions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)
							assert isinstance(UpperCAmelCase__ , UpperCAmelCase__					)
							assert file_instructions.num_examples == 33
							assert file_instructions.file_instructions == [
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train.arrow'''					), "skip": 0, "take": 33}
							]

							lowercase_   :     Dict									=			[SplitInfo(name="""train""" , num_examples=100 , shard_lengths=[10] * 10					)]
							lowercase_   :     Dict									=			make_file_instructions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)
							assert isinstance(UpperCAmelCase__ , UpperCAmelCase__					)
							assert file_instructions.num_examples == 33
							assert file_instructions.file_instructions == [
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train-00000-of-00010.arrow'''					), "skip": 0, "take": -1},
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train-00001-of-00010.arrow'''					), "skip": 0, "take": -1},
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train-00002-of-00010.arrow'''					), "skip": 0, "take": -1},
							    {"filename": os.path.join(UpperCAmelCase__ , F'''{name}-train-00003-of-00010.arrow'''					), "skip": 0, "take": 3},
							]




@pytest.mark.parametrize("""name, expected_exception""" , [(None, TypeError), ("""""", ValueError)]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   Any					)     -> List[str]:
							lowercase_   :     str									=			[SplitInfo(name="""train""" , num_examples=100					)]
							lowercase_   :     Any									=			"""train"""
							lowercase_   :     Optional[int]									=			"""arrow"""
							lowercase_   :     str									=			"""prefix_path"""
							with pytest.raises(UpperCAmelCase__					):
														lowercase_   :     List[str]									=			make_file_instructions(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__					)





