






"""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 						a_ (      snake_case_					):







      '''simple docstring'''

      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=False   ):

       '''simple docstring'''



       lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__	:     Any								=		(
           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(lowerCamelCase_   ),	'wb'   ).close()
       lowerCamelCase__	:     str								=		pa.Table.from_pydict({'filename': [Path(lowerCamelCase_   ).name] * 1_0_0}   )
       if take == -1:
        lowerCamelCase__	:     str								=		len(lowerCamelCase_   ) - skip
       if skip is not None and take is not None:
        lowerCamelCase__	:     Any								=		pa_table.slice(lowerCamelCase_,	lowerCamelCase_   )
       return pa_table



class 						a_ (      snake_case_					):







      '''simple docstring'''

      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		'my_name'
       lowerCamelCase__	:     Tuple								=		SplitInfo(name='train',	num_examples=1_0_0   )
       lowerCamelCase__	:     Union[str, Any]								=		SplitInfo(name='test',	num_examples=1_0_0   )
       lowerCamelCase__	:     str								=		[train_info, test_info]
       lowerCamelCase__	:     Any								=		SplitDict()
       split_dict.add(lowerCamelCase_   )
       split_dict.add(lowerCamelCase_   )
       lowerCamelCase__	:     Union[str, Any]								=		DatasetInfo(splits=lowerCamelCase_   )

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

        lowerCamelCase__	:     List[str]								=		'test[:33%]'
        lowerCamelCase__	:     Optional[Any]								=		Dataset(**reader.read(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   )   )
        self.assertEqual(dset['filename'][0],	f'''{name}-test'''   )
        self.assertEqual(dset.num_rows,	3_3   )
        self.assertEqual(dset.num_columns,	1   )

        lowerCamelCase__	:     Optional[int]								=		['train', 'test[:33%]']
        lowerCamelCase__	:     List[str]								=		[Split.TRAIN, ReadInstruction.from_spec('test[:33%]'   )]
        for instructions in [instructionsa, instructionsa]:
         lowerCamelCase__	:     int								=		[reader.read(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   ) for instr in instructions]
         lowerCamelCase__							,	lowerCamelCase__	:     List[Any]								=		(Dataset(**lowerCamelCase_   ) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset['filename'][0],	f'''{name}-train'''   )
         self.assertEqual(train_dset.num_rows,	1_0_0   )
         self.assertEqual(train_dset.num_columns,	1   )
         self.assertIsInstance(train_dset.split,	lowerCamelCase_   )
         self.assertEqual(str(train_dset.split   ),	'train'   )
         self.assertEqual(test_dset['filename'][0],	f'''{name}-test'''   )
         self.assertEqual(test_dset.num_rows,	3_3   )
         self.assertEqual(test_dset.num_columns,	1   )
         self.assertIsInstance(test_dset.split,	lowerCamelCase_   )
         self.assertEqual(str(test_dset.split   ),	'test[:33%]'   )
         del train_dset, test_dset




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[Any]								=		'my_name'
       lowerCamelCase__	:     List[str]								=		SplitInfo(name='train',	num_examples=1_0_0_0,	shard_lengths=[1_0_0] * 1_0   )
       lowerCamelCase__	:     List[Any]								=		[train_info]
       lowerCamelCase__	:     int								=		SplitDict()
       split_dict.add(lowerCamelCase_   )
       lowerCamelCase__	:     Optional[Any]								=		DatasetInfo(splits=lowerCamelCase_   )

       with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCamelCase__	:     int								=		ReaderTest(lowerCamelCase_,	lowerCamelCase_   )

        lowerCamelCase__	:     int								=		'train[:33%]'
        lowerCamelCase__	:     int								=		Dataset(**reader.read(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_   )   )
        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,	3_3_0   )
        self.assertEqual(dset.num_columns,	1   )





      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		SplitInfo(name='train',	num_examples=1_0_0   )
       lowerCamelCase__	:     Tuple								=		SplitInfo(name='test',	num_examples=1_0_0   )
       lowerCamelCase__	:     Tuple								=		SplitDict()
       split_dict.add(lowerCamelCase_   )
       split_dict.add(lowerCamelCase_   )
       lowerCamelCase__	:     Tuple								=		DatasetInfo(splits=lowerCamelCase_   )

       with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCamelCase__	:     Union[str, Any]								=		ReaderTest(lowerCamelCase_,	lowerCamelCase_   )

        lowerCamelCase__	:     Dict								=		[
            {'filename': os.path.join(lowerCamelCase_,	'train'   )},
            {'filename': os.path.join(lowerCamelCase_,	'test'   ), 'skip': 1_0, 'take': 1_0},
        ]
        lowerCamelCase__	:     List[Any]								=		Dataset(**reader.read_files(lowerCamelCase_,	original_instructions='train+test[10:20]'   )   )
        self.assertEqual(dset.num_rows,	1_1_0   )
        self.assertEqual(dset.num_columns,	1   )
        del dset






@pytest.mark.parametrize('in_memory'     ,  [False, True]							)
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 lowerCamelCase__	:     List[Any]								=		arrow_file
 with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
  lowerCamelCase__	:     Optional[int]								=		ArrowReader.read_table(_lowerCamelCase     ,  in_memory=_lowerCamelCase							)
 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_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 lowerCamelCase__	:     Tuple								=		arrow_file
 lowerCamelCase__	:     Dict								=		ArrowReader(''     ,  _lowerCamelCase							)
 with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
  lowerCamelCase__	:     Dict								=		reader.read_files([{'filename': filename}]     ,  in_memory=_lowerCamelCase							)
 assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
 lowerCamelCase__	:     Dict								=		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_    (		):
 assert ReadInstruction('train'     ,  to=10     ,  unit='abs'							).to_spec() == "train[:10]"
 assert ReadInstruction('train'     ,  from_=-80     ,  to=10     ,  unit='%'							).to_spec() == "train[-80%:10%]"

 lowerCamelCase__	:     List[str]								=		'train+test'
 assert ReadInstruction.from_spec(_lowerCamelCase							).to_spec() == spec_train_test

 lowerCamelCase__	:     Optional[Any]								=		'train[2:10]'
 assert ReadInstruction.from_spec(_lowerCamelCase							).to_spec() == spec_train_abs

 lowerCamelCase__	:     Dict								=		'train[15%:-20%]'
 assert ReadInstruction.from_spec(_lowerCamelCase							).to_spec() == spec_train_pct

 lowerCamelCase__	:     Optional[Any]								=		'train[:10%](closest)'
 assert ReadInstruction.from_spec(_lowerCamelCase							).to_spec() == "train[:10%]"

 lowerCamelCase__	:     Dict								=		'train[:10%](pct1_dropremainder)'
 assert ReadInstruction.from_spec(_lowerCamelCase							).to_spec() == spec_train_pct_rounding

 lowerCamelCase__	:     Any								=		'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
 assert ReadInstruction.from_spec(_lowerCamelCase							).to_spec() == spec_train_test_pct_rounding






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Optional[int]								=		'dummy'
 lowerCamelCase__	:     Optional[int]								=		[SplitInfo(name='train'     ,  num_examples=100							)]
 lowerCamelCase__	:     str								=		'train[:33%]'
 lowerCamelCase__	:     List[Any]								=		'arrow'
 lowerCamelCase__	:     Tuple								=		'prefix'

 lowerCamelCase__	:     Union[str, Any]								=		make_file_instructions(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
 assert isinstance(_lowerCamelCase     ,  _lowerCamelCase							)
 assert file_instructions.num_examples == 33
 assert file_instructions.file_instructions == [
     {"filename": os.path.join(_lowerCamelCase     ,  f'''{name}-train.arrow'''							), "skip": 0, "take": 33}
 ]

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






@pytest.mark.parametrize('name, expected_exception'     ,  [(None, TypeError), ('', ValueError)]							)
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 lowerCamelCase__	:     str								=		[SplitInfo(name='train'     ,  num_examples=100							)]
 lowerCamelCase__	:     Union[str, Any]								=		'train'
 lowerCamelCase__	:     int								=		'arrow'
 lowerCamelCase__	:     str								=		'prefix_path'
 with pytest.raises(_lowerCamelCase							):
  lowerCamelCase__	:     Dict								=		make_file_instructions(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)









"""simple docstring"""
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 						a_ (      snake_case_    ,		unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							Optional[Any]					=      KandinskyPriorPipeline
      lowerCamelCase__			:							Any					=      ['prompt']
      lowerCamelCase__			:							Any					=      ['prompt', 'negative_prompt']
      lowerCamelCase__			:							str					=      [
          'num_images_per_prompt',
          'generator',
          'num_inference_steps',
          'latents',
          'negative_prompt',
          'guidance_scale',
          'output_type',
          'return_dict',
      ]
      lowerCamelCase__			:							Dict					=      False




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return 3_2




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return 3_2




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return self.time_input_dim




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return self.time_input_dim * 4




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return 1_0_0




      @property
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )
       return tokenizer




      @property
      def      a__				(self   ):

       '''simple docstring'''



       torch.manual_seed(0   )
       lowerCamelCase__	:     int								=		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=3_7,	layer_norm_eps=1e-05,	num_attention_heads=4,	num_hidden_layers=5,	pad_token_id=1,	vocab_size=1_0_0_0,	)
       return CLIPTextModelWithProjection(lowerCamelCase_   )




      @property
      def      a__				(self   ):

       '''simple docstring'''



       torch.manual_seed(0   )

       lowerCamelCase__	:     Optional[int]								=		{
           'num_attention_heads': 2,
           'attention_head_dim': 1_2,
           'embedding_dim': self.text_embedder_hidden_size,
           'num_layers': 1,
       }

       lowerCamelCase__	:     Optional[int]								=		PriorTransformer(**lowerCamelCase_   )
       # 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
       lowerCamelCase__	:     Dict								=		nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model




      @property
      def      a__				(self   ):

       '''simple docstring'''



       torch.manual_seed(0   )
       lowerCamelCase__	:     List[str]								=		CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size,	image_size=2_2_4,	projection_dim=self.text_embedder_hidden_size,	intermediate_size=3_7,	num_attention_heads=4,	num_channels=3,	num_hidden_layers=5,	patch_size=1_4,	)

       lowerCamelCase__	:     int								=		CLIPVisionModelWithProjection(lowerCamelCase_   )
       return model




      @property
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		CLIPImageProcessor(
           crop_size=2_2_4,	do_center_crop=lowerCamelCase_,	do_normalize=lowerCamelCase_,	do_resize=lowerCamelCase_,	image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],	image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],	resample=3,	size=2_2_4,	)

       return image_processor




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		self.dummy_prior
       lowerCamelCase__	:     Optional[Any]								=		self.dummy_image_encoder
       lowerCamelCase__	:     Optional[Any]								=		self.dummy_text_encoder
       lowerCamelCase__	:     Union[str, Any]								=		self.dummy_tokenizer
       lowerCamelCase__	:     List[Any]								=		self.dummy_image_processor

       lowerCamelCase__	:     List[Any]								=		UnCLIPScheduler(
           variance_type='fixed_small_log',	prediction_type='sample',	num_train_timesteps=1_0_0_0,	clip_sample=lowerCamelCase_,	clip_sample_range=10.0,	)

       lowerCamelCase__	:     Union[str, Any]								=		{
           'prior': prior,
           'image_encoder': image_encoder,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'scheduler': scheduler,
           'image_processor': image_processor,
       }

       return components




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=0   ):

       '''simple docstring'''



       if str(lowerCamelCase_   ).startswith('mps'   ):
        lowerCamelCase__	:     List[Any]								=		torch.manual_seed(lowerCamelCase_   )
       else:
        lowerCamelCase__	:     Optional[int]								=		torch.Generator(device=lowerCamelCase_   ).manual_seed(lowerCamelCase_   )
       lowerCamelCase__	:     List[Any]								=		{
           'prompt': 'horse',
           'generator': generator,
           'guidance_scale': 4.0,
           'num_inference_steps': 2,
           'output_type': 'np',
       }
       return inputs




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		'cpu'

       lowerCamelCase__	:     str								=		self.get_dummy_components()

       lowerCamelCase__	:     Any								=		self.pipeline_class(**lowerCamelCase_   )
       lowerCamelCase__	:     Dict								=		pipe.to(lowerCamelCase_   )

       pipe.set_progress_bar_config(disable=lowerCamelCase_   )

       lowerCamelCase__	:     Tuple								=		pipe(**self.get_dummy_inputs(lowerCamelCase_   )   )
       lowerCamelCase__	:     Dict								=		output.image_embeds

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

       lowerCamelCase__	:     Dict								=		image[0, -1_0:]
       lowerCamelCase__	:     Tuple								=		image_from_tuple[0, -1_0:]

       assert image.shape == (1, 3_2)

       lowerCamelCase__	:     Dict								=		np.array(
           [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]   )

       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      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		torch_device == 'cpu'
       lowerCamelCase__	:     Any								=		True
       lowerCamelCase__	:     List[Any]								=		False

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





      @skip_mps
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		torch_device == 'cpu'
       lowerCamelCase__	:     int								=		False

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

