
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class    _a				(lowercase_  ,					lowercase_  ,					lowercase_  ,					unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  StableDiffusionControlNetImgaImgPipeline
       SCREAMING_SNAKE_CASE      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       SCREAMING_SNAKE_CASE      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE      =  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'})
       SCREAMING_SNAKE_CASE      =  IMAGE_TO_IMAGE_IMAGE_PARAMS






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				UNetaDConditionModel(
              block_out_channels=(32, 64)       ,							layers_per_block=2       ,							sample_size=32       ,							in_channels=4       ,							out_channels=4       ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,							cross_attention_dim=32       ,							)
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				ControlNetModel(
              block_out_channels=(32, 64)       ,							layers_per_block=2       ,							in_channels=4       ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,							cross_attention_dim=32       ,							conditioning_embedding_out_channels=(16, 32)       ,							)
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				DDIMScheduler(
              beta_start=0.0_0085       ,							beta_end=0.012       ,							beta_schedule="""scaled_linear"""       ,							clip_sample=A__       ,							set_alpha_to_one=A__       ,							)
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				AutoencoderKL(
              block_out_channels=[32, 64]       ,							in_channels=3       ,							out_channels=3       ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,							latent_channels=4       ,							)
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				CLIPTextConfig(
              bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=32       ,							intermediate_size=37       ,							layer_norm_eps=1E-05       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=10_00       ,							)
          _SCREAMING_SNAKE_CASE      =				CLIPTextModel(A__      )
          _SCREAMING_SNAKE_CASE      =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

          _SCREAMING_SNAKE_CASE      =				{
              """unet""": unet,
              """controlnet""": controlnet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components






       def        UpperCamelCase				(							self       ,							A__       ,							A__=0      )    ->			List[str]:
          if str(A__      ).startswith("""mps"""      ):
             _SCREAMING_SNAKE_CASE      =				torch.manual_seed(A__      )
          else:
             _SCREAMING_SNAKE_CASE      =				torch.Generator(device=A__      ).manual_seed(A__      )

          _SCREAMING_SNAKE_CASE      =				2
          _SCREAMING_SNAKE_CASE      =				randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,							generator=A__       ,							device=torch.device(A__      )       ,							)
          _SCREAMING_SNAKE_CASE      =				floats_tensor(control_image.shape       ,							rng=random.Random(A__      )      ).to(A__      )
          _SCREAMING_SNAKE_CASE      =				image.cpu().permute(0       ,							2       ,							3       ,							1      )[0]
          _SCREAMING_SNAKE_CASE      =				Image.fromarray(np.uinta(A__      )      ).convert("""RGB"""      ).resize((64, 64)      )
          _SCREAMING_SNAKE_CASE      =				{
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
              """image""": image,
              """control_image""": control_image,
          }

          return inputs






       def        UpperCamelCase				(							self      )    ->			str:
          return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )






       @unittest.skipIf(
           torch_device != """cuda""" or not is_xformers_available()       ,							reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,							)
       def        UpperCamelCase				(							self      )    ->			Tuple:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )






       def        UpperCamelCase				(							self      )    ->			List[str]:
          self._test_inference_batch_single_identical(expected_max_diff=2E-3      )






class    _a				(lowercase_  ,					lowercase_  ,					unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  StableDiffusionControlNetImgaImgPipeline
       SCREAMING_SNAKE_CASE      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       SCREAMING_SNAKE_CASE      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE      =  frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				UNetaDConditionModel(
              block_out_channels=(32, 64)       ,							layers_per_block=2       ,							sample_size=32       ,							in_channels=4       ,							out_channels=4       ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,							cross_attention_dim=32       ,							)
          torch.manual_seed(0      )

          def init_weights(A__      ):
             if isinstance(A__       ,							torch.nn.Convad      ):
                torch.nn.init.normal(m.weight      )
                m.bias.data.fill_(1.0      )

          _SCREAMING_SNAKE_CASE      =				ControlNetModel(
              block_out_channels=(32, 64)       ,							layers_per_block=2       ,							in_channels=4       ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,							cross_attention_dim=32       ,							conditioning_embedding_out_channels=(16, 32)       ,							)
          controlneta.controlnet_down_blocks.apply(A__      )

          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				ControlNetModel(
              block_out_channels=(32, 64)       ,							layers_per_block=2       ,							in_channels=4       ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,							cross_attention_dim=32       ,							conditioning_embedding_out_channels=(16, 32)       ,							)
          controlneta.controlnet_down_blocks.apply(A__      )

          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				DDIMScheduler(
              beta_start=0.0_0085       ,							beta_end=0.012       ,							beta_schedule="""scaled_linear"""       ,							clip_sample=A__       ,							set_alpha_to_one=A__       ,							)
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				AutoencoderKL(
              block_out_channels=[32, 64]       ,							in_channels=3       ,							out_channels=3       ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,							latent_channels=4       ,							)
          torch.manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				CLIPTextConfig(
              bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=32       ,							intermediate_size=37       ,							layer_norm_eps=1E-05       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=10_00       ,							)
          _SCREAMING_SNAKE_CASE      =				CLIPTextModel(A__      )
          _SCREAMING_SNAKE_CASE      =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

          _SCREAMING_SNAKE_CASE      =				MultiControlNetModel([controlneta, controlneta]      )

          _SCREAMING_SNAKE_CASE      =				{
              """unet""": unet,
              """controlnet""": controlnet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components






       def        UpperCamelCase				(							self       ,							A__       ,							A__=0      )    ->			List[str]:
          if str(A__      ).startswith("""mps"""      ):
             _SCREAMING_SNAKE_CASE      =				torch.manual_seed(A__      )
          else:
             _SCREAMING_SNAKE_CASE      =				torch.Generator(device=A__      ).manual_seed(A__      )

          _SCREAMING_SNAKE_CASE      =				2

          _SCREAMING_SNAKE_CASE      =				[
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,							generator=A__       ,							device=torch.device(A__      )       ,							),
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,							generator=A__       ,							device=torch.device(A__      )       ,							),
          ]

          _SCREAMING_SNAKE_CASE      =				floats_tensor(control_image[0].shape       ,							rng=random.Random(A__      )      ).to(A__      )
          _SCREAMING_SNAKE_CASE      =				image.cpu().permute(0       ,							2       ,							3       ,							1      )[0]
          _SCREAMING_SNAKE_CASE      =				Image.fromarray(np.uinta(A__      )      ).convert("""RGB"""      ).resize((64, 64)      )
          _SCREAMING_SNAKE_CASE      =				{
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
              """image""": image,
              """control_image""": control_image,
          }

          return inputs






       def        UpperCamelCase				(							self      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				self.get_dummy_components()
          _SCREAMING_SNAKE_CASE      =				self.pipeline_class(**A__      )
          pipe.to(A__      )

          _SCREAMING_SNAKE_CASE      =				10.0
          _SCREAMING_SNAKE_CASE      =				4

          _SCREAMING_SNAKE_CASE      =				self.get_dummy_inputs(A__      )
          _SCREAMING_SNAKE_CASE      =				steps
          _SCREAMING_SNAKE_CASE      =				scale
          _SCREAMING_SNAKE_CASE      =				pipe(**A__      )[0]

          _SCREAMING_SNAKE_CASE      =				self.get_dummy_inputs(A__      )
          _SCREAMING_SNAKE_CASE      =				steps
          _SCREAMING_SNAKE_CASE      =				scale
          _SCREAMING_SNAKE_CASE      =				pipe(**A__       ,							control_guidance_start=0.1       ,							control_guidance_end=0.2      )[0]

          _SCREAMING_SNAKE_CASE      =				self.get_dummy_inputs(A__      )
          _SCREAMING_SNAKE_CASE      =				steps
          _SCREAMING_SNAKE_CASE      =				scale
          _SCREAMING_SNAKE_CASE      =				pipe(**A__       ,							control_guidance_start=[0.1, 0.3]       ,							control_guidance_end=[0.2, 0.7]      )[0]

          _SCREAMING_SNAKE_CASE      =				self.get_dummy_inputs(A__      )
          _SCREAMING_SNAKE_CASE      =				steps
          _SCREAMING_SNAKE_CASE      =				scale
          _SCREAMING_SNAKE_CASE      =				pipe(**A__       ,							control_guidance_start=0.4       ,							control_guidance_end=[0.5, 0.8]      )[0]

          # make sure that all outputs are different
          assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
          assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
          assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )






       @unittest.skipIf(
           torch_device != """cuda""" or not is_xformers_available()       ,							reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,							)
       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          self._test_inference_batch_single_identical(expected_max_diff=2E-3      )






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				self.get_dummy_components()
          _SCREAMING_SNAKE_CASE      =				self.pipeline_class(**A__      )
          pipe.to(A__      )
          pipe.set_progress_bar_config(disable=A__      )
          with tempfile.TemporaryDirectory() as tmpdir:
             try:
                # save_pretrained is not implemented for Multi-ControlNet
                pipe.save_pretrained(A__      )
             except NotImplementedError:
                pass






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





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			str:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""      )

          _SCREAMING_SNAKE_CASE      =				StableDiffusionControlNetImgaImgPipeline.from_pretrained(
              """runwayml/stable-diffusion-v1-5"""       ,							safety_checker=A__       ,							controlnet=A__      )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=A__      )

          _SCREAMING_SNAKE_CASE      =				torch.Generator(device="""cpu"""      ).manual_seed(0      )
          _SCREAMING_SNAKE_CASE      =				"""evil space-punk bird"""
          _SCREAMING_SNAKE_CASE      =				load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""      ).resize((5_12, 5_12)      )
          _SCREAMING_SNAKE_CASE      =				load_image(
              """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""      ).resize((5_12, 5_12)      )

          _SCREAMING_SNAKE_CASE      =				pipe(
              A__       ,							A__       ,							control_image=A__       ,							generator=A__       ,							output_type="""np"""       ,							num_inference_steps=50       ,							strength=0.6       ,							)

          _SCREAMING_SNAKE_CASE      =				output.images[0]

          assert image.shape == (5_12, 5_12, 3)

          _SCREAMING_SNAKE_CASE      =				load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"""      )

          assert np.abs(expected_image - image      ).max() < 9E-2








'''simple docstring'''



import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      split=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""    ,      [str, list]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   if issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				parquet_path
   elif issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				[parquet_path]
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=("train",)						)     ->     List[str]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   for split in splits:
      _SCREAMING_SNAKE_CASE      =				dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""    ,      [False, True]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(
          {"""train""": parquet_path}    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize(
    """features"""    ,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader({"""train""": parquet_path}    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if split:
      _SCREAMING_SNAKE_CASE      =				{split: parquet_path}
   else:
      _SCREAMING_SNAKE_CASE      =				"""train"""
      _SCREAMING_SNAKE_CASE      =				{"""train""": parquet_path, """test""": parquet_path}
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      splits=list(path.keys()						)						)
   assert all(dataset[split].split == split for split in path.keys()						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0
   _SCREAMING_SNAKE_CASE      =				pq.ParquetFile(tmp_path / """foo.parquet"""						)
   _SCREAMING_SNAKE_CASE      =				pf.read()
   assert dataset.data.table == output_table
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				str(shared_datadir / """test_image_rgb.jpg"""						)
   _SCREAMING_SNAKE_CASE      =				{"""image""": [image_path]}
   _SCREAMING_SNAKE_CASE      =				Features({"""image""": Image()}						)
   _SCREAMING_SNAKE_CASE      =				Dataset.from_dict(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0

   _SCREAMING_SNAKE_CASE      =				Dataset.from_parquet(str(tmp_path / """foo.parquet"""						)						)
   assert dataset.features == reloaded_dataset.features

   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(str(tmp_path / """foo.parquet"""						)    ,      streaming=SCREAMING_SNAKE_CASE_						).read()
   assert dataset.features == reloaded_iterable_dataset.features







@pytest.mark.parametrize(
    """feature, expected"""    ,      [
        (Features({"""foo""": Value("""int32"""						)}						), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()						)}						), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,      )
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   assert get_writer_batch_size(SCREAMING_SNAKE_CASE_						) == expected






