




# This model implementation is heavily based on:

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,
    StableDiffusionControlNetInpaintPipeline,
    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 (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class 		UpperCAmelCase       (    UpperCAmelCase__	,       UpperCAmelCase__	,       UpperCAmelCase__	,       unittest.TestCase      ):







						'''simple docstring'''





						lowerCAmelCase_							=       StableDiffusionControlNetInpaintPipeline
						lowerCAmelCase_							=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						lowerCAmelCase_							=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						lowerCAmelCase_							=       frozenset({'''control_image'''}      )  # skip `image` and `mask` for now, only test for control_image
						lowerCAmelCase_							=       TEXT_TO_IMAGE_IMAGE_PARAMS






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							torch.manual_seed(0						)
							snake_case_		   =					UNetaDConditionModel(
							    block_out_channels=(32, 64)	,     layers_per_block=2	,     sample_size=32	,     in_channels=9	,     out_channels=4	,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,     cross_attention_dim=32	,     )
							torch.manual_seed(0						)
							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						)
							snake_case_		   =					DDIMScheduler(
							    beta_start=0.0_0085	,     beta_end=0.012	,     beta_schedule="scaled_linear"	,     clip_sample=__lowercase	,     set_alpha_to_one=__lowercase	,     )
							torch.manual_seed(0						)
							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						)
							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	,     )
							snake_case_		   =					CLIPTextModel(__lowercase						)
							snake_case_		   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

							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 	snake_case__						(						self							:					int	,     __lowercase							:					Optional[int]	,     __lowercase							:					Optional[int]=0						):






							"""simple docstring"""






							if str(__lowercase						).startswith("mps"						):
								snake_case_		   =					torch.manual_seed(__lowercase						)
							else:
								snake_case_		   =					torch.Generator(device=__lowercase						).manual_seed(__lowercase						)

							snake_case_		   =					2
							snake_case_		   =					randn_tensor(
							    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,     generator=__lowercase	,     device=torch.device(__lowercase						)	,     )
							snake_case_		   =					floats_tensor((1, 3, 32, 32)	,     rng=random.Random(__lowercase						)						).to(__lowercase						)
							snake_case_		   =					init_image.cpu().permute(0	,     2	,     3	,     1						)[0]

							snake_case_		   =					Image.fromarray(np.uinta(__lowercase						)						).convert("RGB"						).resize((64, 64)						)
							snake_case_		   =					Image.fromarray(np.uinta(init_image + 4						)						).convert("RGB"						).resize((64, 64)						)

							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,
							    "mask_image": mask_image,
							    "control_image": control_image,
							}

							return inputs






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							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 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							self._test_inference_batch_single_identical(expected_max_diff=2E-3						)







class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       StableDiffusionControlNetInpaintPipeline
						lowerCAmelCase_							=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						lowerCAmelCase_							=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						lowerCAmelCase_							=       frozenset([]      )






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							torch.manual_seed(0						)
							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						)
							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						)
							snake_case_		   =					DDIMScheduler(
							    beta_start=0.0_0085	,     beta_end=0.012	,     beta_schedule="scaled_linear"	,     clip_sample=__lowercase	,     set_alpha_to_one=__lowercase	,     )
							torch.manual_seed(0						)
							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						)
							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	,     )
							snake_case_		   =					CLIPTextModel(__lowercase						)
							snake_case_		   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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







class 		UpperCAmelCase       (    UpperCAmelCase__	,       UpperCAmelCase__	,       unittest.TestCase      ):







						'''simple docstring'''





						lowerCAmelCase_							=       StableDiffusionControlNetInpaintPipeline
						lowerCAmelCase_							=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						lowerCAmelCase_							=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							torch.manual_seed(0						)
							snake_case_		   =					UNetaDConditionModel(
							    block_out_channels=(32, 64)	,     layers_per_block=2	,     sample_size=32	,     in_channels=9	,     out_channels=4	,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,     cross_attention_dim=32	,     )
							torch.manual_seed(0						)

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

							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(__lowercase						)

							torch.manual_seed(0						)
							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(__lowercase						)

							torch.manual_seed(0						)
							snake_case_		   =					DDIMScheduler(
							    beta_start=0.0_0085	,     beta_end=0.012	,     beta_schedule="scaled_linear"	,     clip_sample=__lowercase	,     set_alpha_to_one=__lowercase	,     )
							torch.manual_seed(0						)
							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						)
							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	,     )
							snake_case_		   =					CLIPTextModel(__lowercase						)
							snake_case_		   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

							snake_case_		   =					MultiControlNetModel([controlneta, controlneta]						)

							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 	snake_case__						(						self							:					Optional[Any]	,     __lowercase							:					str	,     __lowercase							:					List[str]=0						):






							"""simple docstring"""






							if str(__lowercase						).startswith("mps"						):
								snake_case_		   =					torch.manual_seed(__lowercase						)
							else:
								snake_case_		   =					torch.Generator(device=__lowercase						).manual_seed(__lowercase						)

							snake_case_		   =					2

							snake_case_		   =					[
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,     generator=__lowercase	,     device=torch.device(__lowercase						)	,     ),
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	,     generator=__lowercase	,     device=torch.device(__lowercase						)	,     ),
							]
							snake_case_		   =					floats_tensor((1, 3, 32, 32)	,     rng=random.Random(__lowercase						)						).to(__lowercase						)
							snake_case_		   =					init_image.cpu().permute(0	,     2	,     3	,     1						)[0]

							snake_case_		   =					Image.fromarray(np.uinta(__lowercase						)						).convert("RGB"						).resize((64, 64)						)
							snake_case_		   =					Image.fromarray(np.uinta(init_image + 4						)						).convert("RGB"						).resize((64, 64)						)

							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,
							    "mask_image": mask_image,
							    "control_image": control_image,
							}

							return inputs






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							snake_case_		   =					self.get_dummy_components()
							snake_case_		   =					self.pipeline_class(**__lowercase						)
							pipe.to(__lowercase						)

							snake_case_		   =					10.0
							snake_case_		   =					4

							snake_case_		   =					self.get_dummy_inputs(__lowercase						)
							snake_case_		   =					steps
							snake_case_		   =					scale
							snake_case_		   =					pipe(**__lowercase						)[0]

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

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

							snake_case_		   =					self.get_dummy_inputs(__lowercase						)
							snake_case_		   =					steps
							snake_case_		   =					scale
							snake_case_		   =					pipe(**__lowercase	,     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 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							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 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							self._test_inference_batch_single_identical(expected_max_diff=2E-3						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.get_dummy_components()
							snake_case_		   =					self.pipeline_class(**__lowercase						)
							pipe.to(__lowercase						)
							pipe.set_progress_bar_config(disable=__lowercase						)
							with tempfile.TemporaryDirectory() as tmpdir:
								try:
									# save_pretrained is not implemented for Multi-ControlNet
									pipe.save_pretrained(__lowercase						)
								except NotImplementedError:
									pass







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







						'''simple docstring'''





						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"						)

							snake_case_		   =					StableDiffusionControlNetInpaintPipeline.from_pretrained(
							    "runwayml/stable-diffusion-inpainting"	,     safety_checker=__lowercase	,     controlnet=__lowercase						)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=__lowercase						)

							snake_case_		   =					torch.Generator(device="cpu"						).manual_seed(0						)
							snake_case_		   =					load_image(
							    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"						).resize((5_12, 5_12)						)

							snake_case_		   =					load_image(
							    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
							    "/stable_diffusion_inpaint/input_bench_mask.png"						).resize((5_12, 5_12)						)

							snake_case_		   =					"pitch black hole"

							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)						)

							snake_case_		   =					pipe(
							    __lowercase	,     image=__lowercase	,     mask_image=__lowercase	,     control_image=__lowercase	,     generator=__lowercase	,     output_type="np"	,     num_inference_steps=3	,     )

							snake_case_		   =					output.images[0]

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

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

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






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"						)

							snake_case_		   =					StableDiffusionControlNetInpaintPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	,     safety_checker=__lowercase	,     controlnet=__lowercase						)
							snake_case_		   =					DDIMScheduler.from_config(pipe.scheduler.config						)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=__lowercase						)

							snake_case_		   =					torch.Generator(device="cpu"						).manual_seed(33						)

							snake_case_		   =					load_image(
							    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"						)
							snake_case_		   =					init_image.resize((5_12, 5_12)						)

							snake_case_		   =					load_image(
							    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"						)
							snake_case_		   =					mask_image.resize((5_12, 5_12)						)

							snake_case_		   =					"a handsome man with ray-ban sunglasses"

							def make_inpaint_condition(__lowercase							:					List[str]	,     __lowercase							:					Tuple						):
								snake_case_		   =					np.array(image.convert("RGB"						)						).astype(np.floataa						) / 255.0
								snake_case_		   =					np.array(image_mask.convert("L"						)						).astype(np.floataa						) / 255.0

								assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
								snake_case_		   =					-1.0  # set as masked pixel
								snake_case_		   =					np.expand_dims(__lowercase	,     0						).transpose(0	,     3	,     1	,     2						)
								snake_case_		   =					torch.from_numpy(__lowercase						)
								return image

							snake_case_		   =					make_inpaint_condition(__lowercase	,     __lowercase						)

							snake_case_		   =					pipe(
							    __lowercase	,     image=__lowercase	,     mask_image=__lowercase	,     control_image=__lowercase	,     guidance_scale=9.0	,     eta=1.0	,     generator=__lowercase	,     num_inference_steps=20	,     output_type="np"	,     )
							snake_case_		   =					output.images[0]

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

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

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






import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__    :					str					=						datasets.utils.logging.get_logger(__name__)
@dataclass
class 		UpperCAmelCase       (    datasets.BuilderConfig      ):







						'''simple docstring'''





						lowerCAmelCase_							=       None







class 		UpperCAmelCase       (    datasets.ArrowBasedBuilder      ):







						'''simple docstring'''





						lowerCAmelCase_							=       ArrowConfig






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							return datasets.DatasetInfo(features=self.config.features						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]						):






							"""simple docstring"""






							if not self.config.data_files:
								raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"						)
							snake_case_		   =					dl_manager.download_and_extract(self.config.data_files						)
							if isinstance(__lowercase	,     (str, list, tuple)						):
								snake_case_		   =					data_files
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) for file in files]
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,     gen_kwargs={"files": files}						)]
							snake_case_		   =					[]
							for split_name, files in data_files.items():
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) for file in files]
								# Infer features is they are stoed in the arrow schema
								if self.info.features is None:
									for file in itertools.chain.from_iterable(__lowercase						):
										with open(__lowercase	,     "rb"						) as f:
											snake_case_		   =					datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowercase						).schema						)
										break
								splits.append(datasets.SplitGenerator(name=__lowercase	,     gen_kwargs={"files": files}						)						)
							return splits






						def 	snake_case__						(						self							:					str	,     __lowercase							:					pa.Table						):






							"""simple docstring"""






							if self.info.features is not None:
								# more expensive cast to support nested features with keys in a different order
								# allows str <-> int/float or str to Audio for example
								snake_case_		   =					table_cast(__lowercase	,     self.info.features.arrow_schema						)
							return pa_table






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






							for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase						)						):
								with open(__lowercase	,     "rb"						) as f:
									try:
										for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowercase						)						):
											snake_case_		   =					pa.Table.from_batches([record_batch]						)
											# Uncomment for debugging (will print the Arrow table size and elements)
											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
											yield f"{file_idx}_{batch_idx}", self._cast_table(__lowercase						)
									except ValueError as e:
										logger.error(f"Failed to read file '{file}' with error {type(__lowercase						)}: {e}"						)
										raise
