


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()







class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						StableDiffusionInstructPixaPixPipeline
							_UpperCAmelCase      =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
							_UpperCAmelCase      =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							_UpperCAmelCase      =						IMAGE_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase      =						IMAGE_TO_IMAGE_IMAGE_PARAMS






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)			,						layers_per_block=2			,						sample_size=3_2			,						in_channels=8			,						out_channels=4			,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,						cross_attention_dim=3_2			,						)
										SCREAMING_SNAKE_CASE_		:     Any					=					PNDMScheduler(skip_prk_steps=lowerCAmelCase__       )
										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     int					=					AutoencoderKL(
										    block_out_channels=[3_2, 6_4]			,						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_		:     Union[str, Any]					=					CLIPTextConfig(
										    bos_token_id=0			,						eos_token_id=2			,						hidden_size=3_2			,						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			,						)
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					CLIPTextModel(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					{
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'text_encoder': text_encoder,
										    'tokenizer': tokenizer,
										    'safety_checker': None,
										    'feature_extractor': None,
										}
										return components






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=0       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					floats_tensor((1, 3, 3_2, 3_2)			,						rng=random.Random(lowerCAmelCase__       )       ).to(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					image.cpu().permute(0			,						2			,						3			,						1       )[0]
										SCREAMING_SNAKE_CASE_		:     Tuple					=					Image.fromarray(np.uinta(lowerCAmelCase__       )       ).convert('RGB'       )
										if str(lowerCAmelCase__       ).startswith('mps'       ):
													SCREAMING_SNAKE_CASE_		:     int					=					torch.manual_seed(lowerCAmelCase__       )
										else:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.Generator(device=lowerCAmelCase__       ).manual_seed(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{
										    'prompt': 'A painting of a squirrel eating a burger',
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'guidance_scale': 6.0,
										    'image_guidance_scale': 1,
										    'output_type': 'numpy',
										}
										return inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					sd_pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Dict					=					image[0, -3:, -3:, -1]
										assert image.shape == (1, 3_2, 3_2, 3)
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     int					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'french fries'
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					sd_pipe(**lowerCAmelCase__			,						negative_prompt=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					output.images
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 3_2, 3_2, 3)
										SCREAMING_SNAKE_CASE_		:     str					=					np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					[inputs['prompt']] * 2

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					np.array(inputs['image']       ).astype(np.floataa       ) / 255.0
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.from_numpy(lowerCAmelCase__       ).unsqueeze(0       ).to(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image / 2 + 0.5
										SCREAMING_SNAKE_CASE_		:     int					=					image.permute(0			,						3			,						1			,						2       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image.repeat(2			,						1			,						1			,						1       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					sd_pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Dict					=					image[-1, -3:, -3:, -1]

										assert image.shape == (2, 3_2, 3_2, 3)
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     List[str]					=					EulerAncestralDiscreteScheduler(
										    beta_start=0.00_085			,						beta_end=0.012			,						beta_schedule='scaled_linear'       )
										SCREAMING_SNAKE_CASE_		:     str					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					sd_pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     int					=					image[0, -3:, -3:, -1]

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					[round(lowerCAmelCase__			,						4       ) for x in image_slice.flatten().tolist()]
										print(','.join([str(lowerCAmelCase__       ) for x in slice]       )       )

										assert image.shape == (1, 3_2, 3_2, 3)
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					VaeImageProcessor(do_resize=lowerCAmelCase__			,						do_normalize=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase__			,						input_image_type='pt'       )       )[0]

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					components['vae']
										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_dummy_inputs_by_type(lowerCAmelCase__			,						input_image_type='pt'       )

										for image_param in self.image_latents_params:
													if image_param in inputs.keys():
																SCREAMING_SNAKE_CASE_		:     str					=					vae.encode(inputs[image_param]       ).latent_dist.mode()

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					pipe(**lowerCAmelCase__       )[0]

										SCREAMING_SNAKE_CASE_		:     Tuple					=					np.abs(out - out_latents_inputs       ).max()
										self.assertLess(lowerCAmelCase__			,						1E-4			,						'passing latents as image input generate different result from passing image'       )







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





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__=0       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.manual_seed(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'       )
										SCREAMING_SNAKE_CASE_		:     str					=					{
										    'prompt': 'turn him into a cyborg',
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 3,
										    'guidance_scale': 7.5,
										    'image_guidance_scale': 1.0,
										    'output_type': 'numpy',
										}
										return inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__       )
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     str					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     int					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										SCREAMING_SNAKE_CASE_		:     List[str]					=					np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]       )

										assert np.abs(expected_slice - image_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Any					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]       )

										assert np.abs(expected_slice - image_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					DDIMScheduler.from_config(pipe.scheduler.config       )
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Tuple					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]       )

										assert np.abs(expected_slice - image_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					0

										def callback_fn(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ) -> None:
													SCREAMING_SNAKE_CASE_		:     Any					=					True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																SCREAMING_SNAKE_CASE_		:     str					=					latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 6_4, 6_4)
																SCREAMING_SNAKE_CASE_		:     Dict					=					latents[0, -3:, -3:, -1]
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]       )

																assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2
													elif step == 2:
																SCREAMING_SNAKE_CASE_		:     Tuple					=					latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 6_4, 6_4)
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					latents[0, -3:, -3:, -1]
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]       )

																assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2

										SCREAMING_SNAKE_CASE_		:     int					=					False

										SCREAMING_SNAKE_CASE_		:     Dict					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__			,						torch_dtype=torch.floataa       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.get_inputs()
										pipe(**lowerCAmelCase__			,						callback=lowerCAmelCase__			,						callback_steps=1       )
										assert callback_fn.has_been_called
										assert number_of_steps == 3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'			,						safety_checker=lowerCAmelCase__			,						torch_dtype=torch.floataa       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing(1       )
										pipe.enable_sequential_cpu_offload()

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pipe(**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.cuda.max_memory_allocated()
										# make sure that less than 2.2 GB is allocated
										assert mem_bytes < 2.2 * 1_0**9






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_inputs()
										# resize to resolution that is divisible by 8 but not 16 or 32
										SCREAMING_SNAKE_CASE_		:     Tuple					=					inputs['image'].resize((5_0_4, 5_0_4)       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					'timbrooks/instruct-pix2pix'
										SCREAMING_SNAKE_CASE_		:     List[str]					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    lowerCAmelCase__			,						safety_checker=lowerCAmelCase__			,						)
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Dict					=					pipe(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					output.images[0]

										SCREAMING_SNAKE_CASE_		:     Any					=					image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

										assert image.shape == (5_0_4, 5_0_4, 3)
										SCREAMING_SNAKE_CASE_		:     Any					=					np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-3












import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
		from .features import FeatureType


SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Any			=   False, False, False

@dataclass
class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		None
	__UpperCamelCase   =		True
	__UpperCamelCase   =		True
	__UpperCamelCase   =		None
	# Automatically constructed
	__UpperCamelCase   =		"dict"
	__UpperCamelCase   =		pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}						)
	__UpperCamelCase   =		field(default='''Audio'''     ,     init=UpperCAmelCase_     ,     repr=UpperCAmelCase_						)




	def __call__(						self							:     Optional[Any]   )       -> List[str]:

			'''simple docstring'''




			return self.pa_type




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     Union[str, bytes, dict]   )       -> dict:

			'''simple docstring'''




			try:
					import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
			except ImportError as err:
					raise ImportError('To support encoding audio data, please install \'soundfile\'.'   ) from err
			if isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					return {"bytes": None, "path": value}
			elif isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					return {"bytes": value, "path": None}
			elif "array" in value:
					# convert the audio array to wav bytes
					__lowercase											=     BytesIO()
					sf.write(__lowerCamelCase					,      value['array']					,      value['sampling_rate']					,      format='wav'   )
					return {"bytes": buffer.getvalue(), "path": None}
			elif value.get('path'   ) is not None and os.path.isfile(value['path']   ):
					# we set "bytes": None to not duplicate the data if they're already available locally
					if value["path"].endswith('pcm'   ):
							# "PCM" only has raw audio bytes
							if value.get('sampling_rate'   ) is None:
									# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
									raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object'   )
							if value.get('bytes'   ):
									# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
									__lowercase											=     np.frombuffer(value['bytes']					,      dtype=np.intaa   ).astype(np.floataa   ) / 32_767
							else:
									__lowercase											=     np.memmap(value['path']					,      dtype='h'					,      mode='r'   ).astype(np.floataa   ) / 32_767

							__lowercase											=     BytesIO(bytes()   )
							sf.write(__lowerCamelCase					,      __lowerCamelCase					,      value['sampling_rate']					,      format='wav'   )
							return {"bytes": buffer.getvalue(), "path": None}
					else:
							return {"bytes": None, "path": value.get('path'   )}
			elif value.get('bytes'   ) is not None or value.get('path'   ) is not None:
					# store the audio bytes, and path is used to infer the audio format using the file extension
					return {"bytes": value.get('bytes'   ), "path": value.get('path'   )}
			else:
					raise ValueError(
					    F"An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."   )




	def    UpperCAmelCase			(						self							:     Any					,      __lowerCamelCase							:     dict					,      __lowerCamelCase							:     Optional[Dict[str, Union[str, bool, None]]] = None   )       -> dict:

			'''simple docstring'''




			if not self.decode:
					raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.'   )

			__lowercase						,      __lowercase											=     (value['path'], BytesIO(value['bytes']   )) if value['bytes'] is not None else (value['path'], None)
			if path is None and file is None:
					raise ValueError(F"An audio sample should have one of 'path' or 'bytes' but both are None in {value}."   )

			try:
					import librosa
					import soundfile as sf
			except ImportError as err:
					raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.'   ) from err

			__lowercase											=     xsplitext(__lowerCamelCase   )[1][1:].lower() if path is not None else None
			if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
					raise RuntimeError(
					    'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
					    'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '   )
			elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
					raise RuntimeError(
					    'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
					    'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '   )

			if file is None:
					__lowercase											=     token_per_repo_id or {}
					__lowercase											=     path.split('::'   )[-1]
					try:
							__lowercase											=     string_to_dict(__lowerCamelCase					,      config.HUB_DATASETS_URL   )['repo_id']
							__lowercase											=     token_per_repo_id[repo_id]
					except (ValueError, KeyError):
							__lowercase											=     None

					with xopen(__lowerCamelCase					,      'rb'					,      use_auth_token=__lowerCamelCase   ) as f:
							__lowercase						,      __lowercase											=     sf.read(__lowerCamelCase   )

			else:
					__lowercase						,      __lowercase											=     sf.read(__lowerCamelCase   )

			__lowercase											=     array.T
			if self.mono:
					__lowercase											=     librosa.to_mono(__lowerCamelCase   )
			if self.sampling_rate and self.sampling_rate != sampling_rate:
					__lowercase											=     librosa.resample(__lowerCamelCase					,      orig_sr=__lowerCamelCase					,      target_sr=self.sampling_rate   )
					__lowercase											=     self.sampling_rate

			return {"path": path, "array": array, "sampling_rate": sampling_rate}




	def    UpperCAmelCase			(						self							:     int   )       -> Union["FeatureType", Dict[str, "FeatureType"]]:

			'''simple docstring'''




			from .features import Value

			if self.decode:
					raise ValueError('Cannot flatten a decoded Audio feature.'   )
			return {
			    "bytes": Value('binary'   ),
			    "path": Value('string'   ),
			}




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     Union[pa.StringArray, pa.StructArray]   )       -> pa.StructArray:

			'''simple docstring'''




			if pa.types.is_string(storage.type   ):
					__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.binary()   )
					__lowercase											=     pa.StructArray.from_arrays([bytes_array, storage]					,      ['bytes', 'path']					,      mask=storage.is_null()   )
			elif pa.types.is_binary(storage.type   ):
					__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.string()   )
					__lowercase											=     pa.StructArray.from_arrays([storage, path_array]					,      ['bytes', 'path']					,      mask=storage.is_null()   )
			elif pa.types.is_struct(storage.type   ) and storage.type.get_all_field_indices('array'   ):
					__lowercase											=     pa.array([Audio().encode_example(__lowerCamelCase   ) if x is not None else None for x in storage.to_pylist()]   )
			elif pa.types.is_struct(storage.type   ):
					if storage.type.get_field_index('bytes'   ) >= 0:
							__lowercase											=     storage.field('bytes'   )
					else:
							__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.binary()   )
					if storage.type.get_field_index('path'   ) >= 0:
							__lowercase											=     storage.field('path'   )
					else:
							__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.string()   )
					__lowercase											=     pa.StructArray.from_arrays([bytes_array, path_array]					,      ['bytes', 'path']					,      mask=storage.is_null()   )
			return array_cast(__lowerCamelCase					,      self.pa_type   )






	def    UpperCAmelCase			(						self							:     Union[str, Any]					,      __lowerCamelCase							:     pa.StructArray   )       -> pa.StructArray:

			'''simple docstring'''




			@no_op_if_value_is_null
			def path_to_bytes(__lowerCamelCase							:     Any   ):
					with xopen(__lowerCamelCase					,      'rb'   ) as f:
							__lowercase											=     f.read()
					return bytes_

			__lowercase											=     pa.array(
			    [
			        (path_to_bytes(x['path']   ) if x['bytes'] is None else x['bytes']) if x is not None else None
			        for x in storage.to_pylist()
			    ]					,      type=pa.binary()					,      )
			__lowercase											=     pa.array(
			    [os.path.basename(__lowerCamelCase   ) if path is not None else None for path in storage.field('path'   ).to_pylist()]					,      type=pa.string()					,      )
			__lowercase											=     pa.StructArray.from_arrays([bytes_array, path_array]					,      ['bytes', 'path']					,      mask=bytes_array.is_null()   )
			return array_cast(__lowerCamelCase					,      self.pa_type   )




