
"""simple docstring"""





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


__magic_name__,							__magic_name__,							__magic_name__       =			False, False, False
@dataclass
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  Optional[int]						   =		None
		__lowercase							:  bool						   =		True
		__lowercase							:  bool						   =		True
		__lowercase							:  Optional[str]						   =		None
		# Automatically constructed
		__lowercase							:  ClassVar[str]						   =		"dict"
		__lowercase							:  ClassVar[Any]						   =		pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
		__lowercase							:  str						   =		field(default='''Audio'''     ,				init=__a     ,				repr=__a )




		def __call__(     self):
				return self.pa_type




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				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
						__SCREAMING_SNAKE_CASE            = 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!)
										__SCREAMING_SNAKE_CASE            = np.frombuffer(value["""bytes"""]							,  dtype=np.intaa).astype(np.floataa) / 3_2_7_6_7
								else:
										__SCREAMING_SNAKE_CASE            = np.memmap(value["""path"""]							,  dtype="""h"""							,  mode="""r""").astype(np.floataa) / 3_2_7_6_7

								__SCREAMING_SNAKE_CASE            = 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 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None):
				if not self.decode:
						raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead.""")

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = (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

				__SCREAMING_SNAKE_CASE            = 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:
						__SCREAMING_SNAKE_CASE            = token_per_repo_id or {}
						__SCREAMING_SNAKE_CASE            = path.split("""::""")[-1]
						try:
								__SCREAMING_SNAKE_CASE            = string_to_dict(lowerCAmelCase__							,  config.HUB_DATASETS_URL)["""repo_id"""]
								__SCREAMING_SNAKE_CASE            = token_per_repo_id[repo_id]
						except (ValueError, KeyError):
								__SCREAMING_SNAKE_CASE            = None

						with xopen(lowerCAmelCase__							,  """rb"""							,  use_auth_token=lowerCAmelCase__) as f:
								__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = sf.read(lowerCAmelCase__)

				else:
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = sf.read(lowerCAmelCase__)

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

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




		def 	snake_case_     (     self):
				from .features import Value

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




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				if pa.types.is_string(storage.type):
						__SCREAMING_SNAKE_CASE            = pa.array([None] * len(lowerCAmelCase__)							,  type=pa.binary())
						__SCREAMING_SNAKE_CASE            = pa.StructArray.from_arrays([bytes_array, storage]							,  ["""bytes""", """path"""]							,  mask=storage.is_null())
				elif pa.types.is_binary(storage.type):
						__SCREAMING_SNAKE_CASE            = pa.array([None] * len(lowerCAmelCase__)							,  type=pa.string())
						__SCREAMING_SNAKE_CASE            = 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"""):
						__SCREAMING_SNAKE_CASE            = 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:
								__SCREAMING_SNAKE_CASE            = storage.field("""bytes""")
						else:
								__SCREAMING_SNAKE_CASE            = pa.array([None] * len(lowerCAmelCase__)							,  type=pa.binary())
						if storage.type.get_field_index("""path""") >= 0:
								__SCREAMING_SNAKE_CASE            = storage.field("""path""")
						else:
								__SCREAMING_SNAKE_CASE            = pa.array([None] * len(lowerCAmelCase__)							,  type=pa.string())
						__SCREAMING_SNAKE_CASE            = pa.StructArray.from_arrays([bytes_array, path_array]							,  ["""bytes""", """path"""]							,  mask=storage.is_null())
				return array_cast(lowerCAmelCase__							,  self.pa_type)




		def 	snake_case_     (     self							,  lowerCAmelCase__):

				@no_op_if_value_is_null
				def path_to_bytes(lowerCAmelCase__):
						with xopen(lowerCAmelCase__							,  """rb""") as f:
								__SCREAMING_SNAKE_CASE            = f.read()
						return bytes_

				__SCREAMING_SNAKE_CASE            = 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()							,  )
				__SCREAMING_SNAKE_CASE            = pa.array(
				    [os.path.basename(lowerCAmelCase__) if path is not None else None for path in storage.field("""path""").to_pylist()]							,  type=pa.string()							,  )
				__SCREAMING_SNAKE_CASE            = pa.StructArray.from_arrays([bytes_array, path_array]							,  ["""bytes""", """path"""]							,  mask=bytes_array.is_null())
				return array_cast(lowerCAmelCase__							,  self.pa_type)






"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		KandinskyVaaImgaImgPipeline
		__lowercase							:  str						   =		['''image_embeds''', '''negative_image_embeds''', '''image''']
		__lowercase							:  Dict						   =		[
		    '''image_embeds''',
		    '''negative_image_embeds''',
		    '''image''',
		]
		__lowercase							:  Tuple						   =		[
		    '''generator''',
		    '''height''',
		    '''width''',
		    '''strength''',
		    '''guidance_scale''',
		    '''num_inference_steps''',
		    '''return_dict''',
		    '''guidance_scale''',
		    '''num_images_per_prompt''',
		    '''output_type''',
		    '''return_dict''',
		]
		__lowercase							:  Tuple						   =		False




		@property
		def 	snake_case_     (     self):
				return 3_2




		@property
		def 	snake_case_     (     self):
				return 3_2




		@property
		def 	snake_case_     (     self):
				return self.time_input_dim




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




		@property
		def 	snake_case_     (     self):
				return 1_0_0




		@property
		def 	snake_case_     (     self):
				torch.manual_seed(0)

				__SCREAMING_SNAKE_CASE            = {
				    """in_channels""": 4,
				    # Out channels is double in channels because predicts mean and variance
				    """out_channels""": 8,
				    """addition_embed_type""": """image""",
				    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
				    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
				    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
				    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    """layers_per_block""": 1,
				    """encoder_hid_dim""": self.text_embedder_hidden_size,
				    """encoder_hid_dim_type""": """image_proj""",
				    """cross_attention_dim""": self.cross_attention_dim,
				    """attention_head_dim""": 4,
				    """resnet_time_scale_shift""": """scale_shift""",
				    """class_embed_type""": None,
				}

				__SCREAMING_SNAKE_CASE            = UNetaDConditionModel(**lowerCAmelCase__)
				return model




		@property
		def 	snake_case_     (     self):
				return {
				    "block_out_channels": [3_2, 6_4],
				    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 1_2,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}




		@property
		def 	snake_case_     (     self):
				torch.manual_seed(0)
				__SCREAMING_SNAKE_CASE            = VQModel(**self.dummy_movq_kwargs)
				return model




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.dummy_unet
				__SCREAMING_SNAKE_CASE            = self.dummy_movq

				__SCREAMING_SNAKE_CASE            = {
				    """num_train_timesteps""": 1_0_0_0,
				    """beta_schedule""": """linear""",
				    """beta_start""": 0.0_00_85,
				    """beta_end""": 0.0_12,
				    """clip_sample""": False,
				    """set_alpha_to_one""": False,
				    """steps_offset""": 0,
				    """prediction_type""": """epsilon""",
				    """thresholding""": False,
				}

				__SCREAMING_SNAKE_CASE            = DDIMScheduler(**lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = {
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """movq""": movq,
				}

				return components




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=0):
				__SCREAMING_SNAKE_CASE            = floats_tensor((1, self.text_embedder_hidden_size)							,  rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = floats_tensor((1, self.text_embedder_hidden_size)							,  rng=random.Random(seed + 1)).to(
				    lowerCAmelCase__)
				# create init_image
				__SCREAMING_SNAKE_CASE            = floats_tensor((1, 3, 6_4, 6_4)							,  rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = image.cpu().permute(0							,  2							,  3							,  1)[0]
				__SCREAMING_SNAKE_CASE            = Image.fromarray(np.uinta(lowerCAmelCase__)).convert("""RGB""").resize((2_5_6, 2_5_6))

				if str(lowerCAmelCase__).startswith("""mps"""):
						__SCREAMING_SNAKE_CASE            = torch.manual_seed(lowerCAmelCase__)
				else:
						__SCREAMING_SNAKE_CASE            = torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = {
				    """image""": init_image,
				    """image_embeds""": image_embeds,
				    """negative_image_embeds""": negative_image_embeds,
				    """generator""": generator,
				    """height""": 6_4,
				    """width""": 6_4,
				    """num_inference_steps""": 1_0,
				    """guidance_scale""": 7.0,
				    """strength""": 0.2,
				    """output_type""": """np""",
				}
				return inputs




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """cpu"""

				__SCREAMING_SNAKE_CASE            = self.get_dummy_components()

				__SCREAMING_SNAKE_CASE            = self.pipeline_class(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = pipe.to(lowerCAmelCase__)

				pipe.set_progress_bar_config(disable=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = pipe(**self.get_dummy_inputs(lowerCAmelCase__))
				__SCREAMING_SNAKE_CASE            = output.images

				__SCREAMING_SNAKE_CASE            = pipe(
				    **self.get_dummy_inputs(lowerCAmelCase__)							,  return_dict=lowerCAmelCase__							,  )[0]

				__SCREAMING_SNAKE_CASE            = image[0, -3:, -3:, -1]
				__SCREAMING_SNAKE_CASE            = image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 6_4, 6_4, 3)

				__SCREAMING_SNAKE_CASE            = np.array(
				    [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63])
				assert (
				    np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
				), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
				), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"



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




		"""simple docstring"""





		def 	snake_case_     (     self):
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/kandinskyv22/kandinskyv22_img2img_frog.npy""")

				__SCREAMING_SNAKE_CASE            = load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
				__SCREAMING_SNAKE_CASE            = """A red cartoon frog, 4k"""

				__SCREAMING_SNAKE_CASE            = KandinskyVaaPriorPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-prior"""							,  torch_dtype=torch.floataa)
				pipe_prior.to(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = KandinskyVaaImgaImgPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-2-decoder"""							,  torch_dtype=torch.floataa)
				__SCREAMING_SNAKE_CASE            = pipeline.to(lowerCAmelCase__)

				pipeline.set_progress_bar_config(disable=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = torch.Generator(device="""cpu""").manual_seed(0)
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = pipe_prior(
				    lowerCAmelCase__							,  generator=lowerCAmelCase__							,  num_inference_steps=5							,  negative_prompt=""""""							,  ).to_tuple()

				__SCREAMING_SNAKE_CASE            = pipeline(
				    image=lowerCAmelCase__							,  image_embeds=lowerCAmelCase__							,  negative_image_embeds=lowerCAmelCase__							,  generator=lowerCAmelCase__							,  num_inference_steps=1_0_0							,  height=7_6_8							,  width=7_6_8							,  strength=0.2							,  output_type="""np"""							,  )

				__SCREAMING_SNAKE_CASE            = output.images[0]

				assert image.shape == (7_6_8, 7_6_8, 3)

				assert_mean_pixel_difference(lowerCAmelCase__							,  lowerCAmelCase__)




