



from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							 =					logging.get_logger(__name__)


UpperCAmelCase							 =					{
    """nielsr/canine-s""": 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
UpperCAmelCase							 =					1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
UpperCAmelCase							 =					0

UpperCAmelCase							 =					0Xe0_00
UpperCAmelCase							 =					0Xe0_01
UpperCAmelCase							 =					0Xe0_02
UpperCAmelCase							 =					0Xe0_03
UpperCAmelCase							 =					0Xe0_04

# Maps special codepoints to human-readable names.
UpperCAmelCase							 =					{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
UpperCAmelCase							 =					{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      str         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


							def __init__(							self  ,  a_=chr(a_		)  ,  a_=chr(a_		)  ,  a_=chr(a_		)  ,  a_=chr(a_		)  ,  a_=chr(a_		)  ,  a_=chr(a_		)  ,  a_=False  ,  a_=2_048  ,  **a_  ,  ):
											a__				      =		AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_		) if isinstance(a_  ,  a_		) else bos_token
											a__				      =		AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_		) if isinstance(a_  ,  a_		) else eos_token
											a__				      =		AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_		) if isinstance(a_  ,  a_		) else sep_token
											a__				      =		AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_		) if isinstance(a_  ,  a_		) else cls_token
											a__				      =		AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_		) if isinstance(a_  ,  a_		) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											a__				      =		AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_		) if isinstance(a_  ,  a_		) else mask_token

											super().__init__(
											    bos_token=a_  ,  eos_token=a_  ,  sep_token=a_  ,  cls_token=a_  ,  pad_token=a_  ,  mask_token=a_  ,  add_prefix_space=a_  ,  model_max_length=a_  ,  **a_  ,  )

											# Creates a mapping for looking up the IDs of special symbols.
											a__				      =		{}
											for codepoint, name in SPECIAL_CODEPOINTS.items():
															a__				      =		codepoint

											# Creates a mapping for looking up the string forms of special symbol IDs.
											a__				      =		{
											    codepoint: name for name, codepoint in self._special_codepoints.items()
											}

											a__				      =		UNICODE_VOCAB_SIZE
											a__				      =		len(self._special_codepoints		)


							@property
							def _a      (							self		):
											return self._unicode_vocab_size


							def _a      (							self  ,  a_		):
											return list(a_		)


							def _a      (							self  ,  a_		):
											try:
															return ord(a_		)
											except TypeError:
															raise ValueError(F'''invalid token: \'{token}\''''		)


							def _a      (							self  ,  a_		):
											try:
															if index in SPECIAL_CODEPOINTS:
																			return SPECIAL_CODEPOINTS[index]
															return chr(a_		)
											except TypeError:
															raise ValueError(F'''invalid id: {index}'''		)


							def _a      (							self  ,  a_		):
											return "".join(a_		)


							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		[self.sep_token_id]
											a__				      =		[self.cls_token_id]

											a__				      =		cls + token_ids_a + sep
											if token_ids_a is not None:
															result += token_ids_a + sep
											return result


							def _a      (							self  ,  a_  ,  a_ = None  ,  a_ = False		):
											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=a_  ,  token_ids_a=a_  ,  already_has_special_tokens=a_		)

											a__				      =		[1] + ([0] * len(a_		)) + [1]
											if token_ids_a is not None:
															result += ([0] * len(a_		)) + [1]
											return result


							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		[self.sep_token_id]
											a__				      =		[self.cls_token_id]

											a__				      =		len(cls + token_ids_a + sep		) * [0]
											if token_ids_a is not None:
															result += len(token_ids_a + sep		) * [1]
											return result







							def _a      (							self  ,  a_  ,  a_ = None		):
											return ()






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






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


							@property
							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		UNetaDModel(
											    sample_size=(32, 64)  ,  in_channels=1  ,  out_channels=1  ,  layers_per_block=2  ,  block_out_channels=(128, 128)  ,  down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")  ,  up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")  ,  )
											return model


							@property
							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		UNetaDConditionModel(
											    sample_size=(64, 32)  ,  in_channels=1  ,  out_channels=1  ,  layers_per_block=2  ,  block_out_channels=(128, 128)  ,  down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")  ,  up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")  ,  cross_attention_dim=10  ,  )
											return model


							@property
							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		AutoencoderKL(
											    sample_size=(128, 64)  ,  in_channels=1  ,  out_channels=1  ,  latent_channels=1  ,  layers_per_block=2  ,  block_out_channels=(128, 128)  ,  down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")  ,  up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")  ,  )
											a__				      =		UNetaDModel(
											    sample_size=(64, 32)  ,  in_channels=1  ,  out_channels=1  ,  layers_per_block=2  ,  block_out_channels=(128, 128)  ,  down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")  ,  up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")  ,  )
											return vqvae, unet







							@slow
							def _a      (							self		):
											a__				      =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											a__				      =		Mel(
											    x_res=self.dummy_unet.config.sample_size[1]  ,  y_res=self.dummy_unet.config.sample_size[0]  ,  )

											a__				      =		DDPMScheduler()
											a__				      =		AudioDiffusionPipeline(vqvae=a_  ,  unet=self.dummy_unet  ,  mel=a_  ,  scheduler=a_		)
											a__				      =		pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											a__				      =		torch.Generator(device=a_		).manual_seed(42		)
											a__				      =		pipe(generator=a_  ,  steps=4		)
											a__				      =		output.audios[0]
											a__				      =		output.images[0]

											a__				      =		torch.Generator(device=a_		).manual_seed(42		)
											a__				      =		pipe(generator=a_  ,  steps=4  ,  return_dict=a_		)
											a__				      =		output[0][0]

											assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
											assert (
											    image.height == self.dummy_unet.config.sample_size[0]
											    and image.width == self.dummy_unet.config.sample_size[1]
											)
											a__				      =		np.frombuffer(image.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.frombuffer(image_from_tuple.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() == 0
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() == 0

											a__				      =		Mel(
											    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]  ,  y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]  ,  )

											a__				      =		DDIMScheduler()
											a__				      =		self.dummy_vqvae_and_unet
											a__				      =		AudioDiffusionPipeline(
											    vqvae=self.dummy_vqvae_and_unet[0]  ,  unet=dummy_vqvae_and_unet[1]  ,  mel=a_  ,  scheduler=a_		)
											a__				      =		pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											np.random.seed(0		)
											a__				      =		np.random.uniform(-1  ,  1  ,  ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)		)
											a__				      =		torch.Generator(device=a_		).manual_seed(42		)
											a__				      =		pipe(raw_audio=a_  ,  generator=a_  ,  start_step=5  ,  steps=10		)
											a__				      =		output.images[0]

											assert (
											    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
											    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
											)
											a__				      =		np.frombuffer(image.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() == 0

											a__				      =		self.dummy_unet_condition
											a__				      =		AudioDiffusionPipeline(
											    vqvae=self.dummy_vqvae_and_unet[0]  ,  unet=a_  ,  mel=a_  ,  scheduler=a_		)
											a__				      =		pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											np.random.seed(0		)
											a__				      =		torch.rand((1, 1, 10)		)
											a__				      =		pipe(generator=a_  ,  encoding=a_		)
											a__				      =		output.images[0]
											a__				      =		np.frombuffer(image.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() == 0




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







							'''simple docstring'''






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







							def _a      (							self		):
											a__				      =		torch_device

											a__				      =		DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256"""		)
											a__				      =		pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											a__				      =		torch.Generator(device=a_		).manual_seed(42		)
											a__				      =		pipe(generator=a_		)
											a__				      =		output.audios[0]
											a__				      =		output.images[0]

											assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
											assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
											a__				      =		np.frombuffer(image.tobytes()  ,  dtype="""uint8"""		)[:10]
											a__				      =		np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]		)

											assert np.abs(image_slice.flatten() - expected_slice		).max() == 0

