


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 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		int						)		->  Union[str, Any]:




							"""simple docstring"""


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

					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  List[Any]:




							"""simple docstring"""


							torch.manual_seed(0						)
							lowercase__	     =						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     lowerCamelCase__	(self       :		List[Any]						)		->  List[str]:




							"""simple docstring"""


							torch.manual_seed(0						)
							lowercase__	     =						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     lowerCamelCase__	(self       :		Dict						)		->  Any:




							"""simple docstring"""


							torch.manual_seed(0						)
							lowercase__	     =						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""") ,   )
							lowercase__	     =						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     lowerCamelCase__	(self       :		Optional[int]						)		->  Any:




							"""simple docstring"""


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

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

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

							lowercase__	     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(42						)
							lowercase__	     =						pipe(generator=_UpperCAmelCase ,   steps=4 ,   return_dict=_UpperCAmelCase						)
							lowercase__	     =						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]
							)
							lowercase__	     =						np.frombuffer(image.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						np.frombuffer(image_from_tuple.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						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

							lowercase__	     =						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] ,   )

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

							np.random.seed(0						)
							lowercase__	     =						np.random.uniform(-1 ,   1 ,   ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)						)
							lowercase__	     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(42						)
							lowercase__	     =						pipe(raw_audio=_UpperCAmelCase ,   generator=_UpperCAmelCase ,   start_step=5 ,   steps=10						)
							lowercase__	     =						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]
							)
							lowercase__	     =						np.frombuffer(image.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]						)

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

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

							np.random.seed(0						)
							lowercase__	     =						torch.rand((1, 1, 10)						)
							lowercase__	     =						pipe(generator=_UpperCAmelCase ,   encoding=_UpperCAmelCase						)
							lowercase__	     =						output.images[0]
							lowercase__	     =						np.frombuffer(image.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						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 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  List[str]:




							"""simple docstring"""


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


					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						torch_device

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

							lowercase__	     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(42						)
							lowercase__	     =						pipe(generator=_UpperCAmelCase						)
							lowercase__	     =						output.audios[0]
							lowercase__	     =						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]
							lowercase__	     =						np.frombuffer(image.tobytes() ,   dtype="""uint8"""						)[:10]
							lowercase__	     =						np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]						)

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



"""simple docstring"""




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 			snake_case  (				__lowercase							,      unittest.TestCase ):
							UpperCAmelCase__      =   None
							UpperCAmelCase__      =   BloomTokenizerFast
							UpperCAmelCase__      =   BloomTokenizerFast
							UpperCAmelCase__      =   True
							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   '''tokenizer_file'''
							UpperCAmelCase__      =   {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}

							def _lowercase       (self     ):

									"""simple docstring"""







									super().setUp()
									SCREAMING_SNAKE_CASE_    			=			BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''     )
									tokenizer.save_pretrained(self.tmpdirname     )

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map     )
									return BloomTokenizerFast.from_pretrained(self.tmpdirname							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE_    			=			['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
									SCREAMING_SNAKE_CASE_    			=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

									SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_     )['''input_ids''']
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_=6     ):

									"""simple docstring"""







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
													SCREAMING_SNAKE_CASE_    			=			self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
													# tokenizer_r.pad_token = None # Hotfixing padding = None
													# Simple input
													SCREAMING_SNAKE_CASE_    			=			'''This is a simple input'''
													SCREAMING_SNAKE_CASE_    			=			['''This is a simple input 1''', '''This is a simple input 2''']
													SCREAMING_SNAKE_CASE_    			=			('''This is a simple input''', '''This is a pair''')
													SCREAMING_SNAKE_CASE_    			=			[
													    ('''This is a simple input 1''', '''This is a simple input 2'''),
													    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
													]

													# Simple input tests
													try:
															tokenizer_r.encode(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )

															tokenizer_r.batch_encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.encode(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.batch_encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
													except ValueError:
															self.fail('''Bloom Tokenizer should be able to deal with padding'''     )

													SCREAMING_SNAKE_CASE_    			=			None  # Hotfixing padding = None
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Simple input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Simple input
													self.assertRaises(
													    SCREAMING_SNAKE_CASE_							,							tokenizer_r.batch_encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							)

													# Pair input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Pair input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Pair input
													self.assertRaises(
													    SCREAMING_SNAKE_CASE_							,							tokenizer_r.batch_encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							)

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE_    			=			load_dataset('''xnli'''							,							'''all_languages'''							,							split='''test'''							,							streaming=SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			next(iter(SCREAMING_SNAKE_CASE_     )     )['''premise''']  # pick up one data
									SCREAMING_SNAKE_CASE_    			=			list(sample_data.values()     )

									SCREAMING_SNAKE_CASE_    			=			list(map(tokenizer.encode							,							SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			[tokenizer.decode(SCREAMING_SNAKE_CASE_							,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_     ) for x in output_tokens]
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self     ):

									"""simple docstring"""







									self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )							,							1     )
									self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )							,							1     )