
from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)







class 							lowercase						(   _SCREAMING_SNAKE_CASE						):




							def __init__(							self  ,A__  ,A__  ,A__  ,A__  ,A__  ,A__ = 0.93  ,A__ = True  ,):
												lowercase		      =		mixing_coeff
												lowercase		      =		leapfrog_steps

												super().__init__()
												self.register_modules(
												    vae=_SCREAMING_SNAKE_CASE  ,text_encoder=_SCREAMING_SNAKE_CASE  ,tokenizer=_SCREAMING_SNAKE_CASE  ,unet=_SCREAMING_SNAKE_CASE  ,scheduler=_SCREAMING_SNAKE_CASE  ,)

												lowercase		      =		2 ** (len(self.vae.config.block_out_channels) - 1)
												lowercase		      =		VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)




							def 					A__ (							self  ,A__  ,A__ = None  ,A__ = False):
												lowercase		      =		self.tokenizer(
												    _SCREAMING_SNAKE_CASE  ,padding='''max_length'''  ,max_length=self.tokenizer.model_max_length  ,truncation=_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''  ,)

												lowercase		      =		self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state

												lowercase		      =		prompt_embeds.to(dtype=self.text_encoder.dtype  ,device=self.device)

												if do_classifier_free_guidance:
																	lowercase		      =		'''''' if negative_prompt is None else negative_prompt

																	lowercase		      =		self.tokenizer(
																	    _SCREAMING_SNAKE_CASE  ,padding='''max_length'''  ,max_length=self.tokenizer.model_max_length  ,truncation=_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''  ,)

																	lowercase		      =		self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state

																	lowercase		      =		torch.cat([negative_prompt_embeds, prompt_embeds])

												return prompt_embeds




							def 					A__ (							self  ,A__  ,A__):
												lowercase		      =		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
												lowercase		      =		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

												return [x, y]




							def 					A__ (							self  ,A__  ,A__):
												lowercase		      =		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
												lowercase		      =		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

												return [x, y]




							def 					A__ (							self  ,A__):
												# as self.alphas_cumprod is always in cpu
												lowercase		      =		int(_SCREAMING_SNAKE_CASE)

												lowercase		      =		self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

												return alpha_prod, 1 - alpha_prod




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,):
												lowercase		      =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

												lowercase      ,		lowercase		      =		self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE)
												lowercase      ,		lowercase		      =		self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE)

												lowercase		      =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
												lowercase		      =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

												lowercase		      =		(base - b_t * model_output) / a_t

												return model_input, next_model_input.to(base.dtype)




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,):
												lowercase		      =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

												lowercase      ,		lowercase		      =		self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE)
												lowercase      ,		lowercase		      =		self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE)

												lowercase		      =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
												lowercase		      =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
												lowercase		      =		a_t * base + b_t * model_output

												return model_input, next_model_input.to(base.dtype)




							@torch.no_grad()
							def 					A__ (							self  ,A__):
												lowercase		      =		1 / self.vae.config.scaling_factor * latents
												lowercase		      =		self.vae.decode(_SCREAMING_SNAKE_CASE).sample
												lowercase		      =		(image / 2 + 0.5).clamp(0  ,1)
												return image




							@torch.no_grad()
							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__  ,A__ = None  ,):
												lowercase		      =		guidance_scale > 1.0

												lowercase		      =		image.to(device=self.device  ,dtype=text_embeds.dtype)
												lowercase		      =		self.vae.encode(_SCREAMING_SNAKE_CASE).latent_dist.sample(_SCREAMING_SNAKE_CASE)

												lowercase		      =		self.vae.config.scaling_factor * latent

												lowercase		      =		[latent.clone(), latent.clone()]

												for i, t in tqdm(enumerate(_SCREAMING_SNAKE_CASE)  ,total=len(_SCREAMING_SNAKE_CASE)):
																	lowercase		      =		self.noise_mixing_layer(x=coupled_latents[0]  ,y=coupled_latents[1])

																	# j - model_input index, k - base index
																	for j in range(2):
																						lowercase		      =		j ^ 1

																						if self.leapfrog_steps:
																											if i % 2 == 0:
																																lowercase      ,		lowercase		      =		j, k

																						lowercase		      =		coupled_latents[j]
																						lowercase		      =		coupled_latents[k]

																						lowercase		      =		torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

																						lowercase		      =		self.unet(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,encoder_hidden_states=_SCREAMING_SNAKE_CASE).sample

																						if do_classifier_free_guidance:
																											lowercase      ,		lowercase		      =		noise_pred.chunk(2)
																											lowercase		      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																						lowercase      ,		lowercase		      =		self.noise_step(
																						    base=_SCREAMING_SNAKE_CASE  ,model_input=_SCREAMING_SNAKE_CASE  ,model_output=_SCREAMING_SNAKE_CASE  ,timestep=_SCREAMING_SNAKE_CASE  ,)

																						lowercase		      =		model_input

												return coupled_latents





							@torch.no_grad()
							def __call__(							self  ,A__  ,A__  ,A__  ,A__ = 3.0  ,A__ = 5_0  ,A__ = 0.8  ,A__ = None  ,A__ = None  ,A__ = "pil"  ,):
												lowercase		      =		guidance_scale > 1.0

												lowercase		      =		self.image_processor.preprocess(_SCREAMING_SNAKE_CASE)

												lowercase		      =		self._encode_prompt(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE)
												lowercase		      =		self._encode_prompt(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE)

												self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  ,self.device)

												lowercase		      =		num_inference_steps - int(num_inference_steps * strength)
												lowercase		      =		self.scheduler.timesteps[t_limit:]
												lowercase		      =		fwd_timesteps.flip(0)

												lowercase		      =		self.prepare_latents(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE)

												for i, t in tqdm(enumerate(_SCREAMING_SNAKE_CASE)  ,total=len(_SCREAMING_SNAKE_CASE)):
																	# j - model_input index, k - base index
																	for k in range(2):
																						lowercase		      =		k ^ 1

																						if self.leapfrog_steps:
																											if i % 2 == 1:
																																lowercase      ,		lowercase		      =		j, k

																						lowercase		      =		coupled_latents[j]
																						lowercase		      =		coupled_latents[k]

																						lowercase		      =		torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

																						lowercase		      =		self.unet(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,encoder_hidden_states=_SCREAMING_SNAKE_CASE).sample

																						if do_classifier_free_guidance:
																											lowercase      ,		lowercase		      =		noise_pred.chunk(2)
																											lowercase		      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																						lowercase      ,		lowercase		      =		self.denoise_step(
																						    base=_SCREAMING_SNAKE_CASE  ,model_input=_SCREAMING_SNAKE_CASE  ,model_output=_SCREAMING_SNAKE_CASE  ,timestep=_SCREAMING_SNAKE_CASE  ,)

																						lowercase		      =		model_input

																	lowercase		      =		self.denoise_mixing_layer(x=coupled_latents[0]  ,y=coupled_latents[1])

												# either one is fine
												lowercase		      =		coupled_latents[0]

												if output_type not in ["latent", "pt", "np", "pil"]:
																	lowercase		      =		(
																	    f'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
																	    '''`pil`, `np`, `pt`, `latent`'''
																	)
																	deprecate('''Unsupported output_type'''  ,'''1.0.0'''  ,_SCREAMING_SNAKE_CASE  ,standard_warn=_SCREAMING_SNAKE_CASE)
																	lowercase		      =		'''np'''

												if output_type == "latent":
																	lowercase		      =		final_latent
												else:
																	lowercase		      =		self.decode_latents(_SCREAMING_SNAKE_CASE)
																	lowercase		      =		self.image_processor.postprocess(_SCREAMING_SNAKE_CASE  ,output_type=_SCREAMING_SNAKE_CASE)

												return image








import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
			pass

if is_speech_available():
			from transformers import EncodecFeatureExtractor, MusicgenProcessor


_A							     =       random.Random()

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]     ,					UpperCamelCase		:      Any=1.0     ,					UpperCamelCase		:      Union[str, Any]=None     ,					UpperCamelCase		:      Union[str, Any]=None	)     ->	List[Any]:



	"""simple docstring"""






	if rng is None:
		a_     			=   global_rng

	a_     			=   []
	for batch_idx in range(shape[0]	):
		values.append([]	)
		for _ in range(shape[1]	):
			values[-1].append(rng.random() * scale	)

	return values

@require_torch
@require_sentencepiece
class 			lowerCamelCase_					(    unittest.TestCase    ):





							def   __magic_name__      (							self    ):
								a_     			=   """facebook/musicgen-small"""
								a_     			=   tempfile.mkdtemp()





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return TaTokenizer.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       **_SCREAMING_SNAKE_CASE    ):
								return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,       **_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								shutil.rmtree(self.tmpdirname    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_tokenizer()
								a_     			=   self.get_feature_extractor()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								processor.save_pretrained(self.tmpdirname    )
								a_     			=   MusicgenProcessor.from_pretrained(self.tmpdirname    )

								self.assertEqual(processor.tokenizer.get_vocab()			,       tokenizer.get_vocab()    )
								self.assertIsInstance(processor.tokenizer			,       _SCREAMING_SNAKE_CASE    )

								self.assertEqual(processor.feature_extractor.to_json_string()			,       feature_extractor.to_json_string()    )
								self.assertIsInstance(processor.feature_extractor			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   MusicgenProcessor(tokenizer=self.get_tokenizer()			,       feature_extractor=self.get_feature_extractor()    )
								processor.save_pretrained(self.tmpdirname    )

								a_     			=   self.get_tokenizer(bos_token="""(BOS)"""			,       eos_token="""(EOS)"""    )
								a_     			=   self.get_feature_extractor(do_normalize=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								a_     			=   MusicgenProcessor.from_pretrained(
								    self.tmpdirname			,       bos_token="""(BOS)"""			,       eos_token="""(EOS)"""			,       do_normalize=_SCREAMING_SNAKE_CASE			,       padding_value=1.0    )

								self.assertEqual(processor.tokenizer.get_vocab()			,       tokenizer_add_kwargs.get_vocab()    )
								self.assertIsInstance(processor.tokenizer			,       _SCREAMING_SNAKE_CASE    )

								self.assertEqual(processor.feature_extractor.to_json_string()			,       feature_extractor_add_kwargs.to_json_string()    )
								self.assertIsInstance(processor.feature_extractor			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   floats_list((3, 1000)    )

								a_     			=   feature_extractor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )
								a_     			=   processor(_SCREAMING_SNAKE_CASE			,       return_tensors="""np"""    )

								for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()			,       input_processor[key].sum()			,       delta=1E-2    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   """This is a test string"""

								a_     			=   processor(text=_SCREAMING_SNAKE_CASE    )

								a_     			=   tokenizer(_SCREAMING_SNAKE_CASE    )

								for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]			,       encoded_processor[key]    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								a_     			=   processor.batch_decode(sequences=_SCREAMING_SNAKE_CASE    )
								a_     			=   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    )

								self.assertListEqual(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor()
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								self.assertListEqual(
								    processor.model_input_names			,       feature_extractor.model_input_names			,       msg="""`processor` and `feature_extractor` model input names do not match"""			,       )





							def   __magic_name__      (							self    ):
								a_     			=   self.get_feature_extractor(padding_side="""left"""    )
								a_     			=   self.get_tokenizer()

								a_     			=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,       feature_extractor=_SCREAMING_SNAKE_CASE    )

								a_     			=   [floats_list((1, x)    )[0] for x in range(5			,       20			,       5    )]
								a_     			=   processor(_SCREAMING_SNAKE_CASE    ).padding_mask

								a_     			=   np.asarray(floats_list((3, 20)    )    )[:, None, :]
								a_     			=   processor.batch_decode(_SCREAMING_SNAKE_CASE			,       padding_mask=_SCREAMING_SNAKE_CASE    )

								self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )

								for audio in decoded_audios:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       np.ndarray    )

								self.assertTrue(decoded_audios[0].shape == (1, 10)    )
								self.assertTrue(decoded_audios[1].shape == (1, 15)    )
								self.assertTrue(decoded_audios[2].shape == (1, 20)    )