




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_snake_case            = logging.get_logger(__name__)  # pylint: disable=invalid-name







class 		lowercase							(     UpperCamelCase__	):

					def __init__(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  )		->							Tuple:
											super().__init__()

											if safety_checker is None:
																	logger.warning(
																	    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
																	    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
																	    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
																	    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
																	    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
																	    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""       )

											self.register_modules(
											    speech_model=_a	,  speech_processor=_a	,  vae=_a	,  text_encoder=_a	,  tokenizer=_a	,  unet=_a	,  scheduler=_a	,  feature_extractor=_a	,  )

					def       a__			(						self	,  _a = "auto"       )		->							List[Any]:
											if slice_size == "auto":
																	_A							:  Any    =		self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_a       )

					def       a__			(						self       )		->							Tuple:
											self.enable_attention_slicing(_a       )



					@torch.no_grad()
					def __call__(						self	,  _a	,  _a=1_6000	,  _a = 512	,  _a = 512	,  _a = 50	,  _a = 7.5	,  _a = None	,  _a = 1	,  _a = 0.0	,  _a = None	,  _a = None	,  _a = "pil"	,  _a = True	,  _a = None	,  _a = 1	,  **_a	,  )		->							int:
											_A							:  Dict    =		self.speech_processor.feature_extractor(
											    _a	,  return_tensors="""pt"""	,  sampling_rate=_a       ).input_features.to(self.device       )
											_A							:  Optional[int]    =		self.speech_model.generate(_a	,  max_length=48_0000       )

											_A							:  Union[str, Any]    =		self.speech_processor.tokenizer.batch_decode(_a	,  skip_special_tokens=_a	,  normalize=_a       )[
											    0
											]

											if isinstance(_a	,  _a       ):
																	_A							:  Any    =		1
											elif isinstance(_a	,  _a       ):
																	_A							:  Optional[int]    =		len(_a       )
											else:
																	raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_a       )}'''       )

											if height % 8 != 0 or width % 8 != 0:
																	raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''       )

											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(_a	,  _a       ) or callback_steps <= 0)
											):
																	raise ValueError(
																	    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																	    F''' {type(_a       )}.'''       )

											# get prompt text embeddings
											_A							:  List[Any]    =		self.tokenizer(
											    _a	,  padding="""max_length"""	,  max_length=self.tokenizer.model_max_length	,  return_tensors="""pt"""	,  )
											_A							:  Union[str, Any]    =		text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	_A							:  List[str]    =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
																	logger.warning(
																	    """The following part of your input was truncated because CLIP can only handle sequences up to"""
																	    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''       )
																	_A							:  str    =		text_input_ids[:, : self.tokenizer.model_max_length]
											_A							:  Optional[int]    =		self.text_encoder(text_input_ids.to(self.device       )       )[0]

											# duplicate text embeddings for each generation per prompt, using mps friendly method
											_A							:  List[Any]    =		text_embeddings.shape
											_A							:  Tuple    =		text_embeddings.repeat(1	,  _a	,  1       )
											_A							:  Union[str, Any]    =		text_embeddings.view(bs_embed * num_images_per_prompt	,  _a	,  -1       )

											# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
											# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
											# corresponds to doing no classifier free guidance.
											_A							:  Any    =		guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
																	_A							:  List[str]
																	if negative_prompt is None:
																							_A							:  List[Any]    =		[""""""] * batch_size
																	elif type(_a       ) is not type(_a       ):
																							raise TypeError(
																							    F'''`negative_prompt` should be the same type to `prompt`, but got {type(_a       )} !='''
																							    F''' {type(_a       )}.'''       )
																	elif isinstance(_a	,  _a       ):
																							_A							:  str    =		[negative_prompt]
																	elif batch_size != len(_a       ):
																							raise ValueError(
																							    F'''`negative_prompt`: {negative_prompt} has batch size {len(_a       )}, but `prompt`:'''
																							    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																							    """ the batch size of `prompt`."""       )
																	else:
																							_A							:  Optional[int]    =		negative_prompt

																	_A							:  Tuple    =		text_input_ids.shape[-1]
																	_A							:  Dict    =		self.tokenizer(
																	    _a	,  padding="""max_length"""	,  max_length=_a	,  truncation=_a	,  return_tensors="""pt"""	,  )
																	_A							:  int    =		self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	_A							:  Optional[Any]    =		uncond_embeddings.shape[1]
																	_A							:  Dict    =		uncond_embeddings.repeat(1	,  _a	,  1       )
																	_A							:  str    =		uncond_embeddings.view(batch_size * num_images_per_prompt	,  _a	,  -1       )

																	# For classifier free guidance, we need to do two forward passes.
																	# Here we concatenate the unconditional and text embeddings into a single batch
																	# to avoid doing two forward passes
																	_A							:  Union[str, Any]    =		torch.cat([uncond_embeddings, text_embeddings]       )

											# get the initial random noise unless the user supplied it

											# Unlike in other pipelines, latents need to be generated in the target device
											# for 1-to-1 results reproducibility with the CompVis implementation.
											# However this currently doesn't work in `mps`.
											_A							:  Union[str, Any]    =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
											_A							:  Optional[Any]    =		text_embeddings.dtype
											if latents is None:
																	if self.device.type == "mps":
																							# randn does not exist on mps
																							_A							:  List[str]    =		torch.randn(_a	,  generator=_a	,  device="""cpu"""	,  dtype=_a       ).to(
																							    self.device       )
																	else:
																							_A							:  Dict    =		torch.randn(_a	,  generator=_a	,  device=self.device	,  dtype=_a       )
											else:
																	if latents.shape != latents_shape:
																							raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''       )
																	_A							:  Any    =		latents.to(self.device       )

											# set timesteps
											self.scheduler.set_timesteps(_a       )

											# Some schedulers like PNDM have timesteps as arrays
											# It's more optimized to move all timesteps to correct device beforehand
											_A							:  int    =		self.scheduler.timesteps.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											_A							:  Union[str, Any]    =		latents * self.scheduler.init_noise_sigma

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
											# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
											# and should be between [0, 1]
											_A							:  List[str]    =		"""eta""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
											_A							:  Optional[int]    =		{}
											if accepts_eta:
																	_A							:  Dict    =		eta

											for i, t in enumerate(self.progress_bar(_a       )       ):
																	# expand the latents if we are doing classifier free guidance
																	_A							:  List[str]    =		torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
																	_A							:  Dict    =		self.scheduler.scale_model_input(_a	,  _a       )

																	# predict the noise residual
																	_A							:  Optional[Any]    =		self.unet(_a	,  _a	,  encoder_hidden_states=_a       ).sample

																	# perform guidance
																	if do_classifier_free_guidance:
																							_A							:  Dict    =		noise_pred.chunk(2       )
																							_A							:  Optional[Any]    =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																	# compute the previous noisy sample x_t -> x_t-1
																	_A							:  str    =		self.scheduler.step(_a	,  _a	,  _a	,  **_a       ).prev_sample

																	# call the callback, if provided
																	if callback is not None and i % callback_steps == 0:
																							callback(_a	,  _a	,  _a       )

											_A							:  List[str]    =		1 / 0.18215 * latents
											_A							:  List[str]    =		self.vae.decode(_a       ).sample

											_A							:  int    =		(image / 2 + 0.5).clamp(0	,  1       )

											# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
											_A							:  int    =		image.cpu().permute(0	,  2	,  3	,  1       ).float().numpy()

											if output_type == "pil":
																	_A							:  Dict    =		self.numpy_to_pil(_a       )

											if not return_dict:
																	return image

											return StableDiffusionPipelineOutput(images=_a	,  nsfw_content_detected=_a       )







import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
  import torch

if is_datasets_available():
  from datasets import load_dataset

_snake_case            = random.Random()







def        lowerCAmelCase_			(  snake_case_,snake_case_=1.0,snake_case_=None,snake_case_=None  ):
      if rng is None:
            _A							:  str    =		global_rng

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

      return values







class 		lowercase							(     unittest.TestCase	):

     def __init__(						self	,  _a	,  _a=7	,  _a=400	,  _a=2000	,  _a=2048	,  _a=128	,  _a=1	,  _a=512	,  _a=30	,  _a=4_4100	,  )		->							Tuple:
           _A							:  Any    =		parent
           _A							:  str    =		batch_size
           _A							:  Union[str, Any]    =		min_seq_length
           _A							:  int    =		max_seq_length
           _A							:  List[str]    =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _A							:  Tuple    =		spectrogram_length
           _A							:  int    =		feature_size
           _A							:  str    =		num_audio_channels
           _A							:  Tuple    =		hop_length
           _A							:  List[str]    =		chunk_length
           _A							:  Union[str, Any]    =		sampling_rate

     def       a__			(						self       )		->							Tuple:
           return {
               "spectrogram_length": self.spectrogram_length,
               "feature_size": self.feature_size,
               "num_audio_channels": self.num_audio_channels,
               "hop_length": self.hop_length,
               "chunk_length": self.chunk_length,
               "sampling_rate": self.sampling_rate,
           }



     def       a__			(						self	,  _a=False	,  _a=False       )		->							Optional[int]:
           def _flatten(_a       ):
                 return list(itertools.chain(*_a       )       )

           if equal_length:
                 _A							:  List[str]    =		[floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
           else:
                 # make sure that inputs increase in size
                 _A							:  Union[str, Any]    =		[
                     floats_list((x, self.feature_size)       )
                     for x in range(self.min_seq_length	,  self.max_seq_length	,  self.seq_length_diff       )
                 ]
           if numpify:
                 _A							:  List[Any]    =		[np.asarray(_a       ) for x in speech_inputs]
           return speech_inputs







@require_torch
@require_torchaudio
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		TvltFeatureExtractor

     def       a__			(						self       )		->							Any:
           _A							:  int    =		TvltFeatureExtractionTester(self       )

     def       a__			(						self       )		->							List[Any]:
           _A							:  Tuple    =		self.feature_extraction_class(**self.feat_extract_dict       )
           self.assertTrue(hasattr(_a	,  """spectrogram_length"""       )       )
           self.assertTrue(hasattr(_a	,  """feature_size"""       )       )
           self.assertTrue(hasattr(_a	,  """num_audio_channels"""       )       )
           self.assertTrue(hasattr(_a	,  """hop_length"""       )       )
           self.assertTrue(hasattr(_a	,  """chunk_length"""       )       )
           self.assertTrue(hasattr(_a	,  """sampling_rate"""       )       )

     def       a__			(						self       )		->							Optional[int]:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  Tuple    =		feat_extract_first.save_pretrained(_a       )[0]
                 check_json_file_has_correct_format(_a       )
                 _A							:  Optional[int]    =		self.feature_extraction_class.from_pretrained(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  int    =		feat_extract_second.to_dict()
           _A							:  int    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[int]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							int:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  List[str]    =		os.path.join(_a	,  """feat_extract.json"""       )
                 feat_extract_first.to_json_file(_a       )
                 _A							:  Union[str, Any]    =		self.feature_extraction_class.from_json_file(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  Union[str, Any]    =		feat_extract_second.to_dict()
           _A							:  List[Any]    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[Any]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							Optional[Any]:
           # Initialize feature_extractor
           _A							:  Tuple    =		self.feature_extraction_class(**self.feat_extract_dict       )

           # create three inputs of length 800, 1000, and 1200
           _A							:  Optional[int]    =		[floats_list((1, x)       )[0] for x in range(800	,  1400	,  200       )]
           _A							:  List[str]    =		[np.asarray(_a       ) for speech_input in speech_inputs]

           # Test not batched input
           _A							:  Optional[int]    =		feature_extractor(np_speech_inputs[0]	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test batched
           _A							:  str    =		feature_extractor(_a	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test audio masking
           _A							:  Optional[Any]    =		feature_extractor(
               _a	,  return_tensors="""np"""	,  sampling_rate=4_4100	,  mask_audio=_a       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test 2-D numpy arrays are batched.
           _A							:  Dict    =		[floats_list((1, x)       )[0] for x in (800, 800, 800)]
           _A							:  Optional[int]    =		np.asarray(_a       )
           _A							:  Optional[Any]    =		feature_extractor(_a	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).audio_values
           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

     def       a__			(						self	,  _a       )		->							str:
           _A							:  Optional[Any]    =		load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,  """clean"""	,  split="""validation"""       )
           # automatic decoding with librispeech
           _A							:  Dict    =		ds.sort("""id"""       ).select(range(_a       )       )[:num_samples]["""audio"""]

           return [x["array"] for x in speech_samples]



     def       a__			(						self       )		->							Optional[Any]:
           _A							:  List[str]    =		self._load_datasamples(1       )
           _A							:  List[str]    =		TvltFeatureExtractor()
           _A							:  List[Any]    =		feature_extractor(_a	,  return_tensors="""pt"""       ).audio_values

           self.assertEquals(audio_values.shape	,  (1, 1, 192, 128)       )

           _A							:  int    =		torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]       )
           self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]	,  _a	,  atol=1e-4       )       )

