






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


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


class  _lowerCAmelCase      (		a__   ):



				"""simple docstring"""



				def __init__(   self     :     Tuple      ,		UpperCamelCase__     :     Optional[int]      ,		UpperCamelCase__     :     Union[str, Any]      ,		UpperCamelCase__     :     Tuple      ,		UpperCamelCase__     :     int      ,		UpperCamelCase__     :     Optional[Any]      ,		UpperCamelCase__     :     Optional[Any]      ,		UpperCamelCase__     :     int      ,		UpperCamelCase__     :     List[str]      ,		UpperCamelCase__     :     Optional[int]      ,		):






											'''simple docstring'''







											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        __magic_name__				(   self     :     Optional[int]      ,		UpperCamelCase__     :     str = "auto"):






											'''simple docstring'''







											if slice_size == "auto":
																		snake_case__							=							self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(__a)



				def        __magic_name__				(   self     :     str):






											'''simple docstring'''







											self.enable_attention_slicing(__a)



				@torch.no_grad()
				def __call__(   self     :     int      ,		UpperCamelCase__     :     Dict      ,		UpperCamelCase__     :     Union[str, Any]=1_6_0_0_0      ,		UpperCamelCase__     :     Tuple = 5_1_2      ,		UpperCamelCase__     :     Any = 5_1_2      ,		UpperCamelCase__     :     List[str] = 5_0      ,		UpperCamelCase__     :     Optional[int] = 7.5      ,		UpperCamelCase__     :     Optional[int] = None      ,		UpperCamelCase__     :     Any = 1      ,		UpperCamelCase__     :     Union[str, Any] = 0.0      ,		UpperCamelCase__     :     List[Any] = None      ,		UpperCamelCase__     :     Dict = None      ,		UpperCamelCase__     :     Any = "pil"      ,		UpperCamelCase__     :     List[str] = True      ,		UpperCamelCase__     :     List[str] = None      ,		UpperCamelCase__     :     Tuple = 1      ,		**UpperCamelCase__     :     int      ,		):






											'''simple docstring'''







											snake_case__							=							self.speech_processor.feature_extractor(
											    __a      ,		return_tensors="""pt"""      ,		sampling_rate=__a).input_features.to(self.device)
											snake_case__							=							self.speech_model.generate(__a      ,		max_length=4_8_0_0_0_0)

											snake_case__							=							self.speech_processor.tokenizer.batch_decode(__a      ,		skip_special_tokens=__a      ,		normalize=__a)[
											    0
											]

											if isinstance(__a      ,		__a):
																		snake_case__							=							1
											elif isinstance(__a      ,		__a):
																		snake_case__							=							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
											snake_case__							=							self.tokenizer(
											    __a      ,		padding="""max_length"""      ,		max_length=self.tokenizer.model_max_length      ,		return_tensors="""pt"""      ,		)
											snake_case__							=							text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		snake_case__							=							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}''')
																		snake_case__							=							text_input_ids[:, : self.tokenizer.model_max_length]
											snake_case__							=							self.text_encoder(text_input_ids.to(self.device))[0]

											# duplicate text embeddings for each generation per prompt, using mps friendly method
											snake_case__							=							text_embeddings.shape
											snake_case__							=							text_embeddings.repeat(1      ,		__a      ,		1)
											snake_case__							=							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.
											snake_case__							=							guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
																		snake_case__							=							42
																		if negative_prompt is None:
																									snake_case__							=							[""""""] * 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):
																									snake_case__							=							[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:
																									snake_case__							=							negative_prompt

																		snake_case__							=							text_input_ids.shape[-1]
																		snake_case__							=							self.tokenizer(
																		    __a      ,		padding="""max_length"""      ,		max_length=__a      ,		truncation=__a      ,		return_tensors="""pt"""      ,		)
																		snake_case__							=							self.text_encoder(uncond_input.input_ids.to(self.device))[0]

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		snake_case__							=							uncond_embeddings.shape[1]
																		snake_case__							=							uncond_embeddings.repeat(1      ,		__a      ,		1)
																		snake_case__							=							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
																		snake_case__							=							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`.
											snake_case__							=							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
											snake_case__							=							text_embeddings.dtype
											if latents is None:
																		if self.device.type == "mps":
																									# randn does not exist on mps
																									snake_case__							=							torch.randn(__a      ,		generator=__a      ,		device="""cpu"""      ,		dtype=__a).to(
																									    self.device)
																		else:
																									snake_case__							=							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}''')
																		snake_case__							=							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
											snake_case__							=							self.scheduler.timesteps.to(self.device)

											# scale the initial noise by the standard deviation required by the scheduler
											snake_case__							=							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]
											snake_case__							=							"""eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())
											snake_case__							=							{}
											if accepts_eta:
																		snake_case__							=							eta

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

																		# predict the noise residual
																		snake_case__							=							self.unet(__a      ,		__a      ,		encoder_hidden_states=__a).sample

																		# perform guidance
																		if do_classifier_free_guidance:
																									snake_case__							=							noise_pred.chunk(2)
																									snake_case__							=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		# compute the previous noisy sample x_t -> x_t-1
																		snake_case__							=							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)

											snake_case__							=							1 / 0.1_82_15 * latents
											snake_case__							=							self.vae.decode(__a).sample

											snake_case__							=							(image / 2 + 0.5).clamp(0      ,		1)

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

											if output_type == "pil":
																		snake_case__							=							self.numpy_to_pil(__a)

											if not return_dict:
																		return image

											return StableDiffusionPipelineOutput(images=__a      ,		nsfw_content_detected=__a)






'''simple docstring'''







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase      :   Optional[int]      =       logging.get_logger(__name__)

lowercase      :   Union[str, Any]      =       {
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[Any]          =					"""unispeech-sat"""







     def __init__(				self							,__a=32							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.1							,__a=0.1							,__a=0.02							,__a=1E-5							,__a="group"							,__a="gelu"							,__a=(512, 512, 512, 512, 512, 512, 512)							,__a=(5, 2, 2, 2, 2, 2, 2)							,__a=(10, 3, 3, 3, 3, 2, 2)							,__a=False							,__a=128							,__a=16							,__a=False							,__a=True							,__a=0.05							,__a=10							,__a=2							,__a=0.0							,__a=10							,__a=0							,__a=320							,__a=2							,__a=0.1							,__a=100							,__a=256							,__a=256							,__a=0.1							,__a="mean"							,__a=False							,__a=False							,__a=256							,__a=(512, 512, 512, 512, 1_500)							,__a=(5, 3, 3, 1, 1)							,__a=(1, 2, 3, 1, 1)							,__a=512							,__a=0							,__a=1							,__a=2							,__a=504							,**__a							,)      ->				str:
          super().__init__(**__a							,pad_token_id=__a							,bos_token_id=__a							,eos_token_id=__a							)
          snake_case			:      List[Any]        =			hidden_size
          snake_case			:      Any        =			feat_extract_norm
          snake_case			:      Dict        =			feat_extract_activation
          snake_case			:      List[Any]        =			list(__a							)
          snake_case			:      int        =			list(__a							)
          snake_case			:      Dict        =			list(__a							)
          snake_case			:      int        =			conv_bias
          snake_case			:      Optional[Any]        =			num_conv_pos_embeddings
          snake_case			:      Any        =			num_conv_pos_embedding_groups
          snake_case			:      Dict        =			len(self.conv_dim							)
          snake_case			:      str        =			num_hidden_layers
          snake_case			:      Optional[Any]        =			intermediate_size
          snake_case			:      Optional[Any]        =			hidden_act
          snake_case			:      Union[str, Any]        =			num_attention_heads
          snake_case			:      str        =			hidden_dropout
          snake_case			:      Tuple        =			attention_dropout
          snake_case			:      Tuple        =			activation_dropout
          snake_case			:      List[Any]        =			feat_proj_dropout
          snake_case			:      List[str]        =			final_dropout
          snake_case			:      Union[str, Any]        =			layerdrop
          snake_case			:      Optional[int]        =			layer_norm_eps
          snake_case			:      int        =			initializer_range
          snake_case			:      List[Any]        =			vocab_size
          snake_case			:      Any        =			num_clusters
          snake_case			:      Dict        =			do_stable_layer_norm
          snake_case			:      int        =			use_weighted_layer_sum

          if (
              (len(self.conv_stride							) != self.num_feat_extract_layers)
              or (len(self.conv_kernel							) != self.num_feat_extract_layers)
              or (len(self.conv_dim							) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                   """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                   F''' {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,'''
                   F''' `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          snake_case			:      Dict        =			apply_spec_augment
          snake_case			:      Optional[int]        =			mask_time_prob
          snake_case			:      int        =			mask_time_length
          snake_case			:      int        =			mask_time_min_masks
          snake_case			:      int        =			mask_feature_prob
          snake_case			:      Optional[Any]        =			mask_feature_length
          snake_case			:      List[Any]        =			mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          snake_case			:      Optional[Any]        =			num_codevectors_per_group
          snake_case			:      Optional[int]        =			num_codevector_groups
          snake_case			:      Any        =			contrastive_logits_temperature
          snake_case			:      List[str]        =			feat_quantizer_dropout
          snake_case			:      Dict        =			num_negatives
          snake_case			:      int        =			codevector_dim
          snake_case			:      Optional[int]        =			proj_codevector_dim
          snake_case			:      Union[str, Any]        =			diversity_loss_weight

          # ctc loss
          snake_case			:      Union[str, Any]        =			ctc_loss_reduction
          snake_case			:      Optional[int]        =			ctc_zero_infinity

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          snake_case			:      Dict        =			classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          snake_case			:      int        =			list(__a							)
          snake_case			:      Tuple        =			list(__a							)
          snake_case			:      List[Any]        =			list(__a							)
          snake_case			:      Dict        =			xvector_output_dim







     @property
     def 			snake_case_      (				self							)      ->				List[str]:
          return functools.reduce(operator.mul							,self.conv_stride							,1							)



