"""simple docstring"""



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


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






class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						):
        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=lowerCamelCase  ,						speech_processor=lowerCamelCase  ,						vae=lowerCamelCase  ,						text_encoder=lowerCamelCase  ,						tokenizer=lowerCamelCase  ,						unet=lowerCamelCase  ,						scheduler=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						)


 def 		a__ (				self  ,						lowerCamelCase = "auto"		):
        if slice_size == "auto":
               __a     				=  self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(lowerCamelCase		)


 def 		a__ (				self		):
        self.enable_attention_slicing(lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase  ,						lowerCamelCase=16000  ,						lowerCamelCase = 512  ,						lowerCamelCase = 512  ,						lowerCamelCase = 50  ,						lowerCamelCase = 7.5  ,						lowerCamelCase = None  ,						lowerCamelCase = 1  ,						lowerCamelCase = 0.0  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						lowerCamelCase = 1  ,						**lowerCamelCase  ,						):
        __a     				=  self.speech_processor.feature_extractor(
            lowerCamelCase  ,						return_tensors="pt"  ,						sampling_rate=lowerCamelCase		).input_features.to(self.device		)
        __a     				=  self.speech_model.generate(lowerCamelCase  ,						max_length=480000		)

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

        if isinstance(lowerCamelCase  ,						lowerCamelCase		):
               __a     				=  1
        elif isinstance(lowerCamelCase  ,						lowerCamelCase		):
               __a     				=  len(lowerCamelCase		)
        else:
               raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase		)}"		)

        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(lowerCamelCase  ,						lowerCamelCase		) or callback_steps <= 0)
        ):
               raise ValueError(
                   F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                   F" {type(lowerCamelCase		)}."		)

        # get prompt text embeddings
        __a     				=  self.tokenizer(
            lowerCamelCase  ,						padding="max_length"  ,						max_length=self.tokenizer.model_max_length  ,						return_tensors="pt"  ,						)
        __a     				=  text_inputs.input_ids

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

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        __a				, __a				, __a     				=  text_embeddings.shape
        __a     				=  text_embeddings.repeat(1  ,						lowerCamelCase  ,						1		)
        __a     				=  text_embeddings.view(bs_embed * num_images_per_prompt  ,						lowerCamelCase  ,						-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     				=  guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
               __a     				=  42
               if negative_prompt is None:
                      __a     				=  [""] * batch_size
               elif type(lowerCamelCase		) is not type(lowerCamelCase		):
                      raise TypeError(
                          F"`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase		)} !="
                          F" {type(lowerCamelCase		)}."		)
               elif isinstance(lowerCamelCase  ,						lowerCamelCase		):
                      __a     				=  [negative_prompt]
               elif batch_size != len(lowerCamelCase		):
                      raise ValueError(
                          F"`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase		)}, but `prompt`:"
                          F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                          " the batch size of `prompt`."		)
               else:
                      __a     				=  negative_prompt

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

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
               __a     				=  uncond_embeddings.shape[1]
               __a     				=  uncond_embeddings.repeat(1  ,						lowerCamelCase  ,						1		)
               __a     				=  uncond_embeddings.view(batch_size * num_images_per_prompt  ,						lowerCamelCase  ,						-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     				=  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     				=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        __a     				=  text_embeddings.dtype
        if latents is None:
               if self.device.type == "mps":
                      # randn does not exist on mps
                      __a     				=  torch.randn(lowerCamelCase  ,						generator=lowerCamelCase  ,						device="cpu"  ,						dtype=lowerCamelCase		).to(
                          self.device		)
               else:
                      __a     				=  torch.randn(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device  ,						dtype=lowerCamelCase		)
        else:
               if latents.shape != latents_shape:
                      raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"		)
               __a     				=  latents.to(self.device		)

        # set timesteps
        self.scheduler.set_timesteps(lowerCamelCase		)

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

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

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

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

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

               # compute the previous noisy sample x_t -> x_t-1
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						**lowerCamelCase		).prev_sample

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

        __a     				=  1 / 0.1_8215 * latents
        __a     				=  self.vae.decode(lowerCamelCase		).sample

        __a     				=  (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     				=  image.cpu().permute(0  ,						2  ,						3  ,						1		).float().numpy()

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

        if not return_dict:
               return image

        return StableDiffusionPipelineOutput(images=lowerCamelCase  ,						nsfw_content_detected=lowerCamelCase		)

"""simple docstring"""



import functools
import operator

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


SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:str			=	{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		"""sew-d"""


 def __init__(				self  ,						lowerCamelCase=32  ,						lowerCamelCase=768  ,						lowerCamelCase=12  ,						lowerCamelCase=12  ,						lowerCamelCase=3072  ,						lowerCamelCase=2  ,						lowerCamelCase=512  ,						lowerCamelCase=256  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=("p2c", "c2p")  ,						lowerCamelCase="layer_norm"  ,						lowerCamelCase="gelu_python"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.02  ,						lowerCamelCase=1E-7  ,						lowerCamelCase=1E-5  ,						lowerCamelCase="group"  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,						lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,						lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,						lowerCamelCase=False  ,						lowerCamelCase=128  ,						lowerCamelCase=16  ,						lowerCamelCase=True  ,						lowerCamelCase=0.05  ,						lowerCamelCase=10  ,						lowerCamelCase=2  ,						lowerCamelCase=0.0  ,						lowerCamelCase=10  ,						lowerCamelCase=0  ,						lowerCamelCase="mean"  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=256  ,						lowerCamelCase=0  ,						lowerCamelCase=1  ,						lowerCamelCase=2  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase  ,						pad_token_id=lowerCamelCase  ,						bos_token_id=lowerCamelCase  ,						eos_token_id=lowerCamelCase		)
        __a     				=  hidden_size
        __a     				=  feat_extract_norm
        __a     				=  feat_extract_activation
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  conv_bias
        __a     				=  num_conv_pos_embeddings
        __a     				=  num_conv_pos_embedding_groups
        __a     				=  len(self.conv_dim		)
        __a     				=  num_hidden_layers
        __a     				=  intermediate_size
        __a     				=  squeeze_factor
        __a     				=  max_position_embeddings
        __a     				=  position_buckets
        __a     				=  share_att_key
        __a     				=  relative_attention
        __a     				=  norm_rel_ebd
        __a     				=  list(lowerCamelCase		)
        __a     				=  hidden_act
        __a     				=  num_attention_heads
        __a     				=  hidden_dropout
        __a     				=  attention_dropout
        __a     				=  activation_dropout
        __a     				=  feat_proj_dropout
        __a     				=  final_dropout
        __a     				=  layer_norm_eps
        __a     				=  feature_layer_norm_eps
        __a     				=  initializer_range
        __a     				=  vocab_size

        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)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."		)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a     				=  apply_spec_augment
        __a     				=  mask_time_prob
        __a     				=  mask_time_length
        __a     				=  mask_time_min_masks
        __a     				=  mask_feature_prob
        __a     				=  mask_feature_length
        __a     				=  mask_feature_min_masks

        # ctc loss
        __a     				=  ctc_loss_reduction
        __a     				=  ctc_zero_infinity

        # sequence classification
        __a     				=  use_weighted_layer_sum
        __a     				=  classifier_proj_size






 @property
 def 		a__ (				self		):
        return functools.reduce(operator.mul  ,						self.conv_stride  ,						1		)
