
"""simple docstring"""







import functools
import operator

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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}




class 				A__ (			_lowerCamelCase):
		A_		: Tuple            =			'wavlm'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-5 ,	_SCREAMING_SNAKE_CASE="group" ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,	_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2) ,	_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2) ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=1_28 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=3_20 ,	_SCREAMING_SNAKE_CASE=8_00 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=0.05 ,	_SCREAMING_SNAKE_CASE=10 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=10 ,	_SCREAMING_SNAKE_CASE=3_20 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=1_00 ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE="mean" ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=(5_12, 5_12, 5_12, 5_12, 15_00) ,	_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1) ,	_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1) ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=80 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=None ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE ,	pad_token_id=_SCREAMING_SNAKE_CASE ,	bos_token_id=_SCREAMING_SNAKE_CASE ,	eos_token_id=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_size
			__lowerCAmelCase    :		List[str]		    =   feat_extract_norm
			__lowerCAmelCase    :		str		    =   feat_extract_activation
			__lowerCAmelCase    :		Union[str, Any]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   conv_bias
			__lowerCAmelCase    :		Tuple		    =   num_buckets
			__lowerCAmelCase    :		List[Any]		    =   max_bucket_distance
			__lowerCAmelCase    :		Union[str, Any]		    =   num_conv_pos_embeddings
			__lowerCAmelCase    :		Optional[int]		    =   num_conv_pos_embedding_groups
			__lowerCAmelCase    :		Dict		    =   len(self.conv_dim				)
			__lowerCAmelCase    :		List[Any]		    =   num_hidden_layers
			__lowerCAmelCase    :		Tuple		    =   intermediate_size
			__lowerCAmelCase    :		int		    =   hidden_act
			__lowerCAmelCase    :		Any		    =   num_attention_heads
			__lowerCAmelCase    :		Optional[int]		    =   hidden_dropout
			__lowerCAmelCase    :		Optional[Any]		    =   attention_dropout
			__lowerCAmelCase    :		Dict		    =   activation_dropout
			__lowerCAmelCase    :		Tuple		    =   feat_proj_dropout
			__lowerCAmelCase    :		List[str]		    =   final_dropout
			__lowerCAmelCase    :		Optional[int]		    =   layerdrop
			__lowerCAmelCase    :		int		    =   layer_norm_eps
			__lowerCAmelCase    :		List[Any]		    =   initializer_range
			__lowerCAmelCase    :		str		    =   num_ctc_classes
			__lowerCAmelCase    :		Tuple		    =   vocab_size
			__lowerCAmelCase    :		List[Any]		    =   do_stable_layer_norm
			__lowerCAmelCase    :		int		    =   use_weighted_layer_sum
			__lowerCAmelCase    :		Tuple		    =   classifier_proj_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)`, 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
			__lowerCAmelCase    :		Tuple		    =   apply_spec_augment
			__lowerCAmelCase    :		int		    =   mask_time_prob
			__lowerCAmelCase    :		List[str]		    =   mask_time_length
			__lowerCAmelCase    :		str		    =   mask_time_min_masks
			__lowerCAmelCase    :		Optional[Any]		    =   mask_feature_prob
			__lowerCAmelCase    :		Dict		    =   mask_feature_length

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

			# ctc loss
			__lowerCAmelCase    :		Tuple		    =   ctc_loss_reduction
			__lowerCAmelCase    :		Dict		    =   ctc_zero_infinity

			# adapter
			__lowerCAmelCase    :		str		    =   add_adapter
			__lowerCAmelCase    :		List[Any]		    =   adapter_kernel_size
			__lowerCAmelCase    :		List[str]		    =   adapter_stride
			__lowerCAmelCase    :		List[Any]		    =   num_adapter_layers
			__lowerCAmelCase    :		Union[str, Any]		    =   output_hidden_size or hidden_size

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

			# XVector-specific parameters. Feel free to ignore for other classes.
			__lowerCAmelCase    :		Optional[Any]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Tuple		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   xvector_output_dim







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

"""simple docstring"""







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

lowerCamelCase__           =	"""
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""




@dataclass
class 				A__ (			_lowerCamelCase):
		A_		: Union[PIL.Image.Image, np.ndarray]




class 				A__ (			_lowerCamelCase):

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	):
			super().__init__()

			self.register_modules(
			    prior=_SCREAMING_SNAKE_CASE ,	image_encoder=_SCREAMING_SNAKE_CASE ,	image_processor=_SCREAMING_SNAKE_CASE ,	scheduler=_SCREAMING_SNAKE_CASE ,	renderer=_SCREAMING_SNAKE_CASE ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if latents is None:
				__lowerCAmelCase    :		List[str]		    =   randn_tensor(_SCREAMING_SNAKE_CASE ,	generator=_SCREAMING_SNAKE_CASE ,	device=_SCREAMING_SNAKE_CASE ,	dtype=_SCREAMING_SNAKE_CASE				)
			else:
				if latents.shape != shape:
					raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"				)
				__lowerCAmelCase    :		Any		    =   latents.to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   latents * scheduler.init_noise_sigma
			return latents

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0				):
			if is_accelerate_available():
				from accelerate import cpu_offload
			else:
				raise ImportError('Please install accelerate via `pip install accelerate`'				)

			__lowerCAmelCase    :		Tuple		    =   torch.device(f"cuda:{gpu_id}"				)

			__lowerCAmelCase    :		Union[str, Any]		    =   [self.image_encoder, self.prior]
			for cpu_offloaded_model in models:
				if cpu_offloaded_model is not None:
					cpu_offload(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		@property
		def 							__lowerCamelCase							(   self				):
			if self.device != torch.device('meta'				) or not hasattr(self.image_encoder ,	'_hf_hook'				):
				return self.device
			for module in self.image_encoder.modules():
				if (
				    hasattr(_SCREAMING_SNAKE_CASE ,	'_hf_hook'				)
				    and hasattr(module._hf_hook ,	'execution_device'				)
				    and module._hf_hook.execution_device is not None
				):
					return torch.device(module._hf_hook.execution_device				)
			return self.device

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	):
			if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(image[0] ,	torch.Tensor				):
				__lowerCAmelCase    :		str		    =   torch.cat(_SCREAMING_SNAKE_CASE ,	axis=0				) if image[0].ndim == 4 else torch.stack(_SCREAMING_SNAKE_CASE ,	axis=0				)

			if not isinstance(_SCREAMING_SNAKE_CASE ,	torch.Tensor				):
				__lowerCAmelCase    :		Optional[int]		    =   self.image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

			__lowerCAmelCase    :		Dict		    =   image.to(dtype=self.image_encoder.dtype ,	device=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[str]		    =   self.image_encoder(_SCREAMING_SNAKE_CASE				)['last_hidden_state']
			__lowerCAmelCase    :		Optional[int]		    =   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

			__lowerCAmelCase    :		Tuple		    =   image_embeds.repeat_interleave(_SCREAMING_SNAKE_CASE ,	dim=0				)

			if do_classifier_free_guidance:
				__lowerCAmelCase    :		List[Any]		    =   torch.zeros_like(_SCREAMING_SNAKE_CASE				)

				# 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
				__lowerCAmelCase    :		Any		    =   torch.cat([negative_image_embeds, image_embeds]				)

			return image_embeds







		@torch.no_grad()
		@replace_example_docstring(_SCREAMING_SNAKE_CASE				)
		def __call__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = 1 ,	_SCREAMING_SNAKE_CASE = 25 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 4.0 ,	_SCREAMING_SNAKE_CASE = 64 ,	_SCREAMING_SNAKE_CASE = "pil" ,	_SCREAMING_SNAKE_CASE = True ,	):

			if isinstance(_SCREAMING_SNAKE_CASE ,	PIL.Image.Image				):
				__lowerCAmelCase    :		Union[str, Any]		    =   1
			elif isinstance(_SCREAMING_SNAKE_CASE ,	torch.Tensor				):
				__lowerCAmelCase    :		Tuple		    =   image.shape[0]
			elif isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and isinstance(image[0] ,	(torch.Tensor, PIL.Image.Image)				):
				__lowerCAmelCase    :		Any		    =   len(_SCREAMING_SNAKE_CASE				)
			else:
				raise ValueError(
				    f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_SCREAMING_SNAKE_CASE				)}"				)

			__lowerCAmelCase    :		Optional[Any]		    =   self._execution_device

			__lowerCAmelCase    :		Optional[Any]		    =   batch_size * num_images_per_prompt

			__lowerCAmelCase    :		Any		    =   guidance_scale > 1.0
			__lowerCAmelCase    :		List[Any]		    =   self._encode_image(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			# prior

			self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE ,	device=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   self.scheduler.timesteps

			__lowerCAmelCase    :		Optional[int]		    =   self.prior.config.num_embeddings
			__lowerCAmelCase    :		List[str]		    =   self.prior.config.embedding_dim

			__lowerCAmelCase    :		Any		    =   self.prepare_latents(
			    (batch_size, num_embeddings * embedding_dim) ,	image_embeds.dtype ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	self.scheduler ,	)

			# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
			__lowerCAmelCase    :		str		    =   latents.reshape(latents.shape[0] ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

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

				__lowerCAmelCase    :		Tuple		    =   self.prior(
				    _SCREAMING_SNAKE_CASE ,	timestep=_SCREAMING_SNAKE_CASE ,	proj_embedding=_SCREAMING_SNAKE_CASE ,	).predicted_image_embedding

				# remove the variance
				__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   noise_pred.split(
				    scaled_model_input.shape[2] ,	dim=2				)  # batch_size, num_embeddings, embedding_dim

				if do_classifier_free_guidance is not None:
					__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   noise_pred.chunk(2				)
					__lowerCAmelCase    :		Optional[int]		    =   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

				__lowerCAmelCase    :		Dict		    =   self.scheduler.step(
				    _SCREAMING_SNAKE_CASE ,	timestep=_SCREAMING_SNAKE_CASE ,	sample=_SCREAMING_SNAKE_CASE ,	).prev_sample

			if output_type == "latent":
				return ShapEPipelineOutput(images=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   []
			for i, latent in enumerate(_SCREAMING_SNAKE_CASE				):
				print()
				__lowerCAmelCase    :		int		    =   self.renderer.decode(
				    latent[None, :] ,	_SCREAMING_SNAKE_CASE ,	size=_SCREAMING_SNAKE_CASE ,	ray_batch_size=40_96 ,	n_coarse_samples=64 ,	n_fine_samples=1_28 ,	)

				images.append(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   torch.stack(_SCREAMING_SNAKE_CASE				)

			if output_type not in ["np", "pil"]:
				raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}"				)

			__lowerCAmelCase    :		int		    =   images.cpu().numpy()

			if output_type == "pil":
				__lowerCAmelCase    :		Dict		    =   [self.numpy_to_pil(_SCREAMING_SNAKE_CASE				) for image in images]

			# Offload last model to CPU
			if hasattr(self ,	'final_offload_hook'				) and self.final_offload_hook is not None:
				self.final_offload_hook.offload()

			if not return_dict:
				return (images,)

			return ShapEPipelineOutput(images=_SCREAMING_SNAKE_CASE				)