
from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

lowercase        =  '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      Dict=8       )  ->    Optional[int]:
							lowerCamelCase_							  =					height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							lowerCamelCase_							  =					width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     UNetaDConditionModel   ,					__UpperCamelCase					:     DDPMScheduler   ,					__UpperCamelCase					:     VQModel   ,					):
												super().__init__()

												self.register_modules(
												    unet=__UpperCamelCase   ,					scheduler=__UpperCamelCase   ,					movq=__UpperCamelCase   ,					)
												lowerCamelCase_							  =					2 ** (len(self.movq.config.block_out_channels   ) - 1)

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Dict   ):
												if latents is None:
																			lowerCamelCase_							  =					randn_tensor(__UpperCamelCase   ,					generator=__UpperCamelCase   ,					device=__UpperCamelCase   ,					dtype=__UpperCamelCase   )
												else:
																			if latents.shape != shape:
																										raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''   )
																			lowerCamelCase_							  =					latents.to(__UpperCamelCase   )

												lowerCamelCase_							  =					latents * scheduler.init_noise_sigma
												return latents

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     int=0   ):
												if is_accelerate_available():
																			from accelerate import cpu_offload
												else:
																			raise ImportError("""Please install accelerate via `pip install accelerate`"""   )

												lowerCamelCase_							  =					torch.device(F'''cuda:{gpu_id}'''   )

												lowerCamelCase_							  =					[
												    self.unet,
												    self.movq,
												]
												for cpu_offloaded_model in models:
																			if cpu_offloaded_model is not None:
																										cpu_offload(__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Any=0   ):
												if is_accelerate_available() and is_accelerate_version(""">="""   ,					"""0.17.0.dev0"""   ):
																			from accelerate import cpu_offload_with_hook
												else:
																			raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""   )

												lowerCamelCase_							  =					torch.device(F'''cuda:{gpu_id}'''   )

												if self.device.type != "cpu":
																			self.to("""cpu"""   ,					silence_dtype_warnings=__UpperCamelCase   )
																			torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

												lowerCamelCase_							  =					None
												for cpu_offloaded_model in [self.unet, self.movq]:
																			lowerCamelCase_  ,   lowerCamelCase_							  =					cpu_offload_with_hook(__UpperCamelCase   ,					__UpperCamelCase   ,					prev_module_hook=__UpperCamelCase   )

												# We'll offload the last model manually.
												lowerCamelCase_							  =					hook

					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def    lowercase__      (	self					:     List[Any]   ):
												if not hasattr(self.unet   ,					"""_hf_hook"""   ):
																			return self.device
												for module in self.unet.modules():
																			if (
																			    hasattr(__UpperCamelCase   ,					"""_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



					@torch.no_grad()
					@replace_example_docstring(__UpperCamelCase   )
					def __call__(	self					:     str   ,					__UpperCamelCase					:     Union[torch.FloatTensor, List[torch.FloatTensor]]   ,					__UpperCamelCase					:     Union[torch.FloatTensor, List[torch.FloatTensor]]   ,					__UpperCamelCase					:     torch.FloatTensor   ,					__UpperCamelCase					:     int = 5_1_2   ,					__UpperCamelCase					:     int = 5_1_2   ,					__UpperCamelCase					:     int = 1_0_0   ,					__UpperCamelCase					:     float = 4.0   ,					__UpperCamelCase					:     int = 1   ,					__UpperCamelCase					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,					__UpperCamelCase					:     Optional[torch.FloatTensor] = None   ,					__UpperCamelCase					:     Optional[str] = "pil"   ,					__UpperCamelCase					:     bool = True   ,					):
												lowerCamelCase_							  =					self._execution_device

												lowerCamelCase_							  =					guidance_scale > 1.0

												if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					torch.cat(__UpperCamelCase   ,					dim=0   )
												if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					torch.cat(__UpperCamelCase   ,					dim=0   )
												if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					torch.cat(__UpperCamelCase   ,					dim=0   )

												lowerCamelCase_							  =					image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																			lowerCamelCase_							  =					image_embeds.repeat_interleave(__UpperCamelCase   ,					dim=0   )
																			lowerCamelCase_							  =					negative_image_embeds.repeat_interleave(__UpperCamelCase   ,					dim=0   )
																			lowerCamelCase_							  =					hint.repeat_interleave(__UpperCamelCase   ,					dim=0   )

												lowerCamelCase_							  =					torch.cat([negative_image_embeds, image_embeds]   ,					dim=0   ).to(dtype=self.unet.dtype   ,					device=__UpperCamelCase   )
												lowerCamelCase_							  =					torch.cat([hint, hint]   ,					dim=0   ).to(dtype=self.unet.dtype   ,					device=__UpperCamelCase   )
												self.scheduler.set_timesteps(__UpperCamelCase   ,					device=__UpperCamelCase   )
												lowerCamelCase_							  =					self.scheduler.timesteps

												lowerCamelCase_							  =					self.movq.config.latent_channels

												lowerCamelCase_  ,   lowerCamelCase_							  =					downscale_height_and_width(__UpperCamelCase   ,					__UpperCamelCase   ,					self.movq_scale_factor   )

												# create initial latent
												lowerCamelCase_							  =					self.prepare_latents(
												    (batch_size, num_channels_latents, height, width)   ,					image_embeds.dtype   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					self.scheduler   ,					)

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

																			lowerCamelCase_							  =					{"""image_embeds""": image_embeds, """hint""": hint}
																			lowerCamelCase_							  =					self.unet(
																			    sample=__UpperCamelCase   ,					timestep=__UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					added_cond_kwargs=__UpperCamelCase   ,					return_dict=__UpperCamelCase   ,					)[0]

																			if do_classifier_free_guidance:
																										lowerCamelCase_  ,   lowerCamelCase_							  =					noise_pred.split(latents.shape[1]   ,					dim=1   )
																										lowerCamelCase_  ,   lowerCamelCase_							  =					noise_pred.chunk(2   )
																										lowerCamelCase_  ,   lowerCamelCase_							  =					variance_pred.chunk(2   )
																										lowerCamelCase_							  =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																										lowerCamelCase_							  =					torch.cat([noise_pred, variance_pred_text]   ,					dim=1   )

																			if not (
																			    hasattr(self.scheduler.config   ,					"""variance_type"""   )
																			    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																			):
																										lowerCamelCase_  ,   lowerCamelCase_							  =					noise_pred.split(latents.shape[1]   ,					dim=1   )

																			# compute the previous noisy sample x_t -> x_t-1
																			lowerCamelCase_							  =					self.scheduler.step(
																			    __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					generator=__UpperCamelCase   ,					)[0]
												# post-processing
												lowerCamelCase_							  =					self.movq.decode(__UpperCamelCase   ,					force_not_quantize=__UpperCamelCase   )["""sample"""]

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

												if output_type in ["np", "pil"]:
																			lowerCamelCase_							  =					image * 0.5 + 0.5
																			lowerCamelCase_							  =					image.clamp(0   ,					1   )
																			lowerCamelCase_							  =					image.cpu().permute(0   ,					2   ,					3   ,					1   ).float().numpy()

												if output_type == "pil":
																			lowerCamelCase_							  =					self.numpy_to_pil(__UpperCamelCase   )

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase   )







import functools
import operator

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


lowercase        =  logging.get_logger(__name__)

lowercase        =  {
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''wav2vec2'''

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=3_2   ,					__UpperCamelCase					:     List[str]=7_6_8   ,					__UpperCamelCase					:     Tuple=1_2   ,					__UpperCamelCase					:     str=1_2   ,					__UpperCamelCase					:     Any=3_0_7_2   ,					__UpperCamelCase					:     Tuple="gelu"   ,					__UpperCamelCase					:     Tuple=0.1   ,					__UpperCamelCase					:     Any=0.1   ,					__UpperCamelCase					:     List[str]=0.1   ,					__UpperCamelCase					:     List[Any]=0.0   ,					__UpperCamelCase					:     str=0.0   ,					__UpperCamelCase					:     str=0.1   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     Dict=0.02   ,					__UpperCamelCase					:     int=1E-5   ,					__UpperCamelCase					:     Dict="group"   ,					__UpperCamelCase					:     Tuple="gelu"   ,					__UpperCamelCase					:     Optional[int]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)   ,					__UpperCamelCase					:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,					__UpperCamelCase					:     int=(1_0, 3, 3, 3, 3, 2, 2)   ,					__UpperCamelCase					:     List[Any]=False   ,					__UpperCamelCase					:     Tuple=1_2_8   ,					__UpperCamelCase					:     Any=1_6   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Dict=0.05   ,					__UpperCamelCase					:     Union[str, Any]=1_0   ,					__UpperCamelCase					:     int=2   ,					__UpperCamelCase					:     List[Any]=0.0   ,					__UpperCamelCase					:     List[Any]=1_0   ,					__UpperCamelCase					:     List[str]=0   ,					__UpperCamelCase					:     Optional[Any]=3_2_0   ,					__UpperCamelCase					:     Optional[int]=2   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     int=1_0_0   ,					__UpperCamelCase					:     int=2_5_6   ,					__UpperCamelCase					:     str=2_5_6   ,					__UpperCamelCase					:     Dict=0.1   ,					__UpperCamelCase					:     Union[str, Any]="sum"   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     Optional[int]=False   ,					__UpperCamelCase					:     Dict=2_5_6   ,					__UpperCamelCase					:     List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)   ,					__UpperCamelCase					:     int=(5, 3, 3, 1, 1)   ,					__UpperCamelCase					:     Optional[Any]=(1, 2, 3, 1, 1)   ,					__UpperCamelCase					:     Dict=5_1_2   ,					__UpperCamelCase					:     Any=0   ,					__UpperCamelCase					:     Union[str, Any]=1   ,					__UpperCamelCase					:     List[str]=2   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     int=3   ,					__UpperCamelCase					:     Dict=2   ,					__UpperCamelCase					:     Optional[Any]=3   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     Tuple=None   ,					**__UpperCamelCase					:     Tuple   ,					):
												super().__init__(**__UpperCamelCase   ,					pad_token_id=__UpperCamelCase   ,					bos_token_id=__UpperCamelCase   ,					eos_token_id=__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					feat_extract_norm
												lowerCamelCase_							  =					feat_extract_activation
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					conv_bias
												lowerCamelCase_							  =					num_conv_pos_embeddings
												lowerCamelCase_							  =					num_conv_pos_embedding_groups
												lowerCamelCase_							  =					len(self.conv_dim   )
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					intermediate_size
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					hidden_dropout
												lowerCamelCase_							  =					attention_dropout
												lowerCamelCase_							  =					activation_dropout
												lowerCamelCase_							  =					feat_proj_dropout
												lowerCamelCase_							  =					final_dropout
												lowerCamelCase_							  =					layerdrop
												lowerCamelCase_							  =					layer_norm_eps
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					do_stable_layer_norm
												lowerCamelCase_							  =					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
												lowerCamelCase_							  =					apply_spec_augment
												lowerCamelCase_							  =					mask_time_prob
												lowerCamelCase_							  =					mask_time_length
												lowerCamelCase_							  =					mask_time_min_masks
												lowerCamelCase_							  =					mask_feature_prob
												lowerCamelCase_							  =					mask_feature_length
												lowerCamelCase_							  =					mask_feature_min_masks

												# parameters for pretraining with codevector quantized representations
												lowerCamelCase_							  =					num_codevectors_per_group
												lowerCamelCase_							  =					num_codevector_groups
												lowerCamelCase_							  =					contrastive_logits_temperature
												lowerCamelCase_							  =					feat_quantizer_dropout
												lowerCamelCase_							  =					num_negatives
												lowerCamelCase_							  =					codevector_dim
												lowerCamelCase_							  =					proj_codevector_dim
												lowerCamelCase_							  =					diversity_loss_weight

												# ctc loss
												lowerCamelCase_							  =					ctc_loss_reduction
												lowerCamelCase_							  =					ctc_zero_infinity

												# adapter
												lowerCamelCase_							  =					add_adapter
												lowerCamelCase_							  =					adapter_kernel_size
												lowerCamelCase_							  =					adapter_stride
												lowerCamelCase_							  =					num_adapter_layers
												lowerCamelCase_							  =					output_hidden_size or hidden_size
												lowerCamelCase_							  =					adapter_attn_dim

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												lowerCamelCase_							  =					classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												lowerCamelCase_							  =					xvector_output_dim



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





