

'''simple docstring'''

import functools
import operator

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


_UpperCAmelCase				: str							    =				logging.get_logger(__name__)

_UpperCAmelCase				: Dict							    =				{
    """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       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		int	=	'sew-d'




				def __init__(self       ,			__lowercase=32       ,			__lowercase=7_68       ,			__lowercase=12       ,			__lowercase=12       ,			__lowercase=30_72       ,			__lowercase=2       ,			__lowercase=5_12       ,			__lowercase=2_56       ,			__lowercase=True       ,			__lowercase=True       ,			__lowercase=("p2c", "c2p")       ,			__lowercase="layer_norm"       ,			__lowercase="gelu_python"       ,			__lowercase=0.1       ,			__lowercase=0.1       ,			__lowercase=0.1       ,			__lowercase=0.0       ,			__lowercase=0.1       ,			__lowercase=0.0_2       ,			__lowercase=1e-7       ,			__lowercase=1e-5       ,			__lowercase="group"       ,			__lowercase="gelu"       ,			__lowercase=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)       ,			__lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,			__lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,			__lowercase=False       ,			__lowercase=1_28       ,			__lowercase=16       ,			__lowercase=True       ,			__lowercase=0.0_5       ,			__lowercase=10       ,			__lowercase=2       ,			__lowercase=0.0       ,			__lowercase=10       ,			__lowercase=0       ,			__lowercase="mean"       ,			__lowercase=False       ,			__lowercase=False       ,			__lowercase=2_56       ,			__lowercase=0       ,			__lowercase=1       ,			__lowercase=2       ,			**__lowercase       ,			):
										super().__init__(**__lowercase       ,			pad_token_id=__lowercase       ,			bos_token_id=__lowercase       ,			eos_token_id=__lowercase		)
										__lowerCAmelCase							       =				hidden_size
										__lowerCAmelCase							       =				feat_extract_norm
										__lowerCAmelCase							       =				feat_extract_activation
										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				list(__lowercase		)
										__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							       =				squeeze_factor
										__lowerCAmelCase							       =				max_position_embeddings
										__lowerCAmelCase							       =				position_buckets
										__lowerCAmelCase							       =				share_att_key
										__lowerCAmelCase							       =				relative_attention
										__lowerCAmelCase							       =				norm_rel_ebd
										__lowerCAmelCase							       =				list(__lowercase		)
										__lowerCAmelCase							       =				hidden_act
										__lowerCAmelCase							       =				num_attention_heads
										__lowerCAmelCase							       =				hidden_dropout
										__lowerCAmelCase							       =				attention_dropout
										__lowerCAmelCase							       =				activation_dropout
										__lowerCAmelCase							       =				feat_proj_dropout
										__lowerCAmelCase							       =				final_dropout
										__lowerCAmelCase							       =				layer_norm_eps
										__lowerCAmelCase							       =				feature_layer_norm_eps
										__lowerCAmelCase							       =				initializer_range
										__lowerCAmelCase							       =				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
										__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

										# ctc loss
										__lowerCAmelCase							       =				ctc_loss_reduction
										__lowerCAmelCase							       =				ctc_zero_infinity

										# sequence classification
										__lowerCAmelCase							       =				use_weighted_layer_sum
										__lowerCAmelCase							       =				classifier_proj_size




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





'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			):
										super().__init__()
										self.register_modules(transformer=__lowercase       ,			vae=__lowercase       ,			scheduler=__lowercase		)

										# create a imagenet -> id dictionary for easier use
										__lowerCAmelCase							       =				{}
										if idalabel is not None:
																for key, value in idalabel.items():
																						for label in value.split(''','''		):
																												__lowerCAmelCase							       =				int(__lowercase		)
																__lowerCAmelCase							       =				dict(sorted(self.labels.items()		)		)




				def 					_snake_case  (self       ,			__lowercase		):

										if not isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				list(__lowercase		)

										for l in label:
																if l not in self.labels:
																						raise ValueError(
																						    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

										return [self.labels[l] for l in label]




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = 4.0       ,			__lowercase = None       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				len(__lowercase		)
										__lowerCAmelCase							       =				self.transformer.config.sample_size
										__lowerCAmelCase							       =				self.transformer.config.in_channels

										__lowerCAmelCase							       =				randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)       ,			generator=__lowercase       ,			device=self.device       ,			dtype=self.transformer.dtype       ,			)
										__lowerCAmelCase							       =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

										__lowerCAmelCase							       =				torch.tensor(__lowercase       ,			device=self.device		).reshape(-1		)
										__lowerCAmelCase							       =				torch.tensor([10_00] * batch_size       ,			device=self.device		)
										__lowerCAmelCase							       =				torch.cat([class_labels, class_null]       ,			0		) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__lowercase		)

										for t in self.progress_bar(self.scheduler.timesteps		):
																if guidance_scale > 1:
																						__lowerCAmelCase							       =				latent_model_input[: len(__lowercase		) // 2]
																						__lowerCAmelCase							       =				torch.cat([half, half]       ,			dim=0		)
																__lowerCAmelCase							       =				self.scheduler.scale_model_input(__lowercase       ,			__lowercase		)

																__lowerCAmelCase							       =				t
																if not torch.is_tensor(__lowercase		):
																						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																						# This would be a good case for the `match` statement (Python 3.10+)
																						__lowerCAmelCase							       =				latent_model_input.device.type == '''mps'''
																						if isinstance(__lowercase       ,			__lowercase		):
																												__lowerCAmelCase							       =				torch.floataa if is_mps else torch.floataa
																						else:
																												__lowerCAmelCase							       =				torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=__lowercase       ,			device=latent_model_input.device		)
																elif len(timesteps.shape		) == 0:
																						__lowerCAmelCase							       =				timesteps[None].to(latent_model_input.device		)
																# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																__lowerCAmelCase							       =				timesteps.expand(latent_model_input.shape[0]		)
																# predict noise model_output
																__lowerCAmelCase							       =				self.transformer(
																    __lowercase       ,			timestep=__lowercase       ,			class_labels=__lowercase		).sample

																# perform guidance
																if guidance_scale > 1:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			len(__lowercase		) // 2       ,			dim=0		)

																						__lowerCAmelCase							       =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																						__lowerCAmelCase							       =				torch.cat([half_eps, half_eps]       ,			dim=0		)

																						__lowerCAmelCase							       =				torch.cat([eps, rest]       ,			dim=1		)

																# learned sigma
																if self.transformer.config.out_channels // 2 == latent_channels:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			__lowercase       ,			dim=1		)
																else:
																						__lowerCAmelCase							       =				noise_pred

																# compute previous image: x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase		).prev_sample

										if guidance_scale > 1:
																__lowerCAmelCase       ,					__lowerCAmelCase							       =				latent_model_input.chunk(2       ,			dim=0		)
										else:
																__lowerCAmelCase							       =				latent_model_input

										__lowerCAmelCase							       =				1 / self.vae.config.scaling_factor * latents
										__lowerCAmelCase							       =				self.vae.decode(__lowercase		).sample

										__lowerCAmelCase							       =				(samples / 2 + 0.5).clamp(0       ,			1		)

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

										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=__lowercase		)

