


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        lowerCamelCase      (     _UpperCamelCase   ):


	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
							__UpperCAmelCase :       List[str]  						=  {}
							if idalabel is not None:
													for key, value in idalabel.items():
																			for label in value.split(''','''):
																									__UpperCAmelCase :       Dict  						=  int(lowercase__)
													__UpperCAmelCase :       Tuple  						=  dict(sorted(self.labels.items()))


	def    A(						self							,    lowercase__):

							if not isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Dict  						=  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__ = 5_0							,    lowercase__ = "pil"							,    lowercase__ = True							,    ):
							__UpperCAmelCase :       List[str]  						=  len(lowercase__)
							__UpperCAmelCase :       str  						=  self.transformer.config.sample_size
							__UpperCAmelCase :       List[str]  						=  self.transformer.config.in_channels

							__UpperCAmelCase :       Union[str, Any]  						=  randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)							,    generator=lowercase__							,    device=self.device							,    dtype=self.transformer.dtype							,    )
							__UpperCAmelCase :       Optional[Any]  						=  torch.cat([latents] * 2) if guidance_scale > 1 else latents

							__UpperCAmelCase :       Union[str, Any]  						=  torch.tensor(lowercase__							,    device=self.device).reshape(-1)
							__UpperCAmelCase :       Dict  						=  torch.tensor([1_0_0_0] * batch_size							,    device=self.device)
							__UpperCAmelCase :       int  						=  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:
																			__UpperCAmelCase :       List[str]  						=  latent_model_input[: len(lowercase__) // 2]
																			__UpperCAmelCase :       Optional[Any]  						=  torch.cat([half, half]							,    dim=0)
													__UpperCAmelCase :       Optional[Any]  						=  self.scheduler.scale_model_input(lowercase__							,    lowercase__)

													__UpperCAmelCase :       Any  						=  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+)
																			__UpperCAmelCase :       List[str]  						=  latent_model_input.device.type == '''mps'''
																			if isinstance(lowercase__							,    lowercase__):
																									__UpperCAmelCase :       Tuple  						=  torch.floataa if is_mps else torch.floataa
																			else:
																									__UpperCAmelCase :       Dict  						=  torch.intaa if is_mps else torch.intaa
																			__UpperCAmelCase :       List[str]  						=  torch.tensor([timesteps]							,    dtype=lowercase__							,    device=latent_model_input.device)
													elif len(timesteps.shape) == 0:
																			__UpperCAmelCase :       List[str]  						=  timesteps[None].to(latent_model_input.device)
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													__UpperCAmelCase :       Optional[int]  						=  timesteps.expand(latent_model_input.shape[0])
													# predict noise model_output
													__UpperCAmelCase :       Any  						=  self.transformer(
													    lowercase__							,    timestep=lowercase__							,    class_labels=lowercase__).sample

													# perform guidance
													if guidance_scale > 1:
																			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  torch.split(lowercase__							,    len(lowercase__) // 2							,    dim=0)

																			__UpperCAmelCase :       List[str]  						=  uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			__UpperCAmelCase :       str  						=  torch.cat([half_eps, half_eps]							,    dim=0)

																			__UpperCAmelCase :       Any  						=  torch.cat([eps, rest]							,    dim=1)

													# learned sigma
													if self.transformer.config.out_channels // 2 == latent_channels:
																			__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  torch.split(lowercase__							,    lowercase__							,    dim=1)
													else:
																			__UpperCAmelCase :       Any  						=  noise_pred

													# compute previous image: x_t -> x_t-1
													__UpperCAmelCase :       Dict  						=  self.scheduler.step(lowercase__							,    lowercase__							,    lowercase__).prev_sample

							if guidance_scale > 1:
													__UpperCAmelCase						,			__UpperCAmelCase :       Any  						=  latent_model_input.chunk(2							,    dim=0)
							else:
													__UpperCAmelCase :       List[Any]  						=  latent_model_input

							__UpperCAmelCase :       List[str]  						=  1 / self.vae.config.scaling_factor * latents
							__UpperCAmelCase :       Optional[int]  						=  self.vae.decode(lowercase__).sample

							__UpperCAmelCase :       List[str]  						=  (samples / 2 + 0.5).clamp(0							,    1)

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

							if output_type == "pil":
													__UpperCAmelCase :       Optional[int]  						=  self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (samples,)

							return ImagePipelineOutput(images=lowercase__)





import functools
import operator

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


lowerCAmelCase							=						logging.get_logger(__name__)

lowerCAmelCase							=						{
    """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        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		List[str]        =	'''sew-d'''


	def __init__(						self							,    lowercase__=3_2							,    lowercase__=7_6_8							,    lowercase__=1_2							,    lowercase__=1_2							,    lowercase__=3_0_7_2							,    lowercase__=2							,    lowercase__=5_1_2							,    lowercase__=2_5_6							,    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__=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)							,    lowercase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,    lowercase__=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,    lowercase__=False							,    lowercase__=1_2_8							,    lowercase__=1_6							,    lowercase__=True							,    lowercase__=0.0_5							,    lowercase__=1_0							,    lowercase__=2							,    lowercase__=0.0							,    lowercase__=1_0							,    lowercase__=0							,    lowercase__="mean"							,    lowercase__=False							,    lowercase__=False							,    lowercase__=2_5_6							,    lowercase__=0							,    lowercase__=1							,    lowercase__=2							,    **lowercase__							,    ):
							super().__init__(**lowercase__							,    pad_token_id=lowercase__							,    bos_token_id=lowercase__							,    eos_token_id=lowercase__)
							__UpperCAmelCase :       Dict  						=  hidden_size
							__UpperCAmelCase :       int  						=  feat_extract_norm
							__UpperCAmelCase :       List[str]  						=  feat_extract_activation
							__UpperCAmelCase :       str  						=  list(lowercase__)
							__UpperCAmelCase :       Optional[int]  						=  list(lowercase__)
							__UpperCAmelCase :       Tuple  						=  list(lowercase__)
							__UpperCAmelCase :       Tuple  						=  conv_bias
							__UpperCAmelCase :       int  						=  num_conv_pos_embeddings
							__UpperCAmelCase :       int  						=  num_conv_pos_embedding_groups
							__UpperCAmelCase :       Any  						=  len(self.conv_dim)
							__UpperCAmelCase :       str  						=  num_hidden_layers
							__UpperCAmelCase :       Optional[Any]  						=  intermediate_size
							__UpperCAmelCase :       Union[str, Any]  						=  squeeze_factor
							__UpperCAmelCase :       Union[str, Any]  						=  max_position_embeddings
							__UpperCAmelCase :       List[str]  						=  position_buckets
							__UpperCAmelCase :       Tuple  						=  share_att_key
							__UpperCAmelCase :       int  						=  relative_attention
							__UpperCAmelCase :       str  						=  norm_rel_ebd
							__UpperCAmelCase :       Dict  						=  list(lowercase__)
							__UpperCAmelCase :       int  						=  hidden_act
							__UpperCAmelCase :       int  						=  num_attention_heads
							__UpperCAmelCase :       Optional[int]  						=  hidden_dropout
							__UpperCAmelCase :       int  						=  attention_dropout
							__UpperCAmelCase :       Optional[int]  						=  activation_dropout
							__UpperCAmelCase :       Optional[Any]  						=  feat_proj_dropout
							__UpperCAmelCase :       Optional[Any]  						=  final_dropout
							__UpperCAmelCase :       Optional[int]  						=  layer_norm_eps
							__UpperCAmelCase :       str  						=  feature_layer_norm_eps
							__UpperCAmelCase :       Optional[int]  						=  initializer_range
							__UpperCAmelCase :       Tuple  						=  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
							__UpperCAmelCase :       Optional[int]  						=  apply_spec_augment
							__UpperCAmelCase :       List[str]  						=  mask_time_prob
							__UpperCAmelCase :       Union[str, Any]  						=  mask_time_length
							__UpperCAmelCase :       Optional[int]  						=  mask_time_min_masks
							__UpperCAmelCase :       Optional[int]  						=  mask_feature_prob
							__UpperCAmelCase :       List[str]  						=  mask_feature_length
							__UpperCAmelCase :       List[Any]  						=  mask_feature_min_masks

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

							# sequence classification
							__UpperCAmelCase :       List[str]  						=  use_weighted_layer_sum
							__UpperCAmelCase :       Tuple  						=  classifier_proj_size


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

