






"""simple docstring"""







# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a			:    List[Any]				 =		{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    Tuple				 =		[
			    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """CpmAntForCausalLM""",
			    """CpmAntModel""",
			    """CpmAntPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
			from .tokenization_cpmant import CpmAntTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_cpmant import (
						    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CpmAntForCausalLM,
						    CpmAntModel,
						    CpmAntPreTrainedModel,
						)


else:
			import sys

			a			:    Any				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)














"""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    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  ):
												'''simple docstring'''
												super().__init__()
												self.register_modules(transformer=snake_case__	,  vae=snake_case__	,  scheduler=snake_case__      )

												# create a imagenet -> id dictionary for easier use
												lowercase__    :				int=	{}
												if idalabel is not None:
																		for key, value in idalabel.items():
																								for label in value.split(","      ):
																														lowercase__    :				Tuple=	int(snake_case__      )
																		lowercase__    :				Union[str, Any]=	dict(sorted(self.labels.items()      )      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''

												if not isinstance(snake_case__	,  snake_case__      ):
																		lowercase__    :				List[Any]=	list(snake_case__      )

												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	,  snake_case__	,  snake_case__ = 4.0	,  snake_case__ = None	,  snake_case__ = 50	,  snake_case__ = "pil"	,  snake_case__ = True	,  ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	len(snake_case__      )
												lowercase__    :				Optional[int]=	self.transformer.config.sample_size
												lowercase__    :				List[str]=	self.transformer.config.in_channels

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

												lowercase__    :				Tuple=	torch.tensor(snake_case__	,  device=self.device      ).reshape(-1      )
												lowercase__    :				Any=	torch.tensor([1000] * batch_size	,  device=self.device      )
												lowercase__    :				Tuple=	torch.cat([class_labels, class_null]	,  0      ) if guidance_scale > 1 else class_labels

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

												for t in self.progress_bar(self.scheduler.timesteps      ):
																		if guidance_scale > 1:
																								lowercase__    :				List[str]=	latent_model_input[: len(snake_case__      ) // 2]
																								lowercase__    :				int=	torch.cat([half, half]	,  dim=0      )
																		lowercase__    :				Union[str, Any]=	self.scheduler.scale_model_input(snake_case__	,  snake_case__      )

																		lowercase__    :				Optional[int]=	t
																		if not torch.is_tensor(snake_case__      ):
																								# 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+)
																								lowercase__    :				List[str]=	latent_model_input.device.type == "mps"
																								if isinstance(snake_case__	,  snake_case__      ):
																														lowercase__    :				int=	torch.floataa if is_mps else torch.floataa
																								else:
																														lowercase__    :				Dict=	torch.intaa if is_mps else torch.intaa
																								lowercase__    :				Tuple=	torch.tensor([timesteps]	,  dtype=snake_case__	,  device=latent_model_input.device      )
																		elif len(timesteps.shape      ) == 0:
																								lowercase__    :				Dict=	timesteps[None].to(latent_model_input.device      )
																		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																		lowercase__    :				int=	timesteps.expand(latent_model_input.shape[0]      )
																		# predict noise model_output
																		lowercase__    :				Union[str, Any]=	self.transformer(
																		    snake_case__	,  timestep=snake_case__	,  class_labels=snake_case__      ).sample

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

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

																								lowercase__    :				Optional[int]=	torch.cat([eps, rest]	,  dim=1      )

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

																		# compute previous image: x_t -> x_t-1
																		lowercase__    :				List[Any]=	self.scheduler.step(snake_case__	,  snake_case__	,  snake_case__      ).prev_sample

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

												lowercase__    :				Dict=	1 / self.vae.config.scaling_factor * latents
												lowercase__    :				Any=	self.vae.decode(snake_case__      ).sample

												lowercase__    :				Tuple=	(samples / 2 + 0.5).clamp(0	,  1      )

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

												if output_type == "pil":
																		lowercase__    :				Optional[Any]=	self.numpy_to_pil(snake_case__      )

												if not return_dict:
																		return (samples,)

												return ImagePipelineOutput(images=snake_case__      )






