





from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase__    :      List[Any]       					=logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCAmelCase__    :      Optional[Any]       					='''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''

class    __A  (    a     ):


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

								self.register_modules(
								    unet=UpperCAmelCase_ ,			scheduler=UpperCAmelCase_ ,			)

								lowerCamelCase									=None


				def _snake_case							(		self ,			UpperCAmelCase_=0				):
								if is_accelerate_available() and is_accelerate_version(""">=""" ,			"""0.14.0"""				):
												from accelerate import cpu_offload
								else:
												raise ImportError("""`enable_sequential_cpu_offload` requires `accelerate v0.14.0` 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)

								for cpu_offloaded_model in [self.unet]:
												cpu_offload(UpperCAmelCase_ ,			UpperCAmelCase_				)

								if self.safety_checker is not None:
												cpu_offload(self.safety_checker ,			execution_device=UpperCAmelCase_ ,			offload_buffers=UpperCAmelCase_				)


				def _snake_case							(		self ,			UpperCAmelCase_=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]:
												lowerCamelCase			,	lowerCamelCase									=cpu_offload_with_hook(UpperCAmelCase_ ,			UpperCAmelCase_ ,			prev_module_hook=UpperCAmelCase_				)

								if self.safety_checker is not None:
												lowerCamelCase			,	lowerCamelCase									=cpu_offload_with_hook(self.safety_checker ,			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 _snake_case							(		self				):
								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


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=(batch_size, num_channels, height, width)
								if isinstance(UpperCAmelCase_ ,			UpperCAmelCase_				) and len(UpperCAmelCase_				) != batch_size:
												raise ValueError(
												    f"""You have passed a list of generators of length {len(UpperCAmelCase_				)}, but requested an effective batch"""
												    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

								if latents is None:
												lowerCamelCase									=randn_tensor(UpperCAmelCase_ ,			generator=UpperCAmelCase_ ,			device=UpperCAmelCase_ ,			dtype=UpperCAmelCase_				)
								else:
												lowerCamelCase									=latents.to(device=UpperCAmelCase_ ,			dtype=UpperCAmelCase_				)

								# scale the initial noise by the standard deviation required by the scheduler
								lowerCamelCase									=latents * self.scheduler.init_noise_sigma
								return latents


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = "pil"				):
								if output_type not in ["pt", "np", "pil"]:
												raise ValueError(
												    f"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""				)

								# Equivalent to diffusers.VaeImageProcessor.denormalize
								lowerCamelCase									=(sample / 2 + 0.5).clamp(0 ,			1				)
								if output_type == "pt":
												return sample

								# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
								lowerCamelCase									=sample.cpu().permute(0 ,			2 ,			3 ,			1				).numpy()
								if output_type == "np":
												return sample

								# Output_type must be 'pil'
								lowerCamelCase									=self.numpy_to_pil(UpperCAmelCase_				)
								return sample


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								if self.unet.config.num_class_embeds is not None:
												if isinstance(UpperCAmelCase_ ,			UpperCAmelCase_				):
																lowerCamelCase									=torch.tensor(UpperCAmelCase_ ,			dtype=torch.int				)
												elif isinstance(UpperCAmelCase_ ,			UpperCAmelCase_				):
																assert batch_size == 1, "Batch size must be 1 if classes is an int"
																lowerCamelCase									=torch.tensor([class_labels] ,			dtype=torch.int				)
												elif class_labels is None:
																# Randomly generate batch_size class labels
																# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																lowerCamelCase									=torch.randint(0 ,			self.unet.config.num_class_embeds ,			size=(batch_size,)				)
												lowerCamelCase									=class_labels.to(UpperCAmelCase_				)
								else:
												lowerCamelCase									=None
								return class_labels


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								if num_inference_steps is None and timesteps is None:
												raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""				)

								if num_inference_steps is not None and timesteps is not None:
												logger.warning(
												    f"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
												    """ `timesteps` will be used over `num_inference_steps`."""				)

								if latents is not None:
												lowerCamelCase									=(batch_size, 3, img_size, img_size)
												if latents.shape != expected_shape:
																raise ValueError(f"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""				)

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(UpperCAmelCase_ ,			UpperCAmelCase_				) or callback_steps <= 0)
								):
												raise ValueError(
												    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
												    f""" {type(UpperCAmelCase_				)}."""				)


				@torch.no_grad()
				@replace_example_docstring(UpperCAmelCase_				)
				def __call__(		self ,			UpperCAmelCase_ = 1 ,			UpperCAmelCase_ = None ,			UpperCAmelCase_ = 1 ,			UpperCAmelCase_ = None ,			UpperCAmelCase_ = None ,			UpperCAmelCase_ = None ,			UpperCAmelCase_ = "pil" ,			UpperCAmelCase_ = True ,			UpperCAmelCase_ = None ,			UpperCAmelCase_ = 1 ,			):
								lowerCamelCase									=self.unet.config.sample_size
								lowerCamelCase									=self._execution_device

								# 1. Check inputs
								self.check_inputs(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				)

								# 2. Prepare image latents
								# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
								lowerCamelCase									=self.prepare_latents(
								    batch_size=UpperCAmelCase_ ,			num_channels=self.unet.config.in_channels ,			height=UpperCAmelCase_ ,			width=UpperCAmelCase_ ,			dtype=self.unet.dtype ,			device=UpperCAmelCase_ ,			generator=UpperCAmelCase_ ,			latents=UpperCAmelCase_ ,			)

								# 3. Handle class_labels for class-conditional models
								lowerCamelCase									=self.prepare_class_labels(UpperCAmelCase_ ,			UpperCAmelCase_ ,			class_labels=UpperCAmelCase_				)

								# 4. Prepare timesteps
								if timesteps is not None:
												self.scheduler.set_timesteps(timesteps=UpperCAmelCase_ ,			device=UpperCAmelCase_				)
												lowerCamelCase									=self.scheduler.timesteps
												lowerCamelCase									=len(UpperCAmelCase_				)
								else:
												self.scheduler.set_timesteps(UpperCAmelCase_				)
												lowerCamelCase									=self.scheduler.timesteps

								# 5. Denoising loop
								# Multistep sampling: implements Algorithm 1 in the paper
								with self.progress_bar(total=UpperCAmelCase_				) as progress_bar:
												for i, t in enumerate(UpperCAmelCase_				):
																lowerCamelCase									=self.scheduler.scale_model_input(UpperCAmelCase_ ,			UpperCAmelCase_				)
																lowerCamelCase									=self.unet(UpperCAmelCase_ ,			UpperCAmelCase_ ,			class_labels=UpperCAmelCase_ ,			return_dict=UpperCAmelCase_				)[0]

																lowerCamelCase									=self.scheduler.step(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			generator=UpperCAmelCase_				)[0]

																# call the callback, if provided
																progress_bar.update()
																if callback is not None and i % callback_steps == 0:
																				callback(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_				)

        # 6. Post-process image sample
								lowerCamelCase									=self.postprocess_image(UpperCAmelCase_ ,			output_type=UpperCAmelCase_				)

								# 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 (image,)

								return ImagePipelineOutput(images=UpperCAmelCase_				)












import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase__    :      int       					=logging.get_logger(__name__)

UpperCAmelCase__    :      Optional[Any]       					={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__    :      List[str]       					={
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__    :      Dict       					={
    '''microsoft/layoutlm-base-uncased''': 5_12,
    '''microsoft/layoutlm-large-uncased''': 5_12,
}

UpperCAmelCase__    :      List[Any]       					={
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}

class    __A  (    a     ):
				__A  =					VOCAB_FILES_NAMES
				__A  =					PRETRAINED_VOCAB_FILES_MAP
				__A  =					PRETRAINED_INIT_CONFIGURATION
				__A  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A  =					LayoutLMTokenizer


				def __init__(		self ,			UpperCAmelCase_=None ,			UpperCAmelCase_=None ,			UpperCAmelCase_=True ,			UpperCAmelCase_="[UNK]" ,			UpperCAmelCase_="[SEP]" ,			UpperCAmelCase_="[PAD]" ,			UpperCAmelCase_="[CLS]" ,			UpperCAmelCase_="[MASK]" ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None ,			**UpperCAmelCase_ ,			):
								super().__init__(
								    UpperCAmelCase_ ,			tokenizer_file=UpperCAmelCase_ ,			do_lower_case=UpperCAmelCase_ ,			unk_token=UpperCAmelCase_ ,			sep_token=UpperCAmelCase_ ,			pad_token=UpperCAmelCase_ ,			cls_token=UpperCAmelCase_ ,			mask_token=UpperCAmelCase_ ,			tokenize_chinese_chars=UpperCAmelCase_ ,			strip_accents=UpperCAmelCase_ ,			**UpperCAmelCase_ ,			)

								lowerCamelCase									=json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase""" ,			UpperCAmelCase_				) != do_lower_case
								    or normalizer_state.get("""strip_accents""" ,			UpperCAmelCase_				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars""" ,			UpperCAmelCase_				) != tokenize_chinese_chars
								):
												lowerCamelCase									=getattr(UpperCAmelCase_ ,			normalizer_state.pop("""type"""				)				)
												lowerCamelCase									=do_lower_case
												lowerCamelCase									=strip_accents
												lowerCamelCase									=tokenize_chinese_chars
												lowerCamelCase									=normalizer_class(**UpperCAmelCase_				)

								lowerCamelCase									=do_lower_case


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=[self.sep_token_id]
								lowerCamelCase									=[self.cls_token_id]
								if token_ids_a is None:
												return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=self._tokenizer.model.save(UpperCAmelCase_ ,			name=UpperCAmelCase_				)
								return tuple(UpperCAmelCase_				)





