
import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
   from .tokenization_deberta_va import DebertaVaTokenizer
else:
   _A  					= None

_A  					= logging.get_logger(__name__)

_A  					= {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

_A  					= {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

_A  					= {
    "microsoft/deberta-v2-xlarge": 5_12,
    "microsoft/deberta-v2-xxlarge": 5_12,
    "microsoft/deberta-v2-xlarge-mnli": 5_12,
    "microsoft/deberta-v2-xxlarge-mnli": 5_12,
}

_A  					= {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		VOCAB_FILES_NAMES
     UpperCAmelCase__				:							Tuple           =		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__				:							Optional[Any]           =		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__				:							int           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__				:							List[Any]           =		DebertaVaTokenizer





     def __init__(self      :    int				,    _A      :    Optional[Any]=None				,    _A      :    Tuple=None				,    _A      :    Any=False				,    _A      :    Tuple=False				,    _A      :    Union[str, Any]="[CLS]"				,    _A      :    List[Any]="[SEP]"				,    _A      :    Dict="[UNK]"				,    _A      :    List[Any]="[SEP]"				,    _A      :    str="[PAD]"				,    _A      :    List[str]="[CLS]"				,    _A      :    List[str]="[MASK]"				,    **_A      :    Optional[int]				,    )						->						None:
         super().__init__(
             _A				,    tokenizer_file=_A				,    do_lower_case=_A				,    bos_token=_A				,    eos_token=_A				,    unk_token=_A				,    sep_token=_A				,    pad_token=_A				,    cls_token=_A				,    mask_token=_A				,    split_by_punct=_A				,    **_A				,    )

         snake_case      =					do_lower_case
         snake_case      =					split_by_punct
         snake_case      =					vocab_file
         snake_case      =					False if not self.vocab_file else True





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    str				,    _A      :    List[Any]=None   )						->						str:

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         snake_case      =					[self.cls_token_id]
         snake_case      =					[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    List[str]				,    _A      :    List[str]=None				,    _A      :    Optional[int]=False   )						->						Optional[int]:

         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_A				,    token_ids_a=_A				,    already_has_special_tokens=_A   )

         if token_ids_a is not None:
             return [1] + ([0] * len(_A   )) + [1] + ([0] * len(_A   )) + [1]
         return [1] + ([0] * len(_A   )) + [1]





     def 		UpperCAmelCase(self      :    Tuple				,    _A      :    int				,    _A      :    int=None   )						->						List[Any]:
         snake_case      =					[self.sep_token_id]
         snake_case      =					[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 		UpperCAmelCase(self      :    Union[str, Any]				,    _A      :    str				,    _A      :    Optional[str] = None   )						->						Tuple[str]:
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                 "tokenizer."   )

         if not os.path.isdir(_A   ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
             return
         snake_case      =					os.path.join(
             _A				,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

         if os.path.abspath(self.vocab_file   ) != os.path.abspath(_A   ):
             copyfile(self.vocab_file				,    _A   )

         return (out_vocab_file,)



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


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


_A  					= "\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"







class      lowerCamelCase		(  A_  ):





     def __init__(self      :    Tuple				,    _A      :    UNetaDModel				,    _A      :    CMStochasticIterativeScheduler   )						->						None:
         super().__init__()

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

         snake_case      =					None





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    List[str]=0   )						->						Any:
         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"   )

         snake_case      =					torch.device(f'cuda:{gpu_id}'   )

         if self.device.type != "cpu":
             self.to("cpu"				,    silence_dtype_warnings=_A   )
             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(_A				,    _A   )

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





     def 		UpperCAmelCase(self      :    str				,    _A      :    Optional[Any]=0   )						->						Union[str, Any]:
         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."   )

         snake_case      =					torch.device(f'cuda:{gpu_id}'   )

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

         snake_case      =					None
         for cpu_offloaded_model in [self.unet]:
             snake_case     ,							snake_case      =					cpu_offload_with_hook(_A				,    _A				,    prev_module_hook=_A   )

         if self.safety_checker is not None:
             snake_case     ,							snake_case      =					cpu_offload_with_hook(self.safety_checker				,    _A				,    prev_module_hook=_A   )

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





     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 		UpperCAmelCase(self      :    Dict   )						->						Any:
         if not hasattr(self.unet				,    "_hf_hook"   ):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(_A				,    "_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 		UpperCAmelCase(self      :    Dict				,    _A      :    List[str]				,    _A      :    Tuple				,    _A      :    List[str]				,    _A      :    List[Any]				,    _A      :    Dict				,    _A      :    List[str]				,    _A      :    List[str]				,    _A      :    Optional[Any]=None   )						->						Tuple:
         snake_case      =					(batch_size, num_channels, height, width)
         if isinstance(_A				,    _A   ) and len(_A   ) != batch_size:
             raise ValueError(
                 f'You have passed a list of generators of length {len(_A   )}, 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:
             snake_case      =					randn_tensor(_A				,    generator=_A				,    device=_A				,    dtype=_A   )
         else:
             snake_case      =					latents.to(device=_A				,    dtype=_A   )

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





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    torch.FloatTensor				,    _A      :    str = "pil"   )						->						str:
         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
         snake_case      =					(sample / 2 + 0.5).clamp(0				,    1   )
         if output_type == "pt":
             return sample

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

         # Output_type must be 'pil'
         snake_case      =					self.numpy_to_pil(_A   )
         return sample





     def 		UpperCAmelCase(self      :    str				,    _A      :    List[Any]				,    _A      :    Optional[Any]				,    _A      :    List[Any]=None   )						->						List[str]:
         if self.unet.config.num_class_embeds is not None:
             if isinstance(_A				,    _A   ):
                 snake_case      =					torch.tensor(_A				,    dtype=torch.int   )
             elif isinstance(_A				,    _A   ):
                 assert batch_size == 1, "Batch size must be 1 if classes is an int"
                 snake_case      =					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
                 snake_case      =					torch.randint(0				,    self.unet.config.num_class_embeds				,    size=(batch_size,)   )
             snake_case      =					class_labels.to(_A   )
         else:
             snake_case      =					None
         return class_labels





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    Tuple				,    _A      :    Union[str, Any]				,    _A      :    Dict				,    _A      :    Union[str, Any]				,    _A      :    Any				,    _A      :    str   )						->						Optional[Any]:
         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:
             snake_case      =					(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(_A				,    _A   ) or callback_steps <= 0)
         ):
             raise ValueError(
                 f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                 f' {type(_A   )}.'   )





     @torch.no_grad()
     @replace_example_docstring(_A   )
     def __call__(self      :    Union[str, Any]				,    _A      :    int = 1				,    _A      :    Optional[Union[torch.Tensor, List[int], int]] = None				,    _A      :    int = 1				,    _A      :    List[int] = None				,    _A      :    Optional[Union[torch.Generator, List[torch.Generator]]] = None				,    _A      :    Optional[torch.FloatTensor] = None				,    _A      :    Optional[str] = "pil"				,    _A      :    bool = True				,    _A      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,    _A      :    int = 1				,    )						->						List[str]:
         snake_case      =					self.unet.config.sample_size
         snake_case      =					self._execution_device

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

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

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

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

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

                 snake_case      =					self.scheduler.step(_A				,    _A				,    _A				,    generator=_A   )[0]

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

        # 6. Post-process image sample
         snake_case      =					self.postprocess_image(_A				,    output_type=_A   )

         # 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=_A   )

