
"""simple docstring"""




from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_A			:       Union[str, Any]   		=       logging.get_logger(__name__)  # pylint: disable=invalid-name

_A			:       Any   		=       """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       str     , __snake_case				:       Any=8  ) ->				Dict:
       lowercase    :				Union[str, Any]       		=							height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       lowercase    :				str       		=							width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor

class 		a__						(      a_						):
   def __init__(			self	,    _a	,    _a	,    _a	,    ):
          super().__init__()

          self.register_modules(
              unet=_a	,    scheduler=_a	,    movq=_a	,    )
          lowercase    :				Union[str, Any]       		=							2 ** (len(self.movq.config.block_out_channels				) - 1)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a				):
          if latents is None:
                 lowercase    :				Optional[int]       		=							randn_tensor(_a	,    generator=_a	,    device=_a	,    dtype=_a				)
          else:
                 if latents.shape != shape:
                        raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""				)
                 lowercase    :				Optional[Any]       		=							latents.to(_a				)

          lowercase    :				str       		=							latents * scheduler.init_noise_sigma
          return latents
   def 			__magic_name__			(			self	,    _a=0				):
          if is_accelerate_available():
                 from accelerate import cpu_offload
          else:
                 raise ImportError("Please install accelerate via `pip install accelerate`"				)

          lowercase    :				Dict       		=							torch.device(f"""cuda:{gpu_id}"""				)

          lowercase    :				str       		=							[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                        cpu_offload(_a	,    _a				)
   def 			__magic_name__			(			self	,    _a=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."				)

          lowercase    :				List[Any]       		=							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)

          lowercase    :				str       		=							None
          for cpu_offloaded_model in [self.unet, self.movq]:
                 lowercase		,    lowercase    :				Dict       		=							cpu_offload_with_hook(_a	,    _a	,    prev_module_hook=_a				)

          # We'll offload the last model manually.
          lowercase    :				Union[str, Any]       		=							hook
   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 			__magic_name__			(			self				):
          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

   @torch.no_grad()
   @replace_example_docstring(_a				)
   def __call__(			self	,    _a	,    _a	,    _a = 512	,    _a = 512	,    _a = 100	,    _a = 4.0	,    _a = 1	,    _a = None	,    _a = None	,    _a = "pil"	,    _a = True	,    ):
          lowercase    :				int       		=							self._execution_device

          lowercase    :				Optional[Any]       		=							guidance_scale > 1.0

          if isinstance(_a	,    _a				):
                 lowercase    :				Dict       		=							torch.cat(_a	,    dim=0				)
          lowercase    :				Any       		=							image_embeds.shape[0] * num_images_per_prompt
          if isinstance(_a	,    _a				):
                 lowercase    :				Optional[int]       		=							torch.cat(_a	,    dim=0				)

          if do_classifier_free_guidance:
                 lowercase    :				int       		=							image_embeds.repeat_interleave(_a	,    dim=0				)
                 lowercase    :				Optional[int]       		=							negative_image_embeds.repeat_interleave(_a	,    dim=0				)

          lowercase    :				str       		=							torch.cat([negative_image_embeds, image_embeds]	,    dim=0				).to(dtype=self.unet.dtype	,    device=_a				)

          self.scheduler.set_timesteps(_a	,    device=_a				)
          lowercase    :				Dict       		=							self.scheduler.timesteps

          lowercase    :				List[Any]       		=							self.unet.config.in_channels

          lowercase		,    lowercase    :				Dict       		=							downscale_height_and_width(_a	,    _a	,    self.movq_scale_factor				)

          # create initial latent
          lowercase    :				List[Any]       		=							self.prepare_latents(
              (batch_size, num_channels_latents, height, width)	,    image_embeds.dtype	,    _a	,    _a	,    _a	,    self.scheduler	,    )

          for i, t in enumerate(self.progress_bar(_a				)				):
                 # expand the latents if we are doing classifier free guidance
                 lowercase    :				Any       		=							torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

                 lowercase    :				Optional[int]       		=							{"image_embeds": image_embeds}
                 lowercase    :				Dict       		=							self.unet(
                     sample=_a	,    timestep=_a	,    encoder_hidden_states=_a	,    added_cond_kwargs=_a	,    return_dict=_a	,    )[0]

                 if do_classifier_free_guidance:
                        lowercase		,    lowercase    :				Dict       		=							noise_pred.split(latents.shape[1]	,    dim=1				)
                        lowercase		,    lowercase    :				List[str]       		=							noise_pred.chunk(2				)
                        lowercase		,    lowercase    :				int       		=							variance_pred.chunk(2				)
                        lowercase    :				int       		=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                        lowercase    :				str       		=							torch.cat([noise_pred, variance_pred_text]	,    dim=1				)

                 if not (
                     hasattr(self.scheduler.config	,    "variance_type"				)
                     and self.scheduler.config.variance_type in ["learned", "learned_range"]
                 ):
                        lowercase		,    lowercase    :				Tuple       		=							noise_pred.split(latents.shape[1]	,    dim=1				)

                 # compute the previous noisy sample x_t -> x_t-1
                 lowercase    :				str       		=							self.scheduler.step(
                     _a	,    _a	,    _a	,    generator=_a	,    )[0]
          # post-processing
          lowercase    :				str       		=							self.movq.decode(_a	,    force_not_quantize=_a				)["sample"]

          if output_type not in ["pt", "np", "pil"]:
                 raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""				)

          if output_type in ["np", "pil"]:
                 lowercase    :				Any       		=							image * 0.5 + 0.5
                 lowercase    :				List[Any]       		=							image.clamp(0	,    1				)
                 lowercase    :				Tuple       		=							image.cpu().permute(0	,    2	,    3	,    1				).float().numpy()

          if output_type == "pil":
                 lowercase    :				Optional[Any]       		=							self.numpy_to_pil(_a				)

          if not return_dict:
                 return (image,)

          return ImagePipelineOutput(images=_a				)




"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_A			:       Optional[int]   		=       logging.getLogger(__name__)

class 		a__						(      a_						):
   def __init__(			self	,    _a=-1				):
          # in NER datasets, the last column is usually reserved for NER label
          lowercase    :				List[str]       		=							label_idx
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				Optional[Any]       		=							mode.value
          lowercase    :				List[str]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				str       		=							1
          lowercase    :				Optional[int]       		=							[]
          with open(_a	,    encoding="utf-8"				) as f:
                 lowercase    :				List[Any]       		=							[]
                 lowercase    :				Optional[int]       		=							[]
                 for line in f:
                        if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                               if words:
                                      examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                                      guid_index += 1
                                      lowercase    :				int       		=							[]
                                      lowercase    :				int       		=							[]
                        else:
                               lowercase    :				Optional[Any]       		=							line.split(" "				)
                               words.append(splits[0]				)
                               if len(_a				) > 1:
                                      labels.append(splits[self.label_idx].replace("\n"	,    ""				)				)
                               else:
                                      # Examples could have no label for mode = "test"
                                      labels.append("O"				)
                 if words:
                        examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							0
          for line in test_input_reader:
                 if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                        writer.write(_a				)
                        if not preds_list[example_id]:
                               example_id += 1
                 elif preds_list[example_id]:
                        lowercase    :				Any       		=							line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"
                        writer.write(_a				)
                 else:
                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,    line.split()[0]				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Optional[Any]       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				List[Any]       		=							["O"] + labels
                 return labels
          else:
                 return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]


class 		a__						(      a_						):
   def __init__(			self				):
          # in CONLL2003 dataset chunk column is second-to-last
          super().__init__(label_idx=-2				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Tuple       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				Optional[int]       		=							["O"] + labels
                 return labels
          else:
                 return [
                     "O",
                     "B-ADVP",
                     "B-INTJ",
                     "B-LST",
                     "B-PRT",
                     "B-NP",
                     "B-SBAR",
                     "B-VP",
                     "B-ADJP",
                     "B-CONJP",
                     "B-PP",
                     "I-ADVP",
                     "I-INTJ",
                     "I-LST",
                     "I-PRT",
                     "I-NP",
                     "I-SBAR",
                     "I-VP",
                     "I-ADJP",
                     "I-CONJP",
                     "I-PP",
                 ]


class 		a__						(      a_						):
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				List[Any]       		=							mode.value
          lowercase    :				Optional[int]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				Tuple       		=							1
          lowercase    :				List[str]       		=							[]

          with open(_a	,    encoding="utf-8"				) as f:
                 for sentence in parse_incr(_a				):
                        lowercase    :				Optional[Any]       		=							[]
                        lowercase    :				str       		=							[]
                        for token in sentence:
                               words.append(token["form"]				)
                               labels.append(token["upos"]				)
                        assert len(_a				) == len(_a				)
                        if words:
                               examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                               guid_index += 1
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				str       		=							0
          for sentence in parse_incr(_a				):
                 lowercase    :				List[Any]       		=							preds_list[example_id]
                 lowercase    :				List[str]       		=							""
                 for token in sentence:
                        out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0				)}) """
                 out += "\n"
                 writer.write(_a				)
                 example_id += 1

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        return f.read().splitlines()
          else:
                 return [
                     "ADJ",
                     "ADP",
                     "ADV",
                     "AUX",
                     "CCONJ",
                     "DET",
                     "INTJ",
                     "NOUN",
                     "NUM",
                     "PART",
                     "PRON",
                     "PROPN",
                     "PUNCT",
                     "SCONJ",
                     "SYM",
                     "VERB",
                     "X",
                 ]


