

"""simple docstring"""



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

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


SCREAMING_SNAKE_CASE_					:	str							  =						logging.getLogger(__name__)



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: List[Any]    ,						UpperCamelCase: Union[str, Any]=-1       ):






         """simple docstring"""


         A__										=  label_idx

    def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: Union[Split, str]       ):






         """simple docstring"""


         if isinstance(UpperCamelCase    ,						UpperCamelCase       ):
              A__										=  mode.value
         A__										=  os.path.join(UpperCamelCase    ,						f"""{mode}.txt"""       )
         A__										=  1
         A__										=  []
         with open(UpperCamelCase    ,						encoding="""utf-8"""       ) as f:
              A__										=  []
              A__										=  []
              for line in f:
                   if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
                        if words:
                             examples.append(InputExample(guid=f"""{mode}-{guid_index}"""    ,						words=UpperCamelCase    ,						labels=UpperCamelCase       )       )
                             guid_index += 1
                             A__										=  []
                             A__										=  []
                   else:
                        A__										=  line.split(""" """       )
                        words.append(splits[0]       )
                        if len(UpperCamelCase       ) > 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=UpperCamelCase    ,						labels=UpperCamelCase       )       )
         return examples

    def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: TextIO    ,						UpperCamelCase: TextIO    ,						UpperCamelCase: List       ):






         """simple docstring"""


         A__										=  0
         for line in test_input_reader:
              if line.startswith("""-DOCSTART-"""       ) or line == "" or line == "\n":
                   writer.write(UpperCamelCase       )
                   if not preds_list[example_id]:
                        example_id += 1
              elif preds_list[example_id]:
                   A__										=  line.split()[0] + """ """ + preds_list[example_id].pop(0       ) + """\n"""
                   writer.write(UpperCamelCase       )
              else:
                   logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""    ,						line.split()[0]       )





    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: str       ):






         """simple docstring"""


         if path:
              with open(UpperCamelCase    ,						"""r"""       ) as f:
                   A__										=  f.read().splitlines()
              if "O" not in labels:
                   A__										=  ["""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						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: List[str]       ):






         """simple docstring"""


         super().__init__(label_idx=-2       )





    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: str       ):






         """simple docstring"""


         if path:
              with open(UpperCamelCase    ,						"""r"""       ) as f:
                   A__										=  f.read().splitlines()
              if "O" not in labels:
                   A__										=  ["""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						(       _lowerCamelCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Union[Split, str]       ):






         """simple docstring"""


         if isinstance(UpperCamelCase    ,						UpperCamelCase       ):
              A__										=  mode.value
         A__										=  os.path.join(UpperCamelCase    ,						f"""{mode}.txt"""       )
         A__										=  1
         A__										=  []

         with open(UpperCamelCase    ,						encoding="""utf-8"""       ) as f:
              for sentence in parse_incr(UpperCamelCase       ):
                   A__										=  []
                   A__										=  []
                   for token in sentence:
                        words.append(token["""form"""]       )
                        labels.append(token["""upos"""]       )
                   assert len(UpperCamelCase       ) == len(UpperCamelCase       )
                   if words:
                        examples.append(InputExample(guid=f"""{mode}-{guid_index}"""    ,						words=UpperCamelCase    ,						labels=UpperCamelCase       )       )
                        guid_index += 1
         return examples

    def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: TextIO    ,						UpperCamelCase: TextIO    ,						UpperCamelCase: List       ):






         """simple docstring"""


         A__										=  0
         for sentence in parse_incr(UpperCamelCase       ):
              A__										=  preds_list[example_id]
              A__										=  """"""
              for token in sentence:
                   out += f"""{token['form']} ({token['upos']}|{s_p.pop(0       )}) """
              out += "\n"
              writer.write(UpperCamelCase       )
              example_id += 1





    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str       ):






         """simple docstring"""


         if path:
              with open(UpperCamelCase    ,						"""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",
              ]









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  UNetaDModel(
             block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,						)
         return model





    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

         A__										=  image[0, -3:, -3:, -1]
         A__										=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2



@slow
@require_torch
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

         A__										=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2






