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

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


lowerCamelCase__          =		logging.getLogger(__name__)







class      A__							(     __magic_name__	):






				def __init__(						self					:      Optional[Any]    ,     a					:      Tuple=-1   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		label_idx






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      Optional[Any]    ,     a					:      Union[Split, str]   ):






							'''simple docstring'''







							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      Union[str, Any]         =		mode.value
							lowerCAmelCase__      :      int         =		os.path.join(a    ,     f'''{mode}.txt'''   )
							lowerCAmelCase__      :      Optional[int]         =		1
							lowerCAmelCase__      :      Optional[int]         =		[]
							with open(a    ,     encoding='utf-8'   ) as f:
										lowerCAmelCase__      :      str         =		[]
										lowerCAmelCase__      :      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
																			lowerCAmelCase__      :      Optional[Any]         =		[]
																			lowerCAmelCase__      :      Any         =		[]
													else:
																lowerCAmelCase__      :      Optional[int]         =		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 							_lowerCamelCase		(						self					:      str    ,     a					:      TextIO    ,     a					:      TextIO    ,     a					:      List   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		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]:
													lowerCAmelCase__      :      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 							_lowerCamelCase		(						self					:      Any    ,     a					:      str   ):






							'''simple docstring'''







							if path:
										with open(a    ,     'r'   ) as f:
													lowerCAmelCase__      :      int         =		f.read().splitlines()
										if "O" not in labels:
													lowerCAmelCase__      :      str         =		['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__							(     __magic_name__	):






				def __init__(						self					:      Optional[Any]   ):






							'''simple docstring'''







							super().__init__(label_idx=-2   )






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      str   ):






							'''simple docstring'''







							if path:
										with open(a    ,     'r'   ) as f:
													lowerCAmelCase__      :      List[Any]         =		f.read().splitlines()
										if "O" not in labels:
													lowerCAmelCase__      :      str         =		['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__							(     __magic_name__	):






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      List[str]    ,     a					:      Union[Split, str]   ):






							'''simple docstring'''







							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      Union[str, Any]         =		mode.value
							lowerCAmelCase__      :      Union[str, Any]         =		os.path.join(a    ,     f'''{mode}.txt'''   )
							lowerCAmelCase__      :      Dict         =		1
							lowerCAmelCase__      :      str         =		[]

							with open(a    ,     encoding='utf-8'   ) as f:
										for sentence in parse_incr(a   ):
													lowerCAmelCase__      :      Dict         =		[]
													lowerCAmelCase__      :      List[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 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      TextIO    ,     a					:      TextIO    ,     a					:      List   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      str   ):






							'''simple docstring'''







							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",
										]

import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class      A__							:






				@property
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							return self.get_dummy_input()






				@property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							if self.block_type == "down":
										return (4, 32, 16, 16)
							elif self.block_type == "mid":
										return (4, 32, 32, 32)
							elif self.block_type == "up":
										return (4, 32, 64, 64)

							raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''   )






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[Any]=True    ,     a					:      Any=False    ,     a					:      Dict=False    ,     a					:      Union[str, Any]=False    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		4
							lowerCAmelCase__      :      int         =		32
							lowerCAmelCase__      :      Tuple         =		(32, 32)

							lowerCAmelCase__      :      List[Any]         =		torch.manual_seed(0   )
							lowerCAmelCase__      :      List[Any]         =		torch.device(a   )
							lowerCAmelCase__      :      str         =		(batch_size, num_channels) + sizes
							lowerCAmelCase__      :      Tuple         =		randn_tensor(a    ,     generator=a    ,     device=a   )
							lowerCAmelCase__      :      Optional[Any]         =		{'hidden_states': hidden_states}

							if include_temb:
										lowerCAmelCase__      :      int         =		128
										lowerCAmelCase__      :      List[str]         =		randn_tensor((batch_size, temb_channels)    ,     generator=a    ,     device=a   )

							if include_res_hidden_states_tuple:
										lowerCAmelCase__      :      int         =		torch.manual_seed(1   )
										lowerCAmelCase__      :      str         =		(randn_tensor(a    ,     generator=a    ,     device=a   ),)

							if include_encoder_hidden_states:
										lowerCAmelCase__      :      Any         =		floats_tensor((batch_size, 32, 32)   ).to(a   )

							if include_skip_sample:
										lowerCAmelCase__      :      Union[str, Any]         =		randn_tensor(((batch_size, 3) + sizes)    ,     generator=a    ,     device=a   )

							return dummy_input






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		{
							    'in_channels': 32,
							    'out_channels': 32,
							    'temb_channels': 128,
							}
							if self.block_type == "up":
										lowerCAmelCase__      :      Union[str, Any]         =		32

							if self.block_type == "mid":
										init_dict.pop('out_channels'   )

							lowerCAmelCase__      :      Optional[int]         =		self.dummy_input
							return init_dict, inputs_dict






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      int         =		self.block_class(**a   )
							unet_block.to(a   )
							unet_block.eval()

							with torch.no_grad():
										lowerCAmelCase__      :      int         =		unet_block(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      List[str]         =		output[0]

							self.assertEqual(output.shape    ,     self.output_shape   )

							lowerCAmelCase__      :      List[str]         =		output[0, -1, -3:, -3:]
							lowerCAmelCase__      :      Any         =		torch.tensor(a   ).to(a   )
							assert torch_all_close(output_slice.flatten()    ,     a    ,     atol=5E-3   )






				@unittest.skipIf(torch_device == 'mps'    ,     'Training is not supported in mps'   )
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      Any         =		self.block_class(**a   )
							model.to(a   )
							model.train()
							lowerCAmelCase__      :      int         =		model(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      Dict         =		output[0]

							lowerCAmelCase__      :      Optional[int]         =		torch.device(a   )
							lowerCAmelCase__      :      List[Any]         =		randn_tensor(output.shape    ,     device=a   )
							lowerCAmelCase__      :      List[Any]         =		torch.nn.functional.mse_loss(a    ,     a   )
							loss.backward()