





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

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


_a						=   logging.getLogger(__name__)






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase=-1							):



				'''simple docstring'''







				lowerCamelCase__              =	label_idx





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				if path:
					with open(__lowerCAmelCase ,						'''r'''							) as f:
						lowerCamelCase__              =	f.read().splitlines()
					if "O" not in labels:
						lowerCamelCase__              =	['''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							):



				'''simple docstring'''







				super().__init__(label_idx=-2							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				if path:
					with open(__lowerCAmelCase ,						'''r'''							) as f:
						lowerCamelCase__              =	f.read().splitlines()
					if "O" not in labels:
						lowerCamelCase__              =	['''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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				if isinstance(__lowerCAmelCase ,						__lowerCAmelCase							):
					lowerCamelCase__              =	mode.value
				lowerCamelCase__              =	os.path.join(__lowerCAmelCase ,						F'{mode}.txt'							)
				lowerCamelCase__              =	1
				lowerCamelCase__              =	[]

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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				if path:
					with open(__lowerCAmelCase ,						'''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",
					]









from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		42






class       __A					(					lowerCAmelCase    ,       lowerCAmelCase				):

			'''simple docstring'''







			@register_to_config
			def __init__(					self ,						__lowerCAmelCase = 1_6 ,						__lowerCAmelCase = 8_8 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = 1 ,						__lowerCAmelCase = 0.0 ,						__lowerCAmelCase = 3_2 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = False ,						__lowerCAmelCase = None ,						__lowerCAmelCase = "geglu" ,						__lowerCAmelCase = True ,						__lowerCAmelCase = True ,						):



				'''simple docstring'''







				super().__init__()
				lowerCamelCase__              =	num_attention_heads
				lowerCamelCase__              =	attention_head_dim
				lowerCamelCase__              =	num_attention_heads * attention_head_dim

				lowerCamelCase__              =	in_channels

				lowerCamelCase__              =	torch.nn.GroupNorm(num_groups=__lowerCAmelCase ,						num_channels=__lowerCAmelCase ,						eps=1E-6 ,						affine=__lowerCAmelCase							)
				lowerCamelCase__              =	nn.Linear(__lowerCAmelCase ,						__lowerCAmelCase							)

				# 3. Define transformers blocks
				lowerCamelCase__              =	nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            __lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						dropout=__lowerCAmelCase ,						cross_attention_dim=__lowerCAmelCase ,						activation_fn=__lowerCAmelCase ,						attention_bias=__lowerCAmelCase ,						double_self_attention=__lowerCAmelCase ,						norm_elementwise_affine=__lowerCAmelCase ,						)
				        for d in range(__lowerCAmelCase							)
				    ]							)

				lowerCamelCase__              =	nn.Linear(__lowerCAmelCase ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=1 ,						__lowerCAmelCase=None ,						__lowerCAmelCase = True ,						):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__              =	hidden_states.shape
				lowerCamelCase__              =	batch_frames // num_frames

				lowerCamelCase__              =	hidden_states

				lowerCamelCase__              =	hidden_states[None, :].reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				lowerCamelCase__              =	hidden_states.permute(0 ,						2 ,						1 ,						3 ,						4							)

				lowerCamelCase__              =	self.norm(__lowerCAmelCase							)
				lowerCamelCase__              =	hidden_states.permute(0 ,						3 ,						4 ,						2 ,						1							).reshape(batch_size * height * width ,						__lowerCAmelCase ,						__lowerCAmelCase							)

				lowerCamelCase__              =	self.proj_in(__lowerCAmelCase							)

				# 2. Blocks
				for block in self.transformer_blocks:
					lowerCamelCase__              =	block(
					    __lowerCAmelCase ,						encoder_hidden_states=__lowerCAmelCase ,						timestep=__lowerCAmelCase ,						cross_attention_kwargs=__lowerCAmelCase ,						class_labels=__lowerCAmelCase ,						)

				# 3. Output
				lowerCamelCase__              =	self.proj_out(__lowerCAmelCase							)
				lowerCamelCase__              =	(
				    hidden_states[None, None, :]
				    .reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				    .permute(0 ,						3 ,						4 ,						1 ,						2							)
				    .contiguous()
				)
				lowerCamelCase__              =	hidden_states.reshape(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)

				lowerCamelCase__              =	hidden_states + residual

				if not return_dict:
					return (output,)

				return TransformerTemporalModelOutput(sample=__lowerCAmelCase							)



