






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	None
     UpperCamelCase_							:		Optional[jnp.ndarray]			=	None
     UpperCamelCase_							:		Optional[jnp.ndarray]			=	None  # sigma(t_i)


     @classmethod
     def 				_A       (				cls       :	Optional[Any]      ):
       return cls()





@dataclass
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		jnp.ndarray
     UpperCamelCase_							:		jnp.ndarray
     UpperCamelCase_							:		KarrasVeSchedulerState





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	List[Any]      ):
       return True


     @register_to_config
     def __init__(				self       :	int					, UpperCAmelCase_       :	float = 0.02					, UpperCAmelCase_       :	float = 100					, UpperCAmelCase_       :	float = 1.007					, UpperCAmelCase_       :	float = 80					, UpperCAmelCase_       :	float = 0.05					, UpperCAmelCase_       :	float = 50					, ):
       pass


     def 				_A       (				self       :	List[Any]      ):
       return KarrasVeSchedulerState.create()


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Tuple = ()      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    jnp.arange(0					, UpperCAmelCase_      )[::-1].copy()
       SCREAMING_SNAKE_CASE       :      int							=    [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in timesteps
       ]

       return state.replace(
           num_inference_steps=UpperCAmelCase_					, schedule=jnp.array(UpperCAmelCase_					, dtype=jnp.floataa      )					, timesteps=UpperCAmelCase_					, )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	random.KeyArray					, ):
       if self.config.s_min <= sigma <= self.config.s_max:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    min(self.config.s_churn / state.num_inference_steps					, 2**0.5 - 1      )
       else:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    0

       # sample eps ~ N(0, S_noise^2 * I)
       SCREAMING_SNAKE_CASE       :      Tuple							=    random.split(UpperCAmelCase_					, num=1      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.config.s_noise * random.normal(key=UpperCAmelCase_					, shape=sample.shape      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    sigma + gamma * sigma
       SCREAMING_SNAKE_CASE       :      Any							=    sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	bool = True					, ):
       SCREAMING_SNAKE_CASE       :      int							=    sample_hat + sigma_hat * model_output
       SCREAMING_SNAKE_CASE       :      List[Any]							=    (sample_hat - pred_original_sample) / sigma_hat
       SCREAMING_SNAKE_CASE       :      List[Any]							=    sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
         return (sample_prev, derivative, state)

       return FlaxKarrasVeOutput(prev_sample=UpperCAmelCase_					, derivative=UpperCAmelCase_					, state=UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	bool = True					, ):
       SCREAMING_SNAKE_CASE       :      str							=    sample_prev + sigma_prev * model_output
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (sample_prev - pred_original_sample) / sigma_prev
       SCREAMING_SNAKE_CASE       :      List[Any]							=    sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
         return (sample_prev, derivative, state)

       return FlaxKarrasVeOutput(prev_sample=UpperCAmelCase_					, derivative=UpperCAmelCase_					, state=UpperCAmelCase_      )






     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[Any]      ):
       raise NotImplementedError()














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

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


snake_case    				=		logging.getLogger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Union[str, Any]=-1      ):
       # in NER datasets, the last column is usually reserved for NER label
       SCREAMING_SNAKE_CASE       :      int							=    label_idx


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Union[Split, str]      ):
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    mode.value
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    os.path.join(UpperCAmelCase_					, f'''{mode}.txt'''      )
       SCREAMING_SNAKE_CASE       :      int							=    1
       SCREAMING_SNAKE_CASE       :      Any							=    []
       with open(UpperCAmelCase_					, encoding="utf-8"      ) as f:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
         SCREAMING_SNAKE_CASE       :      List[str]							=    []
         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
               SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    []
               SCREAMING_SNAKE_CASE       :      Optional[int]							=    []
           else:
             SCREAMING_SNAKE_CASE       :      str							=    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 				_A       (				self       :	Tuple					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	List      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    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]:
           SCREAMING_SNAKE_CASE       :      Optional[int]							=    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 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "r"      ) as f:
           SCREAMING_SNAKE_CASE       :      List[Any]							=    f.read().splitlines()
         if "O" not in labels:
           SCREAMING_SNAKE_CASE       :      Tuple							=    ["O"] + labels
         return labels
       else:
         return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	Union[str, Any]      ):
       # in CONLL2003 dataset chunk column is second-to-last
       super().__init__(label_idx=-2      )






     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	str      ):
       if path:
         with open(UpperCAmelCase_					, "r"      ) as f:
           SCREAMING_SNAKE_CASE       :      Dict							=    f.read().splitlines()
         if "O" not in labels:
           SCREAMING_SNAKE_CASE       :      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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Union[Split, str]      ):
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      str							=    mode.value
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    os.path.join(UpperCAmelCase_					, f'''{mode}.txt'''      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    1
       SCREAMING_SNAKE_CASE       :      str							=    []

       with open(UpperCAmelCase_					, encoding="utf-8"      ) as f:
         for sentence in parse_incr(UpperCAmelCase_      ):
           SCREAMING_SNAKE_CASE       :      List[Any]							=    []
           SCREAMING_SNAKE_CASE       :      List[str]							=    []
           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 				_A       (				self       :	str					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	TextIO					, UpperCAmelCase_       :	List      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    0
       for sentence in parse_incr(UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    preds_list[example_id]
         SCREAMING_SNAKE_CASE       :      Any							=    ""
         for token in sentence:
           out += f'''{token['form']} ({token['upos']}|{s_p.pop(0      )}) '''
         out += "\n"
         writer.write(UpperCAmelCase_      )
         example_id += 1






     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	str      ):
       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",
         ]






