


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		)



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def __init__(						self				:     Any   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]=13   ,  UpperCAmelCase				:     Union[str, Any]=7   ,  UpperCAmelCase				:     Union[str, Any]=True   ,  UpperCAmelCase				:     List[Any]=True   ,  UpperCAmelCase				:     Optional[Any]=True   ,  UpperCAmelCase				:     Optional[Any]=True   ,  UpperCAmelCase				:     List[str]=99   ,  UpperCAmelCase				:     Optional[Any]=32   ,  UpperCAmelCase				:     Optional[int]=5   ,  UpperCAmelCase				:     Optional[int]=4   ,  UpperCAmelCase				:     List[str]=37   ,  UpperCAmelCase				:     Optional[Any]="gelu"   ,  UpperCAmelCase				:     Union[str, Any]=0.1   ,  UpperCAmelCase				:     Dict=0.1   ,  UpperCAmelCase				:     Dict=5_12   ,  UpperCAmelCase				:     Optional[Any]=16   ,  UpperCAmelCase				:     List[str]=2   ,  UpperCAmelCase				:     List[str]=0.02   ,  UpperCAmelCase				:     int=4   ,  ):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						parent
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						batch_size
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						seq_length
				SCREAMING_SNAKE_CASE_				:Dict				     =						is_training
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						use_attention_mask
				SCREAMING_SNAKE_CASE_				:int				     =						use_token_type_ids
				SCREAMING_SNAKE_CASE_				:str				     =						use_labels
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						vocab_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						hidden_size
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						num_hidden_layers
				SCREAMING_SNAKE_CASE_				:List[str]				     =						num_attention_heads
				SCREAMING_SNAKE_CASE_				:Dict				     =						intermediate_size
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						hidden_act
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_				:str				     =						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_				:Tuple				     =						max_position_embeddings
				SCREAMING_SNAKE_CASE_				:str				     =						type_vocab_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						type_sequence_label_size
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						initializer_range
				SCREAMING_SNAKE_CASE_				:str				     =						num_choices






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size)

				SCREAMING_SNAKE_CASE_				:int				     =						None
				if self.use_attention_mask:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						random_attention_mask([self.batch_size, self.seq_length])

				SCREAMING_SNAKE_CASE_				:List[str]				     =						None
				if self.use_token_type_ids:
						SCREAMING_SNAKE_CASE_				:Any				     =						ids_tensor([self.batch_size, self.seq_length]   ,  self.type_vocab_size)

				SCREAMING_SNAKE_CASE_				:Any				     =						RobertaPreLayerNormConfig(
				    vocab_size=self.vocab_size   ,  hidden_size=self.hidden_size   ,  num_hidden_layers=self.num_hidden_layers   ,  num_attention_heads=self.num_attention_heads   ,  intermediate_size=self.intermediate_size   ,  hidden_act=self.hidden_act   ,  hidden_dropout_prob=self.hidden_dropout_prob   ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,  max_position_embeddings=self.max_position_embeddings   ,  type_vocab_size=self.type_vocab_size   ,  is_decoder=UpperCAmelCase   ,  initializer_range=self.initializer_range   ,  )

				return config, input_ids, token_type_ids, attention_mask






		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[Any]				     =						config_and_inputs
				SCREAMING_SNAKE_CASE_				:Tuple				     =						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:Any				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						config_and_inputs

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						True
				SCREAMING_SNAKE_CASE_				:Dict				     =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						ids_tensor([self.batch_size, self.seq_length]   ,  vocab_size=2)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 						_UpperCAmelCase      (     lowercase		,		unittest.TestCase							):
		lowerCamelCase_	:			Tuple       			=  True

		lowerCamelCase_	:			Optional[Any]       			=  (
		    (
		        FlaxRobertaPreLayerNormModel,
		        FlaxRobertaPreLayerNormForCausalLM,
		        FlaxRobertaPreLayerNormForMaskedLM,
		        FlaxRobertaPreLayerNormForSequenceClassification,
		        FlaxRobertaPreLayerNormForTokenClassification,
		        FlaxRobertaPreLayerNormForMultipleChoice,
		        FlaxRobertaPreLayerNormForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						FlaxRobertaPreLayerNormModelTester(self)







		@slow
		def _snake_case		(						self				:     Optional[Any]):
				for model_class_name in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,  from_pt=UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:str				     =						model(np.ones((1, 1)))
						self.assertIsNotNone(UpperCAmelCase)




@require_flax
class 						_UpperCAmelCase      (     unittest.TestCase							):






		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,  from_pt=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:str				     =						np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   ,  dtype=jnp.intaa)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						model(UpperCAmelCase)[0]
				SCREAMING_SNAKE_CASE_				:int				     =						[1, 11, 5_02_65]
				self.assertEqual(list(output.shape)   ,  UpperCAmelCase)
				# compare the actual values for a slice.
				SCREAMING_SNAKE_CASE_				:List[str]				     =						np.array(
				    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]   ,  dtype=np.floataa)
				self.assertTrue(np.allclose(output[:, :3, :3]   ,  UpperCAmelCase   ,  atol=1E-4))







		@slow
		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,  from_pt=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   ,  dtype=jnp.intaa)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model(UpperCAmelCase)[0]
				# compare the actual values for a slice.
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						np.array(
				    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]   ,  dtype=np.floataa)
				self.assertTrue(np.allclose(output[:, :3, :3]   ,  UpperCAmelCase   ,  atol=1E-4))










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__     					=			logging.getLogger(__name__)



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Optional[int]   ,  UpperCAmelCase				:     Any=-1):
				# in NER datasets, the last column is usually reserved for NER label
				SCREAMING_SNAKE_CASE_				:Tuple				     =						label_idx






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						1
				SCREAMING_SNAKE_CASE_				:str				     =						[]
				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
						SCREAMING_SNAKE_CASE_				: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=UpperCAmelCase   ,  labels=UpperCAmelCase))
												guid_index += 1
												SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
												SCREAMING_SNAKE_CASE_				:Any				     =						[]
								else:
										SCREAMING_SNAKE_CASE_				:int				     =						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 _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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_				:Union[str, Any]				     =						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 _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Any				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Union[str, 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 						_UpperCAmelCase      (     lowercase							):






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







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Dict				     =						["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 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:List[str]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Dict				     =						1
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[]

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







		def _snake_case		(						self				:     Tuple   ,  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",
						]






