


import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)


def     lowercase	(    a ):



		'''simple docstring'''




		if model.config.model_type == "gpt2":
				return model.transformer.h[0].mlp.c_fc
		return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
		import torch
		import torch.nn as nn



		class 						_UpperCAmelCase      (     nn.Module							):






				def __init__(						self				:     int   ,  UpperCAmelCase				:     nn.Module   ,  UpperCAmelCase				:     int):
						super().__init__()
						SCREAMING_SNAKE_CASE_				:int				     =						module
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						nn.Sequential(
						    nn.Linear(module.in_features   ,  UpperCAmelCase   ,  bias=UpperCAmelCase)   ,  nn.Linear(UpperCAmelCase   ,  module.out_features   ,  bias=UpperCAmelCase)   ,  )
						SCREAMING_SNAKE_CASE_				:Any				     =						(2.0 / (5 * min(module.in_features   ,  module.out_features))) ** 0.5
						nn.init.normal_(self.adapter[0].weight   ,  std=UpperCAmelCase)
						nn.init.zeros_(self.adapter[1].weight)
						self.adapter.to(module.weight.device)







				def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Optional[Any]   ,  *UpperCAmelCase				:     Any   ,  **UpperCAmelCase				:     int):
						return self.module(UpperCAmelCase   ,  *UpperCAmelCase   ,  **UpperCAmelCase) + self.adapter(UpperCAmelCase)



@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 						_UpperCAmelCase      (     unittest.TestCase							):
		# We keep the constants inside the init function and model loading inside setUp function

		# We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
		# Therefore here we use only bloom-1b3 to test our module
		lowerCamelCase_	:			Optional[Any]       			=  """bigscience/bloom-1b7"""

		# Constant values
		lowerCamelCase_	:			Any       			=  2.1_09_65_95_52_69_25_74

		lowerCamelCase_	:			int       			=  """Hello my name is"""
		lowerCamelCase_	:			Optional[Any]       			=  set()
		EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I"""							)
		EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n"""							)
		EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University"""							)
		lowerCamelCase_	:			Optional[Any]       			=  1_0






		def _snake_case		(						self				:     Optional[Any]):
				# Models and tokenizer
				SCREAMING_SNAKE_CASE_				:Any				     =						AutoTokenizer.from_pretrained(self.model_name)




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     Optional[Any]):
				super().setUp()

				# Models and tokenizer
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						AutoModelForCausalLM.from_pretrained(
				    self.model_name   ,  torch_dtype=torch.floataa   ,  device_map="auto")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						AutoModelForCausalLM.from_pretrained(self.model_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")






		def _snake_case		(						self				:     Dict):
				del self.model_fpaa
				del self.model_abit

				gc.collect()
				torch.cuda.empty_cache()






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.model_abit.config

				self.assertTrue(hasattr(UpperCAmelCase   ,  "quantization_config"))

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						config.to_dict()
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						config.to_diff_dict()

				SCREAMING_SNAKE_CASE_				:List[str]				     =						config.to_json_string()






		def _snake_case		(						self				:     int):
				from bitsandbytes.nn import Paramsabit

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.model_fpaa.get_memory_footprint()
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.model_abit.get_memory_footprint()

				self.assertAlmostEqual(mem_fpaa / mem_abit   ,  self.EXPECTED_RELATIVE_DIFFERENCE)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						get_some_linear_layer(self.model_abit)
				self.assertTrue(linear.weight.__class__ == Paramsabit)






		def _snake_case		(						self				:     str):
				from transformers import TaPreTrainedModel

				self.model_fpaa.get_memory_footprint()
				self.model_abit.get_memory_footprint()

				for name, module in self.model_abit.named_modules():
						if isinstance(UpperCAmelCase   ,  torch.nn.Linear):
								if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
										# 4-bit parameters are packed in uint8 variables
										self.assertTrue(module.weight.dtype == torch.uinta)






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Dict				     =						self.tokenizer(self.input_text   ,  return_tensors="pt")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0)   ,  max_new_tokens=10)

				self.assertIn(self.tokenizer.decode(output_sequences[0]   ,  skip_special_tokens=UpperCAmelCase)   ,  self.EXPECTED_OUTPUTS)






		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						BitsAndBytesConfig()
				SCREAMING_SNAKE_CASE_				:Dict				     =						True

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						AutoModelForCausalLM.from_pretrained(
				    self.model_name   ,  quantization_config=UpperCAmelCase   ,  device_map="auto")

				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.tokenizer(self.input_text   ,  return_tensors="pt")
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						model_abit_from_config.generate(
				    input_ids=encoded_input["input_ids"].to(0)   ,  max_new_tokens=10)

				self.assertIn(self.tokenizer.decode(output_sequences[0]   ,  skip_special_tokens=UpperCAmelCase)   ,  self.EXPECTED_OUTPUTS)






		def _snake_case		(						self				:     Any):
				with self.assertRaises(UpperCAmelCase), tempfile.TemporaryDirectory() as tmpdirname:
						self.model_abit.save_pretrained(UpperCAmelCase)






		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						BitsAndBytesConfig()

				with self.assertRaises(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:Any				     =						AutoModelForCausalLM.from_pretrained(
						    self.model_name   ,  quantization_config=UpperCAmelCase   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto"   ,  bnb_abit_quant_type="nf4"   ,  )






		def _snake_case		(						self				:     List[Any]):
				with self.assertRaises(UpperCAmelCase):
						# Tries with `str`
						self.model_abit.to("cpu")

				with self.assertRaises(UpperCAmelCase):
						# Tries with a `dtype``
						self.model_abit.to(torch.floataa)

				with self.assertRaises(UpperCAmelCase):
						# Tries with a `device`
						self.model_abit.to(torch.device("cuda:0"))

				with self.assertRaises(UpperCAmelCase):
						# Tries with a `device`
						self.model_abit.float()

				with self.assertRaises(UpperCAmelCase):
						# Tries with a `device`
						self.model_abit.half()

				# Test if we did not break anything
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.tokenizer(self.input_text   ,  return_tensors="pt")

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.model_fpaa.to(torch.floataa)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0)   ,  max_new_tokens=10)

				# Check this does not throw an error
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.model_fpaa.to("cpu")

				# Check this does not throw an error
				SCREAMING_SNAKE_CASE_				:int				     =						self.model_fpaa.half()

				# Check this does not throw an error
				SCREAMING_SNAKE_CASE_				:int				     =						self.model_fpaa.float()







		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						AutoModelForSeqaSeqLM.from_pretrained("t5-small"   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")
				self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa)




@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 						_UpperCAmelCase      (     unittest.TestCase							):






		@classmethod
		def _snake_case		(						cls				:     Dict):
				SCREAMING_SNAKE_CASE_				:Any				     =						"t5-small"
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
				SCREAMING_SNAKE_CASE_				:int				     =						AutoTokenizer.from_pretrained(cls.model_name)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						"Translate in German: Hello, my dog is cute"






		def _snake_case		(						self				:     Optional[Any]):
				gc.collect()
				torch.cuda.empty_cache()






		def _snake_case		(						self				:     Any):
				from transformers import TaForConditionalGeneration

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						TaForConditionalGeneration._keep_in_fpaa_modules
				SCREAMING_SNAKE_CASE_				:List[str]				     =						None

				# test with `t5-small`
				SCREAMING_SNAKE_CASE_				:Tuple				     =						TaForConditionalGeneration.from_pretrained(self.model_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.tokenizer(self.input_text   ,  return_tensors="pt").to(0)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model.generate(**UpperCAmelCase)

				# test with `flan-t5-small`
				SCREAMING_SNAKE_CASE_				:List[str]				     =						TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.tokenizer(self.input_text   ,  return_tensors="pt").to(0)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						model.generate(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:int				     =						modules







		def _snake_case		(						self				:     Tuple):
				import bitsandbytes as bnb

				from transformers import TaForConditionalGeneration

				# test with `t5-small`
				SCREAMING_SNAKE_CASE_				:Tuple				     =						TaForConditionalGeneration.from_pretrained(self.model_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")

				# there was a bug with decoders - this test checks that it is fixed
				self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q   ,  bnb.nn.Linearabit))

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.tokenizer(self.input_text   ,  return_tensors="pt").to(0)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model.generate(**UpperCAmelCase)

				# test with `flan-t5-small`
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.tokenizer(self.input_text   ,  return_tensors="pt").to(0)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						model.generate(**UpperCAmelCase)




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     Tuple):
				super().setUp()
				# model_name
				SCREAMING_SNAKE_CASE_				:str				     =						"bigscience/bloom-560m"
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						"t5-small"

				# Different types of model

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						AutoModel.from_pretrained(self.model_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")
				# Sequence classification model
				SCREAMING_SNAKE_CASE_				:str				     =						AutoModelForSequenceClassification.from_pretrained(
				    self.model_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")
				# CausalLM model
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						AutoModelForCausalLM.from_pretrained(self.model_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")
				# Seq2seq model
				SCREAMING_SNAKE_CASE_				:List[str]				     =						AutoModelForSeqaSeqLM.from_pretrained(
				    self.seq_to_seq_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="auto")






		def _snake_case		(						self				:     str):
				del self.base_model
				del self.sequence_model
				del self.model_abit
				del self.seq_to_seq_model

				gc.collect()
				torch.cuda.empty_cache()







		def _snake_case		(						self				:     List[Any]):
				from bitsandbytes.nn import Paramsabit

				self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit)

				# Other heads should be nn.Parameter
				self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter)
				self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
				self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     Union[str, Any]):
				super().setUp()






		def _snake_case		(						self				:     Any):
				del self.pipe

				gc.collect()
				torch.cuda.empty_cache()







		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						pipeline(
				    "text-generation"   ,  model=self.model_name   ,  model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}   ,  max_new_tokens=self.MAX_NEW_TOKENS   ,  )

				# Real second forward pass
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.pipe(self.input_text)
				self.assertIn(pipeline_output[0]["generated_text"]   ,  self.EXPECTED_OUTPUTS)




@require_torch_multi_gpu
class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     List[Any]):
				super().setUp()







		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						AutoModelForCausalLM.from_pretrained(
				    self.model_name   ,  load_in_abit=UpperCAmelCase   ,  device_map="balanced")

				# Check correct device map
				self.assertEqual(set(model_parallel.hf_device_map.values())   ,  {0, 1})

				# Check that inference pass works on the model
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.tokenizer(self.input_text   ,  return_tensors="pt")

				# Second real batch
				SCREAMING_SNAKE_CASE_				:Tuple				     =						model_parallel.generate(input_ids=encoded_input["input_ids"].to(0)   ,  max_new_tokens=10)
				self.assertIn(self.tokenizer.decode(output_parallel[0]   ,  skip_special_tokens=UpperCAmelCase)   ,  self.EXPECTED_OUTPUTS)




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_				:Dict				     =						"facebook/opt-350m"
				super().setUp()







		def _snake_case		(						self				:     Dict):
				if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.37.0"):
						return

				# Step 1: freeze all parameters
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						AutoModelForCausalLM.from_pretrained(self.model_name   ,  load_in_abit=UpperCAmelCase)

				self.assertEqual(set(model.hf_device_map.values())   ,  {torch.cuda.current_device()})

				for param in model.parameters():
						SCREAMING_SNAKE_CASE_				:int				     =						False  # freeze the model - train adapters later
						if param.ndim == 1:
								# cast the small parameters (e.g. layernorm) to fp32 for stability
								SCREAMING_SNAKE_CASE_				:Dict				     =						param.data.to(torch.floataa)

        # Step 2: add adapters
				for _, module in model.named_modules():
						if "OPTAttention" in repr(type(UpperCAmelCase)):
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						LoRALayer(module.q_proj   ,  rank=16)
								SCREAMING_SNAKE_CASE_				:str				     =						LoRALayer(module.k_proj   ,  rank=16)
								SCREAMING_SNAKE_CASE_				:str				     =						LoRALayer(module.v_proj   ,  rank=16)

        # Step 3: dummy batch
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.tokenizer("Test batch "   ,  return_tensors="pt").to(0)

				# Step 4: Check if the gradient is not None
				with torch.cuda.amp.autocast():
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model.forward(**UpperCAmelCase)
						out.logits.norm().backward()

				for module in model.modules():
						if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
								self.assertTrue(module.adapter[1].weight.grad is not None)
								self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
						elif isinstance(UpperCAmelCase   ,  nn.Embedding):
								self.assertTrue(module.weight.grad is None)




class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Union[str, Any]       			=  """gpt2-xl"""
		lowerCamelCase_	:			List[Any]       			=  3.31_91_85_48_54_15_21_87










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",
						]






