



"""simple docstring"""







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

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


UpperCamelCase     =							logging.getLogger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=-1		)      ->	Optional[int]:
				# in NER datasets, the last column is usually reserved for NER label
				A__											=					label_idx


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


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







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"r"		) as f:
						A__											=					f.read().splitlines()
					if "O" not in labels:
						A__											=					["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__			(   _lowerCAmelCase      ):

			"""simple docstring"""


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







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"r"		) as f:
						A__											=					f.read().splitlines()
					if "O" not in labels:
						A__											=					["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__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[InputExample]:
				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					A__											=					mode.value
				A__											=					os.path.join(SCREAMING_SNAKE_CASE__						,	f"""{mode}.txt"""		)
				A__											=					1
				A__											=					[]

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				A__											=					0
				for sentence in parse_incr(SCREAMING_SNAKE_CASE__		):
					A__											=					preds_list[example_id]
					A__											=					""
					for token in sentence:
						out += f"""{token['form']} ({token['upos']}|{s_p.pop(0		)}) """
					out += "\n"
					writer.write(SCREAMING_SNAKE_CASE__		)
					example_id += 1







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				if path:
					with open(SCREAMING_SNAKE_CASE__						,	"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 json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.getLogger()

SCREAMING_SNAKE_CASE			:						List[str]				  =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int							):
   os.makedirs(__SCREAMING_SNAKE_CASE  ,				exist_ok=__SCREAMING_SNAKE_CASE							)
   __a							=	{"source": "What is love ?", "target": "life"}
   __a							=	{"train": 12, "val": 2, "test": 2}
   for split in ["train", "test", "val"]:
    for field in ["source", "target"]:
     __a							=	"\n".join([contents[field]] * n_lines[split]							)
     with open(os.path.join(__SCREAMING_SNAKE_CASE  ,				f"""{split}.{field}"""							)  ,				"w"							) as f:
      f.write(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str = "pytorch"							):
   __a							=	self.get_auto_remove_tmp_dir()
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"output"							)
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"data"							)
   self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE							)

   __a							=	f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

   if gpus > 0:
    testargs.append(f"""--gpus={gpus}"""							)
    if is_apex_available():
     testargs.append("--fp16"							)
   else:
    testargs.append("--gpus=0"							)
    testargs.append("--distributed_backend=ddp_cpu"							)
    testargs.append("--num_processes=2"							)

   __a							=	[sys.executable, str(Path(finetune_rag.__file__							).resolve()							)] + testargs
   execute_subprocess_async(__SCREAMING_SNAKE_CASE  ,				env=self.get_env()							)

   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"metrics.json"							)
   with open(__SCREAMING_SNAKE_CASE							) as f:
    __a							=	json.load(__SCREAMING_SNAKE_CASE							)
   return result

  @require_torch_gpu
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self._run_finetune(gpus=1							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self._run_finetune(gpus=2							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)


