
"""simple docstring"""




import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

_A			:       Tuple   		=       logging.getLogger()


def   __magic_name__	(      ) ->				List[Any]:
       lowercase    :				Union[str, Any]       		=							argparse.ArgumentParser()
       parser.add_argument("-f"  )
       lowercase    :				Optional[Any]       		=							parser.parse_args()
       return args.f


def   __magic_name__	(      __snake_case				:       Optional[int]  ) ->				int:
       lowercase    :				Dict       		=							{}
       lowercase    :				Union[str, Any]       		=							os.path.join(__snake_case     , "all_results.json"  )
       if os.path.exists(__snake_case  ):
              with open(__snake_case     , "r"  ) as f:
                     lowercase    :				Any       		=							json.load(__snake_case  )
       else:
              raise ValueError(f"""can't find {path}"""  )
       return results


def   __magic_name__	(      ) ->				Dict:
       lowercase    :				Tuple       		=							torch.cuda.is_available() and torch_device == "cuda"
       return is_using_cuda and is_apex_available()


_A			:       Dict   		=       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class 		a__						(      a_						):
   @classmethod
   def 			__magic_name__			(			cls				):
          # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
          lowercase    :				Optional[int]       		=							tempfile.mkdtemp()
          lowercase    :				str       		=							os.path.join(cls.tmpdir	,    "default_config.yml"				)
          write_basic_config(save_location=cls.configPath				)
          lowercase    :				str       		=							["accelerate", "launch", "--config_file", cls.configPath]
   @classmethod
   def 			__magic_name__			(			cls				):
          shutil.rmtree(cls.tmpdir				)
   @mock.patch.dict(os.environ	,    {"WANDB_MODE": "offline"}				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				str       		=							f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

          if is_cuda_and_apex_available():
                 testargs.append("--fp16"				)

          run_command(self._launch_args + testargs				)
          lowercase    :				Union[str, Any]       		=							get_results(_a				)
          self.assertGreaterEqual(result["eval_accuracy"]	,    0.7_5				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "epoch_0"				)				)				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "glue_no_trainer"				)				)				)
   @mock.patch.dict(os.environ	,    {"WANDB_MODE": "offline"}				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				str       		=							f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

          if torch.cuda.device_count() > 1:
                 # Skipping because there are not enough batches to train the model + would need a drop_last to work.
                 return

          run_command(self._launch_args + testargs				)
          lowercase    :				Optional[int]       		=							get_results(_a				)
          self.assertLess(result["perplexity"]	,    100				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "epoch_0"				)				)				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "clm_no_trainer"				)				)				)
   @mock.patch.dict(os.environ	,    {"WANDB_MODE": "offline"}				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				List[str]       		=							f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

          run_command(self._launch_args + testargs				)
          lowercase    :				List[Any]       		=							get_results(_a				)
          self.assertLess(result["perplexity"]	,    42				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "epoch_0"				)				)				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "mlm_no_trainer"				)				)				)
   @mock.patch.dict(os.environ	,    {"WANDB_MODE": "offline"}				)
   def 			__magic_name__			(			self				):
          # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
          lowercase    :				List[str]       		=							7 if get_gpu_count() > 1 else 2

          lowercase    :				Optional[Any]       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				Optional[Any]       		=							f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

          run_command(self._launch_args + testargs				)
          lowercase    :				Any       		=							get_results(_a				)
          self.assertGreaterEqual(result["eval_accuracy"]	,    0.7_5				)
          self.assertLess(result["train_loss"]	,    0.5				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "epoch_0"				)				)				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "ner_no_trainer"				)				)				)
   @unittest.skip(reason="Fix me @muellerzr"				)
   @mock.patch.dict(os.environ	,    {"WANDB_MODE": "offline"}				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				Union[str, Any]       		=							f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

          run_command(self._launch_args + testargs				)
          lowercase    :				Optional[int]       		=							get_results(_a				)
          # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
          self.assertGreaterEqual(result["eval_f1"]	,    28				)
          self.assertGreaterEqual(result["eval_exact"]	,    28				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "epoch_0"				)				)				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "qa_no_trainer"				)				)				)
   @mock.patch.dict(os.environ	,    {"WANDB_MODE": "offline"}				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				int       		=							f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

          run_command(self._launch_args + testargs				)
          lowercase    :				int       		=							get_results(_a				)
          self.assertGreaterEqual(result["eval_accuracy"]	,    0.8				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "swag_no_trainer"				)				)				)
   @slow
   @mock.patch.dict(os.environ	,    {"WANDB_MODE": "offline"}				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				Union[str, Any]       		=							f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

          run_command(self._launch_args + testargs				)
          lowercase    :				str       		=							get_results(_a				)
          self.assertGreaterEqual(result["eval_rouge1"]	,    10				)
          self.assertGreaterEqual(result["eval_rouge2"]	,    2				)
          self.assertGreaterEqual(result["eval_rougeL"]	,    7				)
          self.assertGreaterEqual(result["eval_rougeLsum"]	,    7				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "epoch_0"				)				)				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "summarization_no_trainer"				)				)				)
   @slow
   @mock.patch.dict(os.environ	,    {"WANDB_MODE": "offline"}				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				Optional[Any]       		=							f"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

          run_command(self._launch_args + testargs				)
          lowercase    :				str       		=							get_results(_a				)
          self.assertGreaterEqual(result["eval_bleu"]	,    30				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "epoch_0"				)				)				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "translation_no_trainer"				)				)				)
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							logging.StreamHandler(sys.stdout				)
          logger.addHandler(_a				)

          lowercase    :				str       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				Dict       		=							f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

          run_command(self._launch_args + testargs				)
          lowercase    :				int       		=							get_results(_a				)
          self.assertGreaterEqual(result["eval_overall_accuracy"]	,    0.1_0				)

   @mock.patch.dict(os.environ	,    {"WANDB_MODE": "offline"}				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.get_auto_remove_tmp_dir()
          lowercase    :				str       		=							f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

          if is_cuda_and_apex_available():
                 testargs.append("--fp16"				)

          run_command(self._launch_args + testargs				)
          lowercase    :				Optional[int]       		=							get_results(_a				)
          # The base model scores a 25%
          self.assertGreaterEqual(result["eval_accuracy"]	,    0.6				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "step_1"				)				)				)
          self.assertTrue(os.path.exists(os.path.join(_a	,    "image_classification_no_trainer"				)				)				)




"""simple docstring"""




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

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


_A			:       Optional[int]   		=       logging.getLogger(__name__)

class 		a__						(      a_						):
   def __init__(			self	,    _a=-1				):
          # in NER datasets, the last column is usually reserved for NER label
          lowercase    :				List[str]       		=							label_idx
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				Optional[Any]       		=							mode.value
          lowercase    :				List[str]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				str       		=							1
          lowercase    :				Optional[int]       		=							[]
          with open(_a	,    encoding="utf-8"				) as f:
                 lowercase    :				List[Any]       		=							[]
                 lowercase    :				Optional[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=_a	,    labels=_a				)				)
                                      guid_index += 1
                                      lowercase    :				int       		=							[]
                                      lowercase    :				int       		=							[]
                        else:
                               lowercase    :				Optional[Any]       		=							line.split(" "				)
                               words.append(splits[0]				)
                               if len(_a				) > 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=_a	,    labels=_a				)				)
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							0
          for line in test_input_reader:
                 if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                        writer.write(_a				)
                        if not preds_list[example_id]:
                               example_id += 1
                 elif preds_list[example_id]:
                        lowercase    :				Any       		=							line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"
                        writer.write(_a				)
                 else:
                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,    line.split()[0]				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Optional[Any]       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				List[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 		a__						(      a_						):
   def __init__(			self				):
          # in CONLL2003 dataset chunk column is second-to-last
          super().__init__(label_idx=-2				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Tuple       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				Optional[int]       		=							["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__						(      a_						):
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				List[Any]       		=							mode.value
          lowercase    :				Optional[int]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				Tuple       		=							1
          lowercase    :				List[str]       		=							[]

          with open(_a	,    encoding="utf-8"				) as f:
                 for sentence in parse_incr(_a				):
                        lowercase    :				Optional[Any]       		=							[]
                        lowercase    :				str       		=							[]
                        for token in sentence:
                               words.append(token["form"]				)
                               labels.append(token["upos"]				)
                        assert len(_a				) == len(_a				)
                        if words:
                               examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                               guid_index += 1
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				str       		=							0
          for sentence in parse_incr(_a				):
                 lowercase    :				List[Any]       		=							preds_list[example_id]
                 lowercase    :				List[str]       		=							""
                 for token in sentence:
                        out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0				)}) """
                 out += "\n"
                 writer.write(_a				)
                 example_id += 1

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "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",
                 ]


