





'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class   UpperCAmelCase      (			UpperCamelCase__							):
 def __init__(							self :Tuple		,			lowercase_ :NestedDataStructureLike[PathLike]		,			lowercase_ :Optional[NamedSplit] = None		,			lowercase_ :Optional[Features] = None		,			lowercase_ :str = None		,			lowercase_ :bool = False		,			lowercase_ :bool = False		,			lowercase_ :Optional[str] = None		,			lowercase_ :Optional[int] = None		,			**lowercase_ :List[str]		,			)->				int:
     super().__init__(
         lowercase_		,			split=lowercase_		,			features=lowercase_		,			cache_dir=lowercase_		,			keep_in_memory=lowercase_		,			streaming=lowercase_		,			num_proc=lowercase_		,			**lowercase_		,			)
     A__							=     field
     A__							=     path_or_paths if isinstance(lowercase_		,			lowercase_				) else {self.split: path_or_paths}
     A__							=     Json(
         cache_dir=lowercase_		,			data_files=lowercase_		,			features=lowercase_		,			field=lowercase_		,			**lowercase_		,			)



 def 	UpperCAmelCase_				(							self :str				)->				Union[str, Any]:
     # Build iterable dataset
     if self.streaming:
         A__							=     self.builder.as_streaming_dataset(split=self.split				)
     # Build regular (map-style) dataset
     else:
         A__							=     None
         A__							=     None
         A__							=     None
         A__							=     None

         self.builder.download_and_prepare(
             download_config=lowercase_		,			download_mode=lowercase_		,			verification_mode=lowercase_		,			base_path=lowercase_		,			num_proc=self.num_proc		,			)
         A__							=     self.builder.as_dataset(
             split=self.split		,			verification_mode=lowercase_		,			in_memory=self.keep_in_memory				)
     return dataset






class   UpperCAmelCase      :
 def __init__(							self :Any		,			lowercase_ :Dataset		,			lowercase_ :Union[PathLike, BinaryIO]		,			lowercase_ :Optional[int] = None		,			lowercase_ :Optional[int] = None		,			**lowercase_ :Dict		,			)->				Tuple:
     if num_proc is not None and num_proc <= 0:
         raise ValueError(F"num_proc {num_proc} must be an integer > 0."				)

     A__							=     dataset
     A__							=     path_or_buf
     A__							=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     A__							=     num_proc
     A__							=     "utf-8"
     A__							=     to_json_kwargs
 def 	UpperCAmelCase_				(							self :List[Any]				)->				int:
     A__							=     self.to_json_kwargs.pop("path_or_buf"		,			lowercase_				)
     A__							=     self.to_json_kwargs.pop("orient"		,			"records"				)
     A__							=     self.to_json_kwargs.pop("lines"		,			True if orient == "records" else False				)
     A__							=     self.to_json_kwargs.pop("index"		,			False if orient in ["split", "table"] else True				)
     A__							=     self.to_json_kwargs.pop("compression"		,			lowercase_				)

     if compression not in [None, "infer", "gzip", "bz2", "xz"]:
         raise NotImplementedError(F"`datasets` currently does not support {compression} compression"				)

     if isinstance(self.path_or_buf		,			(str, bytes, os.PathLike)				):
         with fsspec.open(self.path_or_buf		,			"wb"		,			compression=lowercase_				) as buffer:
             A__							=     self._write(file_obj=lowercase_		,			orient=lowercase_		,			lines=lowercase_		,			index=lowercase_		,			**self.to_json_kwargs				)
     else:
         if compression:
             raise NotImplementedError(
                 F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
                 " was passed. Please provide a local path instead."				)
         A__							=     self._write(
             file_obj=self.path_or_buf		,			orient=lowercase_		,			lines=lowercase_		,			index=lowercase_		,			**self.to_json_kwargs				)
     return written
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :Dict				)->				List[Any]:
     A__,      A__,      A__,      A__,      A__							=     args

     A__							=     query_table(
         table=self.dataset.data		,			key=slice(lowercase_		,			offset + self.batch_size				)		,			indices=self.dataset._indices		,			)
     A__							=     batch.to_pandas().to_json(
         path_or_buf=lowercase_		,			orient=lowercase_		,			lines=lowercase_		,			index=lowercase_		,			**lowercase_				)
     if not json_str.endswith("\n"				):
         json_str += "\n"
     return json_str.encode(self.encoding				)



 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :BinaryIO		,			lowercase_ :Any		,			lowercase_ :Union[str, Any]		,			lowercase_ :List[Any]		,			**lowercase_ :Any		,			)->				int:
     A__							=     0

     if self.num_proc is None or self.num_proc == 1:
         for offset in logging.tqdm(
             range(0		,			len(self.dataset				)		,			self.batch_size				)		,			unit="ba"		,			disable=not logging.is_progress_bar_enabled()		,			desc="Creating json from Arrow format"		,			):
             A__							=     self._batch_json((offset, orient, lines, index, to_json_kwargs)				)
             written += file_obj.write(lowercase_				)
     else:
         A__,      A__							=     len(self.dataset				), self.batch_size
         with multiprocessing.Pool(self.num_proc				) as pool:
             for json_str in logging.tqdm(
                 pool.imap(
                     self._batch_json		,			[(offset, orient, lines, index, to_json_kwargs) for offset in range(0		,			lowercase_		,			lowercase_				)]		,			)		,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size		,			unit="ba"		,			disable=not logging.is_progress_bar_enabled()		,			desc="Creating json from Arrow format"		,			):
                 written += file_obj.write(lowercase_				)

     return written










'''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)

__lowerCAmelCase				:      Dict						      =logging.getLogger()





def 		UpperCamelCase							(				):
    A__							=     argparse.ArgumentParser()
    parser.add_argument("-f"			)
    A__							=     parser.parse_args()
    return args.f





def 		UpperCamelCase							(				_lowerCamelCase   :   List[str]			):
    A__							=     {}
    A__							=     os.path.join(_lowerCamelCase		,			"all_results.json"			)
    if os.path.exists(_lowerCamelCase			):
        with open(_lowerCamelCase		,			"r"			) as f:
            A__							=     json.load(_lowerCamelCase			)
    else:
        raise ValueError(F"can't find {path}"			)
    return results





def 		UpperCamelCase							(				):
    A__							=     torch.cuda.is_available() and torch_device == "cuda"
    return is_using_cuda and is_apex_available()


__lowerCAmelCase				:      Union[str, Any]						      =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






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

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

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_accuracy"]		,			0.7_5				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"glue_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Tuple:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".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				)
     A__							=     get_results(lowercase_				)
     self.assertLess(result["perplexity"]		,			1_00				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"clm_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Any				)->				str:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

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

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

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

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     # 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(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"qa_no_trainer"				)				)				)
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[str]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

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

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     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(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"summarization_no_trainer"				)				)				)
 @slow
 @mock.patch.dict(os.environ		,			{"WANDB_MODE": "offline"}				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.get_auto_remove_tmp_dir()
     A__							=     F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_bleu"]		,			30				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"epoch_0"				)				)				)
     self.assertTrue(os.path.exists(os.path.join(lowercase_		,			"translation_no_trainer"				)				)				)
 @slow
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[Any]:
     A__							=     logging.StreamHandler(sys.stdout				)
     logger.addHandler(lowercase_				)

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

     run_command(self._launch_args + testargs				)
     A__							=     get_results(lowercase_				)
     self.assertGreaterEqual(result["eval_overall_accuracy"]		,			0.1_0				)



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

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

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



