



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)

_snake_case	=						logging.getLogger()






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					argparse.ArgumentParser()
							parser.add_argument("-f"						)
							_lowerCAmelCase  :							Optional[Any]				     =					parser.parse_args()
							return args.f






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




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






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					torch.cuda.is_available() and torch_device == "cuda"
							return is_using_cuda and is_apex_available()


_snake_case	=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class 						UpperCAmelCase_					( a):







							@classmethod
							def      snake_case__   (     cls):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					tempfile.mkdtemp()
														_lowerCAmelCase  :							int				     =					os.path.join(cls.tmpdir,    "default_config.yml")
														write_basic_config(save_location=cls.configPath)
														_lowerCAmelCase  :							int				     =					["accelerate", "launch", "--config_file", cls.configPath]







							@classmethod
							def      snake_case__   (     cls):

														'''simple docstring'''

														shutil.rmtree(cls.tmpdir)







							@mock.patch.dict(os.environ,    {"WANDB_MODE": "offline"})
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							Union[str, Any]				     =					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)
														_lowerCAmelCase  :							List[Any]				     =					get_results(__a)
														self.assertGreaterEqual(result["eval_accuracy"],    0.75)
														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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							Optional[int]				     =					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)
														_lowerCAmelCase  :							Optional[Any]				     =					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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							List[str]				     =					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)
														_lowerCAmelCase  :							Union[str, 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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					7 if get_gpu_count() > 1 else 2

														_lowerCAmelCase  :							int				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							Any				     =					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)
														_lowerCAmelCase  :							List[str]				     =					get_results(__a)
														self.assertGreaterEqual(result["eval_accuracy"],    0.75)
														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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							Optional[int]				     =					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)
														_lowerCAmelCase  :							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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							Tuple				     =					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)
														_lowerCAmelCase  :							Dict				     =					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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							Optional[Any]				     =					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)
														_lowerCAmelCase  :							List[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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							Tuple				     =					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)
														_lowerCAmelCase  :							List[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      snake_case__   (     self):

														'''simple docstring'''

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

														_lowerCAmelCase  :							Optional[int]				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							int				     =					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)
														_lowerCAmelCase  :							Union[str, Any]				     =					get_results(__a)
														self.assertGreaterEqual(result["eval_overall_accuracy"],    0.10)







							@mock.patch.dict(os.environ,    {"WANDB_MODE": "offline"})
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					self.get_auto_remove_tmp_dir()
														_lowerCAmelCase  :							List[Any]				     =					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)
														_lowerCAmelCase  :							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")))





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'roc_bert'







							def __init__(     self,    __a=3_0522,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.1,    __a=0.1,    __a=512,    __a=2,    __a=0.02,    __a=1E-12,    __a=True,    __a=0,    __a="absolute",    __a=None,    __a=True,    __a=True,    __a=768,    __a=910,    __a=512,    __a=2_4858,    __a=True,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					vocab_size
														_lowerCAmelCase  :							Optional[Any]				     =					max_position_embeddings
														_lowerCAmelCase  :							Union[str, Any]				     =					hidden_size
														_lowerCAmelCase  :							str				     =					num_hidden_layers
														_lowerCAmelCase  :							List[Any]				     =					num_attention_heads
														_lowerCAmelCase  :							int				     =					intermediate_size
														_lowerCAmelCase  :							Optional[int]				     =					hidden_act
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Optional[Any]				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Optional[Any]				     =					type_vocab_size
														_lowerCAmelCase  :							int				     =					layer_norm_eps
														_lowerCAmelCase  :							Union[str, Any]				     =					use_cache
														_lowerCAmelCase  :							Optional[int]				     =					enable_pronunciation
														_lowerCAmelCase  :							Dict				     =					enable_shape
														_lowerCAmelCase  :							Optional[Any]				     =					pronunciation_embed_dim
														_lowerCAmelCase  :							Any				     =					pronunciation_vocab_size
														_lowerCAmelCase  :							List[str]				     =					shape_embed_dim
														_lowerCAmelCase  :							int				     =					shape_vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					concat_input
														_lowerCAmelCase  :							Dict				     =					position_embedding_type
														_lowerCAmelCase  :							Tuple				     =					classifier_dropout
														super().__init__(pad_token_id=__a,    **__a)
