
"""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__        =		logging.getLogger()
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						Optional[int]						      =				argparse.ArgumentParser()
	parser.add_argument('-f'		)
	__lowerCAmelCase      :						Tuple						      =				parser.parse_args()
	return args.f
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Tuple						      =				{}
	__lowerCAmelCase      :						Tuple						      =				os.path.join(_UpperCamelCase	,  'all_results.json'		)
	if os.path.exists(_UpperCamelCase		):
		with open(_UpperCamelCase	,  'r'		) as f:
			__lowerCAmelCase      :						int						      =				json.load(_UpperCamelCase		)
	else:
		raise ValueError(F"can't find {path}"		)
	return results
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						Any						      =				torch.cuda.is_available() and torch_device == 'cuda'
	return is_using_cuda and is_apex_available()


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




class 			A__	(     _lowerCamelCase):

		@classmethod
		def 							__lowerCamelCase							(				cls			):
			# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
			__lowerCAmelCase      :						str						      =				tempfile.mkdtemp()
			__lowerCAmelCase      :						Tuple						      =				os.path.join(cls.tmpdir	,		'default_config.yml'			)
			write_basic_config(save_location=cls.configPath			)
			__lowerCAmelCase      :						Any						      =				['accelerate', 'launch', '--config_file', cls.configPath]

		@classmethod
		def 							__lowerCamelCase							(				cls			):
			shutil.rmtree(cls.tmpdir			)

		@mock.patch.dict(os.environ	,		{'WANDB_MODE': 'offline'}			)
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						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      :						Optional[Any]						      =				get_results(_SCREAMING_SNAKE_CASE			)
			self.assertGreaterEqual(result['eval_accuracy']	,		0.75			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'epoch_0'			)			)			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'glue_no_trainer'			)			)			)

		@mock.patch.dict(os.environ	,		{'WANDB_MODE': 'offline'}			)
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.get_auto_remove_tmp_dir()
			__lowerCAmelCase      :						str						      =				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      :						Dict						      =				get_results(_SCREAMING_SNAKE_CASE			)
			self.assertLess(result['perplexity']	,		1_00			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'epoch_0'			)			)			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'clm_no_trainer'			)			)			)

		@mock.patch.dict(os.environ	,		{'WANDB_MODE': 'offline'}			)
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				self.get_auto_remove_tmp_dir()
			__lowerCAmelCase      :						Union[str, Any]						      =				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      :						Tuple						      =				get_results(_SCREAMING_SNAKE_CASE			)
			self.assertLess(result['perplexity']	,		42			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'epoch_0'			)			)			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'mlm_no_trainer'			)			)			)

		@mock.patch.dict(os.environ	,		{'WANDB_MODE': 'offline'}			)
		def 							__lowerCamelCase							(				self			):
			# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
			__lowerCAmelCase      :						Any						      =				7 if get_gpu_count() > 1 else 2

			__lowerCAmelCase      :						Dict						      =				self.get_auto_remove_tmp_dir()
			__lowerCAmelCase      :						List[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      :						Optional[Any]						      =				get_results(_SCREAMING_SNAKE_CASE			)
			self.assertGreaterEqual(result['eval_accuracy']	,		0.75			)
			self.assertLess(result['train_loss']	,		0.5			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'epoch_0'			)			)			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'ner_no_trainer'			)			)			)

		@unittest.skip(reason='Fix me @muellerzr'			)
		@mock.patch.dict(os.environ	,		{'WANDB_MODE': 'offline'}			)
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.get_auto_remove_tmp_dir()
			__lowerCAmelCase      :						Dict						      =				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      :						Dict						      =				get_results(_SCREAMING_SNAKE_CASE			)
			# 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(_SCREAMING_SNAKE_CASE	,		'epoch_0'			)			)			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'qa_no_trainer'			)			)			)

		@mock.patch.dict(os.environ	,		{'WANDB_MODE': 'offline'}			)
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.get_auto_remove_tmp_dir()
			__lowerCAmelCase      :						Optional[int]						      =				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      :						Union[str, Any]						      =				get_results(_SCREAMING_SNAKE_CASE			)
			self.assertGreaterEqual(result['eval_accuracy']	,		0.8			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'swag_no_trainer'			)			)			)

		@slow
		@mock.patch.dict(os.environ	,		{'WANDB_MODE': 'offline'}			)
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[Any]						      =				self.get_auto_remove_tmp_dir()
			__lowerCAmelCase      :						str						      =				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      :						Optional[Any]						      =				get_results(_SCREAMING_SNAKE_CASE			)
			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(_SCREAMING_SNAKE_CASE	,		'epoch_0'			)			)			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'summarization_no_trainer'			)			)			)

		@slow
		@mock.patch.dict(os.environ	,		{'WANDB_MODE': 'offline'}			)
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[Any]						      =				self.get_auto_remove_tmp_dir()
			__lowerCAmelCase      :						Union[str, Any]						      =				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      :						Any						      =				get_results(_SCREAMING_SNAKE_CASE			)
			self.assertGreaterEqual(result['eval_bleu']	,		30			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'epoch_0'			)			)			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'translation_no_trainer'			)			)			)

		@slow
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				logging.StreamHandler(sys.stdout			)
			logger.addHandler(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				self.get_auto_remove_tmp_dir()
			__lowerCAmelCase      :						Optional[Any]						      =				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      :						Optional[int]						      =				get_results(_SCREAMING_SNAKE_CASE			)
			self.assertGreaterEqual(result['eval_overall_accuracy']	,		0.10			)







		@mock.patch.dict(os.environ	,		{'WANDB_MODE': 'offline'}			)
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				self.get_auto_remove_tmp_dir()
			__lowerCAmelCase      :						Optional[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      :						Any						      =				get_results(_SCREAMING_SNAKE_CASE			)
			# The base model scores a 25%
			self.assertGreaterEqual(result['eval_accuracy']	,		0.6			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'step_1'			)			)			)
			self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE	,		'image_classification_no_trainer'			)			)			)

"""simple docstring"""







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None	,  ):
	__lowerCAmelCase      :						Union[str, Any]						      =				aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    F"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"		)
	__lowerCAmelCase      :						str						      =				formatter_cls
	for alias in set(aliases + [format_type]		):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    F"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"		)
		__lowerCAmelCase      :						Any						      =				format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	__lowerCAmelCase      :						int						      =				aliases if aliases is not None else []
	for alias in set(aliases + [format_type]		):
		__lowerCAmelCase      :						str						      =				unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
							lowerCamelCase__        =		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
							_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
							lowerCamelCase__        =		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
							_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
							lowerCamelCase__        =		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
							_register_unavailable_formatter(_jax_error, """jax""", aliases=[])
def    __lowerCAmelCase    (_UpperCamelCase		):
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
	__lowerCAmelCase      :						Any						      =				get_format_type_from_alias(_UpperCamelCase		)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**_UpperCamelCase		)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    F"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"		)