

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											=     logging.getLogger()





def a():





	'''simple docstring'''
	snake_case_      		= argparse.ArgumentParser()
	parser.add_argument('-f'	)
	snake_case_      		= parser.parse_args()
	return args.f





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= {}
	snake_case_      		= os.path.join(lowercase__			, 'all_results.json'	)
	if os.path.exists(lowercase__	):
		with open(lowercase__			, 'r'	) as f:
			snake_case_      		= json.load(lowercase__	)
	else:
		raise ValueError(f"""can't find {path}"""	)
	return results





def a():





	'''simple docstring'''
	snake_case_      		= torch.cuda.is_available() and torch_device == 'cuda'
	return is_using_cuda and is_apex_available()


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






class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			@classmethod
			def 			__lowerCAmelCase (    cls							):

				"""simple docstring"""


				snake_case_      		= tempfile.mkdtemp()
				snake_case_      		= os.path.join(cls.tmpdir ,				'default_config.yml'							)
				write_basic_config(save_location=cls.configPath							)
				snake_case_      		= ['accelerate', 'launch', '--config_file', cls.configPath]


			@classmethod
			def 			__lowerCAmelCase (    cls							):

				"""simple docstring"""


				shutil.rmtree(cls.tmpdir							)


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

				"""simple docstring"""


				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				self.assertGreaterEqual(result['eval_accuracy'] ,				0.75							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'epoch_0'							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'glue_no_trainer'							)							)							)


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

				"""simple docstring"""


				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				self.assertLess(result['perplexity'] ,				1_00							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'epoch_0'							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'clm_no_trainer'							)							)							)


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

				"""simple docstring"""


				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				self.assertLess(result['perplexity'] ,				42							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'epoch_0'							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'mlm_no_trainer'							)							)							)


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

				"""simple docstring"""


				snake_case_      		= 7 if get_gpu_count() > 1 else 2

				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				self.assertGreaterEqual(result['eval_accuracy'] ,				0.75							)
				self.assertLess(result['train_loss'] ,				0.5							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'epoch_0'							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'ner_no_trainer'							)							)							)


			@unittest.skip(reason='Fix me @muellerzr'							)
			@mock.patch.dict(os.environ ,				{'WANDB_MODE': 'offline'}							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				# 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(__UpperCamelCase ,				'epoch_0'							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'qa_no_trainer'							)							)							)


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

				"""simple docstring"""


				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				self.assertGreaterEqual(result['eval_accuracy'] ,				0.8							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'swag_no_trainer'							)							)							)


			@slow
			@mock.patch.dict(os.environ ,				{'WANDB_MODE': 'offline'}							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				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(__UpperCamelCase ,				'epoch_0'							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'summarization_no_trainer'							)							)							)


			@slow
			@mock.patch.dict(os.environ ,				{'WANDB_MODE': 'offline'}							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				self.assertGreaterEqual(result['eval_bleu'] ,				30							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'epoch_0'							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'translation_no_trainer'							)							)							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= logging.StreamHandler(sys.stdout							)
				logger.addHandler(__UpperCamelCase							)

				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				self.assertGreaterEqual(result['eval_overall_accuracy'] ,				0.10							)







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

				"""simple docstring"""


				snake_case_      		= self.get_auto_remove_tmp_dir()
				snake_case_      		= 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							)
				snake_case_      		= get_results(__UpperCamelCase							)
				# The base model scores a 25%
				self.assertGreaterEqual(result['eval_accuracy'] ,				0.6							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'step_1'							)							)							)
				self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase ,				'image_classification_no_trainer'							)							)							)



from ..utils import DummyObject, requires_backends


class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)
