



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)

SCREAMING_SNAKE_CASE_:str             =						logging.getLogger()




def 	__UpperCamelCase	(  )       ->  Tuple:



    """simple docstring"""






    A :       Dict          =   argparse.ArgumentParser()
    parser.add_argument("""-f"""		)
    A :       Dict          =   parser.parse_args()
    return args.f




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  List[str]:



    """simple docstring"""






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




def 	__UpperCamelCase	(  )       ->  Any:



    """simple docstring"""






    A :       Any          =   torch.cuda.is_available() and torch_device == """cuda"""
    return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE_:str             =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @classmethod
    def 			_lowerCAmelCase					(       cls		):
        # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
        A :       Optional[int]          =   tempfile.mkdtemp()
        A :       Any          =   os.path.join(cls.tmpdir,		"""default_config.yml"""		)
        write_basic_config(save_location=cls.configPath		)
        A :       List[str]          =   ["""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		):
        A :       Dict          =   self.get_auto_remove_tmp_dir()
        A :       Optional[int]          =   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		)
        A :       str          =   get_results(lowerCamelCase__		)
        self.assertGreaterEqual(result["""eval_accuracy"""],		0.75		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""epoch_0"""		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""glue_no_trainer"""		)		)		)






    @mock.patch.dict(os.environ,		{"""WANDB_MODE""": """offline"""}		)
    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.get_auto_remove_tmp_dir()
        A :       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		)
        A :       Dict          =   get_results(lowerCamelCase__		)
        self.assertLess(result["""perplexity"""],		100		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""epoch_0"""		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""clm_no_trainer"""		)		)		)






    @mock.patch.dict(os.environ,		{"""WANDB_MODE""": """offline"""}		)
    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.get_auto_remove_tmp_dir()
        A :       Any          =   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		)
        A :       Tuple          =   get_results(lowerCamelCase__		)
        self.assertLess(result["""perplexity"""],		42		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""epoch_0"""		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""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
        A :       Optional[Any]          =   7 if get_gpu_count() > 1 else 2

        A :       List[Any]          =   self.get_auto_remove_tmp_dir()
        A :       List[str]          =   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		)
        A :       Any          =   get_results(lowerCamelCase__		)
        self.assertGreaterEqual(result["""eval_accuracy"""],		0.75		)
        self.assertLess(result["""train_loss"""],		0.5		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""epoch_0"""		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""ner_no_trainer"""		)		)		)






    @unittest.skip(reason="""Fix me @muellerzr"""		)
    @mock.patch.dict(os.environ,		{"""WANDB_MODE""": """offline"""}		)
    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   self.get_auto_remove_tmp_dir()
        A :       Dict          =   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		)
        A :       Optional[int]          =   get_results(lowerCamelCase__		)
        # 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(lowerCamelCase__,		"""epoch_0"""		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""qa_no_trainer"""		)		)		)






    @mock.patch.dict(os.environ,		{"""WANDB_MODE""": """offline"""}		)
    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   self.get_auto_remove_tmp_dir()
        A :       Union[str, Any]          =   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		)
        A :       List[str]          =   get_results(lowerCamelCase__		)
        self.assertGreaterEqual(result["""eval_accuracy"""],		0.8		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""swag_no_trainer"""		)		)		)






    @slow
    @mock.patch.dict(os.environ,		{"""WANDB_MODE""": """offline"""}		)
    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   self.get_auto_remove_tmp_dir()
        A :       List[str]          =   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		)
        A :       List[Any]          =   get_results(lowerCamelCase__		)
        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(lowerCamelCase__,		"""epoch_0"""		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""summarization_no_trainer"""		)		)		)






    @slow
    @mock.patch.dict(os.environ,		{"""WANDB_MODE""": """offline"""}		)
    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.get_auto_remove_tmp_dir()
        A :       int          =   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		)
        A :       Union[str, Any]          =   get_results(lowerCamelCase__		)
        self.assertGreaterEqual(result["""eval_bleu"""],		30		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""epoch_0"""		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""translation_no_trainer"""		)		)		)






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       List[Any]          =   logging.StreamHandler(sys.stdout		)
        logger.addHandler(lowerCamelCase__		)

        A :       int          =   self.get_auto_remove_tmp_dir()
        A :       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		)
        A :       str          =   get_results(lowerCamelCase__		)
        self.assertGreaterEqual(result["""eval_overall_accuracy"""],		0.10		)






    @mock.patch.dict(os.environ,		{"""WANDB_MODE""": """offline"""}		)
    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.get_auto_remove_tmp_dir()
        A :       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		)
        A :       List[Any]          =   get_results(lowerCamelCase__		)
        # The base model scores a 25%
        self.assertGreaterEqual(result["""eval_accuracy"""],		0.6		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""step_1"""		)		)		)
        self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__,		"""image_classification_no_trainer"""		)		)		)







from ..utils import DummyObject, requires_backends






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






class SCREAMING_SNAKE_CASE__     (		metaclass=SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      ["torch", "transformers", "onnx"]






    def __init__(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(self,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		*lowerCamelCase__,		**lowerCamelCase__		):
        requires_backends(cls,		["""torch""", """transformers""", """onnx"""]		)


