


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_(   )					->		int:
    _lowercase :							Any   	=	argparse.ArgumentParser()
    parser.add_argument('-f'				)
    _lowercase :							List[str]   	=	parser.parse_args()
    return args.f



def        UpperCamelCase_(   lowerCamelCase_				)					->		int:
    _lowercase :							List[str]   	=	{}
    _lowercase :							int   	=	os.path.join(lowerCamelCase_							,				'all_results.json'				)
    if os.path.exists(lowerCamelCase_				):
        with open(lowerCamelCase_							,				'r'				) as f:
            _lowercase :							Optional[Any]   	=	json.load(lowerCamelCase_				)
    else:
        raise ValueError(F'''can\'t find {path}'''				)
    return results



def        UpperCamelCase_(   )					->		Optional[int]:
    _lowercase :							Optional[int]   	=	torch.cuda.is_available() and torch_device == 'cuda'
    return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE :   Any							      =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class _lowerCamelCase(		_a     ):
       @classmethod
       def     UpperCamelCase  (      cls) ->     str:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	tempfile.mkdtemp()
           _lowercase :							int   	=	os.path.join(cls.tmpdir,   'default_config.yml')
           write_basic_config(save_location=cls.configPath)
           _lowercase :							Union[str, Any]   	=	['accelerate', 'launch', '--config_file', cls.configPath]
       @classmethod
       def     UpperCamelCase  (      cls) ->     Optional[Any]:




           """simple docstring"""

           shutil.rmtree(cls.tmpdir)
       @mock.patch.dict(os.environ,   {'WANDB_MODE': 'offline'})
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							Any   	=	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)
           _lowercase :							Union[str, Any]   	=	get_results(lowerCamelCase)
           self.assertGreaterEqual(result['eval_accuracy'],   0.7_5)
           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     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							Dict   	=	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)
           _lowercase :							Optional[int]   	=	get_results(lowerCamelCase)
           self.assertLess(result['perplexity'],   1_00)
           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     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							List[str]   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							List[str]   	=	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)
           _lowercase :							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     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

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

           _lowercase :							Any   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							List[Any]   	=	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)
           _lowercase :							Tuple   	=	get_results(lowerCamelCase)
           self.assertGreaterEqual(result['eval_accuracy'],   0.7_5)
           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     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							int   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							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)
           _lowercase :							List[Any]   	=	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     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							str   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							str   	=	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)
           _lowercase :							Optional[int]   	=	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     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							Optional[Any]   	=	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)
           _lowercase :							Optional[int]   	=	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     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							List[str]   	=	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)
           _lowercase :							Tuple   	=	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     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

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

           _lowercase :							Optional[Any]   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							Any   	=	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)
           _lowercase :							str   	=	get_results(lowerCamelCase)
           self.assertGreaterEqual(result['eval_overall_accuracy'],   0.1_0)




       @mock.patch.dict(os.environ,   {'WANDB_MODE': 'offline'})
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							str   	=	self.get_auto_remove_tmp_dir()
           _lowercase :							Optional[Any]   	=	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)
           _lowercase :							Tuple   	=	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')))






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


SCREAMING_SNAKE_CASE :   List[Any]							      =				subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
SCREAMING_SNAKE_CASE :   Optional[int]							      =				(
    subprocess.check_output(F"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode("utf-8").split()
)

SCREAMING_SNAKE_CASE :   Any							      =				"|".join(sys.argv[1:])
SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				re.compile(rF"^({joined_dirs}).*?\.py$")

SCREAMING_SNAKE_CASE :   List[Any]							      =				[x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")


