

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)

_UpperCAmelCase						:  Optional[Any]									=			logging.getLogger()





def    __lowerCamelCase      (					):







							'''simple docstring'''






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





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






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





def    __lowerCamelCase      (					):







							'''simple docstring'''






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


_UpperCAmelCase						:  int									=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 					lowercase							(			lowercase_     ):




				@classmethod
				def        a				(   cls			):
											# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
											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        a				(   cls			):
											shutil.rmtree(cls.tmpdir			)




				@mock.patch.dict(os.environ ,  {'WANDB_MODE': 'offline'}			)
				def        a				(   self			):
											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(snake_case			)
											self.assertGreaterEqual(result['eval_accuracy'] ,  0.75			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'epoch_0'			)			)			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'glue_no_trainer'			)			)			)




				@mock.patch.dict(os.environ ,  {'WANDB_MODE': 'offline'}			)
				def        a				(   self			):
											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(snake_case			)
											self.assertLess(result['perplexity'] ,  100			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'epoch_0'			)			)			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'clm_no_trainer'			)			)			)




				@mock.patch.dict(os.environ ,  {'WANDB_MODE': 'offline'}			)
				def        a				(   self			):
											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(snake_case			)
											self.assertLess(result['perplexity'] ,  42			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'epoch_0'			)			)			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'mlm_no_trainer'			)			)			)




				@mock.patch.dict(os.environ ,  {'WANDB_MODE': 'offline'}			)
				def        a				(   self			):
											# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
											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(snake_case			)
											self.assertGreaterEqual(result['eval_accuracy'] ,  0.75			)
											self.assertLess(result['train_loss'] ,  0.5			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'epoch_0'			)			)			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'ner_no_trainer'			)			)			)




				@unittest.skip(reason='Fix me @muellerzr'			)
				@mock.patch.dict(os.environ ,  {'WANDB_MODE': 'offline'}			)
				def        a				(   self			):
											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(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(snake_case ,  'epoch_0'			)			)			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'qa_no_trainer'			)			)			)




				@mock.patch.dict(os.environ ,  {'WANDB_MODE': 'offline'}			)
				def        a				(   self			):
											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(snake_case			)
											self.assertGreaterEqual(result['eval_accuracy'] ,  0.8			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'swag_no_trainer'			)			)			)




				@slow
				@mock.patch.dict(os.environ ,  {'WANDB_MODE': 'offline'}			)
				def        a				(   self			):
											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(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(snake_case ,  'epoch_0'			)			)			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'summarization_no_trainer'			)			)			)




				@slow
				@mock.patch.dict(os.environ ,  {'WANDB_MODE': 'offline'}			)
				def        a				(   self			):
											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(snake_case			)
											self.assertGreaterEqual(result['eval_bleu'] ,  30			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'epoch_0'			)			)			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'translation_no_trainer'			)			)			)




				@slow
				def        a				(   self			):
											snake_case_     							=       logging.StreamHandler(sys.stdout			)
											logger.addHandler(snake_case			)

											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(snake_case			)
											self.assertGreaterEqual(result['eval_overall_accuracy'] ,  0.10			)




				@mock.patch.dict(os.environ ,  {'WANDB_MODE': 'offline'}			)
				def        a				(   self			):
											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(snake_case			)
											# The base model scores a 25%
											self.assertGreaterEqual(result['eval_accuracy'] ,  0.6			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'step_1'			)			)			)
											self.assertTrue(os.path.exists(os.path.join(snake_case ,  'image_classification_no_trainer'			)			)			)







import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_UpperCAmelCase						:  Dict									=			"""sshleifer/bart-tiny-random"""
_UpperCAmelCase						:  Optional[Any]									=			"""patrickvonplaten/t5-tiny-random"""







@require_torch
class 					lowercase							(			unittest.TestCase     ):




				@cached_property
				def        a				(   self			):
											return AutoConfig.from_pretrained(snake_case			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=1			)
											self.assertEqual(student.config.num_hidden_layers ,  1			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=snake_case			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=snake_case			)
											self.assertEqual(student.config.encoder_layers ,  1			)
											self.assertEqual(student.config.decoder_layers ,  self.teacher_config.encoder_layers			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=1			)
											self.assertEqual(student.config.encoder_layers ,  1			)
											self.assertEqual(student.config.decoder_layers ,  1			)




				def        a				(   self			):
											with self.assertRaises(snake_case			):
																		create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=snake_case ,  d=snake_case			)




