





'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

a__							:							Optional[int] =[
    '''cross_validation.py''',
    '''gradient_accumulation.py''',
    '''local_sgd.py''',
    '''multi_process_metrics.py''',
    '''memory.py''',
    '''automatic_gradient_accumulation.py''',
    '''fsdp_with_peak_mem_tracking.py''',
    '''deepspeed_with_config_support.py''',
    '''megatron_lm_gpt_pretraining.py''',
]

class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							int							,	__A       :							str							,	__A       :							bool							,	__A       :							str = None							,	__A       :							list = None	):
        __UpperCamelCase       =  None
        __UpperCamelCase       =  os.path.abspath(os.path.join('examples'							,	'by_feature'	)	)
        __UpperCamelCase       =  os.path.abspath('examples'	)
        for item in os.listdir(__A	):
               if item not in EXCLUDE_EXAMPLES:
                      __UpperCamelCase       =  os.path.join(__A							,	__A	)
                      if os.path.isfile(__A	) and ".py" in item_path:
                             with self.subTest(
                                 tested_script=__A							,	feature_script=__A							,	tested_section='main()' if parser_only else 'training_function()'							,	):
                                    __UpperCamelCase       =  compare_against_test(
                                        os.path.join(__A							,	__A	)							,	__A							,	__A							,	__A	)
                                    __UpperCamelCase       =  '\n'.join(__A	)
                                    if special_strings is not None:
                                           for string in special_strings:
                                                  __UpperCamelCase       =  diff.replace(__A							,	''	)
                                    self.assertEqual(__A							,	''	)






 def        _lowerCamelCase					(       self       :							int	):
        self.one_complete_example('complete_nlp_example.py'							,	__A	)
        self.one_complete_example('complete_nlp_example.py'							,	__A	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  os.path.abspath(os.path.join('examples'							,	'cv_example.py'	)	)
        __UpperCamelCase       =  [
            ' ' * 1_6 + '{\n\n',
            ' ' * 2_0 + '"accuracy": eval_metric["accuracy"],\n\n',
            ' ' * 2_0 + '"f1": eval_metric["f1"],\n\n',
            ' ' * 2_0 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
            ' ' * 2_0 + '"epoch": epoch,\n\n',
            ' ' * 1_6 + '},\n\n',
            ' ' * 1_6 + 'step=epoch,\n',
            ' ' * 1_2,
            ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
        ]
        self.one_complete_example('complete_cv_example.py'							,	__A							,	__A							,	__A	)
        self.one_complete_example('complete_cv_example.py'							,	__A							,	__A							,	__A	)






@mock.patch.dict(os.environ			,       {"TESTING_MOCKED_DATALOADERS": "1"}       )
class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=False






 @classmethod
 def        _lowerCamelCase					(       cls       :							Any	):
        super().setUpClass()
        __UpperCamelCase       =  tempfile.mkdtemp()
        __UpperCamelCase       =  os.path.join(cls._tmpdir							,	'default_config.yml'	)

        write_basic_config(save_location=cls.configPath	)
        __UpperCamelCase       =  ['accelerate', 'launch', '--config_file', cls.configPath]






 @classmethod
 def        _lowerCamelCase					(       cls       :							Any	):
        super().tearDownClass()
        shutil.rmtree(cls._tmpdir	)






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        '''.split()
        run_command(self._launch_args + testargs	)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir							,	'epoch_0'	)	)	)






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        '''.split()
        __UpperCamelCase       =  run_command(self._launch_args + testargs	)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir							,	'step_2'	)	)	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir							,	'epoch_0'	)}
        '''.split()
        __UpperCamelCase       =  run_command(self._launch_args + testargs							,	return_stdout=__A	)
        self.assertNotIn('epoch 0:'							,	__A	)
        self.assertIn('epoch 1:'							,	__A	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir							,	'step_2'	)}
        '''.split()
        __UpperCamelCase       =  run_command(self._launch_args + testargs							,	return_stdout=__A	)
        if torch.cuda.is_available():
               __UpperCamelCase       =  torch.cuda.device_count()
        else:
               __UpperCamelCase       =  1
        if num_processes > 1:
               self.assertNotIn('epoch 0:'							,	__A	)
               self.assertIn('epoch 1:'							,	__A	)
        else:
               self.assertIn('epoch 0:'							,	__A	)
               self.assertIn('epoch 1:'							,	__A	)






 @slow
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  '\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
        with mock.patch.dict(os.environ							,	{'TESTING_MOCKED_DATALOADERS': '0'}	):
               __UpperCamelCase       =  run_command(self._launch_args + testargs							,	return_stdout=__A	)
               __UpperCamelCase       =  re.findall('({.+})'							,	__A	)
               __UpperCamelCase       =  [r for r in results if 'accuracy' in r][-1]
               __UpperCamelCase       =  ast.literal_eval(__A	)
               self.assertGreaterEqual(results['accuracy']							,	0.75	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  ['examples/by_feature/multi_process_metrics.py']
        run_command(self._launch_args + testargs	)






 @require_trackers
 @mock.patch.dict(os.environ							,	{'WANDB_MODE': 'offline'}	)
 def        _lowerCamelCase					(       self       :							List[str]	):
        with tempfile.TemporaryDirectory() as tmpdir:
               __UpperCamelCase       =  f'''
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            '''.split()
               run_command(self._launch_args + testargs	)
               self.assertTrue(os.path.exists(os.path.join(__A							,	'tracking'	)	)	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  ['examples/by_feature/gradient_accumulation.py']
        run_command(self._launch_args + testargs	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  ['examples/by_feature/local_sgd.py']
        run_command(self._launch_args + testargs	)

'''simple docstring'''




import math

import flax.linen as nn
import jax.numpy as jnp






def 				snake_case__							(			_A:					jnp.ndarray      ,   _A:					int      ,   _A:					float = 1      ,   _A:					float = 1      ,   _A:					float = 1.0e4      ,   _A:					bool = False      ,   _A:					float = 1.0      ,   )    ->	jnp.ndarray:

		'''simple docstring'''


		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
		lowerCAmelCase   					=  float(embedding_dim // 2 )
		lowerCAmelCase   					=  math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
		lowerCAmelCase   					=  min_timescale * jnp.exp(jnp.arange(_A      ,   dtype=jnp.floataa ) * -log_timescale_increment )
		lowerCAmelCase   					=  jnp.expand_dims(_A      ,   1 ) * jnp.expand_dims(_A      ,   0 )

		# scale embeddings
		lowerCAmelCase   					=  scale * emb

		if flip_sin_to_cos:
				lowerCAmelCase   					=  jnp.concatenate([jnp.cos(_A ), jnp.sin(_A )]      ,   axis=1 )
		else:
				lowerCAmelCase   					=  jnp.concatenate([jnp.sin(_A ), jnp.cos(_A )]      ,   axis=1 )
		lowerCAmelCase   					=  jnp.reshape(_A      ,   [jnp.shape(_A )[0], embedding_dim] )
		return signal






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_1""")(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.silu(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_2""")(__lowerCAmelCase)
					return temb







class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     bool      =       False
			UpperCAmelCase_			:     float      =       1



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return get_sinusoidal_embeddings(
					    __lowerCAmelCase       ,    embedding_dim=self.dim       ,    flip_sin_to_cos=self.flip_sin_to_cos       ,    freq_shift=self.freq_shift)




