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)

_lowerCamelCase					:   Dict							=		[
    '''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 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					List[Any] = None	,							_UpperCamelCase   :					Tuple = None      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None
												SCREAMING_SNAKE_CASE    			=       os.path.abspath(os.path.join("examples"	,							"by_feature"      )      )
												SCREAMING_SNAKE_CASE    			=       os.path.abspath("examples"      )
												for item in os.listdir(A__      ):
																	if item not in EXCLUDE_EXAMPLES:
																						SCREAMING_SNAKE_CASE    			=       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()"	,							):
																																SCREAMING_SNAKE_CASE    			=       compare_against_test(
																																    os.path.join(A__	,							A__      )	,							A__	,							A__	,							A__      )
																																SCREAMING_SNAKE_CASE    			=       "\n".join(A__      )
																																if special_strings is not None:
																																					for string in special_strings:
																																										SCREAMING_SNAKE_CASE    			=       diff.replace(A__	,							""      )
																																self.assertEqual(A__	,							""      )




							def 	__snake_case(						self   :					Tuple      )					->				Optional[int]:



												'''simple docstring'''

												self.one_complete_example("complete_nlp_example.py"	,							A__      )
												self.one_complete_example("complete_nlp_example.py"	,							A__      )





							def 	__snake_case(						self   :					List[str]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       os.path.abspath(os.path.join("examples"	,							"cv_example.py"      )      )
												SCREAMING_SNAKE_CASE    			=       [
												    " " * 16 + "{\n\n",
												    " " * 20 + "\"accuracy\": eval_metric[\"accuracy\"],\n\n",
												    " " * 20 + "\"f1\": eval_metric[\"f1\"],\n\n",
												    " " * 20 + "\"train_loss\": total_loss.item() / len(train_dataloader),\n\n",
												    " " * 20 + "\"epoch\": epoch,\n\n",
												    " " * 16 + "},\n\n",
												    " " * 16 + "step=epoch,\n",
												    " " * 12,
												    " " * 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 						lowercase    (     __a     ):
							lowercase__						:       List[Any]								=							False




							@classmethod
							def 	__snake_case(						cls   :					List[str]      )					->				Union[str, Any]:



												'''simple docstring'''

												super().setUpClass()
												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()
												SCREAMING_SNAKE_CASE    			=       os.path.join(cls._tmpdir	,							"default_config.yml"      )

												write_basic_config(save_location=cls.configPath      )
												SCREAMING_SNAKE_CASE    			=       ["accelerate", "launch", "--config_file", cls.configPath]




							@classmethod
							def 	__snake_case(						cls   :					Optional[int]      )					->				str:



												'''simple docstring'''

												super().tearDownClass()
												shutil.rmtree(cls._tmpdir      )




							def 	__snake_case(						self   :					Any      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       F"\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        ".split()
												run_command(self._launch_args + testargs      )
												self.assertTrue(os.path.exists(os.path.join(self.tmpdir	,							"epoch_0"      )      )      )




							def 	__snake_case(						self   :					List[Any]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       F"\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        ".split()
												SCREAMING_SNAKE_CASE    			=       run_command(self._launch_args + testargs      )
												self.assertTrue(os.path.exists(os.path.join(self.tmpdir	,							"step_2"      )      )      )




							def 	__snake_case(						self   :					Optional[int]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       F"\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir	,							'epoch_0'      )}\n        ".split()
												SCREAMING_SNAKE_CASE    			=       run_command(self._launch_args + testargs	,							return_stdout=A__      )
												self.assertNotIn("epoch 0:"	,							A__      )
												self.assertIn("epoch 1:"	,							A__      )




							def 	__snake_case(						self   :					Any      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       F"\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir	,							'step_2'      )}\n        ".split()
												SCREAMING_SNAKE_CASE    			=       run_command(self._launch_args + testargs	,							return_stdout=A__      )
												if torch.cuda.is_available():
																	SCREAMING_SNAKE_CASE    			=       torch.cuda.device_count()
												else:
																	SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Optional[int]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        ".split()
												with mock.patch.dict(os.environ	,							{"TESTING_MOCKED_DATALOADERS": "0"}      ):
																	SCREAMING_SNAKE_CASE    			=       run_command(self._launch_args + testargs	,							return_stdout=A__      )
																	SCREAMING_SNAKE_CASE    			=       re.findall("({.+})"	,							A__      )
																	SCREAMING_SNAKE_CASE    			=       [r for r in results if "accuracy" in r][-1]
																	SCREAMING_SNAKE_CASE    			=       ast.literal_eval(A__      )
																	self.assertGreaterEqual(results["accuracy"]	,							0.7_5      )




							def 	__snake_case(						self   :					List[str]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ["examples/by_feature/multi_process_metrics.py"]
												run_command(self._launch_args + testargs      )




							@require_trackers
							@mock.patch.dict(os.environ	,							{"WANDB_MODE": "offline"}      )
							def 	__snake_case(						self   :					Dict      )					->				Tuple:



												'''simple docstring'''

												with tempfile.TemporaryDirectory() as tmpdir:
																	SCREAMING_SNAKE_CASE    			=       F"\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            ".split()
																	run_command(self._launch_args + testargs      )
																	self.assertTrue(os.path.exists(os.path.join(A__	,							"tracking"      )      )      )




							def 	__snake_case(						self   :					int      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ["examples/by_feature/gradient_accumulation.py"]
												run_command(self._launch_args + testargs      )





							def 	__snake_case(						self   :					Union[str, Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ["examples/by_feature/local_sgd.py"]
												run_command(self._launch_args + testargs      )





import numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , input_matrix.conj().T  )

					# Set convergence to False. Will define convergence when we exceed max_iterations
					# or when we have small changes from one iteration to next.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

					while not convergence:
										# Multiple matrix by the vector.
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , UpperCAmelCase__  )
										# Normalize the resulting output vector.
										SCREAMING_SNAKE_CASE    			=       w / np.linalg.norm(UpperCAmelCase__  )
										# Find rayleigh quotient
										# (faster than usual b/c we know vector is normalized already)
										SCREAMING_SNAKE_CASE    			=       vector.conj().T if is_complex else vector.T
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , np.dot(UpperCAmelCase__      , UpperCAmelCase__  )  )

										# Check convergence.
										SCREAMING_SNAKE_CASE    			=       np.abs(lambda_ - lambda_previous  ) / lambda_
										iterations += 1

										if error <= error_tol or iterations >= max_iterations:
															SCREAMING_SNAKE_CASE    			=       True

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

					for problem_type in ["real", "complex"]:
										if problem_type == "real":
															SCREAMING_SNAKE_CASE    			=       real_input_matrix
															SCREAMING_SNAKE_CASE    			=       real_vector
										elif problem_type == "complex":
															SCREAMING_SNAKE_CASE    			=       complex_input_matrix
															SCREAMING_SNAKE_CASE    			=       complex_vector

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       np.linalg.eigh(UpperCAmelCase__  )
										# Last eigenvalue is the maximum one.
										SCREAMING_SNAKE_CASE    			=       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										SCREAMING_SNAKE_CASE    			=       eigen_vectors[:, -1]

										# Check our implementation and numpy gives close answers.
										assert np.abs(eigen_value - eigen_value_max  ) <= 1e-6
										# Take absolute values element wise of each eigenvector.
										# as they are only unique to a minus sign.
										assert np.linalg.norm(np.abs(UpperCAmelCase__  ) - np.abs(UpperCAmelCase__  )  ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




