
import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_       =					1_0
	def 						_lowercase  (       self  :			int       )       ->     List[str]:
					lowercase_       =					[1, 2, 3, 4]
					lowercase_       =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
					self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_       ,							self.block_size       ,							0       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     Optional[Any]:
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
					self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_       ,							self.block_size       ,							0       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
					self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_       ,							self.block_size       ,							0       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Any       )       ->     List[Any]:
					lowercase_       =					'''It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this.'''
					lowercase_  ,	lowercase_       =					process_story(SCREAMING_SNAKE_CASE_       )
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							[]       )
	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					lowercase_       =					''''''
					lowercase_  ,	lowercase_       =					process_story(SCREAMING_SNAKE_CASE_       )
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							[]       )
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							[]       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Union[str, Any]:
					lowercase_       =					(
					    '''It was the year of Our Lord one thousand seven hundred and '''
					    '''seventy-five\n\nSpiritual revelations were conceded to England '''
					    '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
					)
					lowercase_  ,	lowercase_       =					process_story(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					[
					    '''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
					    '''Spiritual revelations were conceded to England at that favoured period, as at this.''',
					]
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					['''It was the best of times.''']
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[Any]:
					lowercase_       =					torch.tensor([1, 2, 3, 4]       )
					lowercase_       =					torch.tensor([1, 1, 1, 1]       )
					np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_       ,							0       ).numpy()       ,							expected.numpy()       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     Tuple:
					lowercase_       =					torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]       )
					lowercase_       =					torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
					np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_       ,							2_3       ).numpy()       ,							expected.numpy()       )
	def 						_lowercase  (       self  :			int       )       ->     Dict:
					lowercase_       =					torch.tensor([8, 2, 3, 4, 1, 1, 1]       )
					lowercase_       =					torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
					np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_       ,							1       ).numpy()       ,							expected.numpy()       )




	def 						_lowercase  (       self  :			List[str]       )       ->     Tuple:
					lowercase_       =					1_0_1
					lowercase_       =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]       )
					lowercase_       =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]       )

					lowercase_       =					compute_token_type_ids(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )








"""simple docstring"""




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				__a           =			inspect.getfile(accelerate.test_utils      )
				__a           =			os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_script.py''']      )
				__a           =			os.path.sep.join(
				    mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']      )
				__a           =			os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_ops.py''']      )

		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				print(f'''Found {torch.cuda.device_count()} devices.'''      )
				__a           =			['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
				with patch_environment(omp_num_threads=1      ):
						execute_subprocess_async(_a							, env=os.environ.copy()      )

		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				print(f'''Found {torch.cuda.device_count()} devices.'''      )
				__a           =			['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
				print(f'''Command: {cmd}'''      )
				with patch_environment(omp_num_threads=1      ):
						execute_subprocess_async(_a							, env=os.environ.copy()      )

		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				__a           =			['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
				with patch_environment(omp_num_threads=1      ):
						execute_subprocess_async(_a							, env=os.environ.copy()      )





		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''      )
				__a           =			['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
				with patch_environment(omp_num_threads=1							, cuda_visible_devices='''0,1'''      ):
						execute_subprocess_async(_a							, env=os.environ.copy()      )


if __name__ == "__main__":
				lowercase_			     =  Accelerator()
				lowercase_			     =  (accelerator.state.process_index + 2, 1_0)
				lowercase_			     =  torch.randint(0, 1_0, shape).to(accelerator.device)

				lowercase_			     =  ""

				lowercase_			     =  accelerator.pad_across_processes(tensor)
				if tensora.shape[0] != accelerator.state.num_processes + 1:
								error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
				if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
								error_msg += "Tensors have different values."
				if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
								error_msg += "Padding was not done with the right value (0)."

				lowercase_			     =  accelerator.pad_across_processes(tensor, pad_first=True)
				if tensora.shape[0] != accelerator.state.num_processes + 1:
								error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
				lowercase_			     =  accelerator.state.num_processes - accelerator.state.process_index - 1
				if not torch.equal(tensora[index:], tensor):
								error_msg += "Tensors have different values."
				if not torch.all(tensora[:index] == 0):
								error_msg += "Padding was not done with the right value (0)."

				# Raise error at the end to make sure we don't stop at the first failure.
				if len(error_msg) > 0:
								raise ValueError(error_msg)






