





import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil


@pytest.fixture
def 				SCREAMING_SNAKE_CASE__    (     __a ):
	snake_case_		:      Optional[int]           =						tmp_path / 'text.txt'
	snake_case_		:      Union[str, Any]           =						textwrap.dedent(
	    '\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        ' )
	with open(__a ,			'w' ,			encoding='utf-8' ) as f:
		f.write(__a )
	return str(__a )


@pytest.fixture
def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      Dict           =						tmp_path / 'text_with_image.txt'
	with open(__a ,			'w' ,			encoding='utf-8' ) as f:
		f.write(__a )
	return str(__a )


@pytest.mark.parametrize('keep_linebreaks' ,			[True, False] )
def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	with open(__a ,			encoding='utf-8' ) as f:
		snake_case_		:      Any           =						f.read().splitlines(keepends=__a )
	snake_case_		:      str           =						Text(keep_linebreaks=__a ,			encoding='utf-8' )
	snake_case_		:      Optional[int]           =						text._generate_tables([[text_file]] )
	snake_case_		:      Dict           =						pa.concat_tables([table for _, table in generator] ).to_pydict()['text']
	assert generated_content == expected_content


@require_pil
def 				SCREAMING_SNAKE_CASE__    (     __a ):
	with open(__a ,			encoding='utf-8' ) as f:
		snake_case_		:      Dict           =						f.read().splitlines()[0]
	snake_case_		:      Any           =						Text(encoding='utf-8' ,			features=Features({'image': Image()} ) )
	snake_case_		:      Union[str, Any]           =						text._generate_tables([[text_file_with_image]] )
	snake_case_		:      List[str]           =						pa.concat_tables([table for _, table in generator] )
	assert pa_table.schema.field('image' ).type == Image()()
	snake_case_		:      int           =						pa_table.to_pydict()['image']
	assert generated_content == [{"path": image_file, "bytes": None}]


@pytest.mark.parametrize('sample_by' ,			['line', 'paragraph', 'document'] )
def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	with open(__a ,			encoding='utf-8' ) as f:
		snake_case_		:      str           =						f.read()
	if sample_by == "line":
		snake_case_		:      Dict           =						expected_content.splitlines()
	elif sample_by == "paragraph":
		snake_case_		:      Optional[int]           =						expected_content.split('\n\n' )
	elif sample_by == "document":
		snake_case_		:      Optional[Any]           =						[expected_content]
	snake_case_		:      List[Any]           =						Text(sample_by=__a ,			encoding='utf-8' ,			chunksize=1_00 )
	snake_case_		:      int           =						text._generate_tables([[text_file]] )
	snake_case_		:      List[str]           =						pa.concat_tables([table for _, table in generator] ).to_pydict()['text']
	assert generated_content == expected_content



import torch

from accelerate import Accelerator, DistributedType







class 	A__				:






				def __enter__(		self   : Optional[Any]  ):






							'''simple docstring'''







							if self.enabled:
										lowerCAmelCase__ : Any   =						self.model.no_sync()
										self.model_sync_obj.__enter__()

							return self






				def __exit__(		self   : int       ,	a   : int       ,	a   : Optional[Any]       ,	a   : str  ):






							'''simple docstring'''







							if self.enabled:
										# Average all models on exit
										self._sync_and_avg_model_params()
										self.model_sync_obj.__exit__(a       ,	a       ,	a  )






				def __init__(		self   : List[str]       ,	a   : Accelerator       ,	a   : torch.nn.Module       ,	a   : int       ,	a   : bool = True  ):






							'''simple docstring'''







							if accelerator.distributed_type not in [
							    DistributedType.NO,
							    DistributedType.MULTI_CPU,
							    DistributedType.MULTI_GPU,
							]:
										raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'  )
							lowerCAmelCase__ : Union[str, Any]   =						enabled and accelerator.distributed_type != DistributedType.NO
							lowerCAmelCase__ : str   =						0
							if self.enabled:
										lowerCAmelCase__ : Dict   =						accelerator
										lowerCAmelCase__ : Dict   =						model
										lowerCAmelCase__ : Optional[int]   =						local_sgd_steps






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							self.num_steps += 1
							if not self.enabled:
										return

							if self.num_steps % self.local_sgd_steps == 0:
										self._sync_and_avg_model_params()






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							self.accelerator.wait_for_everyone()
							with self.accelerator.autocast():
										for param in self.model.parameters():
													lowerCAmelCase__ : Dict   =						self.accelerator.reduce(param.data       ,	reduction='mean'  )