





"""simple docstring"""


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_   (       snake_case   :					Any		)->		Dict:
       _lowerCamelCase    	=					tmp_path / 'text.txt'
       _lowerCamelCase    	=					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(snake_case						,  'w'						,  encoding='utf-8'		) as f:
              f.write(snake_case		)
       return str(snake_case		)





@pytest.fixture
def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Any]						,  snake_case   :					str		)->		str:
       _lowerCamelCase    	=					tmp_path / 'text_with_image.txt'
       with open(snake_case						,  'w'						,  encoding='utf-8'		) as f:
              f.write(snake_case		)
       return str(snake_case		)





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





@require_pil
def SCREAMING_SNAKE_CASE_   (       snake_case   :					Any		)->		Dict:
       with open(snake_case						,  encoding='utf-8'		) as f:
              _lowerCamelCase    	=					f.read().splitlines()[0]
       _lowerCamelCase    	=					Text(encoding='utf-8'						,  features=Features({'image': Image()}		)		)
       _lowerCamelCase    	=					text._generate_tables([[text_file_with_image]]		)
       _lowerCamelCase    	=					pa.concat_tables([table for _, table in generator]		)
       assert pa_table.schema.field('image'		).type == Image()()
       _lowerCamelCase    	=					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_   (       snake_case   :					Any						,  snake_case   :					Dict		)->		int:
       with open(snake_case						,  encoding='utf-8'		) as f:
              _lowerCamelCase    	=					f.read()
       if sample_by == "line":
              _lowerCamelCase    	=					expected_content.splitlines()
       elif sample_by == "paragraph":
              _lowerCamelCase    	=					expected_content.split('\n\n'		)
       elif sample_by == "document":
              _lowerCamelCase    	=					[expected_content]
       _lowerCamelCase    	=					Text(sample_by=snake_case						,  encoding='utf-8'						,  chunksize=100		)
       _lowerCamelCase    	=					text._generate_tables([[text_file]]		)
       _lowerCamelCase    	=					pa.concat_tables([table for _, table in generator]		).to_pydict()['text']
       assert generated_content == expected_content













"""simple docstring"""


import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int						,  snake_case   :					bool = False		)->		Any:
       if device_specific:
              seed += AcceleratorState().process_index
       random.seed(snake_case		)
       np.random.seed(snake_case		)
       torch.manual_seed(snake_case		)
       if is_xpu_available():
              torch.xpu.manual_seed_all(snake_case		)
       elif is_npu_available():
              torch.npu.manual_seed_all(snake_case		)
       else:
              torch.cuda.manual_seed_all(snake_case		)
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
              xm.set_rng_state(snake_case		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[RNGType] = None						,  snake_case   :					Optional[torch.Generator] = None		)->		str:
       # Get the proper rng state
       if rng_type == RNGType.TORCH:
              _lowerCamelCase    	=					torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              _lowerCamelCase    	=					torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
              assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
              _lowerCamelCase    	=					torch.tensor(xm.get_rng_state()		)
       elif rng_type == RNGType.NPU:
              assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
              _lowerCamelCase    	=					torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
              assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
              _lowerCamelCase    	=					torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              _lowerCamelCase    	=					generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       _lowerCamelCase    	=					AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              _lowerCamelCase    	=					rng_state.to(xm.xla_device()		)
              xm.collective_broadcast([rng_state]		)
              xm.mark_step()
              _lowerCamelCase    	=					rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
              _lowerCamelCase    	=					rng_state.to(state.device		)
              torch.distributed.broadcast(snake_case						,  0		)
              _lowerCamelCase    	=					rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(snake_case						,  0		)

       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
              torch.set_rng_state(snake_case		)
       elif rng_type == RNGType.CUDA:
              torch.cuda.set_rng_state(snake_case		)
       elif rng_type == RNGType.NPU:
              torch.npu.set_rng_state(snake_case		)
       elif rng_type == RNGType.XPU:
              torch.xpu.set_rng_state(snake_case		)
       elif rng_type == RNGType.XLA:
              xm.set_rng_state(rng_state.item()		)
       elif rng_type == RNGType.GENERATOR:
              generator.set_state(snake_case		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Union[str, RNGType]]						,  snake_case   :					Optional[torch.Generator] = None		)->		int:
       for rng_type in rng_types:
              synchronize_rng_state(RNGType(snake_case		)						,  generator=snake_case		)






