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   _UpperCamelCase   (			snake_case__,    snake_case__ = False							)						->   Optional[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   _UpperCamelCase   (			snake_case__ = None,    snake_case__ = None							)						->   str:
							# Get the proper rng state
							if rng_type == RNGType.TORCH:
														__UpperCAmelCase						:			Dict         =   torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														__UpperCAmelCase						:			Union[str, Any]         =   torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														__UpperCAmelCase						:			Tuple         =   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."
														__UpperCAmelCase						:			List[str]         =   torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														__UpperCAmelCase						:			Optional[int]         =   torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														__UpperCAmelCase						:			List[Any]         =   generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							__UpperCAmelCase						:			Optional[int]         =   AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														__UpperCAmelCase						:			Any         =   rng_state.to(xm.xla_device()							)
														xm.collective_broadcast([rng_state]							)
														xm.mark_step()
														__UpperCAmelCase						:			Optional[int]         =   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
							):
														__UpperCAmelCase						:			Optional[int]         =   rng_state.to(state.device							)
														torch.distributed.broadcast(snake_case__,    0							)
														__UpperCAmelCase						:			Tuple         =   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   _UpperCamelCase   (			snake_case__,    snake_case__ = None							)						->   str:
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(snake_case__							),    generator=snake_case__							)





import io
import json
import subprocess


_snake_case			=						[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

_snake_case			=						8




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   List[Any]:
							__UpperCAmelCase						:			int         =   {}
							for src, tgt in pairs:
														__UpperCAmelCase						:			Optional[Any]         =   f'''{src}-{tgt}'''
														__UpperCAmelCase						:			Union[str, Any]         =   f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
														__UpperCAmelCase						:			Any         =   subprocess.run(snake_case__,    stdout=subprocess.PIPE							).stdout.decode("utf-8"							).splitlines()
														__UpperCAmelCase						:			Tuple         =   f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
														__UpperCAmelCase						:			List[Any]         =   subprocess.run(snake_case__,    stdout=subprocess.PIPE							).stdout.decode("utf-8"							).splitlines()
														__UpperCAmelCase						:			str         =   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
							return text


_snake_case			=						get_all_data(pairs, n_objs)
_snake_case			=						'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
	_snake_case			=						json.dump(text, f, indent=2, ensure_ascii=False)




