




'''simple docstring'''




from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

snake_case__ 							=    logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):




							def     snake_case__     (			cls					: Union[str, Any]      ) -> List[Any]:
												return cls




def     snake_case__     (			lowerCamelCase__					: Union[Tuple, List]      ,		lowerCamelCase__					: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None      ,		lowerCamelCase__					: Optional["torch.device"] = None      ,		lowerCamelCase__					: Optional["torch.dtype"] = None      ,		lowerCamelCase__					: Optional["torch.layout"] = None      ,		) -> List[Any]:
					A_			:  Union[str, Any]			=				device
					A_			:  Union[str, Any]			=				shape[0]

					A_			:  Dict			=				layout or torch.strided
					A_			:  Any			=				device or torch.device('''cpu'''      )

					if generator is not None:
										A_			:  List[str]			=				generator.device.type if not isinstance(lowerCamelCase__      ,		lowerCamelCase__      ) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															A_			:  Union[str, Any]			=				'''cpu'''
															if device != "mps":
																				logger.info(
																				    f'The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'
																				    f' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'
																				    f' slighly speed up this function by passing a generator that was created on the {device} device.'      )
										elif gen_device_type != device.type and gen_device_type == "cuda":
															raise ValueError(f'Cannot generate a {device} tensor from a generator of type {gen_device_type}.'      )

					if isinstance(lowerCamelCase__      ,		lowerCamelCase__      ):
										A_			:  Dict			=				(1,) + shape[1:]
										A_			:  int			=				[
										    torch.randn(lowerCamelCase__      ,		generator=generator[i]      ,		device=lowerCamelCase__      ,		dtype=lowerCamelCase__      ,		layout=lowerCamelCase__      )
										    for i in range(lowerCamelCase__      )
										]
										A_			:  int			=				torch.cat(lowerCamelCase__      ,		dim=0      ).to(lowerCamelCase__      )
					else:
										A_			:  int			=				torch.randn(lowerCamelCase__      ,		generator=lowerCamelCase__      ,		device=lowerCamelCase__      ,		dtype=lowerCamelCase__      ,		layout=lowerCamelCase__      ).to(lowerCamelCase__      )

					return latents





def     snake_case__     (			lowerCamelCase__					: Optional[Any]      ) -> int:
					if is_torch_version('''<'''      ,		'''2.0.0'''      ) or not hasattr(lowerCamelCase__      ,		'''_dynamo'''      ):
										return False
					return isinstance(lowerCamelCase__      ,		torch._dynamo.eval_frame.OptimizedModule      )









'''simple docstring'''




import io
import json
import subprocess


snake_case__ 							=    [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

snake_case__ 							=    8




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



