"""simple docstring"""




import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=False     ):





		"""simple docstring"""




		try:
				lowerCAmelCase__        =   os.environ[key]
		except KeyError:
				# KEY isn't set, default to `default`.
				lowerCAmelCase__        =   default
		else:
				# KEY is set, convert it to True or False.
				try:
						lowerCAmelCase__        =   strtobool(lowerCamelCase__     )
				except ValueError:
						# More values are supported, but let's keep the message simple.
						raise ValueError(f"""If set, {key} must be yes or no."""     )
		return _value


__lowerCAmelCase    :							Optional[int]													=						parse_flag_from_env("RUN_SLOW", default=False)







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skip("""Test was skipped"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(_run_slow_tests			,  """test is slow"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(not torch.cuda.is_available()			,  """test requires only a CPU"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(torch.cuda.is_available()			,  """test requires a GPU"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_xpu_available()			,  """test requires a XPU"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_mps_available()			,  """test requires a `mps` backend support in `torch`"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(
		    is_transformers_available() and is_datasets_available()			,  """test requires the Hugging Face suite"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_bnb_available()			,  """test requires the bitsandbytes library"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_tpu_available()			,  """test requires TPU"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(torch.cuda.device_count() == 1			,  """test requires a GPU"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(torch.xpu.device_count() == 1			,  """test requires a XPU"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(torch.cuda.device_count() > 1			,  """test requires multiple GPUs"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(torch.xpu.device_count() > 1			,  """test requires multiple XPUs"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_safetensors_available()			,  """test requires safetensors"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_deepspeed_available()			,  """test requires DeepSpeed"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_torch_version(""">="""			,  """1.12.0"""     )			,  """test requires torch version >= 1.12.0"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__=None			,  lowerCamelCase__=None     ):





		"""simple docstring"""




		if test_case is None:
				return partial(lowerCamelCase__			,  version=lowerCamelCase__     )
		return unittest.skipUnless(is_torch_version(""">="""			,  lowerCamelCase__     )			,  f"""test requires torch version >= {version}"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_tensorboard_available()			,  """test requires Tensorboard"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_wandb_available()			,  """test requires wandb"""     )(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(is_comet_ml_available()			,  """test requires comet_ml"""     )(lowerCamelCase__     )


__lowerCAmelCase    :							Dict													=						(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		return unittest.skipUnless(
		    _atleast_one_tracker_available			,  """test requires at least one tracker to be available and for `comet_ml` to not be installed"""			,  )(lowerCamelCase__     )

class       a_      (     unittest.TestCase			):
					UpperCamelCase_ :	Union[str, Any]			=    True




					@classmethod
					def 						_SCREAMING_SNAKE_CASE						(	cls						:     Union[str, Any]     ):
							lowerCAmelCase__        =   tempfile.mkdtemp()




					@classmethod
					def 						_SCREAMING_SNAKE_CASE						(	cls						:     List[Any]     ):
							if os.path.exists(cls.tmpdir     ):
									shutil.rmtree(cls.tmpdir     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							if self.clear_on_setup:
									for path in Path(self.tmpdir     ).glob("""**/*"""     ):
											if path.is_file():
													path.unlink()
											elif path.is_dir():
													shutil.rmtree(snake_case__     )

class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							super().tearDown()
							# Reset the state of the AcceleratorState singleton.
							AcceleratorState._reset_state()
							PartialState._reset_state()

class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Union[mock.Mock, List[mock.Mock]]     ):
							lowerCAmelCase__        =   mocks if isinstance(snake_case__  ,						(tuple, list)     ) else [mocks]
							for m in self.mocks:
									m.start()
									self.addCleanup(m.stop     )







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   AcceleratorState()
		lowerCAmelCase__        =   tensor[None].clone().to(state.device     )
		lowerCAmelCase__        =   gather(lowerCamelCase__     ).cpu()
		lowerCAmelCase__        =   tensor[0].cpu()
		for i in range(tensors.shape[0]     ):
				if not torch.equal(tensors[i]			,  lowerCamelCase__     ):
						return False
		return True

class       a_      :




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Any  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   returncode
							lowerCAmelCase__        =   stdout
							lowerCAmelCase__        =   stderr







async def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		while True:
				lowerCAmelCase__        =   await stream.readline()
				if line:
						callback(lowerCamelCase__     )
				else:
						break







async def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=False			,  lowerCamelCase__=False     ):





		"""simple docstring"""




		if echo:
				print("""\nRunning: """			,  """ """.join(lowerCamelCase__     )     )

		lowerCAmelCase__        =   await asyncio.create_subprocess_exec(
		    cmd[0]			,  *cmd[1:]			,  stdin=lowerCamelCase__			,  stdout=asyncio.subprocess.PIPE			,  stderr=asyncio.subprocess.PIPE			,  env=lowerCamelCase__			,  )

		# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
		# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
		#
		# If it starts hanging, will need to switch to the following code. The problem is that no data
		# will be seen until it's done and if it hangs for example there will be no debug info.
		# out, err = await p.communicate()
		# return _RunOutput(p.returncode, out, err)

		lowerCAmelCase__        =   []
		lowerCAmelCase__        =   []

		def tee(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=""     ):
				lowerCAmelCase__        =   line.decode("""utf-8"""     ).rstrip()
				sink.append(lowerCamelCase__     )
				if not quiet:
						print(lowerCamelCase__			,  lowerCamelCase__			,  file=lowerCamelCase__     )

    # XXX: the timeout doesn't seem to make any difference here
		await asyncio.wait(
		    [
		        asyncio.create_task(_read_stream(p.stdout			,  lambda lowerCamelCase__     : tee(lowerCamelCase__			,  lowerCamelCase__			,  sys.stdout			,  label="""stdout:"""     )     )     ),
		        asyncio.create_task(_read_stream(p.stderr			,  lambda lowerCamelCase__     : tee(lowerCamelCase__			,  lowerCamelCase__			,  sys.stderr			,  label="""stderr:"""     )     )     ),
		    ]			,  timeout=lowerCamelCase__			,  )
		return _RunOutput(await p.wait()			,  lowerCamelCase__			,  lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=180			,  lowerCamelCase__=False			,  lowerCamelCase__=True     ):





		"""simple docstring"""




		lowerCAmelCase__        =   asyncio.get_event_loop()
		lowerCAmelCase__        =   loop.run_until_complete(
		    _stream_subprocess(lowerCamelCase__			,  env=lowerCamelCase__			,  stdin=lowerCamelCase__			,  timeout=lowerCamelCase__			,  quiet=lowerCamelCase__			,  echo=lowerCamelCase__     )     )

		lowerCAmelCase__        =   """ """.join(lowerCamelCase__     )
		if result.returncode > 0:
				lowerCAmelCase__        =   """\n""".join(result.stderr     )
				raise RuntimeError(
				    f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
				    f"""The combined stderr from workers follows:\n{stderr}"""     )

		return result

class       a_      (     __UpperCamelCase			):
					pass







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=False     ):





		"""simple docstring"""




		try:
				lowerCAmelCase__        =   subprocess.check_output(lowerCamelCase__			,  stderr=subprocess.STDOUT     )
				if return_stdout:
						if hasattr(lowerCamelCase__			,  """decode"""     ):
								lowerCAmelCase__        =   output.decode("""utf-8"""     )
						return output
		except subprocess.CalledProcessError as e:
				raise SubprocessCallException(
				    f"""Command `{' '.join(lowerCamelCase__     )}` failed with the following error:\n\n{e.output.decode()}"""     ) from e





"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__lowerCAmelCase    :							Union[str, Any]													=						get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
			__lowerCAmelCase    :							Optional[int]													=						json.load(f)

@require_torch
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Dict     ):
							return FSMTTokenizer.from_pretrained(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Any     ):
							lowerCAmelCase__        =   FSMTForConditionalGeneration.from_pretrained(snake_case__     ).to(snake_case__     )
							if torch_device == "cuda":
									model.half()
							return model




					@parameterized.expand(
					    [
					        ["""en-ru""", 26.0],
					        ["""ru-en""", 22.0],
					        ["""en-de""", 22.0],
					        ["""de-en""", 29.0],
					    ]     )
					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Any  ,						snake_case__						:     int     ):
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowerCAmelCase__        =   F"""facebook/wmt19-{pair}"""
							lowerCAmelCase__        =   self.get_tokenizer(snake_case__     )
							lowerCAmelCase__        =   self.get_model(snake_case__     )

							lowerCAmelCase__        =   bleu_data[pair]["""src"""]
							lowerCAmelCase__        =   bleu_data[pair]["""tgt"""]

							lowerCAmelCase__        =   tokenizer(snake_case__  ,						return_tensors="""pt"""  ,						truncation=snake_case__  ,						padding="""longest"""     ).to(snake_case__     )
							lowerCAmelCase__        =   model.generate(
							    input_ids=batch.input_ids  ,						num_beams=8  ,						)
							lowerCAmelCase__        =   tokenizer.batch_decode(
							    snake_case__  ,						skip_special_tokens=snake_case__  ,						clean_up_tokenization_spaces=snake_case__     )
							lowerCAmelCase__        =   calculate_bleu(snake_case__  ,						snake_case__     )
							print(snake_case__     )
							self.assertGreaterEqual(scores["""bleu"""]  ,						snake_case__     )




