



'''simple docstring'''

import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse('3.8'):
	import importlib_metadata
else:
	import importlib.metadata as importlib_metadata







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=False					)				->					Union[str, Any]:





			"""simple docstring"""







			try:
						snake_case:    Optional[int]      							=os.environ[key]
			except KeyError:
						# KEY isn't set, default to `default`.
						snake_case:    Union[str, Any]      							=default
			else:
						# KEY is set, convert it to True or False.
						try:
									snake_case:    Optional[Any]      							=strtobool(__UpperCAmelCase					)
						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


a							   =				parse_flag_from_env('RUN_SLOW', default=False)
a							   =				parse_flag_from_env('RUN_REMOTE', default=False)
a							   =				parse_flag_from_env('RUN_LOCAL', default=True)
a							   =				parse_flag_from_env('RUN_PACKAGED', default=True)

# Compression
a							   =				pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='test requires lz4')
a							   =				pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='test requires py7zr')
a							   =				pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='test requires zstandard')

# Audio
a							   =				pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec('soundfile') is None or version.parse(importlib_metadata.version('soundfile')) < version.parse('0.12.0'),
    reason='test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ',
)

# Beam
a							   =				pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('0.3.2'),
    reason='test requires apache-beam and a compatible dill version',
)

# Dill-cloudpickle compatibility
a							   =				pytest.mark.skipif(
    config.DILL_VERSION <= version.parse('0.3.2'),
    reason='test requires dill>0.3.2 for cloudpickle compatibility',
)

# Windows
a							   =				pytest.mark.skipif(
    sys.platform == 'win32',
    reason='test should not be run on Windows',
)







def a_     (    __UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			try:
						import faiss  # noqa
			except ImportError:
						snake_case:    Dict      							=unittest.skip('test requires faiss'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			try:
						import regex  # noqa
			except ImportError:
						snake_case:    int      							=unittest.skip('test requires regex'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			try:
						import elasticsearch  # noqa
			except ImportError:
						snake_case:    Optional[Any]      							=unittest.skip('test requires elasticsearch'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			try:
						import sqlalchemy  # noqa
			except ImportError:
						snake_case:    str      							=unittest.skip('test requires sqlalchemy'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			if not config.TORCH_AVAILABLE:
						snake_case:    Any      							=unittest.skip('test requires PyTorch'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			if not config.TF_AVAILABLE:
						snake_case:    Tuple      							=unittest.skip('test requires TensorFlow'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					str:





			"""simple docstring"""







			if not config.JAX_AVAILABLE:
						snake_case:    Optional[int]      							=unittest.skip('test requires JAX'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					List[str]:





			"""simple docstring"""







			if not config.PIL_AVAILABLE:
						snake_case:    Dict      							=unittest.skip('test requires Pillow'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			try:
						import transformers  # noqa F401
			except ImportError:
						return unittest.skip('test requires transformers'					)(__UpperCAmelCase					)
			else:
						return test_case







def a_     (    __UpperCAmelCase					)				->					Any:





			"""simple docstring"""







			try:
						import tiktoken  # noqa F401
			except ImportError:
						return unittest.skip('test requires tiktoken'					)(__UpperCAmelCase					)
			else:
						return test_case







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			try:
						import spacy  # noqa F401
			except ImportError:
						return unittest.skip('test requires spacy'					)(__UpperCAmelCase					)
			else:
						return test_case







def a_     (    __UpperCAmelCase					)				->					Any:





			"""simple docstring"""







			def _require_spacy_model(__UpperCAmelCase					):
						try:
									import spacy  # noqa F401

									spacy.load(__UpperCAmelCase					)
						except ImportError:
									return unittest.skip('test requires spacy'					)(__UpperCAmelCase					)
						except OSError:
									return unittest.skip('test requires spacy model \'{}\''.format(__UpperCAmelCase					)					)(__UpperCAmelCase					)
						else:
									return test_case

			return _require_spacy_model







def a_     (    __UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			try:
						import pyspark  # noqa F401
			except ImportError:
						return unittest.skip('test requires pyspark'					)(__UpperCAmelCase					)
			else:
						return test_case







def a_     (    __UpperCAmelCase					)				->					List[Any]:





			"""simple docstring"""







			try:
						import joblibspark  # noqa F401
			except ImportError:
						return unittest.skip('test requires joblibspark'					)(__UpperCAmelCase					)
			else:
						return test_case







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			if not _run_slow_tests or _run_slow_tests == 0:
						snake_case:    str      							=unittest.skip('test is slow'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					str:





			"""simple docstring"""







			if not _run_local_tests or _run_local_tests == 0:
						snake_case:    Any      							=unittest.skip('test is local'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					str:





			"""simple docstring"""







			if not _run_packaged_tests or _run_packaged_tests == 0:
						snake_case:    List[Any]      							=unittest.skip('test is packaged'					)(__UpperCAmelCase					)
			return test_case







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			if not _run_remote_tests or _run_remote_tests == 0:
						snake_case:    List[Any]      							=unittest.skip('test requires remote'					)(__UpperCAmelCase					)
			return test_case







def a_     (    *__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			def decorate(cls					):
						for name, fn in cls.__dict__.items():
									if callable(__UpperCAmelCase					) and name.startswith('test'					):
												for decorator in decorators:
															snake_case:    List[Any]      							=decorator(__UpperCAmelCase					)
												setattr(cls				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						return cls

			return decorate






class 						a_		(     snake_case    ):
							pass






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		Optional[Any]        =				0
							UpperCAmelCase			:		str        =				1
							UpperCAmelCase			:		Optional[int]        =				2







@contextmanager
def a_     (    __UpperCAmelCase=OfflineSimulationMode.CONNECTION_FAILS				,							__UpperCAmelCase=1e-16					)				->					int:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=requests.Session().request

			def timeout_request(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							**__UpperCAmelCase					):
						# Change the url to an invalid url so that the connection hangs
						snake_case:    Any      							='https://10.255.255.1'
						if kwargs.get('timeout'					) is None:
									raise RequestWouldHangIndefinitelyError(
									    f'''Tried a call to {url} in offline mode with no timeout set. Please set a timeout.'''					)
						snake_case:    Optional[Any]      							=timeout
						try:
									return online_request(__UpperCAmelCase				,							__UpperCAmelCase				,							**__UpperCAmelCase					)
						except Exception as e:
									# The following changes in the error are just here to make the offline timeout error prettier
									snake_case:    Tuple      							=url
									snake_case:    Tuple      							=e.args[0]
									snake_case:    str      							=(max_retry_error.args[0].replace('10.255.255.1'				,							f'''OfflineMock[{url}]'''					),)
									snake_case:    Optional[int]      							=(max_retry_error,)
									raise

			def raise_connection_error(__UpperCAmelCase				,							__UpperCAmelCase				,							**__UpperCAmelCase					):
						raise requests.ConnectionError('Offline mode is enabled.'				,							request=__UpperCAmelCase					)

			if mode is OfflineSimulationMode.CONNECTION_FAILS:
						with patch('requests.Session.send'				,							__UpperCAmelCase					):
									yield
			elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
						# inspired from https://stackoverflow.com/a/904609
						with patch('requests.Session.request'				,							__UpperCAmelCase					):
									yield
			elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
						with patch('datasets.config.HF_DATASETS_OFFLINE'				,							__UpperCAmelCase					):
									yield
			else:
						raise ValueError('Please use a value from the OfflineSimulationMode enum.'					)







@contextmanager
def a_     (    *__UpperCAmelCase				,							**__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    List[Any]      							=str(Path().resolve()					)
			with tempfile.TemporaryDirectory(*__UpperCAmelCase				,							**__UpperCAmelCase					) as tmp_dir:
						try:
									os.chdir(__UpperCAmelCase					)
									yield
						finally:
									os.chdir(__UpperCAmelCase					)







@contextmanager
def a_     (    )				->					Dict:





			"""simple docstring"""







			import gc

			gc.collect()
			snake_case:    List[str]      							=pa.total_allocated_bytes()
			yield
			assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."







@contextmanager
def a_     (    )				->					Any:





			"""simple docstring"""







			import gc

			gc.collect()
			snake_case:    Optional[int]      							=pa.total_allocated_bytes()
			yield
			assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			return deepcopy(__UpperCAmelCase					).integers(0				,							1_00				,							10					).tolist() == deepcopy(__UpperCAmelCase					).integers(0				,							1_00				,							10					).tolist()







def a_     (    __UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			import decorator
			from requests.exceptions import HTTPError

			def _wrapper(__UpperCAmelCase				,							*__UpperCAmelCase				,							**__UpperCAmelCase					):
						try:
									return func(*__UpperCAmelCase				,							**__UpperCAmelCase					)
						except HTTPError as err:
									if str(__UpperCAmelCase					).startswith('500'					) or str(__UpperCAmelCase					).startswith('502'					):
												pytest.xfail(str(__UpperCAmelCase					)					)
									raise err

			return decorator.decorator(_wrapper				,							__UpperCAmelCase					)






class 						a_		:


							def __init__(				self   :						Optional[int]      ,						a_   :						Tuple      ,						a_   :						Optional[Any]      ,						a_   :						List[Any]			)			->					str:
										snake_case:    int      							=returncode
										snake_case:    Union[str, Any]      							=stdout
										snake_case:    str      							=stderr







async def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			while True:
						snake_case:    Optional[Any]      							=await stream.readline()
						if line:
									callback(__UpperCAmelCase					)
						else:
									break







async def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					_RunOutput:





			"""simple docstring"""







			if echo:
						print('\nRunning: '				,							' '.join(__UpperCAmelCase					)					)

			snake_case:    List[str]      							=await asyncio.create_subprocess_exec(
			    cmd[0]				,							*cmd[1:]				,							stdin=__UpperCAmelCase				,							stdout=asyncio.subprocess.PIPE				,							stderr=asyncio.subprocess.PIPE				,							env=__UpperCAmelCase				,							)

			# 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)

			snake_case:    Union[str, Any]      							=[]
			snake_case:    Optional[int]      							=[]

			def tee(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=""					):
						snake_case:    str      							=line.decode('utf-8'					).rstrip()
						sink.append(__UpperCAmelCase					)
						if not quiet:
									print(__UpperCAmelCase				,							__UpperCAmelCase				,							file=__UpperCAmelCase					)

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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None				,							__UpperCAmelCase=1_80				,							__UpperCAmelCase=False				,							__UpperCAmelCase=True					)				->					_RunOutput:





			"""simple docstring"""







			snake_case:    List[str]      							=asyncio.get_event_loop()
			snake_case:    Dict      							=loop.run_until_complete(
			    _stream_subprocess(__UpperCAmelCase				,							env=__UpperCAmelCase				,							stdin=__UpperCAmelCase				,							timeout=__UpperCAmelCase				,							quiet=__UpperCAmelCase				,							echo=__UpperCAmelCase					)					)

			snake_case:    Dict      							=' '.join(__UpperCAmelCase					)
			if result.returncode > 0:
						snake_case:    int      							='\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}'''					)

			# check that the subprocess actually did run and produced some output, should the test rely on
			# the remote side to do the testing
			if not result.stdout and not result.stderr:
						raise RuntimeError(f'''\'{cmd_str}\' produced no output.'''					)

			return result







def a_     (    )				->					List[Any]:





			"""simple docstring"""







			snake_case:    Any      							=os.environ.get('PYTEST_XDIST_WORKER'				,							'gw0'					)
			snake_case:    Any      							=re.sub(R'^gw'				,							''				,							__UpperCAmelCase				,							0				,							re.M					)
			return int(__UpperCAmelCase					)







def a_     (    )				->					Optional[int]:





			"""simple docstring"""







			snake_case:    List[str]      							=2_95_00
			snake_case:    int      							=pytest_xdist_worker_id()
			return port + uniq_delta









'''simple docstring'''

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


a							   =				logging.get_logger(__name__)
logging.set_verbosity_info()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if "xprophetnet" in prophetnet_checkpoint_path:
						snake_case:    Optional[Any]      							=XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    str      							=XLMProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)
			else:
						snake_case:    Optional[int]      							=ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    List[str]      							=ProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)

			snake_case:    str      							=['key_proj', 'value_proj', 'query_proj']

			snake_case:    str      							={
			    'self_attn': 'ngram_self_attn',
			    'cross_attn': 'encoder_attn',
			    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
			    'feed_forward_layer_norm': 'final_layer_norm',
			    'feed_forward': '',
			    'intermediate': 'fc1',
			    'output': 'fc2',
			    'key_proj': 'k_proj',
			    'query_proj': 'q_proj',
			    'value_proj': 'v_proj',
			    'word_embeddings': 'embed_tokens',
			    'embeddings_layer_norm': 'emb_layer_norm',
			    'relative_pos_embeddings': 'relative_linear',
			    'ngram_embeddings': 'ngram_input_embed',
			    'position_embeddings': 'embed_positions',
			}

			for key in loading_info["missing_keys"]:
						snake_case:    List[str]      							=key.split('.'					)

						if attributes[0] == "lm_head":
									snake_case:    Dict      							=prophet
									snake_case:    List[str]      							=prophet_old
						else:
									snake_case:    Any      							=prophet.prophetnet
									snake_case:    int      							=prophet_old.model

						snake_case:    int      							=False
						for attribute in attributes:
									if attribute in mapping:
												snake_case:    Dict      							=mapping[attribute]
												if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					) and len(__UpperCAmelCase					) > 0:
															snake_case:    Optional[int]      							=attribute
									elif hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
												snake_case:    Optional[Any]      							=attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												snake_case:    Optional[Any]      							=old_model.weight
												logger.info(f'''{attribute} is initialized.'''					)
												snake_case:    List[str]      							=True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												snake_case:    List[Any]      							=old_model.bias
												logger.info(f'''{attribute} is initialized'''					)
												snake_case:    List[Any]      							=True
												break
									elif attribute in special_keys and hasattr(__UpperCAmelCase				,							'in_proj_weight'					):
												snake_case:    Optional[int]      							=old_model.in_proj_weight.shape[0] // 3
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)
												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
												if attribute == "query_proj":
															snake_case:    int      							=nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
															snake_case:    Any      							=nn.Parameter(old_model.in_proj_bias[:embed_dim]					)

												elif attribute == "key_proj":
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
												elif attribute == "value_proj":
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
												snake_case:    Any      							=True
												break
									elif attribute == "position_embeddings":
												assert (
												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
												), "Hidden size has to match"
												assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
												snake_case:    Dict      							=nn.Parameter(old_model.embed_positions.weight[:5_12, :]					)
												snake_case:    Dict      							=True
												break

									if attribute.isdigit():
												snake_case:    int      							=model[int(__UpperCAmelCase					)]
												snake_case:    Optional[int]      							=old_model[int(__UpperCAmelCase					)]
									else:
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

												if old_attribute == "":
															snake_case:    Union[str, Any]      							=old_model
												else:
															if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
																		raise ValueError(f'''{old_model} does not have {old_attribute}'''					)
															snake_case:    List[Any]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

						if not is_key_init:
									raise ValueError(f'''{key} was not correctly initialized!'''					)

			print(f'''Saving model to {pytorch_dump_folder_path}'''					)
			prophet.save_pretrained(__UpperCAmelCase					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	a							   =				parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




