
'''simple docstring'''






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
							import torch

							from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer

@require_torch
class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	@slow
	def 	__A      (						self				: Any      ):
			lowerCAmelCase_   :       List[Any]    							=XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''      )
			model.to(UpperCamelCase_      )

			# encoder-decoder outputs
			lowerCAmelCase_   :       Any    							=torch.tensor([[17, 96208, 103471, 2]]      ).to(UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[Any]    							=torch.tensor(
			    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]      ).to(UpperCamelCase_      )
			lowerCAmelCase_   :       Union[str, Any]    							=model(
			    input_ids=UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						encoder_outputs=UpperCamelCase_					,						decoder_input_ids=UpperCamelCase_      )
			lowerCAmelCase_   :       Dict    							=output[0]
			lowerCAmelCase_   :       str    							=torch.Size((1, 14, 250012)      )
			self.assertEqual(output_predited_logis.shape					,						UpperCamelCase_      )
			lowerCAmelCase_   :       int    							=torch.tensor(
			    [[[-6.3_9_8_6, -8.2_3_9_1, 1_2.5_1_8_9], [-6.3_2_8_9, -8.0_8_6_4, 1_2.6_2_1_1], [-6.2_4_1_8, -8.0_4_4_5, 1_2.7_9_6_8]]]      ).to(UpperCamelCase_      )
			self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					,						UpperCamelCase_					,						atol=1E-4      )      )

			# encoder outputs
			lowerCAmelCase_   :       Optional[Any]    							=model.prophetnet.encoder(UpperCamelCase_      )[0]
			lowerCAmelCase_   :       Optional[int]    							=torch.tensor(
			    [[[-1.4_2_6_0, -0.7_6_2_8, 0.8_4_5_3], [-1.4_7_1_9, -0.1_3_9_1, 0.7_8_0_7], [-1.7_6_7_8, 0.0_1_1_4, 0.4_6_4_6]]]      ).to(UpperCamelCase_      )
			lowerCAmelCase_   :       Tuple    							=torch.Size((1, 4, 1024)      )
			self.assertEqual(encoder_outputs.shape					,						UpperCamelCase_      )
			self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]					,						UpperCamelCase_					,						atol=1E-4      )      )

			# decoder outputs
			lowerCAmelCase_   :       int    							=model.prophetnet.decoder(
			    UpperCamelCase_					,						encoder_hidden_states=UpperCamelCase_					,						)
			lowerCAmelCase_   :       int    							=decoder_outputs[1].view(1					,						model.config.ngram					,						14					,						-1      )
			lowerCAmelCase_   :       Optional[Any]    							=model.lm_head(UpperCamelCase_      )
			lowerCAmelCase_   :       List[str]    							=predicting_streams_logits[:, 0]
			self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]					,						UpperCamelCase_					,						atol=1E-4      )      )




	@slow
	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       Tuple    							=XLMProphetNetForConditionalGeneration.from_pretrained(
			    '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''      )
			model.to(UpperCamelCase_      )

			lowerCAmelCase_   :       Any    							=torch.tensor([[17, 96208, 103471, 2]]      ).to(UpperCamelCase_      )
			lowerCAmelCase_   :       Any    							=torch.tensor(
			    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]      ).to(UpperCamelCase_      )
			lowerCAmelCase_   :       str    							=model(
			    input_ids=UpperCamelCase_					,						attention_mask=UpperCamelCase_					,						encoder_outputs=UpperCamelCase_					,						decoder_input_ids=UpperCamelCase_      )
			lowerCAmelCase_   :       Any    							=output[0]
			lowerCAmelCase_   :       List[Any]    							=torch.Size((1, 14, 250012)      )
			self.assertEqual(output_predited_logis.shape					,						UpperCamelCase_      )
			# compare the actual values for a slice.
			lowerCAmelCase_   :       Optional[Any]    							=torch.tensor(
			    [[[-9.2_2_5_3, -9.7_1_7_3, -6.3_5_2_9], [-7.6_7_0_1, -9.0_1_4_5, -1.9_3_8_2], [-8.0_1_9_5, -7.0_0_0_4, -0.1_5_2_3]]]      ).to(UpperCamelCase_      )

			self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					,						UpperCamelCase_					,						atol=1E-4      )      )





	@slow
	def 	__A      (						self				: int      ):
			lowerCAmelCase_   :       Union[str, Any]    							=XLMProphetNetForConditionalGeneration.from_pretrained(
			    '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''      )
			model.to(UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[Any]    							=512

			lowerCAmelCase_   :       int    							=XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''      )

			lowerCAmelCase_   :       Tuple    							=(
			    '''Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'''
			    ''' January 14, 2020, according to the official portal of the organization. From that day, users of this'''
			    ''' system will not be able to receive security updates, which could make their computers vulnerable to'''
			    ''' cyber attacks.'''
			)
			lowerCAmelCase_   :       str    							=(
			    '''орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'''
			    ''' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'''
			    ''' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'''
			    ''' к кибератакам.'''
			)
			lowerCAmelCase_   :       Dict    							=(
			    '''根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'''
			)

			lowerCAmelCase_   :       Dict    							=tokenizer(
			    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]					,						padding=UpperCamelCase_					,						max_length=255					,						return_tensors='''pt'''      ).input_ids
			lowerCAmelCase_   :       Any    							=input_ids.to(UpperCamelCase_      )

			lowerCAmelCase_   :       int    							=model.generate(
			    UpperCamelCase_					,						num_beams=10					,						length_penalty=1.0					,						no_repeat_ngram_size=3					,						early_stopping=UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[Any]    							=[tokenizer.decode(UpperCamelCase_					,						skip_special_tokens=UpperCamelCase_      ) for g in summary_ids]
			lowerCAmelCase_   :       str    							='''Microsoft to end Windows 7 free support after January 14, 2020'''
			lowerCAmelCase_   :       Tuple    							='''Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'''
			lowerCAmelCase_   :       Optional[int]    							='''微软打算终止对Windows 7操作系统的免费支持'''
			self.assertListEqual(
			    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]					,						UpperCamelCase_					,						)

			lowerCAmelCase_   :       List[Any]    							=model.generate(
			    UpperCamelCase_					,						num_beams=1					,						length_penalty=1.0					,						no_repeat_ngram_size=3					,						early_stopping=UpperCamelCase_      )
			lowerCAmelCase_   :       List[str]    							=[
			    tokenizer.convert_ids_to_tokens(UpperCamelCase_					,						skip_special_tokens=UpperCamelCase_      ) for g in summary_ids_beama
			]
			lowerCAmelCase_   :       Optional[Any]    							='''▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'''.split(''' '''      )
			lowerCAmelCase_   :       List[str]    							='''▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'''.split(
			    ''' '''      )
			lowerCAmelCase_   :       Union[str, Any]    							='''微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'''.split(''' '''      )
			self.assertListEqual(
			    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]					,						UpperCamelCase_					,						)








'''simple docstring'''






import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


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


__lowercase	=    '''lfs-multipart-upload'''

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	@staticmethod
	def 	__A      (						UpperCamelCase_				: ArgumentParser      ):
			lowerCAmelCase_   :       Dict    							=parser.add_parser(
			    '''lfs-enable-largefiles'''					,						help=(
			        '''Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'''
			    )					,						)
			enable_parser.add_argument('''path'''					,						type=UpperCamelCase_					,						help='''Local path to repository you want to configure.'''      )
			enable_parser.set_defaults(func=lambda UpperCamelCase_      : LfsEnableCommand(UpperCamelCase_      )      )

			lowerCAmelCase_   :       str    							=parser.add_parser(
			    UpperCamelCase_					,						help=(
			        '''Deprecated: use `huggingface-cli` instead. '''
			        '''Command will get called by git-lfs, do not call it directly.'''
			    )					,						)
			upload_parser.set_defaults(func=lambda UpperCamelCase_      : LfsUploadCommand(UpperCamelCase_      )      )







class 							_snake_case     :
	"""simple docstring"""





	def __init__(						self				: Any					,						UpperCamelCase_				: str      ):
			lowerCAmelCase_   :       Any    							=args





	def 	__A      (						self				: Optional[int]      ):
			warnings.warn(
			    '''Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'''      )
			lowerCAmelCase_   :       Optional[Any]    							=os.path.abspath(self.args.path      )
			if not os.path.isdir(UpperCamelCase_      ):
					print('''This does not look like a valid git repo.'''      )
					exit(1      )
			subprocess.run(
			    '''git config lfs.customtransfer.multipart.path transformers-cli'''.split()					,						check=UpperCamelCase_					,						cwd=UpperCamelCase_      )
			subprocess.run(
			    F'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()					,						check=UpperCamelCase_					,						cwd=UpperCamelCase_					,						)
			print('''Local repo set up for largefiles'''      )







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Optional[int]    							=json.dumps(_SCREAMING_SNAKE_CASE  ) + '''\n'''
		sys.stdout.write(_SCREAMING_SNAKE_CASE  )
		sys.stdout.flush()







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       List[Any]    							=json.loads(sys.stdin.readline().strip()  )

		if "terminate" in (msg.get('''type'''  ), msg.get('''event'''  )):
				# terminate message received
				return None

		if msg.get('''event'''  ) not in ("download", "upload"):
				logger.critical('''Received unexpected message'''  )
				sys.exit(1  )

		return msg

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	def __init__(						self				: Tuple					,						UpperCamelCase_				: str					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int      ):
			lowerCAmelCase_   :       int    							=filepath
			lowerCAmelCase_   :       Optional[Any]    							=seek_from
			lowerCAmelCase_   :       Tuple    							=read_limit
			lowerCAmelCase_   :       Tuple    							=0




	def __enter__(						self				: int      ):
			lowerCAmelCase_   :       str    							=open(self.filepath					,						'''rb'''      )
			self.f.seek(self.seek_from      )
			return self




	def __len__(						self				: Optional[Any]      ):
			lowerCAmelCase_   :       Tuple    							=os.fstat(self.f.fileno()      ).st_size
			return min(self.read_limit					,						total_length - self.seek_from      )




	def 	__A      (						self				: Any					,						UpperCamelCase_				: str=-1      ):
			if self.n_seen >= self.read_limit:
					return b""
			lowerCAmelCase_   :       str    							=self.read_limit - self.n_seen
			lowerCAmelCase_   :       Optional[int]    							=self.f.read(remaining_amount if n < 0 else min(UpperCamelCase_					,						UpperCamelCase_      )      )
			self.n_seen += len(UpperCamelCase_      )
			return data




	def __iter__(						self				: int      ):
			yield self.read(n=4 * 1024 * 1024      )





	def __exit__(						self				: Tuple					,						*UpperCamelCase_				: Tuple      ):
			self.f.close()







class 							_snake_case     :
	"""simple docstring"""





	def __init__(						self				: List[str]					,						UpperCamelCase_				: List[str]      ):
			lowerCAmelCase_   :       int    							=args





	def 	__A      (						self				: List[Any]      ):
			# Immediately after invoking a custom transfer process, git-lfs
			# sends initiation data to the process over stdin.
			# This tells the process useful information about the configuration.
			lowerCAmelCase_   :       Union[str, Any]    							=json.loads(sys.stdin.readline().strip()      )
			if not (init_msg.get('''event'''      ) == "init" and init_msg.get('''operation'''      ) == "upload"):
					write_msg({'''error''': {'''code''': 32, '''message''': '''Wrong lfs init operation'''}}      )
					sys.exit(1      )

			# The transfer process should use the information it needs from the
			# initiation structure, and also perform any one-off setup tasks it
			# needs to do. It should then respond on stdout with a simple empty
			# confirmation structure, as follows:
			write_msg({}      )

			# After the initiation exchange, git-lfs will send any number of
			# transfer requests to the stdin of the transfer process, in a serial sequence.
			while True:
					lowerCAmelCase_   :       List[Any]    							=read_msg()
					if msg is None:
							# When all transfers have been processed, git-lfs will send
							# a terminate event to the stdin of the transfer process.
							# On receiving this message the transfer process should
							# clean up and terminate. No response is expected.
							sys.exit(0      )

					lowerCAmelCase_   :       List[Any]    							=msg['''oid''']
					lowerCAmelCase_   :       List[str]    							=msg['''path''']
					lowerCAmelCase_   :       int    							=msg['''action''']['''href''']
					lowerCAmelCase_   :       List[Any]    							=msg['''action''']['''header''']
					lowerCAmelCase_   :       Union[str, Any]    							=int(header.pop('''chunk_size'''      )      )
					lowerCAmelCase_   :       List[str]    							=list(header.values()      )

					lowerCAmelCase_   :       Dict    							=[]
					for i, presigned_url in enumerate(UpperCamelCase_      ):
							with FileSlice(UpperCamelCase_					,						seek_from=i * chunk_size					,						read_limit=UpperCamelCase_      ) as data:
									lowerCAmelCase_   :       Optional[Any]    							=requests.put(UpperCamelCase_					,						data=UpperCamelCase_      )
									r.raise_for_status()
									parts.append(
									    {
									        '''etag''': r.headers.get('''etag'''      ),
									        '''partNumber''': i + 1,
									    }      )
									# In order to support progress reporting while data is uploading / downloading,
									# the transfer process should post messages to stdout
									write_msg(
									    {
									        '''event''': '''progress''',
									        '''oid''': oid,
									        '''bytesSoFar''': (i + 1) * chunk_size,
									        '''bytesSinceLast''': chunk_size,
									    }      )
									# Not precise but that's ok.

					lowerCAmelCase_   :       Optional[Any]    							=requests.post(
					    UpperCamelCase_					,						json={
					        '''oid''': oid,
					        '''parts''': parts,
					    }					,						)
					r.raise_for_status()

					write_msg({'''event''': '''complete''', '''oid''': oid}      )






