


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 	a__						(    unittest.TestCase     ):

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Union[str, Any]				      =     XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""     )
            model.to(lowerCamelCase_     )

            # encoder-decoder outputs
            a_     :  Optional[int]				      =     torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(lowerCamelCase_     )
            a_     :  List[Any]				      =     torch.tensor(
                [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]     ).to(lowerCamelCase_     )
            a_     :  Any				      =     model(
                input_ids=lowerCamelCase_   ,				attention_mask=lowerCamelCase_   ,				encoder_outputs=lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_     )
            a_     :  List[str]				      =     output[0]
            a_     :  Any				      =     torch.Size((1, 1_4, 2_5_0_0_1_2)     )
            self.assertEqual(output_predited_logis.shape   ,				lowerCamelCase_     )
            a_     :  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(lowerCamelCase_     )
            self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   ,				lowerCamelCase_   ,				atol=1E-4     )     )

            # encoder outputs
            a_     :  Dict				      =     model.prophetnet.encoder(lowerCamelCase_     )[0]
            a_     :  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(lowerCamelCase_     )
            a_     :  Tuple				      =     torch.Size((1, 4, 1_0_2_4)     )
            self.assertEqual(encoder_outputs.shape   ,				lowerCamelCase_     )
            self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]   ,				lowerCamelCase_   ,				atol=1E-4     )     )

            # decoder outputs
            a_     :  Optional[Any]				      =     model.prophetnet.decoder(
                lowerCamelCase_   ,				encoder_hidden_states=lowerCamelCase_   ,				)
            a_     :  Union[str, Any]				      =     decoder_outputs[1].view(1   ,				model.config.ngram   ,				1_4   ,				-1     )
            a_     :  int				      =     model.lm_head(lowerCamelCase_     )
            a_     :  Optional[Any]				      =     predicting_streams_logits[:, 0]
            self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]   ,				lowerCamelCase_   ,				atol=1E-4     )     )

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  List[Any]				      =     XLMProphetNetForConditionalGeneration.from_pretrained(
                """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )
            model.to(lowerCamelCase_     )

            a_     :  List[Any]				      =     torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]     ).to(lowerCamelCase_     )
            a_     :  Union[str, Any]				      =     torch.tensor(
                [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]     ).to(lowerCamelCase_     )
            a_     :  List[Any]				      =     model(
                input_ids=lowerCamelCase_   ,				attention_mask=lowerCamelCase_   ,				encoder_outputs=lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_     )
            a_     :  str				      =     output[0]
            a_     :  str				      =     torch.Size((1, 1_4, 2_5_0_0_1_2)     )
            self.assertEqual(output_predited_logis.shape   ,				lowerCamelCase_     )
            # compare the actual values for a slice.
            a_     :  int				      =     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(lowerCamelCase_     )

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

       @slow
       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  str				      =     XLMProphetNetForConditionalGeneration.from_pretrained(
                """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )
            model.to(lowerCamelCase_     )
            a_     :  Tuple				      =     5_1_2

            a_     :  Optional[Any]				      =     XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""     )

            a_     :  Dict				      =     (
                """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."""
            )
            a_     :  List[Any]				      =     (
                """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."""
            )
            a_     :  Optional[int]				      =     (
                """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。"""
            )

            a_     :  Union[str, Any]				      =     tokenizer(
                [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]   ,				padding=lowerCamelCase_   ,				max_length=2_5_5   ,				return_tensors="""pt"""     ).input_ids
            a_     :  str				      =     input_ids.to(lowerCamelCase_     )

            a_     :  int				      =     model.generate(
                lowerCamelCase_   ,				num_beams=1_0   ,				length_penalty=1.0   ,				no_repeat_ngram_size=3   ,				early_stopping=lowerCamelCase_     )
            a_     :  int				      =     [tokenizer.decode(lowerCamelCase_   ,				skip_special_tokens=lowerCamelCase_     ) for g in summary_ids]
            a_     :  str				      =     """Microsoft to end Windows 7 free support after January 14, 2020"""
            a_     :  Dict				      =     """Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
            a_     :  Dict				      =     """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]   ,				lowerCamelCase_   ,				)

            a_     :  int				      =     model.generate(
                lowerCamelCase_   ,				num_beams=1   ,				length_penalty=1.0   ,				no_repeat_ngram_size=3   ,				early_stopping=lowerCamelCase_     )
            a_     :  List[str]				      =     [
                tokenizer.convert_ids_to_tokens(lowerCamelCase_   ,				skip_special_tokens=lowerCamelCase_     ) for g in summary_ids_beama
            ]
            a_     :  int				      =     """▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """     )
            a_     :  str				      =     """▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
                """ """     )
            a_     :  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]   ,				lowerCamelCase_   ,				)











"""simple docstring"""







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import tensorflow as tf







class 							_SCREAMING_SNAKE_CASE    (	TensorFormatter[Mapping, 'tf.Tensor', Mapping]				):





							"""simple docstring"""





							def __init__( self:  Union[str, Any]					,		__A:  Union[str, Any]=None					,		**__A:  Union[str, Any]     ):

												'''simple docstring'''

												super().__init__(features=__A     )
												a__ 							=	tf_tensor_kwargs
												import tensorflow as tf  # noqa: import tf at initialization




							def 			lowercase		( self:  Optional[int]					,		__A:  List[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if isinstance(__A					,		__A     ) and column:
																	if all(
																	    isinstance(__A					,		tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
																						return tf.stack(__A     )
																	elif all(
																	    isinstance(__A					,		(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
																	    for x in column     ):
																						# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																						return tf.ragged.stack(__A     )

												return column




							def 			lowercase		( self:  int					,		__A:  Optional[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if value is None:
																	return value

												a__ 							=	{}

												if isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.integer     ):
																	a__ 							=	{'''dtype''': tf.intaa}
												elif isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.floating     ):
																	a__ 							=	{'''dtype''': tf.floataa}
												elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(__A					,		PIL.Image.Image     ):
																						a__ 							=	np.asarray(__A     )

												return tf.convert_to_tensor(__A					,		**{**default_dtype, **self.tf_tensor_kwargs}     )




							def 			lowercase		( self:  List[Any]					,		__A:  str     ):

												'''simple docstring'''

												import tensorflow as tf

												# support for torch, tf, jax etc.
												if config.TORCH_AVAILABLE and "torch" in sys.modules:
																	import torch

																	if isinstance(__A					,		torch.Tensor     ):
																						return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
												if hasattr(__A					,		'''__array__'''     ) and not isinstance(__A					,		tf.Tensor     ):
																	a__ 							=	data_struct.__array__()
												# support for nested types like struct of list of struct
												if isinstance(__A					,		np.ndarray     ):
																	if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																						return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												elif isinstance(__A					,		(list, tuple)     ):
																	return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												return self._tensorize(__A     )




							def 			lowercase		( self:  int					,		__A:  dict     ):

												'''simple docstring'''

												return map_nested(self._recursive_tensorize					,		__A					,		map_list=__A     )




							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_row(__A     )
												a__ 							=	self.python_features_decoder.decode_row(__A     )
												return self.recursive_tensorize(__A     )




							def 			lowercase		( self:  Dict					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_column(__A     )
												a__ 							=	self.python_features_decoder.decode_column(__A					,		pa_table.column_names[0]     )
												a__ 							=	self.recursive_tensorize(__A     )
												a__ 							=	self._consolidate(__A     )
												return column





							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_batch(__A     )
												a__ 							=	self.python_features_decoder.decode_batch(__A     )
												a__ 							=	self.recursive_tensorize(__A     )
												for column_name in batch:
																	a__ 							=	self._consolidate(batch[column_name]     )
												return batch


