'''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 				lowercase    (    unittest.TestCase			):


   @slow
   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Optional[Any]:
         lowercase_     			=   XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased")
         model.to(lowerCAmelCase__)

         # encoder-decoder outputs
         lowercase_     			=   torch.tensor([[17, 9_6208, 10_3471, 2]]).to(lowerCAmelCase__)
         lowercase_     			=   torch.tensor(
             [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]).to(lowerCAmelCase__)
         lowercase_     			=   model(
             input_ids=lowerCAmelCase__ ,		attention_mask=lowerCAmelCase__ ,		encoder_outputs=lowerCAmelCase__ ,		decoder_input_ids=lowerCAmelCase__)
         lowercase_     			=   output[0]
         lowercase_     			=   torch.Size((1, 14, 25_0012))
         self.assertEqual(output_predited_logis.shape ,		lowerCAmelCase__)
         lowercase_     			=   torch.tensor(
             [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]).to(lowerCAmelCase__)
         self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3] ,		lowerCAmelCase__ ,		atol=1e-4))

         # encoder outputs
         lowercase_     			=   model.prophetnet.encoder(lowerCAmelCase__)[0]
         lowercase_     			=   torch.tensor(
             [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]).to(lowerCAmelCase__)
         lowercase_     			=   torch.Size((1, 4, 1024))
         self.assertEqual(encoder_outputs.shape ,		lowerCAmelCase__)
         self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3] ,		lowerCAmelCase__ ,		atol=1e-4))

         # decoder outputs
         lowercase_     			=   model.prophetnet.decoder(
             lowerCAmelCase__ ,		encoder_hidden_states=lowerCAmelCase__ ,		)
         lowercase_     			=   decoder_outputs[1].view(1 ,		model.config.ngram ,		14 ,		-1)
         lowercase_     			=   model.lm_head(lowerCAmelCase__)
         lowercase_     			=   predicting_streams_logits[:, 0]
         self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3] ,		lowerCAmelCase__ ,		atol=1e-4))


   @slow
   def __UpperCAmelCase							( self					:						int)   ->       List[Any]:
         lowercase_     			=   XLMProphetNetForConditionalGeneration.from_pretrained(
             "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
         model.to(lowerCAmelCase__)

         lowercase_     			=   torch.tensor([[17, 9_6208, 10_3471, 2]]).to(lowerCAmelCase__)
         lowercase_     			=   torch.tensor(
             [[2, 250, 9953, 34, 6_9489, 1620, 32, 11_8424, 624, 210, 105, 2913, 1032, 351]]).to(lowerCAmelCase__)
         lowercase_     			=   model(
             input_ids=lowerCAmelCase__ ,		attention_mask=lowerCAmelCase__ ,		encoder_outputs=lowerCAmelCase__ ,		decoder_input_ids=lowerCAmelCase__)
         lowercase_     			=   output[0]
         lowercase_     			=   torch.Size((1, 14, 25_0012))
         self.assertEqual(output_predited_logis.shape ,		lowerCAmelCase__)
         # compare the actual values for a slice.
         lowercase_     			=   torch.tensor(
             [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]).to(lowerCAmelCase__)

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


   @slow
   def __UpperCAmelCase							( self					:						Dict)   ->       Tuple:
         lowercase_     			=   XLMProphetNetForConditionalGeneration.from_pretrained(
             "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
         model.to(lowerCAmelCase__)
         lowercase_     			=   512

         lowercase_     			=   XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")

         lowercase_     			=   (
             """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."""
         )
         lowercase_     			=   (
             """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."""
         )
         lowercase_     			=   (
             """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。"""
         )

         lowercase_     			=   tokenizer(
             [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE] ,		padding=lowerCAmelCase__ ,		max_length=255 ,		return_tensors="pt").input_ids
         lowercase_     			=   input_ids.to(lowerCAmelCase__)

         lowercase_     			=   model.generate(
             lowerCAmelCase__ ,		num_beams=10 ,		length_penalty=1.0 ,		no_repeat_ngram_size=3 ,		early_stopping=lowerCAmelCase__)
         lowercase_     			=   [tokenizer.decode(lowerCAmelCase__ ,		skip_special_tokens=lowerCAmelCase__) for g in summary_ids]
         lowercase_     			=   """Microsoft to end Windows 7 free support after January 14, 2020"""
         lowercase_     			=   """Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
         lowercase_     			=   """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__ ,		)

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

import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType




@require_torch
class    lowerCamelCase__  :
   __UpperCAmelCase           =						()
   __UpperCAmelCase           =						True



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =self.config_class

        # test default config
        _UpperCamelCase  :List[Any]       =config_class()
        self.assertIsNotNone(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =["""stem"""] + [f'''stage{idx}''' for idx in range(1					,      len(config.depths     ) + 1     )]
        self.assertEqual(config.stage_names					,      lowerCAmelCase__     )
        self.assertTrue(set(config.out_features     ).issubset(set(config.stage_names     )     )     )

        # Test out_features and out_indices are correctly set
        # out_features and out_indices both None
        _UpperCamelCase  :Any       =config_class(out_features=lowerCAmelCase__					,      out_indices=lowerCAmelCase__     )
        self.assertEqual(config.out_features					,      [config.stage_names[-1]]     )
        self.assertEqual(config.out_indices					,      [len(config.stage_names     ) - 1]     )

        # out_features and out_indices both set
        _UpperCamelCase  :int       =config_class(out_features=["""stem""", """stage1"""]					,      out_indices=[0, 1]     )
        self.assertEqual(config.out_features					,      ["""stem""", """stage1"""]     )
        self.assertEqual(config.out_indices					,      [0, 1]     )

        # Only out_features set
        _UpperCamelCase  :Dict       =config_class(out_features=["""stage1""", """stage3"""]     )
        self.assertEqual(config.out_features					,      ["""stage1""", """stage3"""]     )
        self.assertEqual(config.out_indices					,      [1, 3]     )

        # Only out_indices set
        _UpperCamelCase  :Tuple       =config_class(out_indices=[0, 2]     )
        self.assertEqual(config.out_features					,      [config.stage_names[0], config.stage_names[2]]     )
        self.assertEqual(config.out_indices					,      [0, 2]     )

        # Error raised when out_indices do not correspond to out_features
        with self.assertRaises(lowerCAmelCase__     ):
             _UpperCamelCase  :List[Any]       =config_class(out_features=["""stage1""", """stage2"""]					,      out_indices=[0, 2]     )



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _UpperCamelCase  :Union[str, Any]       =model_class(lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =inspect.signature(model.forward     )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _UpperCamelCase  :Tuple       =[*signature.parameters.keys()]
             _UpperCamelCase  :Any       =["""pixel_values"""]
             self.assertListEqual(arg_names[:1]					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _UpperCamelCase  :int       =model_class(lowerCAmelCase__     )
             self.assertEqual(len(model.channels     )					,      len(config.out_features     )     )
             _UpperCamelCase  :List[str]       =model.num_features
             _UpperCamelCase  :int       =[config.stage_names.index(lowerCAmelCase__     ) for feat in config.out_features]
             _UpperCamelCase  :List[str]       =[num_features[idx] for idx in out_indices]
             self.assertListEqual(model.channels					,      lowerCAmelCase__     )

             _UpperCamelCase  :Any       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :List[Any]       =None
             _UpperCamelCase  :Optional[Any]       =model_class(lowerCAmelCase__     )
             self.assertEqual(len(model.channels     )					,      1     )
             self.assertListEqual(model.channels					,      [num_features[-1]]     )

             _UpperCamelCase  :Tuple       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :int       =None
             _UpperCamelCase  :int       =model_class(lowerCAmelCase__     )
             self.assertEqual(len(model.channels     )					,      1     )
             self.assertListEqual(model.channels					,      [num_features[-1]]     )



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Any       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _UpperCamelCase  :List[Any]       =model_class(lowerCAmelCase__     )
             model.to(lowerCAmelCase__     )
             model.eval()
             _UpperCamelCase  :str       =model(**lowerCAmelCase__     )

             self.assertEqual(len(result.feature_maps     )					,      len(config.out_features     )     )
             self.assertEqual(len(model.channels     )					,      len(config.out_features     )     )
             self.assertEqual(len(result.feature_maps     )					,      len(config.out_indices     )     )
             self.assertEqual(len(model.channels     )					,      len(config.out_indices     )     )

             # Check output of last stage is taken if out_features=None, out_indices=None
             _UpperCamelCase  :Any       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :List[Any]       =None
             _UpperCamelCase  :Dict       =model_class(lowerCAmelCase__     )
             model.to(lowerCAmelCase__     )
             model.eval()
             _UpperCamelCase  :str       =model(**lowerCAmelCase__     )

             self.assertEqual(len(result.feature_maps     )					,      1     )
             self.assertEqual(len(model.channels     )					,      1     )

             _UpperCamelCase  :str       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :Dict       =None
             _UpperCamelCase  :Tuple       =model_class(lowerCAmelCase__     )
             model.to(lowerCAmelCase__     )
             model.eval()
             _UpperCamelCase  :Dict       =model(**lowerCAmelCase__     )

             self.assertEqual(len(result.feature_maps     )					,      1     )
             self.assertEqual(len(model.channels     )					,      1     )

             # Check backbone can be initialized with fresh weights
             _UpperCamelCase  :Any       =copy.deepcopy(lowerCAmelCase__     )
             _UpperCamelCase  :Optional[int]       =False
             _UpperCamelCase  :Any       =model_class(lowerCAmelCase__     )
             model.to(lowerCAmelCase__     )
             model.eval()
             _UpperCamelCase  :Optional[int]       =model(**lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs_for_common()

        for backbone_class in self.all_model_classes:
             _UpperCamelCase  :Optional[int]       =backbone_class(lowerCAmelCase__     )

             self.assertTrue(hasattr(lowerCAmelCase__					,      """backbone_type"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """stage_names"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """num_features"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """out_indices"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """out_features"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """out_feature_channels"""     )     )
             self.assertTrue(hasattr(lowerCAmelCase__					,      """channels"""     )     )

             self.assertIsInstance(backbone.backbone_type					,      lowerCAmelCase__     )
             # Verify num_features has been initialized in the backbone init
             self.assertIsNotNone(backbone.num_features     )
             self.assertTrue(len(backbone.channels     ) == len(backbone.out_indices     )     )
             self.assertTrue(len(backbone.stage_names     ) == len(backbone.num_features     )     )
             self.assertTrue(len(backbone.channels     ) <= len(backbone.num_features     )     )
             self.assertTrue(len(backbone.out_feature_channels     ) == len(backbone.stage_names     )     )




   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :List[Any]       =self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase  :Union[str, Any]       =inputs_dict["""pixel_values"""].shape[0]

        for backbone_class in self.all_model_classes:
             _UpperCamelCase  :str       =backbone_class(lowerCAmelCase__     )
             backbone.to(lowerCAmelCase__     )
             backbone.eval()

             _UpperCamelCase  :Any       =backbone(**lowerCAmelCase__     )

             # Test default outputs and verify feature maps
             self.assertIsInstance(outputs.feature_maps					,      lowerCAmelCase__     )
             self.assertTrue(len(outputs.feature_maps     ) == len(backbone.channels     )     )
             for feature_map, n_channels in zip(outputs.feature_maps					,      backbone.channels     ):
                  self.assertTrue(feature_map.shape[:2]					,      (batch_size, n_channels)     )
             self.assertIsNone(outputs.hidden_states     )
             self.assertIsNone(outputs.attentions     )

             # Test output_hidden_states=True
             _UpperCamelCase  :Any       =backbone(**lowerCAmelCase__					,      output_hidden_states=lowerCAmelCase__     )
             self.assertIsNotNone(outputs.hidden_states     )
             self.assertTrue(len(outputs.hidden_states     )					,      len(backbone.stage_names     )     )
             for hidden_state, n_channels in zip(outputs.hidden_states					,      backbone.channels     ):
                  self.assertTrue(hidden_state.shape[:2]					,      (batch_size, n_channels)     )

             # Test output_attentions=True
             if self.has_attentions:
                  _UpperCamelCase  :int       =backbone(**lowerCAmelCase__					,      output_attentions=lowerCAmelCase__     )
                  self.assertIsNotNone(outputs.attentions     )