

'''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 lowerCamelCase   (    unittest.TestCase				):

				@slow
				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased' )
					model.to(lowercase_ )

					# encoder-decoder outputs
					snake_case							=					torch.tensor([[17, 96208, 103471, 2]] ).to(lowercase_ )
					snake_case							=					torch.tensor(
					    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]] ).to(lowercase_ )
					snake_case							=					model(
					    input_ids=lowercase_,						attention_mask=lowercase_,						encoder_outputs=lowercase_,						decoder_input_ids=lowercase_ )
					snake_case							=					output[0]
					snake_case							=					torch.Size((1, 14, 250012) )
					self.assertEqual(output_predited_logis.shape,						lowercase_ )
					snake_case							=					torch.tensor(
					    [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]] ).to(lowercase_ )
					self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3],						lowercase_,						atol=1E-4 ) )

					# encoder outputs
					snake_case							=					model.prophetnet.encoder(lowercase_ )[0]
					snake_case							=					torch.tensor(
					    [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]] ).to(lowercase_ )
					snake_case							=					torch.Size((1, 4, 1024) )
					self.assertEqual(encoder_outputs.shape,						lowercase_ )
					self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3],						lowercase_,						atol=1E-4 ) )

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

				@slow
				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					XLMProphetNetForConditionalGeneration.from_pretrained(
					    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg' )
					model.to(lowercase_ )

					snake_case							=					torch.tensor([[17, 96208, 103471, 2]] ).to(lowercase_ )
					snake_case							=					torch.tensor(
					    [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]] ).to(lowercase_ )
					snake_case							=					model(
					    input_ids=lowercase_,						attention_mask=lowercase_,						encoder_outputs=lowercase_,						decoder_input_ids=lowercase_ )
					snake_case							=					output[0]
					snake_case							=					torch.Size((1, 14, 250012) )
					self.assertEqual(output_predited_logis.shape,						lowercase_ )
					# compare the actual values for a slice.
					snake_case							=					torch.tensor(
					    [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]] ).to(lowercase_ )

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






				@slow
				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					XLMProphetNetForConditionalGeneration.from_pretrained(
					    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg' )
					model.to(lowercase_ )
					snake_case							=					512

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

					snake_case							=					(
					    '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.'
					)
					snake_case							=					(
					    '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.'
					)
					snake_case							=					(
					    '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。'
					)

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

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

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






'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.ctrl.modeling_tf_ctrl import (
          TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCTRLForSequenceClassification,
          TFCTRLLMHeadModel,
          TFCTRLModel,
      )



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    def __init__(					self     :						Dict							,							UpperCAmelCase_     :						List[str]							,							):


         """simple docstring"""







         a					:							Any							= parent
         a					:							Tuple							= 1_3
         a					:							Any							= 7
         a					:							Any							= True
         a					:							Optional[Any]							= True
         a					:							Optional[Any]							= True
         a					:							Dict							= True
         a					:							List[str]							= True
         a					:							Dict							= 9_9
         a					:							Tuple							= 3_2
         a					:							Optional[int]							= 2
         a					:							str							= 4
         a					:							str							= 3_7
         a					:							Union[str, Any]							= 'gelu'
         a					:							Any							= 0.1
         a					:							Any							= 0.1
         a					:							List[Any]							= 5_1_2
         a					:							Any							= 1_6
         a					:							int							= 2
         a					:							Dict							= 0.02
         a					:							Optional[int]							= 3
         a					:							Union[str, Any]							= 4
         a					:							int							= None
         a					:							Tuple							= self.vocab_size - 1





    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a					:							Optional[int]							= ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

         a					:							List[str]							= None
         if self.use_input_mask:
              a					:							Optional[Any]							= random_attention_mask([self.batch_size, self.seq_length])

         a					:							List[Any]							= None
         if self.use_token_type_ids:
              a					:							Any							= ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size)

         a					:							List[str]							= None
         if self.use_mc_token_ids:
              a					:							List[str]							= ids_tensor([self.batch_size, self.num_choices]							,							self.seq_length)

         a					:							str							= None
         a					:							Optional[Any]							= None
         a					:							Optional[int]							= None
         if self.use_labels:
              a					:							Tuple							= ids_tensor([self.batch_size]							,							self.type_sequence_label_size)
              a					:							Any							= ids_tensor([self.batch_size, self.seq_length]							,							self.num_labels)
              a					:							List[Any]							= ids_tensor([self.batch_size]							,							self.num_choices)

         a					:							Dict							= CTRLConfig(
             vocab_size=self.vocab_size							,							n_embd=self.hidden_size							,							n_layer=self.num_hidden_layers							,							n_head=self.num_attention_heads							,							n_positions=self.max_position_embeddings							,							pad_token_id=self.pad_token_id							,							)

         a					:							Tuple							= ids_tensor([self.num_hidden_layers, self.num_attention_heads]							,							2)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						List[Any]							,							UpperCAmelCase_     :						str							,							*UpperCAmelCase_     :						List[str]):


         """simple docstring"""







         a					:							Union[str, Any]							= TFCTRLModel(config=UpperCAmelCase_)
         a					:							Dict							= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         a					:							Tuple							= model(UpperCAmelCase_)

         a					:							str							= [input_ids, None, input_mask]  # None is the input for 'past'
         a					:							Any							= model(UpperCAmelCase_)

         a					:							List[str]							= model(UpperCAmelCase_)

         self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size))





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						List[str]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Dict							,							*UpperCAmelCase_     :						str):


         """simple docstring"""







         a					:							Optional[int]							= TFCTRLLMHeadModel(config=UpperCAmelCase_)
         a					:							Tuple							= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         a					:							int							= model(UpperCAmelCase_)
         self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size))





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							UpperCAmelCase_     :						str							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Optional[Any]							,							*UpperCAmelCase_     :						List[str]):


         """simple docstring"""







         a					:							Tuple							= self.num_labels
         a					:							List[str]							= ids_tensor([self.batch_size]							,							self.type_sequence_label_size)
         a					:							int							= {
             'input_ids': input_ids,
             'token_type_ids': token_type_ids,
             'labels': sequence_labels,
         }
         a					:							str							= TFCTRLForSequenceClassification(UpperCAmelCase_)
         a					:							Union[str, Any]							= model(UpperCAmelCase_)
         self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels))







    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a					:							Optional[int]							= self.prepare_config_and_inputs()

         (
             (
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			(
             a
         )							,			
         )					:							int							= config_and_inputs

         a					:							Tuple							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_tf
class   UpperCamelCase					(       a_    ,     a_    ,     unittest.TestCase       ):







    """simple docstring"""





    A     :    str				=			(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
    A     :    Optional[int]				=			(TFCTRLLMHeadModel,) if is_tf_available() else ()
    A     :    str				=			(
        {
            "feature-extraction": TFCTRLModel,
            "text-classification": TFCTRLForSequenceClassification,
            "text-generation": TFCTRLLMHeadModel,
            "zero-shot": TFCTRLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    A     :    int				=			False
    A     :    Optional[Any]				=			False





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						Any							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Optional[Any]):


         """simple docstring"""







         if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
              # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
              # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
              # config could not be created.
              return True

         return False





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a					:							Any							= TFCTRLModelTester(self)
         a					:							Dict							= ConfigTester(self							,							config_class=UpperCAmelCase_							,							n_embd=3_7)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         self.config_tester.run_common_tests()





    def    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         a					:							Optional[Any]							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         a					:							List[Any]							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_lm_head(*UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







         a					:							str							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a							,			a					:							Optional[Any]							= self.model_tester.prepare_config_and_inputs_for_common()
         a					:							int							= [TFCTRLLMHeadModel]
         a					:							int							= [TFCTRLForSequenceClassification]

         for model_class in self.all_model_classes:
              a					:							int							= model_class(UpperCAmelCase_)
              model.build()  # may be needed for the get_bias() call below
              assert isinstance(model.get_input_embeddings()							,							tf.keras.layers.Layer)

              if model_class in list_lm_models:
                   a					:							Optional[Any]							= model.get_output_embeddings()
                   assert isinstance(UpperCAmelCase_							,							tf.keras.layers.Layer)
                   a					:							int							= model.get_bias()
                   assert isinstance(UpperCAmelCase_							,							UpperCAmelCase_)
                   for k, v in name.items():
                        assert isinstance(UpperCAmelCase_							,							tf.Variable)
              elif model_class in list_other_models_with_output_ebd:
                   a					:							List[str]							= model.get_output_embeddings()
                   assert isinstance(UpperCAmelCase_							,							tf.keras.layers.Layer)
                   a					:							List[Any]							= model.get_bias()
                   assert name is None
              else:
                   a					:							List[str]							= model.get_output_embeddings()
                   assert x is None
                   a					:							Optional[Any]							= model.get_bias()
                   assert name is None







    @slow
    def    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              a					:							Dict							= TFCTRLModel.from_pretrained(UpperCAmelCase_)
              self.assertIsNotNone(UpperCAmelCase_)






@require_tf
class   UpperCamelCase					(       unittest.TestCase       ):







    """simple docstring"""





    @slow
    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a					:							List[Any]							= TFCTRLLMHeadModel.from_pretrained('ctrl')
         a					:							Union[str, Any]							= tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]							,							dtype=tf.intaa)  # Legal the president is
         a					:							List[Any]							= [
             1_1_8_5_9,
             0,
             1_6_1_1,
             8,
             5,
             1_5_0,
             2_6_4_4_9,
             2,
             1_9,
             3_4_8,
             4_6_9,
             3,
             2_5_9_5,
             4_8,
             2_0_7_4_0,
             2_4_6_5_3_3,
             2_4_6_5_3_3,
             1_9,
             3_0,
             5,
         ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

         a					:							List[str]							= model.generate(UpperCAmelCase_							,							do_sample=UpperCAmelCase_)
         self.assertListEqual(output_ids[0].numpy().tolist()							,							UpperCAmelCase_)

