





"""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   ):

      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''						)
       model.to(_lowerCamelCase						)

       # encoder-decoder outputs
       __SCREAMING_SNAKE_CASE :			List[str]												=       torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]						).to(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       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						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       model(
           input_ids=_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							encoder_outputs=_lowerCamelCase						,							decoder_input_ids=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       output[0]
       __SCREAMING_SNAKE_CASE :			int												=       torch.Size((1, 1_4, 2_5_0_0_1_2)						)
       self.assertEqual(output_predited_logis.shape						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       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
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model.prophetnet.encoder(_lowerCamelCase						)[0]
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       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
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       model.prophetnet.decoder(
           _lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE :			Dict												=       decoder_outputs[1].view(1						,							model.config.ngram						,							1_4						,							-1						)
       __SCREAMING_SNAKE_CASE :			Dict												=       model.lm_head(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       predicting_streams_logits[:, 0]
       self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]						,							_lowerCamelCase						,							atol=1e-4						)						)

      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       XLMProphetNetForConditionalGeneration.from_pretrained(
           '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''						)
       model.to(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]						).to(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       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						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       model(
           input_ids=_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							encoder_outputs=_lowerCamelCase						,							decoder_input_ids=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Any												=       output[0]
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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.
       __SCREAMING_SNAKE_CASE :			List[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(_lowerCamelCase						)

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



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       XLMProphetNetForConditionalGeneration.from_pretrained(
           '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''						)
       model.to(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       5_1_2

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

       __SCREAMING_SNAKE_CASE :			str												=       (
           '''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.'''
       )
       __SCREAMING_SNAKE_CASE :			Union[str, 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.'''
       )
       __SCREAMING_SNAKE_CASE :			List[Any]												=       (
           '''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。'''
       )

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       tokenizer(
           [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]						,							padding=_lowerCamelCase						,							max_length=2_5_5						,							return_tensors='''pt'''						).input_ids
       __SCREAMING_SNAKE_CASE :			List[Any]												=       input_ids.to(_lowerCamelCase						)

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

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

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
    _lowerCamelCase       =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

