
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					):



  """simple docstring"""





  @slow
  def 					lowercase_						(					self					):
      A_								=					XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"					)
      model.to(__UpperCamelCase					)

      # encoder-decoder outputs
      A_								=					torch.tensor([[17, 96208, 103471, 2]]					).to(__UpperCamelCase					)
      A_								=					torch.tensor(
          [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]					).to(__UpperCamelCase					)
      A_								=					model(
          input_ids=__UpperCamelCase						,    attention_mask=__UpperCamelCase						,    encoder_outputs=__UpperCamelCase						,    decoder_input_ids=__UpperCamelCase					)
      A_								=					output[0]
      A_								=					torch.Size((1, 14, 250012)					)
      self.assertEqual(output_predited_logis.shape						,    __UpperCamelCase					)
      A_								=					torch.tensor(
          [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]					).to(__UpperCamelCase					)
      self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]						,    __UpperCamelCase						,    atol=1E-4					)					)

      # encoder outputs
      A_								=					model.prophetnet.encoder(__UpperCamelCase					)[0]
      A_								=					torch.tensor(
          [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]					).to(__UpperCamelCase					)
      A_								=					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
      A_								=					model.prophetnet.decoder(
          __UpperCamelCase						,    encoder_hidden_states=__UpperCamelCase						,    )
      A_								=					decoder_outputs[1].view(1						,    model.config.ngram						,    14						,    -1					)
      A_								=					model.lm_head(__UpperCamelCase					)
      A_								=					predicting_streams_logits[:, 0]
      self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]						,    __UpperCamelCase						,    atol=1E-4					)					)

  @slow
  def 					lowercase_						(					self					):
      A_								=					XLMProphetNetForConditionalGeneration.from_pretrained(
          "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"					)
      model.to(__UpperCamelCase					)

      A_								=					torch.tensor([[17, 96208, 103471, 2]]					).to(__UpperCamelCase					)
      A_								=					torch.tensor(
          [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]					).to(__UpperCamelCase					)
      A_								=					model(
          input_ids=__UpperCamelCase						,    attention_mask=__UpperCamelCase						,    encoder_outputs=__UpperCamelCase						,    decoder_input_ids=__UpperCamelCase					)
      A_								=					output[0]
      A_								=					torch.Size((1, 14, 250012)					)
      self.assertEqual(output_predited_logis.shape						,    __UpperCamelCase					)
      # compare the actual values for a slice.
      A_								=					torch.tensor(
          [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]					).to(__UpperCamelCase					)

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

  @slow
  def 					lowercase_						(					self					):
      A_								=					XLMProphetNetForConditionalGeneration.from_pretrained(
          "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"					)
      model.to(__UpperCamelCase					)
      A_								=					512

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

      A_								=					(
          "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_								=					(
          "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_								=					(
          "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_								=					tokenizer(
          [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]						,    padding=__UpperCamelCase						,    max_length=255						,    return_tensors="pt"					).input_ids
      A_								=					input_ids.to(__UpperCamelCase					)

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

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








import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__magic_name__  :  List[str]     			=   logging.getLogger(__name__)





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Optional[Any]:
    np.random.seed(args.seed )
    torch.manual_seed(args.seed )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed )





def        lowerCAmelCase       (				)->			int:
    A_								=					argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path"				,					default=snake_case__				,					type=snake_case__				,					required=snake_case__				,					)
    parser.add_argument("--prompt"				,					type=snake_case__				,					default="" )
    parser.add_argument("--length"				,					type=snake_case__				,					default=20 )
    parser.add_argument("--stop_token"				,					type=snake_case__				,					default=snake_case__				,					help="Token at which text generation is stopped" )
    parser.add_argument(
        "--temperature"				,					type=snake_case__				,					default=1.0				,					help="temperature of 1.0 has no effect, lower tend toward greedy sampling"				,					)
    parser.add_argument(
        "--repetition_penalty"				,					type=snake_case__				,					default=1.0				,					help="primarily useful for CTRL model; in that case, use 1.2" )
    parser.add_argument("--k"				,					type=snake_case__				,					default=0 )
    parser.add_argument("--penalty_alpha"				,					type=snake_case__				,					default=0.0 )
    parser.add_argument("--p"				,					type=snake_case__				,					default=0.9 )

    parser.add_argument("--prefix"				,					type=snake_case__				,					default=""				,					help="Text added prior to input." )
    parser.add_argument("--padding_text"				,					type=snake_case__				,					default=""				,					help="Deprecated, the use of `--prefix` is preferred." )
    parser.add_argument("--xlm_language"				,					type=snake_case__				,					default=""				,					help="Optional language when used with the XLM model." )

    parser.add_argument("--seed"				,					type=snake_case__				,					default=42				,					help="random seed for initialization" )
    parser.add_argument("--no_cuda"				,					action="store_true"				,					help="Avoid using CUDA when available" )
    parser.add_argument(
        "--fp16"				,					action="store_true"				,					help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"				,					)
    A_								=					parser.parse_args()

    A_								=					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
    A_								=					0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}' )

    set_seed(snake_case__ )

    # Initialize the model and tokenizer
    A_								=					AutoTokenizer.from_pretrained(args.model_name_or_path )
    A_								=					AutoModelForCausalLM.from_pretrained(args.model_name_or_path )

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device )

    if args.fpaa:
        model.half()

    logger.info(snake_case__ )
    A_								=					args.prompt if args.prompt else input("Model prompt >>> " )

    A_								=					tokenizer(snake_case__				,					return_tensors="pt"				,					add_special_tokens=snake_case__ )
    A_								=					{key: value.to(args.device ) for key, value in inputs.items()}

    A_								=					model.generate(
        **snake_case__				,					max_length=args.length + len(inputs["input_ids"][0] )				,					penalty_alpha=args.penalty_alpha				,					top_k=args.k				,					)

    A_								=					[]
    for generated_sequence_idx, generated_sequence in enumerate(snake_case__ ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
        A_								=					generated_sequence.tolist()

        # Decode text
        A_								=					tokenizer.decode(snake_case__				,					clean_up_tokenization_spaces=snake_case__				,					add_special_tokens=snake_case__ )

        # Remove all text after the stop token
        A_								=					text[: text.find(args.stop_token ) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        A_								=					(
            prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]				,					clean_up_tokenization_spaces=snake_case__ ) ) :]
        )

        generated_sequences.append(snake_case__ )
        print(snake_case__ )

    return generated_sequences


if __name__ == "__main__":
     main()






