


'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import DeiTImageProcessor







class 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   def __init__(	self	:					Dict					, lowerCamelCase	:					str					, lowerCamelCase	:					Dict=7					, lowerCamelCase	:					Union[str, Any]=3					, lowerCamelCase	:					List[Any]=18					, lowerCamelCase	:					List[Any]=30					, lowerCamelCase	:					str=400					, lowerCamelCase	:					Any=True					, lowerCamelCase	:					Tuple=None					, lowerCamelCase	:					str=True					, lowerCamelCase	:					Dict=None					, lowerCamelCase	:					int=True					, lowerCamelCase	:					Optional[int]=[0.5, 0.5, 0.5]					, lowerCamelCase	:					Optional[int]=[0.5, 0.5, 0.5]					, )->	Optional[int]:
          snake_case__					:	Optional[int]          =					size if size is not None else {"""height""": 20, """width""": 20}
          snake_case__					:	Optional[Any]          =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

          snake_case__					:	List[Any]          =					parent
          snake_case__					:	Any          =					batch_size
          snake_case__					:	List[str]          =					num_channels
          snake_case__					:	List[Any]          =					image_size
          snake_case__					:	Any          =					min_resolution
          snake_case__					:	Union[str, Any]          =					max_resolution
          snake_case__					:	Any          =					do_resize
          snake_case__					:	int          =					size
          snake_case__					:	str          =					do_center_crop
          snake_case__					:	str          =					crop_size
          snake_case__					:	Tuple          =					do_normalize
          snake_case__					:	Optional[Any]          =					image_mean
          snake_case__					:	Dict          =					image_std




   def   __lowerCAmelCase						(	self	:					Tuple						)->	Tuple:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
          }







@require_torch
@require_vision
class 					_A						(						UpperCamelCase    ,  unittest.TestCase							):



   '''simple docstring'''






   _lowercase    						= DeiTImageProcessor if is_vision_available() else None
   _lowercase    						= True




   def   __lowerCAmelCase						(	self	:					Dict						)->	List[Any]:
          snake_case__					:	List[Any]          =					DeiTImageProcessingTester(self						)




   @property
   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Union[str, Any]:
          return self.image_processor_tester.prepare_image_processor_dict()




   def   __lowerCAmelCase						(	self	:					Dict						)->	Optional[Any]:
          snake_case__					:	str          =					self.image_processing_class(**self.image_processor_dict						)
          self.assertTrue(hasattr(lowerCamelCase					, """do_resize"""						)						)
          self.assertTrue(hasattr(lowerCamelCase					, """size"""						)						)
          self.assertTrue(hasattr(lowerCamelCase					, """do_center_crop"""						)						)
          self.assertTrue(hasattr(lowerCamelCase					, """center_crop"""						)						)
          self.assertTrue(hasattr(lowerCamelCase					, """do_normalize"""						)						)
          self.assertTrue(hasattr(lowerCamelCase					, """image_mean"""						)						)
          self.assertTrue(hasattr(lowerCamelCase					, """image_std"""						)						)




   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	int:
          snake_case__					:	str          =					self.image_processing_class.from_dict(self.image_processor_dict						)
          self.assertEqual(image_processor.size					, {"""height""": 20, """width""": 20}						)
          self.assertEqual(image_processor.crop_size					, {"""height""": 18, """width""": 18}						)

          snake_case__					:	Optional[Any]          =					self.image_processing_class.from_dict(self.image_processor_dict					, size=42					, crop_size=84						)
          self.assertEqual(image_processor.size					, {"""height""": 42, """width""": 42}						)
          self.assertEqual(image_processor.crop_size					, {"""height""": 84, """width""": 84}						)




   def   __lowerCAmelCase						(	self	:					Optional[int]						)->	Dict:
          pass




   def   __lowerCAmelCase						(	self	:					str						)->	Optional[Any]:
          # Initialize image_processing
          snake_case__					:	List[Any]          =					self.image_processing_class(**self.image_processor_dict						)
          # create random PIL images
          snake_case__					:	Dict          =					prepare_image_inputs(self.image_processor_tester					, equal_resolution=lowerCamelCase						)
          for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase					, Image.Image						)

          # Test not batched input
          snake_case__					:	List[str]          =					image_processing(image_inputs[0]					, return_tensors="""pt"""						).pixel_values
          self.assertEqual(
              encoded_images.shape					, (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )					, )

          # Test batched
          snake_case__					:	List[str]          =					image_processing(lowerCamelCase					, return_tensors="""pt"""						).pixel_values
          self.assertEqual(
              encoded_images.shape					, (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )					, )




   def   __lowerCAmelCase						(	self	:					int						)->	Union[str, Any]:
          # Initialize image_processing
          snake_case__					:	int          =					self.image_processing_class(**self.image_processor_dict						)
          # create random numpy tensors
          snake_case__					:	Any          =					prepare_image_inputs(self.image_processor_tester					, equal_resolution=lowerCamelCase					, numpify=lowerCamelCase						)
          for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase					, np.ndarray						)

          # Test not batched input
          snake_case__					:	Union[str, Any]          =					image_processing(image_inputs[0]					, return_tensors="""pt"""						).pixel_values
          self.assertEqual(
              encoded_images.shape					, (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )					, )

          # Test batched
          snake_case__					:	Dict          =					image_processing(lowerCamelCase					, return_tensors="""pt"""						).pixel_values
          self.assertEqual(
              encoded_images.shape					, (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )					, )




   def   __lowerCAmelCase						(	self	:					List[Any]						)->	Optional[Any]:
          # Initialize image_processing
          snake_case__					:	Tuple          =					self.image_processing_class(**self.image_processor_dict						)
          # create random PyTorch tensors
          snake_case__					:	str          =					prepare_image_inputs(self.image_processor_tester					, equal_resolution=lowerCamelCase					, torchify=lowerCamelCase						)
          for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase					, torch.Tensor						)

          # Test not batched input
          snake_case__					:	str          =					image_processing(image_inputs[0]					, return_tensors="""pt"""						).pixel_values
          self.assertEqual(
              encoded_images.shape					, (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )					, )

          # Test batched
          snake_case__					:	Union[str, Any]          =					image_processing(lowerCamelCase					, return_tensors="""pt"""						).pixel_values
          self.assertEqual(
              encoded_images.shape					, (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )					, )








'''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 					_A						(						unittest.TestCase							):



   '''simple docstring'''






   @slow
   def   __lowerCAmelCase						(	self	:					Tuple						)->	Optional[int]:
          snake_case__					:	Any          =					XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""						)
          model.to(lowerCamelCase						)

          # encoder-decoder outputs
          snake_case__					:	Optional[int]          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[Any]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          snake_case__					:	Optional[int]          =					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(lowerCamelCase						)
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

          # encoder outputs
          snake_case__					:	Dict          =					model.prophetnet.encoder(lowerCamelCase						)[0]
          snake_case__					:	Optional[Any]          =					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(lowerCamelCase						)
          snake_case__					:	str          =					torch.Size((1, 4, 1_024)						)
          self.assertEqual(encoder_outputs.shape					, lowerCamelCase						)
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

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




   @slow
   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Optional[Any]:
          snake_case__					:	Tuple          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)

          snake_case__					:	str          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	List[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[str]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          # compare the actual values for a slice.
          snake_case__					:	List[str]          =					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(lowerCamelCase						)

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




   @slow
   def   __lowerCAmelCase						(	self	:					str						)->	str:
          snake_case__					:	Dict          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)
          snake_case__					:	Optional[int]          =					512

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

          snake_case__					:	Optional[int]          =					(
              """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__					:	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."""
          )
          snake_case__					:	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。"""
          )

          snake_case__					:	int          =					tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]					, padding=lowerCamelCase					, max_length=255					, return_tensors="""pt"""						).input_ids
          snake_case__					:	Optional[int]          =					input_ids.to(lowerCamelCase						)

          snake_case__					:	Optional[Any]          =					model.generate(
              lowerCamelCase					, num_beams=10					, length_penalty=1.0					, no_repeat_ngram_size=3					, early_stopping=lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					[tokenizer.decode(lowerCamelCase					, skip_special_tokens=lowerCamelCase						) for g in summary_ids]
          snake_case__					:	Optional[Any]          =					"""Microsoft to end Windows 7 free support after January 14, 2020"""
          snake_case__					:	Dict          =					"""Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
          snake_case__					:	Optional[int]          =					"""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					, )

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




