





'''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		):

		@slow
		def   lowercase__    (      self				:      Dict )				->						Union[str, Any]:


					"""simple docstring"""

					__snake_case				:			Dict       			=    XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )
					model.to(__magic_name__ )

					# encoder-decoder outputs
					__snake_case				:			str       			=    torch.tensor([[17, 9_62_08, 10_34_71, 2]] ).to(__magic_name__ )
					__snake_case				:			Any       			=    torch.tensor(
					    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]] ).to(__magic_name__ )
					__snake_case				:			Tuple       			=    model(
					    input_ids=__magic_name__     ,			attention_mask=__magic_name__     ,			encoder_outputs=__magic_name__     ,			decoder_input_ids=__magic_name__ )
					__snake_case				:			List[Any]       			=    output[0]
					__snake_case				:			List[Any]       			=    torch.Size((1, 14, 25_00_12) )
					self.assertEqual(output_predited_logis.shape     ,			__magic_name__ )
					__snake_case				:			Optional[int]       			=    torch.tensor(
					    [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]] ).to(__magic_name__ )
					self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]     ,			__magic_name__     ,			atol=1E-4 ) )

					# encoder outputs
					__snake_case				:			Union[str, Any]       			=    model.prophetnet.encoder(__magic_name__ )[0]
					__snake_case				:			List[Any]       			=    torch.tensor(
					    [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]] ).to(__magic_name__ )
					__snake_case				:			Optional[int]       			=    torch.Size((1, 4, 10_24) )
					self.assertEqual(encoder_outputs.shape     ,			__magic_name__ )
					self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]     ,			__magic_name__     ,			atol=1E-4 ) )

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

		@slow
		def   lowercase__    (      self				:      Union[str, Any] )				->						int:


					"""simple docstring"""

					__snake_case				:			int       			=    XLMProphetNetForConditionalGeneration.from_pretrained(
					    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""" )
					model.to(__magic_name__ )

					__snake_case				:			Optional[int]       			=    torch.tensor([[17, 9_62_08, 10_34_71, 2]] ).to(__magic_name__ )
					__snake_case				:			str       			=    torch.tensor(
					    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]] ).to(__magic_name__ )
					__snake_case				:			Optional[Any]       			=    model(
					    input_ids=__magic_name__     ,			attention_mask=__magic_name__     ,			encoder_outputs=__magic_name__     ,			decoder_input_ids=__magic_name__ )
					__snake_case				:			Any       			=    output[0]
					__snake_case				:			Optional[Any]       			=    torch.Size((1, 14, 25_00_12) )
					self.assertEqual(output_predited_logis.shape     ,			__magic_name__ )
					# compare the actual values for a slice.
					__snake_case				:			Any       			=    torch.tensor(
					    [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]] ).to(__magic_name__ )

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

		@slow
		def   lowercase__    (      self				:      Dict )				->						Dict:


					"""simple docstring"""

					__snake_case				:			Optional[int]       			=    XLMProphetNetForConditionalGeneration.from_pretrained(
					    """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""" )
					model.to(__magic_name__ )
					__snake_case				:			Optional[int]       			=    5_12

					__snake_case				:			Optional[int]       			=    XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg""" )

					__snake_case				:			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				:			int       			=    (
					    """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				:			Optional[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				:			Tuple       			=    tokenizer(
					    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]     ,			padding=__magic_name__     ,			max_length=2_55     ,			return_tensors="""pt""" ).input_ids
					__snake_case				:			Union[str, Any]       			=    input_ids.to(__magic_name__ )

					__snake_case				:			Any       			=    model.generate(
					    __magic_name__     ,			num_beams=10     ,			length_penalty=1.0     ,			no_repeat_ngram_size=3     ,			early_stopping=__magic_name__ )
					__snake_case				:			Tuple       			=    [tokenizer.decode(__magic_name__     ,			skip_special_tokens=__magic_name__ ) for g in summary_ids]
					__snake_case				:			Dict       			=    """Microsoft to end Windows 7 free support after January 14, 2020"""
					__snake_case				:			int       			=    """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]     ,			__magic_name__     ,			)

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









'''simple docstring'''






def 				_a	(					_lowerCamelCase   )   ->							bool:



			"""simple docstring"""


			__snake_case				:			Optional[Any]       			=    [list(_lowerCamelCase   ) for row in matrix]
			if matrix and isinstance(_lowerCamelCase						,  _lowerCamelCase   ):
						if isinstance(matrix[0]						,  _lowerCamelCase   ):
									__snake_case				:			str       			=    0
									for row in matrix:
												if prev_len == 0:
															__snake_case				:			Dict       			=    len(_lowerCamelCase   )
															__snake_case				:			List[Any]       			=    True
												else:
															__snake_case				:			Union[str, Any]       			=    prev_len == len(_lowerCamelCase   )
						else:
									__snake_case				:			List[Any]       			=    True
			else:
						__snake_case				:			List[str]       			=    False

			return result







def 				_a	(					_lowerCamelCase   )   ->							None:



			"""simple docstring"""


			if check_matrix(_lowerCamelCase   ) and len(_lowerCamelCase   ) > 0:
						__snake_case				:			List[str]       			=    [list(_lowerCamelCase   ) for row in a]
						__snake_case				:			Optional[Any]       			=    len(_lowerCamelCase   )
						if isinstance(a[0]						,  _lowerCamelCase   ):
									__snake_case				:			Tuple       			=    len(a[0]   )
						else:
									for dat in a:
												print(_lowerCamelCase   )
									return

						# horizotal printing increasing
						for i in range(0						,  _lowerCamelCase   ):
									print(a[0][i]   )
						# vertical printing down
						for i in range(1						,  _lowerCamelCase   ):
									print(a[i][mat_col - 1]   )
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2						,  -1						,  -1   ):
												print(a[mat_row - 1][i]   )
        # vertical printing up
						for i in range(mat_row - 2						,  0						,  -1   ):
									print(a[i][0]   )
						__snake_case				:			Union[str, Any]       			=    [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(_lowerCamelCase   ) > 0:
									spiral_print_clockwise(_lowerCamelCase   )
						else:
									return
			else:
						print("""Not a valid matrix"""   )
						return







def 				_a	(					_lowerCamelCase   )   ->							list[int]:



			"""simple docstring"""


			if matrix:
						return list(matrix.pop(0   )   ) + spiral_traversal(list(zip(*_lowerCamelCase   )   )[::-1]   )
			else:
						return []


# driver code
if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase										=  [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
	spiral_print_clockwise(a)


