


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       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"	)
             model.to(__lowerCamelCase	)

             # encoder-decoder outputs
             lowerCamelCase__		:    Optional[int]   =  torch.tensor([[17, 96208, 103471, 2]]	).to(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[int]   =  torch.tensor(
                 [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]	).to(__lowerCamelCase	)
             lowerCamelCase__		:    Union[str, Any]   =  model(
                 input_ids=__lowerCamelCase ,				attention_mask=__lowerCamelCase ,				encoder_outputs=__lowerCamelCase ,				decoder_input_ids=__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  output[0]
             lowerCamelCase__		:    List[str]   =  torch.Size((1, 14, 250012)	)
             self.assertEqual(output_predited_logis.shape ,				__lowerCamelCase	)
             lowerCamelCase__		:    Any   =  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
             lowerCamelCase__		:    Tuple   =  model.prophetnet.encoder(__lowerCamelCase	)[0]
             lowerCamelCase__		:    Tuple   =  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	)
             lowerCamelCase__		:    Union[str, Any]   =  torch.Size((1, 4, 1024)	)
             self.assertEqual(encoder_outputs.shape ,				__lowerCamelCase	)
             self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3] ,				__lowerCamelCase ,				atol=1E-4	)	)

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





       @slow
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  XLMProphetNetForConditionalGeneration.from_pretrained(
                 "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"	)
             model.to(__lowerCamelCase	)

             lowerCamelCase__		:    Any   =  torch.tensor([[17, 96208, 103471, 2]]	).to(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  torch.tensor(
                 [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]	).to(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[int]   =  model(
                 input_ids=__lowerCamelCase ,				attention_mask=__lowerCamelCase ,				encoder_outputs=__lowerCamelCase ,				decoder_input_ids=__lowerCamelCase	)
             lowerCamelCase__		:    Optional[int]   =  output[0]
             lowerCamelCase__		:    Optional[Any]   =  torch.Size((1, 14, 250012)	)
             self.assertEqual(output_predited_logis.shape ,				__lowerCamelCase	)
             # compare the actual values for a slice.
             lowerCamelCase__		:    Union[str, 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 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  XLMProphetNetForConditionalGeneration.from_pretrained(
                 "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"	)
             model.to(__lowerCamelCase	)
             lowerCamelCase__		:    Tuple   =  512

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

             lowerCamelCase__		:    Any   =  (
                 "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."
             )
             lowerCamelCase__		:    Optional[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."
             )
             lowerCamelCase__		:    Tuple   =  (
                 "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。"
             )

             lowerCamelCase__		:    int   =  tokenizer(
                 [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE] ,				padding=__lowerCamelCase ,				max_length=255 ,				return_tensors="pt"	).input_ids
             lowerCamelCase__		:    Tuple   =  input_ids.to(__lowerCamelCase	)

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

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






import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch


A				:   Union[str, Any]		= random.Random()

def 				lowercase_		(		_A      :   Any   , _A      :   int=1.0   , _A      :   Optional[int]=None   , _A      :   Union[str, Any]=None						):

      """simple docstring"""




      if rng is None:
            lowerCamelCase__		:    Any   =  global_rng

      lowerCamelCase__		:    Optional[Any]   =  []
      for batch_idx in range(shape[0]						):
            values.append([]						)
            for _ in range(shape[1]						):
                  values[-1].append(rng.random() * scale						)

      return values







@require_torch
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       def __init__(       self							:      Tuple ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      Union[str, Any]=7 ,				__lowerCamelCase							:      int=400 ,				__lowerCamelCase							:      Tuple=2000 ,				__lowerCamelCase							:      int=1 ,				__lowerCamelCase							:      int=0.0 ,				__lowerCamelCase							:      int=24000 ,				__lowerCamelCase							:      Union[str, Any]=True ,				):


             '''simple docstring'''



             lowerCamelCase__		:    str   =  parent
             lowerCamelCase__		:    Union[str, Any]   =  batch_size
             lowerCamelCase__		:    str   =  min_seq_length
             lowerCamelCase__		:    int   =  max_seq_length
             lowerCamelCase__		:    List[Any]   =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             lowerCamelCase__		:    List[str]   =  feature_size
             lowerCamelCase__		:    List[Any]   =  padding_value
             lowerCamelCase__		:    Union[str, Any]   =  sampling_rate
             lowerCamelCase__		:    Tuple   =  return_attention_mask





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {
                 "feature_size": self.feature_size,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
             }





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      Tuple=False ,				__lowerCamelCase							:      Optional[int]=False	):


             '''simple docstring'''



             def _flatten(__lowerCamelCase							:      Dict	):
                   return list(itertools.chain(*__lowerCamelCase	)	)

             if equal_length:
                   lowerCamelCase__		:    List[Any]   =  floats_list((self.batch_size, self.max_seq_length)	)
             else:
                   # make sure that inputs increase in size
                   lowerCamelCase__		:    Dict   =  [
                       _flatten(floats_list((x, self.feature_size)	)	)
                       for x in range(self.min_seq_length ,				self.max_seq_length ,				self.seq_length_diff	)
                   ]

             if numpify:
                   lowerCamelCase__		:    Optional[int]   =  [np.asarray(__lowerCamelCase	) for x in audio_inputs]

             return audio_inputs







@require_torch
class       _lowercase			(							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__ 						=      EncodecFeatureExtractor





       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  EnCodecFeatureExtractionTester(self	)





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             # create three inputs of length 800, 1000, and 1200
             lowerCamelCase__		:    Union[str, Any]   =  [floats_list((1, x)	)[0] for x in range(800 ,				1400 ,				200	)]
             lowerCamelCase__		:    Dict   =  [np.asarray(__lowerCamelCase	) for audio_input in audio_inputs]

             # Test not batched input
             lowerCamelCase__		:    Dict   =  feat_extract(audio_inputs[0] ,				return_tensors="np"	).input_values
             lowerCamelCase__		:    Optional[int]   =  feat_extract(np_audio_inputs[0] ,				return_tensors="np"	).input_values
             self.assertTrue(np.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=1E-3	)	)

             # Test batched
             lowerCamelCase__		:    Dict   =  feat_extract(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="np"	).input_values
             lowerCamelCase__		:    Any   =  feat_extract(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="np"	).input_values
             for enc_seq_a, enc_seq_a in zip(__lowerCamelCase ,				__lowerCamelCase	):
                   self.assertTrue(np.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=1E-3	)	)





       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             lowerCamelCase__		:    Tuple   =  np.random.rand(100	).astype(np.floataa	)
             lowerCamelCase__		:    str   =  np_audio_inputs.tolist()

             for inputs in [py_audio_inputs, np_audio_inputs]:
                   lowerCamelCase__		:    List[Any]   =  feature_extractor.pad([{"input_values": inputs}] ,				return_tensors="np"	)
                   self.assertTrue(np_processed.input_values.dtype == np.floataa	)
                   lowerCamelCase__		:    int   =  feature_extractor.pad([{"input_values": inputs}] ,				return_tensors="pt"	)
                   self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)





       def 						lowerCAmelCase					(       self							:      str ,				__lowerCamelCase							:      Any	):


             '''simple docstring'''



             from datasets import load_dataset

             lowerCamelCase__		:    List[Any]   =  load_dataset("hf-internal-testing/librispeech_asr_dummy" ,				"clean" ,				split="validation"	)
             # automatic decoding with librispeech
             lowerCamelCase__		:    Union[str, Any]   =  ds.sort("id"	).select(range(__lowerCamelCase	)	)[:num_samples]["audio"]

             return [x["array"] for x in audio_samples]





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  torch.tensor(
                 [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                  3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                  2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                  4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                  7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                  4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]	)
             # fmt: on
             lowerCamelCase__		:    str   =  self._load_datasamples(1	)
             lowerCamelCase__		:    Any   =  EncodecFeatureExtractor()
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(input_values.shape ,				(1, 1, 93680)	)
             self.assertTrue(torch.allclose(input_values[0, 0, :30] ,				__lowerCamelCase ,				atol=1E-6	)	)





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  torch.tensor(
                 [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                  3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                  2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                  4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                  7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                  4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]	)
             # fmt: on
             lowerCamelCase__		:    Any   =  self._load_datasamples(1	)
             lowerCamelCase__		:    int   =  [np.tile(input_audio[0][None] ,				reps=(2, 1)	)]
             input_audio[0][1] *= 0.5
             lowerCamelCase__		:    List[str]   =  EncodecFeatureExtractor(feature_size=2	)
             lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(input_values.shape ,				(1, 2, 93680)	)
             self.assertTrue(torch.allclose(input_values[0, 0, :30] ,				__lowerCamelCase ,				atol=1E-6	)	)
             self.assertTrue(torch.allclose(input_values[0, 1, :30] ,				EXPECTED_INPUT_VALUES * 0.5 ,				atol=1E-6	)	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  self._load_datasamples(2	)
             # would be easier if the stride was like
             lowerCamelCase__		:    Optional[Any]   =  EncodecFeatureExtractor(feature_size=1 ,				chunk_length_s=1 ,				overlap=0.0_1	)

             # pad and trunc raise an error ?
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Both padding and truncation were set. Make sure you only set one.$" ,				):
                   lowerCamelCase__		:    str   =  feature_extractor(
                       __lowerCamelCase ,				padding="max_length" ,				truncation=__lowerCamelCase ,				return_tensors="pt"	).input_values

             # truncate to chunk
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				truncation=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 71520)	)  # 2 chunks

             # force truncate to max_length
             lowerCamelCase__		:    List[str]   =  feature_extractor(
                 __lowerCamelCase ,				truncation=__lowerCamelCase ,				max_length=48000 ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 48000)	)

             # pad to chunk
             lowerCamelCase__		:    int   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(padded_outputs.shape ,				(2, 1, 95280)	)

             # pad to chunk
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 95280)	)

             # force pad to max length
             lowerCamelCase__		:    Tuple   =  feature_extractor(
                 __lowerCamelCase ,				padding="max_length" ,				max_length=100000 ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 100000)	)

             # force no pad
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    str   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)

             # no pad if no chunk_length_s
             lowerCamelCase__		:    Tuple   =  None
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    List[str]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    Tuple   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)

             # no pad if no overlap
             lowerCamelCase__		:    List[Any]   =  2
             lowerCamelCase__		:    List[Any]   =  None
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    Optional[Any]   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)


