





'''simple docstring'''






import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import BertGenerationDecoder, BertGenerationEncoder


class   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=13    ,   _UpperCAmelCase=7    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=99    ,   _UpperCAmelCase=32    ,   _UpperCAmelCase=5    ,   _UpperCAmelCase=4    ,   _UpperCAmelCase=37    ,   _UpperCAmelCase="gelu"    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=50    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   ):
       A       :       Optional[int]							=						parent
       A       :       int							=						batch_size
       A       :       Optional[int]							=						seq_length
       A       :       Optional[int]							=						is_training
       A       :       Dict							=						use_input_mask
       A       :       Optional[int]							=						vocab_size
       A       :       Dict							=						hidden_size
       A       :       Union[str, Any]							=						num_hidden_layers
       A       :       Dict							=						num_attention_heads
       A       :       List[Any]							=						intermediate_size
       A       :       Dict							=						hidden_act
       A       :       Optional[int]							=						hidden_dropout_prob
       A       :       Optional[Any]							=						attention_probs_dropout_prob
       A       :       Union[str, Any]							=						max_position_embeddings
       A       :       List[str]							=						initializer_range
       A       :       Any							=						use_labels
       A       :       List[str]							=						scope


   def 					snake_case				( self					):
       A       :       str							=						ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size					)

       A       :       Union[str, Any]							=						None
       if self.use_input_mask:
           A       :       List[Any]							=						random_attention_mask([self.batch_size, self.seq_length]					)

       if self.use_labels:
           A       :       Tuple							=						ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size					)

       A       :       int							=						self.get_config()

       return config, input_ids, input_mask, token_labels


   def 					snake_case				( self					):
       return BertGenerationConfig(
           vocab_size=self.vocab_size    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   max_position_embeddings=self.max_position_embeddings    ,   is_decoder=_UpperCAmelCase    ,   initializer_range=self.initializer_range    ,   )


   def 					snake_case				( self					):
       (
           (
           A					
       ),	(
           A					
       ),	(
           A					
       ),	(
           A					
       ),						
       )       :       Optional[Any]							=						self.prepare_config_and_inputs()

       A       :       Optional[int]							=						True
       A       :       Tuple							=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
       A       :       Dict							=						ids_tensor([self.batch_size, self.seq_length]    ,   vocab_size=2					)

       return (
           config,
           input_ids,
           input_mask,
           token_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   **_UpperCAmelCase    ,   ):
       A       :       List[str]							=						BertGenerationEncoder(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       Optional[Any]							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					)
       A       :       List[str]							=						model(_UpperCAmelCase					)
       self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   **_UpperCAmelCase    ,   ):
       A       :       int							=						True
       A       :       Optional[Any]							=						BertGenerationEncoder(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       Tuple							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   )
       A       :       int							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   )
       self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   **_UpperCAmelCase    ,   ):
       A       :       Optional[int]							=						True
       A       :       Optional[Any]							=						True
       A       :       Tuple							=						BertGenerationDecoder(config=_UpperCAmelCase					).to(_UpperCAmelCase					).eval()

       # first forward pass
       A       :       Optional[Any]							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   use_cache=_UpperCAmelCase    ,   )
       A       :       Optional[int]							=						outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       A       :       Optional[int]							=						ids_tensor((self.batch_size, 3)    ,   config.vocab_size					)
       A       :       List[str]							=						ids_tensor((self.batch_size, 3)    ,   vocab_size=2					)

       # append to next input_ids and
       A       :       Dict							=						torch.cat([input_ids, next_tokens]    ,   dim=-1					)
       A       :       Any							=						torch.cat([input_mask, next_mask]    ,   dim=-1					)

       A       :       List[str]							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   output_hidden_states=_UpperCAmelCase    ,   )['''hidden_states'''][0]
       A       :       Optional[Any]							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   past_key_values=_UpperCAmelCase    ,   output_hidden_states=_UpperCAmelCase    ,   )['''hidden_states'''][0]

       # select random slice
       A       :       List[str]							=						ids_tensor((1,)    ,   output_from_past.shape[-1]					).item()
       A       :       List[Any]							=						output_from_no_past[:, -3:, random_slice_idx].detach()
       A       :       Optional[Any]							=						output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(_UpperCAmelCase    ,   _UpperCAmelCase    ,   atol=1E-3					)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   *_UpperCAmelCase    ,   ):
       A       :       List[str]							=						BertGenerationDecoder(_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       Any							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   labels=_UpperCAmelCase					)
       self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.vocab_size)					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self.prepare_config_and_inputs()
       A       :       Any							=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_torch
class   _lowercase						(							a		,					a		,					a		,					unittest.TestCase				):
   _UpperCamelCase       		=   (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
   _UpperCamelCase       		=   (BertGenerationDecoder,) if is_torch_available() else ()
   _UpperCamelCase       		=   (
       {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
       if is_torch_available()
       else {}
   )


   def 					snake_case				( self					):
       A       :       Any							=						BertGenerationEncoderTester(self					)
       A       :       Any							=						ConfigTester(self    ,   config_class=_UpperCAmelCase    ,   hidden_size=37					)


   def 					snake_case				( self					):
       self.config_tester.run_common_tests()


   def 					snake_case				( self					):
       A       :       List[Any]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       int							=						self.model_tester.prepare_config_and_inputs()
       A       :       Optional[int]							=						'''bert'''
       self.model_tester.create_and_check_model(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       Optional[Any]							=						self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       List[Any]							=						self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       (
           (
           A					
       ),	(
           A					
       ),	(
           A					
       ),	(
           A					
       ),	(
           A					
       ),	(
           A					
       ),						
       )       :       Dict							=						self.model_tester.prepare_config_and_inputs_for_decoder()

       A       :       str							=						None

       self.model_tester.create_and_check_model_as_decoder(
           _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   )


   def 					snake_case				( self					):
       A       :       List[Any]							=						self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_for_causal_lm(*_UpperCAmelCase					)


   @slow
   def 					snake_case				( self					):
       A       :       Any							=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''					)
       self.assertIsNotNone(_UpperCAmelCase					)







@require_torch
class   _lowercase						(							unittest.TestCase				):


   @slow
   def 					snake_case				( self					):
       A       :       Dict							=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''					)
       A       :       Optional[int]							=						torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]					)
       with torch.no_grad():
           A       :       Tuple							=						model(_UpperCAmelCase					)[0]
       A       :       List[str]							=						torch.Size([1, 8, 1_024]					)
       self.assertEqual(output.shape    ,   _UpperCAmelCase					)
       A       :       Optional[Any]							=						torch.tensor(
           [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]					)
       self.assertTrue(torch.allclose(output[:, :3, :3]    ,   _UpperCAmelCase    ,   atol=1E-4					)					)







@require_torch
class   _lowercase						(							unittest.TestCase				):


   @slow
   def 					snake_case				( self					):
       A       :       Optional[Any]							=						BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''					)
       A       :       Optional[Any]							=						torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]					)
       with torch.no_grad():
           A       :       int							=						model(_UpperCAmelCase					)[0]
       A       :       Any							=						torch.Size([1, 8, 50_358]					)
       self.assertEqual(output.shape    ,   _UpperCAmelCase					)
       A       :       Any							=						torch.tensor(
           [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]					)
       self.assertTrue(torch.allclose(output[:, :3, :3]    ,   _UpperCAmelCase    ,   atol=1E-4					)					)







'''simple docstring'''






import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_     =	get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
class   _lowercase						(							a		,					unittest.TestCase				):
   _UpperCamelCase       		=   XLMProphetNetTokenizer
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   True


   def 					snake_case				( self					):
       super().setUp()

       # We have a SentencePiece fixture for testing
       A       :       Dict							=						XLMProphetNetTokenizer(_UpperCAmelCase    ,   keep_accents=_UpperCAmelCase					)
       tokenizer.save_pretrained(self.tmpdirname					)


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						'''[PAD]'''
       A       :       Optional[int]							=						0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase					)    ,   _UpperCAmelCase					)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase					)    ,   _UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						list(self.get_tokenizer().get_vocab().keys()					)

       self.assertEqual(vocab_keys[0]    ,   '''[PAD]'''					)
       self.assertEqual(vocab_keys[1]    ,   '''[CLS]'''					)
       self.assertEqual(vocab_keys[-1]    ,   '''j'''					)
       self.assertEqual(len(_UpperCAmelCase					)    ,   1_012					)


   def 					snake_case				( self					):
       self.assertEqual(self.get_tokenizer().vocab_size    ,   1_012					)


   def 					snake_case				( self					):
       A       :       Dict							=						XLMProphetNetTokenizer(_UpperCAmelCase    ,   keep_accents=_UpperCAmelCase					)

       A       :       int							=						tokenizer.tokenize('''This is a test'''					)
       self.assertListEqual(_UpperCAmelCase    ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']					)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)    ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,   )

       A       :       Optional[Any]							=						tokenizer.tokenize('''I was born in 92000, and this is falsé.'''					)
       self.assertListEqual(
           _UpperCAmelCase    ,   [
               SPIECE_UNDERLINE + '''I''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''9''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''s''',
               '''é''',
               '''.''',
           ]    ,   )
       A       :       int							=						tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)
       self.assertListEqual(
           _UpperCAmelCase    ,   [
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
           ]    ,   )

       A       :       Union[str, Any]							=						tokenizer.convert_ids_to_tokens(_UpperCAmelCase					)
       self.assertListEqual(
           _UpperCAmelCase    ,   [
               SPIECE_UNDERLINE + '''I''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''[UNK]''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''s''',
               '''[UNK]''',
               '''.''',
           ]    ,   )


   @cached_property
   def 					snake_case				( self					):
       return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''					)


   @slow
   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						'''Hello World!'''
       A       :       Any							=						[35_389, 6_672, 49, 2]
       self.assertListEqual(_UpperCAmelCase    ,   self.big_tokenizer.encode(_UpperCAmelCase					)					)


   @slow
   def 					snake_case				( self					):
       # fmt: off
       A       :       List[Any]							=						{'''input_ids''': [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_UpperCAmelCase    ,   model_name='''microsoft/xprophetnet-large-wiki100-cased'''    ,   revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''    ,   )
