"""simple docstring"""
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


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

if is_sentencepiece_available():
					import sentencepiece as sp


SCREAMING_SNAKE_CASE__   =					5
SCREAMING_SNAKE_CASE__   =					10



@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[int]     					=  SpeechaTextTokenizer
				_lowerCAmelCase       :      Dict     					=  False
				_lowerCAmelCase       :      Dict     					=  True

				def 						snake_case	(			self      ):






										"""simple docstring"""

										super().setUp()

										snake_case        =     sp.SentencePieceProcessor()
										spm_model.Load(lowerCAmelCase      )
										snake_case        =     ['<s>', '<pad>', '</s>', '<unk>']

										vocab += [spm_model.IdToPiece(id_      ) for id_ in range(len(lowerCAmelCase      )      )]
										snake_case        =     dict(zip(lowerCAmelCase						,      range(len(lowerCAmelCase      )      )      )      )

										snake_case        =     Path(self.tmpdirname      )
										save_json(lowerCAmelCase						,      save_dir / VOCAB_FILES_NAMES['vocab_file']      )
										if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																copyfile(lowerCAmelCase						,      save_dir / VOCAB_FILES_NAMES['spm_file']      )

										snake_case        =     SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
										tokenizer.save_pretrained(self.tmpdirname      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '<pad>'
										snake_case        =     1

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     list(self.get_tokenizer().get_vocab().keys()      )

										self.assertEqual(vocab_keys[0]						,      '<s>'      )
										self.assertEqual(vocab_keys[1]						,      '<pad>'      )
										self.assertEqual(vocab_keys[-1]						,      'j'      )
										self.assertEqual(len(lowerCAmelCase      )						,      10_01      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.assertEqual(self.get_tokenizer().vocab_size						,      10_01      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )

										snake_case        =     tokenizer.tokenize('This is a test'      )
										self.assertListEqual(lowerCAmelCase						,      ['▁This', '▁is', '▁a', '▁t', 'est']      )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowerCAmelCase      )						,      [2_89, 50, 14, 1_74, 3_86]						,      )

										snake_case        =     tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
										self.assertListEqual(
										    lowerCAmelCase						,      [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', '.']						,      )
										snake_case        =     tokenizer.convert_tokens_to_ids(lowerCAmelCase      )
										self.assertListEqual(lowerCAmelCase						,      [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8]      )

										snake_case        =     tokenizer.convert_ids_to_tokens(lowerCAmelCase      )
										self.assertListEqual(
										    lowerCAmelCase						,      [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>', '.']						,      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     {'input_ids': [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 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], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '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, 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, 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, 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, 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=lowerCAmelCase						,      model_name='facebook/s2t-small-mustc-en-de-st'						,      revision='a14f04cf0776c02f62a8cb800cf7909e15ea23ad'						,      )





@require_sentencepiece
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  """valhalla/s2t_mustc_multilinguial_medium"""

				_lowerCAmelCase       :      List[str]     					=  """C'est trop cool"""
				_lowerCAmelCase       :      List[str]     					=  """Esto es genial"""

				@classmethod
				def 						snake_case	(			cls      ):






										"""simple docstring"""

										snake_case        =     SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name      )
										return cls

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.assertEqual(self.tokenizer.lang_code_to_id['pt']						,      4      )
										self.assertEqual(self.tokenizer.lang_code_to_id['ru']						,      6      )
										self.assertEqual(self.tokenizer.lang_code_to_id['it']						,      9      )
										self.assertEqual(self.tokenizer.lang_code_to_id['de']						,      11      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.assertEqual(self.tokenizer.vocab_size						,      1_00_00      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.assertIn(lowerCAmelCase						,      self.tokenizer.all_special_ids      )
										snake_case        =     [ES_CODE, 4, 16_01, 47, 76_47, 2]
										snake_case        =     self.tokenizer.decode(lowerCAmelCase						,      skip_special_tokens=lowerCAmelCase      )
										snake_case        =     self.tokenizer.decode(generated_ids[1:]						,      skip_special_tokens=lowerCAmelCase      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )
										self.assertNotIn(self.tokenizer.eos_token						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     'fr'
										snake_case        =     self.tokenizer(self.french_text      ).input_ids
										self.assertEqual(encoded[0]						,      lowerCAmelCase      )
										self.assertEqual(encoded[-1]						,      self.tokenizer.eos_token_id      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     'fr'
										self.assertListEqual(self.tokenizer.prefix_tokens						,      [FR_CODE]      )

										snake_case        =     'es'
										self.assertListEqual(self.tokenizer.prefix_tokens						,      [ES_CODE]      )



"""simple docstring"""
import math
import unittest

from transformers import BioGptConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    BioGptForCausalLM,
					    BioGptForSequenceClassification,
					    BioGptForTokenClassification,
					    BioGptModel,
					    BioGptTokenizer,
					)
					from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST



class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=13						,      lowerCAmelCase=7						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=False						,      lowerCAmelCase=True						,      lowerCAmelCase=99						,      lowerCAmelCase=32						,      lowerCAmelCase=5						,      lowerCAmelCase=4						,      lowerCAmelCase=37						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=16						,      lowerCAmelCase=2						,      lowerCAmelCase=0.02						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=None						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     seq_length
										snake_case        =     is_training
										snake_case        =     use_input_mask
										snake_case        =     use_token_type_ids
										snake_case        =     use_labels
										snake_case        =     vocab_size
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     intermediate_size
										snake_case        =     hidden_act
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     max_position_embeddings
										snake_case        =     type_vocab_size
										snake_case        =     type_sequence_label_size
										snake_case        =     initializer_range
										snake_case        =     num_labels
										snake_case        =     num_choices
										snake_case        =     scope

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size      )

										snake_case        =     None
										if self.use_input_mask:
																snake_case        =     random_attention_mask([self.batch_size, self.seq_length]      )

										snake_case        =     None
										if self.use_token_type_ids:
																snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.type_vocab_size      )

										snake_case        =     None
										snake_case        =     None
										snake_case        =     None
										if self.use_labels:
																snake_case        =     ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )
																snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.num_labels      )
																snake_case        =     ids_tensor([self.batch_size]						,      self.num_choices      )

										snake_case        =     self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

				def 						snake_case	(			self      ):






										"""simple docstring"""

										return BioGptConfig(
										    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						,      type_vocab_size=self.type_vocab_size						,      is_decoder=lowerCAmelCase						,      initializer_range=self.initializer_range						,      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )
										snake_case        =     model(lowerCAmelCase      )
										self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()

										# create attention mask
										snake_case        =     torch.ones(input_ids.shape						,      dtype=torch.long						,      device=lowerCAmelCase      )
										snake_case        =     self.seq_length // 2
										snake_case        =     0

										# first forward pass
										snake_case   ,snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      ).to_tuple()

										# create hypothetical next token and extent to next_input_ids
										snake_case        =     ids_tensor((self.batch_size, 1)						,      config.vocab_size      )

										# change a random masked slice from input_ids
										snake_case        =     ids_tensor((1,)						,      lowerCAmelCase      ).item() + 1
										snake_case        =     ids_tensor((self.batch_size, 1)						,      config.vocab_size      ).squeeze(-1      )
										snake_case        =     random_other_next_tokens

										# append to next input_ids and attn_mask
										snake_case        =     torch.cat([input_ids, next_tokens]						,      dim=-1      )
										snake_case        =     torch.cat(
										    [attn_mask, torch.ones((attn_mask.shape[0], 1)						,      dtype=torch.long						,      device=lowerCAmelCase      )]						,      dim=1						,      )

										# get two different outputs
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )['last_hidden_state']
										snake_case        =     model(lowerCAmelCase						,      past_key_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase      )['last_hidden_state']

										# select random slice
										snake_case        =     ids_tensor((1,)						,      output_from_past.shape[-1]      ).item()
										snake_case        =     output_from_no_past[:, -1, random_slice_idx].detach()
										snake_case        =     output_from_past[:, 0, random_slice_idx].detach()

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(config=lowerCAmelCase      ).to(lowerCAmelCase      ).eval()

										snake_case        =     torch.ones(input_ids.shape						,      dtype=torch.long						,      device=lowerCAmelCase      )

										# first forward pass
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      use_cache=lowerCAmelCase      )

										snake_case   ,snake_case        =     outputs.to_tuple()

										# create hypothetical multiple next token and extent to next_input_ids
										snake_case        =     ids_tensor((self.batch_size, 3)						,      config.vocab_size      )
										snake_case        =     ids_tensor((self.batch_size, 3)						,      2      )

										# append to next input_ids and
										snake_case        =     torch.cat([input_ids, next_tokens]						,      dim=-1      )
										snake_case        =     torch.cat([attention_mask, next_attn_mask]						,      dim=-1      )

										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )['last_hidden_state']
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      past_key_values=lowerCAmelCase      )[
										    'last_hidden_state'
										]

										# select random slice
										snake_case        =     ids_tensor((1,)						,      output_from_past.shape[-1]      ).item()
										snake_case        =     output_from_no_past[:, -3:, random_slice_idx].detach()
										snake_case        =     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(lowerCAmelCase						,      lowerCAmelCase						,      atol=1E-3      )      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase						,      lowerCAmelCase=False      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										if gradient_checkpointing:
																model.gradient_checkpointing_enable()

										snake_case        =     model(lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.parent.assertEqual(result.loss.shape						,      ()      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)      )
										result.loss.backward()

				def 						snake_case	(			self						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(lowerCAmelCase      )
										snake_case        =     model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers      )
										for key in model.state_dict().keys():
																if "c_proj" in key and "weight" in key:
																						self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]      ) - model_std      )						,      0.0_01      )
																						self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]      ) - 0.0      )						,      0.01      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     BioGptForTokenClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.num_labels)      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     config_and_inputs
										snake_case        =     {'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict





@require_torch
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      List[Any]     					=  (
				    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
				    if is_torch_available()
				    else ()
				)
				_lowerCAmelCase       :      str     					=  (BioGptForCausalLM,) if is_torch_available() else ()
				_lowerCAmelCase       :      str     					=  (
				    {
				        """feature-extraction""": BioGptModel,
				        """text-classification""": BioGptForSequenceClassification,
				        """text-generation""": BioGptForCausalLM,
				        """token-classification""": BioGptForTokenClassification,
				        """zero-shot""": BioGptForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_lowerCAmelCase       :      List[str]     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptModelTester(self      )
										snake_case        =     ConfigTester(self						,      config_class=lowerCAmelCase						,      hidden_size=37      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																snake_case        =     type
																self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase						,      gradient_checkpointing=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
										model.to(lowerCAmelCase      )
										snake_case        =     BioGptTokenizer.from_pretrained('microsoft/biogpt'      )

										snake_case        =     'left'

										# Define PAD Token = EOS Token = 50256
										snake_case        =     tokenizer.eos_token
										snake_case        =     model.config.eos_token_id

										# use different length sentences to test batching
										snake_case        =     [
										    'Hello, my dog is a little',
										    'Today, I',
										]

										snake_case        =     tokenizer(lowerCAmelCase						,      return_tensors='pt'						,      padding=lowerCAmelCase      )
										snake_case        =     inputs['input_ids'].to(lowerCAmelCase      )

										snake_case        =     model.generate(
										    input_ids=lowerCAmelCase						,      attention_mask=inputs['attention_mask'].to(lowerCAmelCase      )						,      )

										snake_case        =     tokenizer(sentences[0]						,      return_tensors='pt'      ).input_ids.to(lowerCAmelCase      )
										snake_case        =     model.generate(input_ids=lowerCAmelCase      )

										snake_case        =     inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
										snake_case        =     tokenizer(sentences[1]						,      return_tensors='pt'      ).input_ids.to(lowerCAmelCase      )
										snake_case        =     model.generate(input_ids=lowerCAmelCase						,      max_length=model.config.max_length - num_paddings      )

										snake_case        =     tokenizer.batch_decode(lowerCAmelCase						,      skip_special_tokens=lowerCAmelCase      )
										snake_case        =     tokenizer.decode(output_non_padded[0]						,      skip_special_tokens=lowerCAmelCase      )
										snake_case        =     tokenizer.decode(output_padded[0]						,      skip_special_tokens=lowerCAmelCase      )

										snake_case        =     [
										    'Hello, my dog is a little bit bigger than a little bit.',
										    'Today, I have a good idea of how to use the information',
										]
										self.assertListEqual(lowerCAmelCase						,      lowerCAmelCase      )
										self.assertListEqual(lowerCAmelCase						,      [non_padded_sentence, padded_sentence]      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case        =     BioGptModel.from_pretrained(lowerCAmelCase      )
																self.assertIsNotNone(lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
										snake_case        =     3
										snake_case        =     input_dict['input_ids']
										snake_case        =     input_ids.ne(1      ).to(lowerCAmelCase      )
										snake_case        =     ids_tensor([self.model_tester.batch_size]						,      self.model_tester.type_sequence_label_size      )
										snake_case        =     BioGptForSequenceClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.assertEqual(result.logits.shape						,      (self.model_tester.batch_size, self.model_tester.num_labels)      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
										snake_case        =     3
										snake_case        =     'multi_label_classification'
										snake_case        =     input_dict['input_ids']
										snake_case        =     input_ids.ne(1      ).to(lowerCAmelCase      )
										snake_case        =     ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]						,      self.model_tester.type_sequence_label_size      ).to(torch.float      )
										snake_case        =     BioGptForSequenceClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.assertEqual(result.logits.shape						,      (self.model_tester.batch_size, self.model_tester.num_labels)      )





@require_torch
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
										snake_case        =     torch.tensor([[2, 48_05, 9, 6_56, 21]]      )
										snake_case        =     model(lowerCAmelCase      )[0]

										snake_case        =     4_23_84

										snake_case        =     torch.Size((1, 5, vocab_size)      )
										self.assertEqual(output.shape						,      lowerCAmelCase      )

										snake_case        =     torch.tensor(
										    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]      )

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

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptTokenizer.from_pretrained('microsoft/biogpt'      )
										snake_case        =     BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
										model.to(lowerCAmelCase      )

										torch.manual_seed(0      )
										snake_case        =     tokenizer('COVID-19 is'						,      return_tensors='pt'      ).to(lowerCAmelCase      )
										snake_case        =     model.generate(
										    **lowerCAmelCase						,      min_length=1_00						,      max_length=10_24						,      num_beams=5						,      early_stopping=lowerCAmelCase						,      )
										snake_case        =     tokenizer.decode(output_ids[0]						,      skip_special_tokens=lowerCAmelCase      )

										snake_case        =     (
										    'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
										    ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
										    ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
										    ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
										    ' more than 800,000 deaths.'
										)
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )


