


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       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=13       ,  lowercase_=7       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=False       ,  lowercase_=True       ,  lowercase_=99       ,  lowercase_=32       ,  lowercase_=5       ,  lowercase_=4       ,  lowercase_=37       ,  lowercase_="gelu"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=512       ,  lowercase_=16       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=3       ,  lowercase_=4       ,  lowercase_=None       ,  )  -> List[Any]:
											a__      =parent
											a__      =batch_size
											a__      =seq_length
											a__      =is_training
											a__      =use_input_mask
											a__      =use_token_type_ids
											a__      =use_labels
											a__      =vocab_size
											a__      =hidden_size
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =max_position_embeddings
											a__      =type_vocab_size
											a__      =type_sequence_label_size
											a__      =initializer_range
											a__      =num_labels
											a__      =num_choices
											a__      =scope





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size)

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

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

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

											a__      =self.get_config()

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





				def 						__UpperCamelCase    (				self)  -> Any:
											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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> Union[str, Any]:
											a__      =BioGptModel(config=_lowerCamelCase)
											model.to(_lowerCamelCase)
											model.eval()
											a__      =model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase)
											a__      =model(_lowerCamelCase)
											self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size))





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  )  -> Dict:
											a__      =BioGptForCausalLM(config=_lowerCamelCase)
											model.to(_lowerCamelCase)
											model.eval()
											a__      =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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  *lowercase_)  -> Optional[int]:
											a__      =BioGptModel(config=_lowerCamelCase)
											model.to(_lowerCamelCase)
											model.eval()

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

											# first forward pass
											a__      =model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase).to_tuple()

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

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

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

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

											# select random slice
											a__      =ids_tensor((1,)       ,  output_from_past.shape[-1]).item()
											a__      =output_from_no_past[:, -1, random_slice_idx].detach()
											a__      =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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  *lowercase_)  -> List[Any]:
											a__      =BioGptModel(config=_lowerCamelCase).to(_lowerCamelCase).eval()

											a__      =torch.ones(input_ids.shape       ,  dtype=torch.long       ,  device=_lowerCamelCase)

											# first forward pass
											a__      =model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  use_cache=_lowerCamelCase)

											a__      =outputs.to_tuple()

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

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

											a__      =model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase)['''last_hidden_state''']
											a__      =model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  past_key_values=_lowerCamelCase)[
											    '''last_hidden_state'''
											]

											# select random slice
											a__      =ids_tensor((1,)       ,  output_from_past.shape[-1]).item()
											a__      =output_from_no_past[:, -3:, random_slice_idx].detach()
											a__      =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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  *lowercase_       ,  lowercase_=False)  -> Tuple:
											a__      =BioGptForCausalLM(_lowerCamelCase)
											model.to(_lowerCamelCase)
											if gradient_checkpointing:
																		model.gradient_checkpointing_enable()

											a__      =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 						__UpperCamelCase    (				self       ,  lowercase_       ,  *lowercase_)  -> int:
											a__      =BioGptModel(_lowerCamelCase)
											a__      =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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  *lowercase_)  -> Tuple:
											a__      =self.num_labels
											a__      =BioGptForTokenClassification(_lowerCamelCase)
											model.to(_lowerCamelCase)
											model.eval()
											a__      =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 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =self.prepare_config_and_inputs()
											(
											    a__							
											)      =config_and_inputs
											a__      ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
											return config, inputs_dict







@require_torch
class       lowercase_					(_snake_case      ,					_snake_case      ,					_snake_case      ,					unittest.TestCase   ):
				snake_case	    =(
				    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
				    if is_torch_available()
				    else ()
				)
				snake_case	    =(BioGptForCausalLM,) if is_torch_available() else ()
				snake_case	    =(
				    {
				        'feature-extraction': BioGptModel,
				        'text-classification': BioGptForSequenceClassification,
				        'text-generation': BioGptForCausalLM,
				        'token-classification': BioGptForTokenClassification,
				        'zero-shot': BioGptForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				snake_case	    =False





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =BioGptModelTester(self)
											a__      =ConfigTester(self       ,  config_class=_lowerCamelCase       ,  hidden_size=37)





				def 						__UpperCamelCase    (				self)  -> Any:
											self.config_tester.run_common_tests()





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_lowerCamelCase)





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		a__      =type
																		self.model_tester.create_and_check_model(*_lowerCamelCase)





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_lowerCamelCase)





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_forward_and_backwards(*_lowerCamelCase       ,  gradient_checkpointing=_lowerCamelCase)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_lowerCamelCase)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_biogpt_weight_initialization(*_lowerCamelCase)





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_biogpt_for_token_classification(*_lowerCamelCase)





				@slow
				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =BioGptForCausalLM.from_pretrained('microsoft/biogpt')
											model.to(_lowerCamelCase)
											a__      =BioGptTokenizer.from_pretrained('microsoft/biogpt')

											a__      ='''left'''

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

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

											a__      =tokenizer(_lowerCamelCase       ,  return_tensors='pt'       ,  padding=_lowerCamelCase)
											a__      =inputs['''input_ids'''].to(_lowerCamelCase)

											a__      =model.generate(
											    input_ids=_lowerCamelCase       ,  attention_mask=inputs['attention_mask'].to(_lowerCamelCase)       ,  )

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

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

											a__      =tokenizer.batch_decode(_lowerCamelCase       ,  skip_special_tokens=_lowerCamelCase)
											a__      =tokenizer.decode(output_non_padded[0]       ,  skip_special_tokens=_lowerCamelCase)
											a__      =tokenizer.decode(output_padded[0]       ,  skip_special_tokens=_lowerCamelCase)

											a__      =[
											    '''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 						__UpperCamelCase    (				self)  -> List[str]:
											for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		a__      =BioGptModel.from_pretrained(_lowerCamelCase)
																		self.assertIsNotNone(_lowerCamelCase)





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =self.model_tester.prepare_config_and_inputs_for_common()
											a__      =3
											a__      =input_dict['''input_ids''']
											a__      =input_ids.ne(1).to(_lowerCamelCase)
											a__      =ids_tensor([self.model_tester.batch_size]       ,  self.model_tester.type_sequence_label_size)
											a__      =BioGptForSequenceClassification(_lowerCamelCase)
											model.to(_lowerCamelCase)
											model.eval()
											a__      =model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  labels=_lowerCamelCase)
											self.assertEqual(result.logits.shape       ,  (self.model_tester.batch_size, self.model_tester.num_labels))





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.model_tester.prepare_config_and_inputs_for_common()
											a__      =3
											a__      ='''multi_label_classification'''
											a__      =input_dict['''input_ids''']
											a__      =input_ids.ne(1).to(_lowerCamelCase)
											a__      =ids_tensor(
											    [self.model_tester.batch_size, config.num_labels]       ,  self.model_tester.type_sequence_label_size).to(torch.float)
											a__      =BioGptForSequenceClassification(_lowerCamelCase)
											model.to(_lowerCamelCase)
											model.eval()
											a__      =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       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> int:
											a__      =BioGptForCausalLM.from_pretrained('microsoft/biogpt')
											a__      =torch.tensor([[2, 4805, 9, 656, 21]])
											a__      =model(_lowerCamelCase)[0]

											a__      =42384

											a__      =torch.Size((1, 5, vocab_size))
											self.assertEqual(output.shape       ,  _lowerCamelCase)

											a__      =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 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =BioGptTokenizer.from_pretrained('microsoft/biogpt')
											a__      =BioGptForCausalLM.from_pretrained('microsoft/biogpt')
											model.to(_lowerCamelCase)

											torch.manual_seed(0)
											a__      =tokenizer('COVID-19 is'       ,  return_tensors='pt').to(_lowerCamelCase)
											a__      =model.generate(
											    **_lowerCamelCase       ,  min_length=100       ,  max_length=1024       ,  num_beams=5       ,  early_stopping=_lowerCamelCase       ,  )
											a__      =tokenizer.decode(output_ids[0]       ,  skip_special_tokens=_lowerCamelCase)

											a__      =(
											    '''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)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:       List[Any]										=	logging.get_logger(__name__)

snake_case							:       Dict										=	{
    '''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'visual_bert'




 def __init__(						self   ,    _lowerCamelCase=3_0522   ,    _lowerCamelCase=768   ,    _lowerCamelCase=512   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-12   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )

       a		:Any									=			vocab_size
       a		:str									=			max_position_embeddings
       a		:str									=			hidden_size
       a		:List[Any]									=			visual_embedding_dim
       a		:str									=			num_hidden_layers
       a		:Optional[int]									=			num_attention_heads
       a		:int									=			intermediate_size
       a		:int									=			hidden_act
       a		:Union[str, Any]									=			hidden_dropout_prob
       a		:List[Any]									=			attention_probs_dropout_prob
       a		:int									=			initializer_range
       a		:List[Any]									=			type_vocab_size
       a		:str									=			layer_norm_eps
       a		:Optional[int]									=			bypass_transformer
       a		:str									=			special_visual_initialize





