






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 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[str],      snake_case	:List[str],      snake_case	:str=13,      snake_case	:Dict=7,      snake_case	:Tuple=True,      snake_case	:Optional[Any]=True,      snake_case	:str=False,      snake_case	:Optional[int]=True,      snake_case	:Optional[Any]=99,      snake_case	:List[Any]=32,      snake_case	:Tuple=5,      snake_case	:Any=4,      snake_case	:Union[str, Any]=37,      snake_case	:Any="gelu",      snake_case	:List[Any]=0.1,      snake_case	:Union[str, Any]=0.1,      snake_case	:Any=512,      snake_case	:List[Any]=16,      snake_case	:Optional[Any]=2,      snake_case	:Any=0.0_2,      snake_case	:List[str]=3,      snake_case	:Optional[int]=4,      snake_case	:Optional[int]=None,      ):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

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

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

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

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

          _lowercase              =self.get_config()

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





       def 				UpperCamelCase__						(				self	:Any):



          """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=snake_case,      initializer_range=self.initializer_range,      )





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:Optional[int],      snake_case	:Dict,      snake_case	:Optional[int],      snake_case	:Optional[int],      snake_case	:str,      snake_case	:List[Any],      snake_case	:int):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:Dict,      snake_case	:Optional[int],      snake_case	:Dict,      snake_case	:Any,      snake_case	:List[Any],      snake_case	:int,      snake_case	:Optional[int],      snake_case	:List[Any],      snake_case	:str,      ):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:str,      snake_case	:int,      snake_case	:List[str],      snake_case	:Tuple,      snake_case	:Optional[int],      snake_case	:Tuple,      *snake_case	:Optional[Any]):



          """simple docstring"""

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

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

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

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

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

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

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

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

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





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:str,      snake_case	:Optional[int],      snake_case	:Dict,      snake_case	:int,      snake_case	:Optional[int],      *snake_case	:str):



          """simple docstring"""

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

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

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

          _lowercase       ,       _lowercase              =outputs.to_tuple()

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

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

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

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





       def 				UpperCamelCase__						(				self	:str,      snake_case	:Union[str, Any],      snake_case	:Union[str, Any],      snake_case	:Union[str, Any],      snake_case	:Union[str, Any],      snake_case	:Any,      *snake_case	:List[str],      snake_case	:int=False):



          """simple docstring"""

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

          _lowercase              =model(snake_case,      labels=snake_case)
          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	:Union[str, Any],      snake_case	:List[str],      *snake_case	:Optional[Any]):



          """simple docstring"""

          _lowercase              =BioGptModel(snake_case)
          _lowercase              =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_0_1)
                self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0),      0.0_1)





       def 				UpperCamelCase__						(				self	:Optional[Any],      snake_case	:int,      snake_case	:Any,      snake_case	:Optional[int],      snake_case	:Optional[int],      snake_case	:Optional[int],      *snake_case	:Tuple):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

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






@require_torch
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						_a  ,						_a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		List[Any]					=(
           (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
           if is_torch_available()
           else ()
       )
       __lowerCAmelCase    :		List[Any]					=(BioGptForCausalLM,) if is_torch_available() else ()
       __lowerCAmelCase    :		Union[str, Any]					=(
           {
               '''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 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          self.config_tester.run_common_tests()





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



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

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





       @slow
       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

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

          _lowercase              ='left'

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

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

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

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

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

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

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

          _lowercase              =[
              '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(snake_case,      snake_case)
          self.assertListEqual(snake_case,      [non_padded_sentence, padded_sentence])





       @slow
       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

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





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



          """simple docstring"""

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






@require_torch
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @slow
       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =BioGptForCausalLM.from_pretrained('microsoft/biogpt')
          _lowercase              =torch.tensor([[2, 4805, 9, 656, 21]])
          _lowercase              =model(snake_case)[0]

          _lowercase              =4_2384

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

          _lowercase              =torch.tensor(
              [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]])

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





       @slow
       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

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

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

          _lowercase              =(
              '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(snake_case,      snake_case)










from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=['''image_processor''', '''tokenizer''']
       __lowerCAmelCase    :		List[Any]					='''BlipImageProcessor'''
       __lowerCAmelCase    :		Union[str, Any]					=('''BertTokenizer''', '''BertTokenizerFast''')





       def __init__(				self	:Optional[int],      snake_case	:int,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =False
          super().__init__(snake_case,      snake_case)
          _lowercase              =self.image_processor





       def __call__(				self	:Dict,      snake_case	:ImageInput = None,      snake_case	:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,      snake_case	:bool = True,      snake_case	:Union[bool, str, PaddingStrategy] = False,      snake_case	:Union[bool, str, TruncationStrategy] = None,      snake_case	:Optional[int] = None,      snake_case	:int = 0,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = True,      snake_case	:Optional[Union[str, TensorType]] = None,      **snake_case	:str,      ):



          """simple docstring"""

          if images is None and text is None:
             raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
             _lowercase              =self.tokenizer
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
             return text_encoding

          # add pixel_values
          _lowercase              =self.image_processor(snake_case,      return_tensors=snake_case)

          if text is not None:
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
          else:
             _lowercase              =None

          if text_encoding is not None:
             encoding_image_processor.update(snake_case)

          return encoding_image_processor





       def 				UpperCamelCase__						(				self	:List[Any],      *snake_case	:int,      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.batch_decode(*snake_case,      **snake_case)





       def 				UpperCamelCase__						(				self	:Tuple,      *snake_case	:List[str],      **snake_case	:str):



          """simple docstring"""

          return self.tokenizer.decode(*snake_case,      **snake_case)





       @property
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =self.tokenizer.model_input_names
          _lowercase              =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


