





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 		UpperCamelCase__ :



     '''simple docstring'''

     def __init__(							self						:    Optional[Any]						,lowerCamelCase__						:    Union[str, Any]						,lowerCamelCase__						:    List[Any]=13						,lowerCamelCase__						:    Dict=7						,lowerCamelCase__						:    Union[str, Any]=True						,lowerCamelCase__						:    Dict=True						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    List[str]=True						,lowerCamelCase__						:    List[Any]=99						,lowerCamelCase__						:    List[str]=32						,lowerCamelCase__						:    Union[str, Any]=5						,lowerCamelCase__						:    Union[str, Any]=4						,lowerCamelCase__						:    List[str]=37						,lowerCamelCase__						:    str="gelu"						,lowerCamelCase__						:    Optional[Any]=0.1						,lowerCamelCase__						:    Dict=0.1						,lowerCamelCase__						:    Dict=512						,lowerCamelCase__						:    List[Any]=16						,lowerCamelCase__						:    Dict=2						,lowerCamelCase__						:    List[Any]=0.02						,lowerCamelCase__						:    Optional[int]=3						,lowerCamelCase__						:    Dict=4						,lowerCamelCase__						:    str=None						,)     ->		List[str]:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Optional[int]:




           '''simple docstring'''


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

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

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

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

           SCREAMING_SNAKE_CASE		=						self.get_config()

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		Tuple:




           '''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 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]						,lowerCamelCase__						:    Optional[int]						,lowerCamelCase__						:    str						,lowerCamelCase__						:    str						,lowerCamelCase__						:    Dict						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    Any       )     ->		Union[str, Any]:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]						,lowerCamelCase__						:    Union[str, Any]						,lowerCamelCase__						:    str						,lowerCamelCase__						:    List[str]						,lowerCamelCase__						:    Dict						,lowerCamelCase__						:    str						,lowerCamelCase__						:    List[str]						,lowerCamelCase__						:    List[str]						,lowerCamelCase__						:    Optional[int]						,lowerCamelCase__						:    Optional[int]						,)     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BioGptForCausalLM(config=lowerCamelCase__       )
           model.to(lowerCamelCase__       )
           model.eval()
           SCREAMING_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 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    str						,lowerCamelCase__						:    Union[str, Any]						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    int						,lowerCamelCase__						:    List[str]						,*lowerCamelCase__						:    Optional[int]       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BioGptModel(config=lowerCamelCase__       )
           model.to(lowerCamelCase__       )
           model.eval()

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

           # first forward pass
           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,attention_mask=lowerCamelCase__       ).to_tuple()

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

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

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

           # get two different outputs
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,attention_mask=lowerCamelCase__       )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,past_key_values=lowerCamelCase__						,attention_mask=lowerCamelCase__       )["""last_hidden_state"""]

           # select random slice
           SCREAMING_SNAKE_CASE		=						ids_tensor((1,)						,output_from_past.shape[-1]       ).item()
           SCREAMING_SNAKE_CASE		=						output_from_no_past[:, -1, random_slice_idx].detach()
           SCREAMING_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 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    int						,lowerCamelCase__						:    Dict						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    Dict						,*lowerCamelCase__						:    str       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BioGptModel(config=lowerCamelCase__       ).to(lowerCamelCase__       ).eval()

           SCREAMING_SNAKE_CASE		=						torch.ones(input_ids.shape						,dtype=torch.long						,device=lowerCamelCase__       )

           # first forward pass
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,attention_mask=lowerCamelCase__						,use_cache=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						outputs.to_tuple()

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

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

           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,attention_mask=lowerCamelCase__       )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__						,attention_mask=lowerCamelCase__						,past_key_values=lowerCamelCase__       )[
               """last_hidden_state"""
           ]

           # select random slice
           SCREAMING_SNAKE_CASE		=						ids_tensor((1,)						,output_from_past.shape[-1]       ).item()
           SCREAMING_SNAKE_CASE		=						output_from_no_past[:, -3:, random_slice_idx].detach()
           SCREAMING_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 				SCREAMING_SNAKE_CASE__  (							self						:    str						,lowerCamelCase__						:    int						,lowerCamelCase__						:    int						,lowerCamelCase__						:    str						,lowerCamelCase__						:    Union[str, Any]						,lowerCamelCase__						:    List[Any]						,*lowerCamelCase__						:    str						,lowerCamelCase__						:    Tuple=False       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BioGptForCausalLM(lowerCamelCase__       )
           model.to(lowerCamelCase__       )
           if gradient_checkpointing:
                 model.gradient_checkpointing_enable()

           SCREAMING_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 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    List[Any]						,*lowerCamelCase__						:    List[str]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BioGptModel(lowerCamelCase__       )
           SCREAMING_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       )						,0.001       )
                       self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]       ) - 0.0       )						,0.01       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    str						,lowerCamelCase__						:    List[str]						,lowerCamelCase__						:    Optional[int]						,lowerCamelCase__						:    Union[str, Any]						,lowerCamelCase__						:    Tuple						,*lowerCamelCase__						:    Optional[int]       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.num_labels
           SCREAMING_SNAKE_CASE		=						BioGptForTokenClassification(lowerCamelCase__       )
           model.to(lowerCamelCase__       )
           model.eval()
           SCREAMING_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 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		List[str]:




           '''simple docstring'''


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

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



     '''simple docstring'''

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BioGptModelTester(self       )
           SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__						,hidden_size=37       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Dict:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Tuple:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_forward_and_backwards(*lowerCamelCase__						,gradient_checkpointing=lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCamelCase__       )






     @slow
     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""       )
           model.to(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""       )

           SCREAMING_SNAKE_CASE		=						"""left"""

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

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

           SCREAMING_SNAKE_CASE		=						tokenizer(lowerCamelCase__						,return_tensors="""pt"""						,padding=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						inputs["""input_ids"""].to(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						model.generate(
               input_ids=lowerCamelCase__						,attention_mask=inputs["""attention_mask"""].to(lowerCamelCase__       )						,)

           SCREAMING_SNAKE_CASE		=						tokenizer(sentences[0]						,return_tensors="""pt"""       ).input_ids.to(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						model.generate(input_ids=lowerCamelCase__       )

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

           SCREAMING_SNAKE_CASE		=						tokenizer.batch_decode(lowerCamelCase__						,skip_special_tokens=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						tokenizer.decode(output_non_padded[0]						,skip_special_tokens=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						tokenizer.decode(output_padded[0]						,skip_special_tokens=lowerCamelCase__       )

           SCREAMING_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 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE		=						3
           SCREAMING_SNAKE_CASE		=						input_dict["""input_ids"""]
           SCREAMING_SNAKE_CASE		=						input_ids.ne(1       ).to(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						ids_tensor([self.model_tester.batch_size]						,self.model_tester.type_sequence_label_size       )
           SCREAMING_SNAKE_CASE		=						BioGptForSequenceClassification(lowerCamelCase__       )
           model.to(lowerCamelCase__       )
           model.eval()
           SCREAMING_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 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE		=						3
           SCREAMING_SNAKE_CASE		=						"""multi_label_classification"""
           SCREAMING_SNAKE_CASE		=						input_dict["""input_ids"""]
           SCREAMING_SNAKE_CASE		=						input_ids.ne(1       ).to(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						ids_tensor(
               [self.model_tester.batch_size, config.num_labels]						,self.model_tester.type_sequence_label_size       ).to(torch.float       )
           SCREAMING_SNAKE_CASE		=						BioGptForSequenceClassification(lowerCamelCase__       )
           model.to(lowerCamelCase__       )
           model.eval()
           SCREAMING_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 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     @slow
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""       )
           SCREAMING_SNAKE_CASE		=						torch.tensor([[2, 4805, 9, 656, 21]]       )
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__       )[0]

           SCREAMING_SNAKE_CASE		=						42384

           SCREAMING_SNAKE_CASE		=						torch.Size((1, 5, vocab_size)       )
           self.assertEqual(output.shape						,lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.tensor(
               [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]       )

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






     @slow
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""       )
           SCREAMING_SNAKE_CASE		=						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""       )
           model.to(lowerCamelCase__       )

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

           SCREAMING_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__       )









import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						UNetaDModel(
               block_out_channels=(32, 64)						,layers_per_block=2						,sample_size=32						,in_channels=3						,out_channels=3						,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,)
           return model






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.dummy_uncond_unet
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""						,return_dict=lowerCamelCase__       )[0]

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE		=						image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2

@slow
@require_torch
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""google/ncsnpp-celebahq-256"""
           SCREAMING_SNAKE_CASE		=						UNetaDModel.from_pretrained(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=20						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           assert image.shape == (1, 256, 256, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


