


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






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

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

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

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

         __UpperCamelCase    							=self.get_config()

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






    def _a					(			self    )  ->       Optional[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=A_					,   initializer_range=self.initializer_range					,   )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       List[str]:
         __UpperCamelCase    							=BioGptModel(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_    )
         __UpperCamelCase    							=model(A_    )
         self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=BioGptForCausalLM(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   token_type_ids=A_					,   labels=A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   *A_    )  ->       str:
         __UpperCamelCase    							=BioGptModel(config=A_    )
         model.to(A_    )
         model.eval()

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

         # first forward pass
         __UpperCamelCase							,      __UpperCamelCase    							=model(A_					,   attention_mask=A_    ).to_tuple()

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

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

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

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

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

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






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   *A_    )  ->       Tuple:
         __UpperCamelCase    							=BioGptModel(config=A_    ).to(A_    ).eval()

         __UpperCamelCase    							=torch.ones(input_ids.shape					,   dtype=torch.long					,   device=A_    )

         # first forward pass
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   use_cache=A_    )

         __UpperCamelCase							,      __UpperCamelCase    							=outputs.to_tuple()

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

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

         __UpperCamelCase    							=model(A_					,   attention_mask=A_    )['last_hidden_state']
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   past_key_values=A_    )[
             'last_hidden_state'
         ]

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






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   *A_					,   A_=False    )  ->       List[str]:
         __UpperCamelCase    							=BioGptForCausalLM(A_    )
         model.to(A_    )
         if gradient_checkpointing:
              model.gradient_checkpointing_enable()

         __UpperCamelCase    							=model(A_					,   labels=A_    )
         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 _a					(			self					,   A_					,   *A_    )  ->       Tuple:
         __UpperCamelCase    							=BioGptModel(A_    )
         __UpperCamelCase    							=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 _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   *A_    )  ->       Tuple:
         __UpperCamelCase    							=self.num_labels
         __UpperCamelCase    							=BioGptForTokenClassification(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   token_type_ids=A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.num_labels)    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=self.prepare_config_and_inputs()
         (
             (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      
         )    							=config_and_inputs
         __UpperCamelCase    							={'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_torch
class     UpperCAmelCase__       (					A_   ,  A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]      =  (
        (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__						:		int      =  (BioGptForCausalLM,) if is_torch_available() else ()
    UpperCAmelCase__						:		Any      =  (
        {
            "feature-extraction": BioGptModel,
            "text-classification": BioGptForSequenceClassification,
            "text-generation": BioGptForCausalLM,
            "token-classification": BioGptForTokenClassification,
            "zero-shot": BioGptForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    UpperCAmelCase__						:		Tuple      =  False






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=BioGptModelTester(self    )
         __UpperCamelCase    							=ConfigTester(self					,   config_class=A_					,   hidden_size=37    )






    def _a					(			self    )  ->       Optional[int]:
         self.config_tester.run_common_tests()






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              __UpperCamelCase    							=type
              self.model_tester.create_and_check_model(*A_    )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_forward_and_backwards(*A_					,   gradient_checkpointing=A_    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A_    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_weight_initialization(*A_    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_for_token_classification(*A_    )






    @slow
    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=BioGptForCausalLM.from_pretrained('microsoft/biogpt'    )
         model.to(A_    )
         __UpperCamelCase    							=BioGptTokenizer.from_pretrained('microsoft/biogpt'    )

         __UpperCamelCase    							='left'

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

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

         __UpperCamelCase    							=tokenizer(A_					,   return_tensors='pt'					,   padding=A_    )
         __UpperCamelCase    							=inputs['input_ids'].to(A_    )

         __UpperCamelCase    							=model.generate(
             input_ids=A_					,   attention_mask=inputs['attention_mask'].to(A_    )					,   )

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

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

         __UpperCamelCase    							=tokenizer.batch_decode(A_					,   skip_special_tokens=A_    )
         __UpperCamelCase    							=tokenizer.decode(output_non_padded[0]					,   skip_special_tokens=A_    )
         __UpperCamelCase    							=tokenizer.decode(output_padded[0]					,   skip_special_tokens=A_    )

         __UpperCamelCase    							=[
             '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(A_					,   A_    )
         self.assertListEqual(A_					,   [non_padded_sentence, padded_sentence]    )






    @slow
    def _a					(			self    )  ->       List[Any]:
         for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase    							=BioGptModel.from_pretrained(A_    )
              self.assertIsNotNone(A_    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=3
         __UpperCamelCase    							=input_dict['input_ids']
         __UpperCamelCase    							=input_ids.ne(1    ).to(A_    )
         __UpperCamelCase    							=ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size    )
         __UpperCamelCase    							=BioGptForSequenceClassification(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   labels=A_    )
         self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=3
         __UpperCamelCase    							='multi_label_classification'
         __UpperCamelCase    							=input_dict['input_ids']
         __UpperCamelCase    							=input_ids.ne(1    ).to(A_    )
         __UpperCamelCase    							=ids_tensor(
             [self.model_tester.batch_size, config.num_labels]					,   self.model_tester.type_sequence_label_size    ).to(torch.float    )
         __UpperCamelCase    							=BioGptForSequenceClassification(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   labels=A_    )
         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 _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=BioGptForCausalLM.from_pretrained('microsoft/biogpt'    )
         __UpperCamelCase    							=torch.tensor([[2, 4805, 9, 656, 21]]    )
         __UpperCamelCase    							=model(A_    )[0]

         __UpperCamelCase    							=42384

         __UpperCamelCase    							=torch.Size((1, 5, vocab_size)    )
         self.assertEqual(output.shape					,   A_    )

         __UpperCamelCase    							=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]					,   A_					,   atol=1E-4    )    )






    @slow
    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=BioGptTokenizer.from_pretrained('microsoft/biogpt'    )
         __UpperCamelCase    							=BioGptForCausalLM.from_pretrained('microsoft/biogpt'    )
         model.to(A_    )

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

         __UpperCamelCase    							=(
             '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(A_					,   A_    )









from numpy import exp, pi, sqrt

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					float = 0.0			,     SCREAMING_SNAKE_CASE__			:					float = 1.0				):
     return 1 / sqrt(2 * pi * sigma**2				) * exp(-((x - mu) ** 2) / (2 * sigma**2)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





