





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  __A:

     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,       ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return BioGptConfig(
                vocab_size=self.vocab_size,       hidden_size=self.hidden_size,       num_hidden_layers=self.num_hidden_layers,       num_attention_heads=self.num_attention_heads,       intermediate_size=self.intermediate_size,       hidden_act=self.hidden_act,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       max_position_embeddings=self.max_position_embeddings,       type_vocab_size=self.type_vocab_size,       is_decoder=A,       initializer_range=self.initializer_range,       )

     def 	_UpperCamelCase     (      self,       A,       A,       A,       A,       A,       A,       A      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self,       A,       A,       A,       A,       A,       A,       A,       A,       A,       ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self,       A,       A,       A,       A,       A,       *A      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self,       A,       A,       A,       A,       A,       *A      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self,       A,       A,       A,       A,       A,       *A,       A=False      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self,       A,       *A      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self,       A,       A,       A,       A,       A,       *A      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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  __A(						__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			unittest.TestCase ):
     __A	  =       (
         (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
         if is_torch_available()
         else ()
     )
     __A	  =       (BioGptForCausalLM,) if is_torch_available() else ()
     __A	  =       (
         {
             "feature-extraction": BioGptModel,
             "text-classification": BioGptForSequenceClassification,
             "text-generation": BioGptForCausalLM,
             "token-classification": BioGptForTokenClassification,
             "zero-shot": BioGptForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __A	  =       False

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						BioGptModelTester(self      )
            _UpperCamelCase              =						ConfigTester(self,       config_class=A,       hidden_size=37      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            self.config_tester.run_common_tests()

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_forward_and_backwards(*A,       gradient_checkpointing=A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_weight_initialization(*A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_for_token_classification(*A      )

     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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  __A(						unittest.TestCase ):

     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						4_2384

            _UpperCamelCase              =						torch.Size((1, 5, vocab_size)      )
            self.assertEqual(output.shape,       A      )

            _UpperCamelCase              =						torch.tensor(
                [[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]]      )

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



     @slow
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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      )













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase = 0    ,       lowerCAmelCase = 0					):
       _UpperCamelCase              =						right or len(lowerCAmelCase					) - 1
       if left > right:
              return -1
       elif list_data[left] == key:
              return left
       elif list_data[right] == key:
              return right
       else:
              return search(lowerCAmelCase    ,       lowerCAmelCase    ,       left + 1    ,       right - 1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






