"""simple docstring"""





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  _snake_case     :






      def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Optional[int]=13							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=True							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			List[Any]=False							,					UpperCAmelCase      :			int=True							,					UpperCAmelCase      :			List[str]=99							,					UpperCAmelCase      :			str=32							,					UpperCAmelCase      :			Dict=5							,					UpperCAmelCase      :			Optional[int]=4							,					UpperCAmelCase      :			str=37							,					UpperCAmelCase      :			List[str]="gelu"							,					UpperCAmelCase      :			int=0.1							,					UpperCAmelCase      :			Optional[int]=0.1							,					UpperCAmelCase      :			Union[str, Any]=512							,					UpperCAmelCase      :			Dict=16							,					UpperCAmelCase      :			Any=2							,					UpperCAmelCase      :			Any=0.0_2							,					UpperCAmelCase      :			List[Any]=3							,					UpperCAmelCase      :			Dict=4							,					UpperCAmelCase      :			int=None							,					):
             __lowerCamelCase					:		Optional[int]		   =     parent
             __lowerCamelCase					:		Optional[int]		   =     batch_size
             __lowerCamelCase					:		Optional[Any]		   =     seq_length
             __lowerCamelCase					:		Union[str, Any]		   =     is_training
             __lowerCamelCase					:		Tuple		   =     use_input_mask
             __lowerCamelCase					:		List[Any]		   =     use_token_type_ids
             __lowerCamelCase					:		List[str]		   =     use_labels
             __lowerCamelCase					:		Union[str, Any]		   =     vocab_size
             __lowerCamelCase					:		Any		   =     hidden_size
             __lowerCamelCase					:		List[str]		   =     num_hidden_layers
             __lowerCamelCase					:		Tuple		   =     num_attention_heads
             __lowerCamelCase					:		int		   =     intermediate_size
             __lowerCamelCase					:		Any		   =     hidden_act
             __lowerCamelCase					:		Union[str, Any]		   =     hidden_dropout_prob
             __lowerCamelCase					:		int		   =     attention_probs_dropout_prob
             __lowerCamelCase					:		Union[str, Any]		   =     max_position_embeddings
             __lowerCamelCase					:		Optional[int]		   =     type_vocab_size
             __lowerCamelCase					:		Any		   =     type_sequence_label_size
             __lowerCamelCase					:		Optional[int]		   =     initializer_range
             __lowerCamelCase					:		Union[str, Any]		   =     num_labels
             __lowerCamelCase					:		int		   =     num_choices
             __lowerCamelCase					:		Any		   =     scope






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase					:		List[Any]		   =     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

             __lowerCamelCase					:		str		   =     None
             if self.use_input_mask:
                    __lowerCamelCase					:		Tuple		   =     random_attention_mask([self.batch_size, self.seq_length]    )

             __lowerCamelCase					:		str		   =     None
             if self.use_token_type_ids:
                    __lowerCamelCase					:		Optional[Any]		   =     ids_tensor([self.batch_size, self.seq_length]							,					self.type_vocab_size    )

             __lowerCamelCase					:		Dict		   =     None
             __lowerCamelCase					:		List[str]		   =     None
             __lowerCamelCase					:		Dict		   =     None
             if self.use_labels:
                    __lowerCamelCase					:		List[Any]		   =     ids_tensor([self.batch_size]							,					self.type_sequence_label_size    )
                    __lowerCamelCase					:		Optional[int]		   =     ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels    )
                    __lowerCamelCase					:		List[str]		   =     ids_tensor([self.batch_size]							,					self.num_choices    )

             __lowerCamelCase					:		Union[str, Any]		   =     self.get_config()

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






      def       lowerCamelCase__     (       self      :			Tuple    ):
             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=UpperCAmelCase							,					initializer_range=self.initializer_range							,					)






      def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
             __lowerCamelCase					:		Tuple		   =     BioGptModel(config=UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		Tuple		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase    )
             __lowerCamelCase					:		List[Any]		   =     model(UpperCAmelCase    )
             self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)    )






      def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int							,					):
             __lowerCamelCase					:		Optional[int]		   =     BioGptForCausalLM(config=UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		Tuple		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					token_type_ids=UpperCAmelCase							,					labels=UpperCAmelCase    )
             self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)    )






      def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			str							,					*UpperCAmelCase      :			Optional[Any]    ):
             __lowerCamelCase					:		Optional[int]		   =     BioGptModel(config=UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()

             # create attention mask
             __lowerCamelCase					:		Tuple		   =     torch.ones(input_ids.shape							,					dtype=torch.long							,					device=UpperCAmelCase    )
             __lowerCamelCase					:		Optional[int]		   =     self.seq_length // 2
             __lowerCamelCase					:		str		   =     0

             # first forward pass
             __lowerCamelCase					:		str		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase    ).to_tuple()

             # create hypothetical next token and extent to next_input_ids
             __lowerCamelCase					:		Union[str, Any]		   =     ids_tensor((self.batch_size, 1)							,					config.vocab_size    )

             # change a random masked slice from input_ids
             __lowerCamelCase					:		Optional[int]		   =     ids_tensor((1,)							,					UpperCAmelCase    ).item() + 1
             __lowerCamelCase					:		Tuple		   =     ids_tensor((self.batch_size, 1)							,					config.vocab_size    ).squeeze(-1    )
             __lowerCamelCase					:		Dict		   =     random_other_next_tokens

             # append to next input_ids and attn_mask
             __lowerCamelCase					:		int		   =     torch.cat([input_ids, next_tokens]							,					dim=-1    )
             __lowerCamelCase					:		Any		   =     torch.cat(
                 [attn_mask, torch.ones((attn_mask.shape[0], 1)							,					dtype=torch.long							,					device=UpperCAmelCase    )]							,					dim=1							,					)

             # get two different outputs
             __lowerCamelCase					:		int		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase    )["last_hidden_state"]
             __lowerCamelCase					:		List[str]		   =     model(UpperCAmelCase							,					past_key_values=UpperCAmelCase							,					attention_mask=UpperCAmelCase    )["last_hidden_state"]

             # select random slice
             __lowerCamelCase					:		Optional[Any]		   =     ids_tensor((1,)							,					output_from_past.shape[-1]    ).item()
             __lowerCamelCase					:		Tuple		   =     output_from_no_past[:, -1, random_slice_idx].detach()
             __lowerCamelCase					:		List[str]		   =     output_from_past[:, 0, random_slice_idx].detach()

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






      def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Any							,					*UpperCAmelCase      :			Any    ):
             __lowerCamelCase					:		int		   =     BioGptModel(config=UpperCAmelCase    ).to(UpperCAmelCase    ).eval()

             __lowerCamelCase					:		List[Any]		   =     torch.ones(input_ids.shape							,					dtype=torch.long							,					device=UpperCAmelCase    )

             # first forward pass
             __lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					use_cache=UpperCAmelCase    )

             __lowerCamelCase					:		Dict		   =     outputs.to_tuple()

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

             # append to next input_ids and
             __lowerCamelCase					:		List[str]		   =     torch.cat([input_ids, next_tokens]							,					dim=-1    )
             __lowerCamelCase					:		Dict		   =     torch.cat([attention_mask, next_attn_mask]							,					dim=-1    )

             __lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase    )["last_hidden_state"]
             __lowerCamelCase					:		int		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					past_key_values=UpperCAmelCase    )[
                 "last_hidden_state"
             ]

             # select random slice
             __lowerCamelCase					:		List[Any]		   =     ids_tensor((1,)							,					output_from_past.shape[-1]    ).item()
             __lowerCamelCase					:		Tuple		   =     output_from_no_past[:, -3:, random_slice_idx].detach()
             __lowerCamelCase					:		str		   =     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(UpperCAmelCase							,					UpperCAmelCase							,					atol=1E-3    )    )






      def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[str]							,					*UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			int=False    ):
             __lowerCamelCase					:		Union[str, Any]		   =     BioGptForCausalLM(UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             if gradient_checkpointing:
                    model.gradient_checkpointing_enable()

             __lowerCamelCase					:		str		   =     model(UpperCAmelCase							,					labels=UpperCAmelCase    )
             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       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					*UpperCAmelCase      :			str    ):
             __lowerCamelCase					:		List[Any]		   =     BioGptModel(UpperCAmelCase    )
             __lowerCamelCase					:		Optional[Any]		   =     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       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Dict							,					*UpperCAmelCase      :			Tuple    ):
             __lowerCamelCase					:		Union[str, Any]		   =     self.num_labels
             __lowerCamelCase					:		Union[str, Any]		   =     BioGptForTokenClassification(UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		int		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					token_type_ids=UpperCAmelCase    )
             self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)    )







      def       lowerCamelCase__     (       self      :			List[Any]    ):
             __lowerCamelCase					:		Any		   =     self.prepare_config_and_inputs()
             (
                 __lowerCamelCase  
             )					:		Optional[Any]		   =     config_and_inputs
             __lowerCamelCase					:		Tuple		   =     {"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict

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






      def       lowerCamelCase__     (       self      :			Optional[Any]    ):
             __lowerCamelCase					:		Union[str, Any]		   =     BioGptModelTester(self    )
             __lowerCamelCase					:		Optional[int]		   =     ConfigTester(self							,					config_class=UpperCAmelCase							,					hidden_size=37    )






      def       lowerCamelCase__     (       self      :			Any    ):
             self.config_tester.run_common_tests()






      def       lowerCamelCase__     (       self      :			Tuple    ):
             __lowerCamelCase					:		Union[str, Any]		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Any    ):
             __lowerCamelCase					:		List[Any]		   =     self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    __lowerCamelCase					:		Tuple		   =     type
                    self.model_tester.create_and_check_model(*UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Optional[int]    ):
             __lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			List[str]    ):
             __lowerCamelCase					:		List[Any]		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_forward_and_backwards(*UpperCAmelCase							,					gradient_checkpointing=UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Tuple    ):
             __lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Optional[int]    ):
             __lowerCamelCase					:		Any		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			List[str]    ):
             __lowerCamelCase					:		Dict		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCAmelCase    )






      @slow
      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase					:		Any		   =     BioGptForCausalLM.from_pretrained("microsoft/biogpt"    )
             model.to(UpperCAmelCase    )
             __lowerCamelCase					:		Tuple		   =     BioGptTokenizer.from_pretrained("microsoft/biogpt"    )

             __lowerCamelCase					:		int		   =     "left"

             # Define PAD Token = EOS Token = 50256
             __lowerCamelCase					:		Union[str, Any]		   =     tokenizer.eos_token
             __lowerCamelCase					:		Union[str, Any]		   =     model.config.eos_token_id

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

             __lowerCamelCase					:		str		   =     tokenizer(UpperCAmelCase							,					return_tensors="pt"							,					padding=UpperCAmelCase    )
             __lowerCamelCase					:		Any		   =     inputs["input_ids"].to(UpperCAmelCase    )

             __lowerCamelCase					:		Union[str, Any]		   =     model.generate(
                 input_ids=UpperCAmelCase							,					attention_mask=inputs["attention_mask"].to(UpperCAmelCase    )							,					)

             __lowerCamelCase					:		Optional[Any]		   =     tokenizer(sentences[0]							,					return_tensors="pt"    ).input_ids.to(UpperCAmelCase    )
             __lowerCamelCase					:		int		   =     model.generate(input_ids=UpperCAmelCase    )

             __lowerCamelCase					:		str		   =     inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
             __lowerCamelCase					:		Tuple		   =     tokenizer(sentences[1]							,					return_tensors="pt"    ).input_ids.to(UpperCAmelCase    )
             __lowerCamelCase					:		Optional[Any]		   =     model.generate(input_ids=UpperCAmelCase							,					max_length=model.config.max_length - num_paddings    )

             __lowerCamelCase					:		Tuple		   =     tokenizer.batch_decode(UpperCAmelCase							,					skip_special_tokens=UpperCAmelCase    )
             __lowerCamelCase					:		Dict		   =     tokenizer.decode(output_non_padded[0]							,					skip_special_tokens=UpperCAmelCase    )
             __lowerCamelCase					:		Dict		   =     tokenizer.decode(output_padded[0]							,					skip_special_tokens=UpperCAmelCase    )

             __lowerCamelCase					:		Any		   =     [
                 "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(UpperCAmelCase							,					UpperCAmelCase    )
             self.assertListEqual(UpperCAmelCase							,					[non_padded_sentence, padded_sentence]    )






      @slow
      def       lowerCamelCase__     (       self      :			List[Any]    ):
             for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __lowerCamelCase					:		str		   =     BioGptModel.from_pretrained(UpperCAmelCase    )
                    self.assertIsNotNone(UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase					:		str		   =     self.model_tester.prepare_config_and_inputs_for_common()
             __lowerCamelCase					:		List[Any]		   =     3
             __lowerCamelCase					:		Optional[int]		   =     input_dict["input_ids"]
             __lowerCamelCase					:		int		   =     input_ids.ne(1    ).to(UpperCAmelCase    )
             __lowerCamelCase					:		int		   =     ids_tensor([self.model_tester.batch_size]							,					self.model_tester.type_sequence_label_size    )
             __lowerCamelCase					:		str		   =     BioGptForSequenceClassification(UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					labels=UpperCAmelCase    )
             self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels)    )







      def       lowerCamelCase__     (       self      :			Tuple    ):
             __lowerCamelCase					:		List[Any]		   =     self.model_tester.prepare_config_and_inputs_for_common()
             __lowerCamelCase					:		int		   =     3
             __lowerCamelCase					:		Optional[int]		   =     "multi_label_classification"
             __lowerCamelCase					:		List[str]		   =     input_dict["input_ids"]
             __lowerCamelCase					:		Union[str, Any]		   =     input_ids.ne(1    ).to(UpperCAmelCase    )
             __lowerCamelCase					:		Any		   =     ids_tensor(
                 [self.model_tester.batch_size, config.num_labels]							,					self.model_tester.type_sequence_label_size    ).to(torch.float    )
             __lowerCamelCase					:		str		   =     BioGptForSequenceClassification(UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		Optional[int]		   =     model(UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					labels=UpperCAmelCase    )
             self.assertEqual(result.logits.shape							,					(self.model_tester.batch_size, self.model_tester.num_labels)    )

@require_torch
class  _snake_case     (					unittest.TestCase		):






      @slow
      def       lowerCamelCase__     (       self      :			List[Any]    ):
             __lowerCamelCase					:		Any		   =     BioGptForCausalLM.from_pretrained("microsoft/biogpt"    )
             __lowerCamelCase					:		Dict		   =     torch.tensor([[2, 4805, 9, 656, 21]]    )
             __lowerCamelCase					:		List[str]		   =     model(UpperCAmelCase    )[0]

             __lowerCamelCase					:		int		   =     42384

             __lowerCamelCase					:		int		   =     torch.Size((1, 5, vocab_size)    )
             self.assertEqual(output.shape							,					UpperCAmelCase    )

             __lowerCamelCase					:		List[str]		   =     torch.tensor(
                 [[[-9.5_2_3_6, -9.8_9_1_8, 10.4557], [-11.0469, -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]							,					UpperCAmelCase							,					atol=1E-4    )    )







      @slow
      def       lowerCamelCase__     (       self      :			Tuple    ):
             __lowerCamelCase					:		int		   =     BioGptTokenizer.from_pretrained("microsoft/biogpt"    )
             __lowerCamelCase					:		str		   =     BioGptForCausalLM.from_pretrained("microsoft/biogpt"    )
             model.to(UpperCAmelCase    )

             torch.manual_seed(0    )
             __lowerCamelCase					:		Union[str, Any]		   =     tokenizer("COVID-19 is"							,					return_tensors="pt"    ).to(UpperCAmelCase    )
             __lowerCamelCase					:		List[Any]		   =     model.generate(
                 **UpperCAmelCase							,					min_length=100							,					max_length=1024							,					num_beams=5							,					early_stopping=UpperCAmelCase							,					)
             __lowerCamelCase					:		List[str]		   =     tokenizer.decode(output_ids[0]							,					skip_special_tokens=UpperCAmelCase    )

             __lowerCamelCase					:		Any		   =     (
                 "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(UpperCAmelCase							,					UpperCAmelCase    )
"""simple docstring"""





from __future__ import annotations


def    lowercase_   (   _lowerCamelCase:	list[list[int]]      ) ->      bool:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     len(_lowerCamelCase      )
							# We need to create solution object to save path.
							__lowerCamelCase					:		List[Any]		   =     [[0 for _ in range(_lowerCamelCase      )] for _ in range(_lowerCamelCase      )]
							__lowerCamelCase					:		Dict		   =     run_maze(_lowerCamelCase						,   0						,   0						,   _lowerCamelCase      )
							if solved:
														print("\n".join(str(_lowerCamelCase      ) for row in solutions      )      )
							else:
														print("No solution exists!"      )
							return solved


def    lowercase_   (   _lowerCamelCase:	list[list[int]]						,   _lowerCamelCase:	int						,   _lowerCamelCase:	int						,   _lowerCamelCase:	list[list[int]]      ) ->      bool:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     len(_lowerCamelCase      )
							# Final check point.
							if i == j == (size - 1):
														__lowerCamelCase					:		Optional[Any]		   =     1
														return True

							__lowerCamelCase					:		int		   =     (not i < 0) and (not j < 0)  # Check lower bounds
							__lowerCamelCase					:		Union[str, Any]		   =     (i < size) and (j < size)  # Check upper bounds

							if lower_flag and upper_flag:
														# check for already visited and block points.
														__lowerCamelCase					:		List[Any]		   =     (not solutions[i][j]) and (not maze[i][j])
														if block_flag:
																					# check visited
																					__lowerCamelCase					:		Dict		   =     1

																					# check for directions
																					if (
																					    run_maze(_lowerCamelCase						,   i + 1						,   _lowerCamelCase						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   _lowerCamelCase						,   j + 1						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   i - 1						,   _lowerCamelCase						,   _lowerCamelCase      )
																					    or run_maze(_lowerCamelCase						,   _lowerCamelCase						,   j - 1						,   _lowerCamelCase      )
																					):
																												return True

																					__lowerCamelCase					:		List[str]		   =     0
																					return False
							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()