





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       :





       '''simple docstring'''







       def __init__(  self :    Optional[Any]							,				A :    Any							,				A :    Union[str, Any]=13							,				A :    str=7							,				A :    str=True							,				A :    Optional[int]=True							,				A :    List[Any]=False							,				A :    Dict=True							,				A :    Optional[int]=99							,				A :    Tuple=32							,				A :    List[str]=5							,				A :    List[str]=4							,				A :    Any=37							,				A :    List[Any]="gelu"							,				A :    Union[str, Any]=0.1							,				A :    Dict=0.1							,				A :    Dict=512							,				A :    Optional[int]=16							,				A :    Dict=2							,				A :    int=0.02							,				A :    List[Any]=3							,				A :    Tuple=4							,				A :    Tuple=None							,				):
         __snake_case:						Tuple   =						parent
         __snake_case:						Tuple   =						batch_size
         __snake_case:						str   =						seq_length
         __snake_case:						str   =						is_training
         __snake_case:						Optional[Any]   =						use_input_mask
         __snake_case:						List[Any]   =						use_token_type_ids
         __snake_case:						int   =						use_labels
         __snake_case:						Dict   =						vocab_size
         __snake_case:						Any   =						hidden_size
         __snake_case:						Union[str, Any]   =						num_hidden_layers
         __snake_case:						int   =						num_attention_heads
         __snake_case:						Optional[Any]   =						intermediate_size
         __snake_case:						Tuple   =						hidden_act
         __snake_case:						int   =						hidden_dropout_prob
         __snake_case:						Optional[Any]   =						attention_probs_dropout_prob
         __snake_case:						Optional[int]   =						max_position_embeddings
         __snake_case:						int   =						type_vocab_size
         __snake_case:						List[Any]   =						type_sequence_label_size
         __snake_case:						Any   =						initializer_range
         __snake_case:						Dict   =						num_labels
         __snake_case:						Union[str, Any]   =						num_choices
         __snake_case:						List[str]   =						scope




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						List[Any]   =						ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size		)

         __snake_case:						Optional[int]   =						None
         if self.use_input_mask:
           __snake_case:						List[str]   =						random_attention_mask([self.batch_size, self.seq_length]		)

         __snake_case:						Optional[int]   =						None
         if self.use_token_type_ids:
           __snake_case:						List[Any]   =						ids_tensor([self.batch_size, self.seq_length]							,				self.type_vocab_size		)

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

         __snake_case:						Optional[Any]   =						self.get_config()

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




       def 							UpperCAmelCase__ (  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=A							,				initializer_range=self.initializer_range							,				)




       def 							UpperCAmelCase__ (  self :    int							,				A :    Optional[Any]							,				A :    Dict							,				A :    Tuple							,				A :    Optional[int]							,				A :    Tuple							,				A :    Union[str, Any]							,				A :    Union[str, Any]		):
         __snake_case:						Tuple   =						BioGptModel(config=A		)
         model.to(A		)
         model.eval()
         __snake_case:						Union[str, Any]   =						model(A							,				attention_mask=A		)
         __snake_case:						Tuple   =						model(A		)
         self.parent.assertEqual(result.last_hidden_state.shape							,				(self.batch_size, self.seq_length, self.hidden_size)		)




       def 							UpperCAmelCase__ (  self :    str							,				A :    Tuple							,				A :    Any							,				A :    Tuple							,				A :    Union[str, Any]							,				A :    str							,				A :    str							,				A :    List[str]							,				A :    Union[str, Any]							,				A :    Optional[Any]							,				):
         __snake_case:						str   =						BioGptForCausalLM(config=A		)
         model.to(A		)
         model.eval()
         __snake_case:						int   =						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 :    Any							,				A :    Any							,				A :    Union[str, Any]							,				A :    int							,				A :    int							,				A :    Optional[Any]							,				*A :    Tuple		):
         __snake_case:						int   =						BioGptModel(config=A		)
         model.to(A		)
         model.eval()

         # create attention mask
         __snake_case:						Optional[Any]   =						torch.ones(input_ids.shape							,				dtype=torch.long							,				device=A		)
         __snake_case:						Union[str, Any]   =						self.seq_length // 2
         __snake_case:						List[str]   =						0

         # first forward pass
         __snake_case  ,   __snake_case:						Optional[Any]   =						model(A							,				attention_mask=A		).to_tuple()

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

         # change a random masked slice from input_ids
         __snake_case:						Dict   =						ids_tensor((1,)							,				A		).item() + 1
         __snake_case:						int   =						ids_tensor((self.batch_size, 1)							,				config.vocab_size		).squeeze(-1		)
         __snake_case:						List[Any]   =						random_other_next_tokens

         # append to next input_ids and attn_mask
         __snake_case:						Tuple   =						torch.cat([input_ids, next_tokens]							,				dim=-1		)
         __snake_case:						Dict   =						torch.cat(
             [attn_mask, torch.ones((attn_mask.shape[0], 1)							,				dtype=torch.long							,				device=A		)]							,				dim=1							,				)

         # get two different outputs
         __snake_case:						int   =						model(A							,				attention_mask=A		)["""last_hidden_state"""]
         __snake_case:						Any   =						model(A							,				past_key_values=A							,				attention_mask=A		)["""last_hidden_state"""]

         # select random slice
         __snake_case:						Union[str, Any]   =						ids_tensor((1,)							,				output_from_past.shape[-1]		).item()
         __snake_case:						List[str]   =						output_from_no_past[:, -1, random_slice_idx].detach()
         __snake_case:						str   =						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 :    Dict							,				A :    int							,				A :    int							,				A :    Any							,				A :    str							,				A :    Optional[Any]							,				*A :    Tuple		):
         __snake_case:						Any   =						BioGptModel(config=A		).to(A		).eval()

         __snake_case:						Dict   =						torch.ones(input_ids.shape							,				dtype=torch.long							,				device=A		)

         # first forward pass
         __snake_case:						str   =						model(A							,				attention_mask=A							,				use_cache=A		)

         __snake_case  ,   __snake_case:						Any   =						outputs.to_tuple()

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

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

         __snake_case:						List[str]   =						model(A							,				attention_mask=A		)["""last_hidden_state"""]
         __snake_case:						List[str]   =						model(A							,				attention_mask=A							,				past_key_values=A		)[
             """last_hidden_state"""
         ]

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




       def 							UpperCAmelCase__ (  self :    str							,				A :    Tuple							,				A :    List[str]							,				A :    Union[str, Any]							,				A :    Tuple							,				A :    List[Any]							,				*A :    str							,				A :    List[Any]=False		):
         __snake_case:						Dict   =						BioGptForCausalLM(A		)
         model.to(A		)
         if gradient_checkpointing:
           model.gradient_checkpointing_enable()

         __snake_case:						str   =						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 :    Dict							,				A :    List[Any]							,				*A :    Tuple		):
         __snake_case:						Optional[int]   =						BioGptModel(A		)
         __snake_case:						Tuple   =						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 :    Any							,				A :    List[str]							,				A :    Any							,				A :    Optional[int]							,				A :    Tuple							,				A :    Optional[Any]							,				*A :    Tuple		):
         __snake_case:						Tuple   =						self.num_labels
         __snake_case:						List[str]   =						BioGptForTokenClassification(A		)
         model.to(A		)
         model.eval()
         __snake_case:						Optional[Any]   =						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 :    Any		):
         __snake_case:						int   =						self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   (
             __snake_case
         )  ,   
         ):						str   =						config_and_inputs
         __snake_case:						str   =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict







@require_torch
class     __snake_case       (							__lowerCamelCase   ,			__lowerCamelCase   ,			__lowerCamelCase   ,			unittest.TestCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     (
           (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__      		=     (BioGptForCausalLM,) if is_torch_available() else ()
       lowerCAmelCase__      		=     (
           {
               """feature-extraction""": BioGptModel,
               """text-classification""": BioGptForSequenceClassification,
               """text-generation""": BioGptForCausalLM,
               """token-classification""": BioGptForTokenClassification,
               """zero-shot""": BioGptForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__      		=     False




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						int   =						BioGptModelTester(self		)
         __snake_case:						Optional[int]   =						ConfigTester(self							,				config_class=A							,				hidden_size=37		)




       def 							UpperCAmelCase__ (  self :    Tuple		):
         self.config_tester.run_common_tests()




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Optional[Any]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A		)




       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						Optional[Any]   =						self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
           __snake_case:						int   =						type
           self.model_tester.create_and_check_model(*A		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						Optional[Any]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						int   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_forward_and_backwards(*A							,				gradient_checkpointing=A		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						Tuple   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Optional[int]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_weight_initialization(*A		)




       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						List[Any]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_biogpt_for_token_classification(*A		)




       @slow
       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						List[str]   =						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""		)
         model.to(A		)
         __snake_case:						Tuple   =						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""		)

         __snake_case:						List[Any]   =						"""left"""

         # Define PAD Token = EOS Token = 50256
         __snake_case:						List[Any]   =						tokenizer.eos_token
         __snake_case:						str   =						model.config.eos_token_id

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

         __snake_case:						Any   =						tokenizer(A							,				return_tensors="""pt"""							,				padding=A		)
         __snake_case:						Optional[int]   =						inputs["""input_ids"""].to(A		)

         __snake_case:						List[str]   =						model.generate(
             input_ids=A							,				attention_mask=inputs["""attention_mask"""].to(A		)							,				)

         __snake_case:						List[str]   =						tokenizer(sentences[0]							,				return_tensors="""pt"""		).input_ids.to(A		)
         __snake_case:						List[Any]   =						model.generate(input_ids=A		)

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

         __snake_case:						Optional[Any]   =						tokenizer.batch_decode(A							,				skip_special_tokens=A		)
         __snake_case:						Dict   =						tokenizer.decode(output_non_padded[0]							,				skip_special_tokens=A		)
         __snake_case:						List[str]   =						tokenizer.decode(output_padded[0]							,				skip_special_tokens=A		)

         __snake_case:						Tuple   =						[
             """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 :    Dict		):
         for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __snake_case:						int   =						BioGptModel.from_pretrained(A		)
           self.assertIsNotNone(A		)




       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case  ,   __snake_case:						Tuple   =						self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case:						Any   =						3
         __snake_case:						Any   =						input_dict["""input_ids"""]
         __snake_case:						str   =						input_ids.ne(1		).to(A		)
         __snake_case:						Optional[Any]   =						ids_tensor([self.model_tester.batch_size]							,				self.model_tester.type_sequence_label_size		)
         __snake_case:						Optional[int]   =						BioGptForSequenceClassification(A		)
         model.to(A		)
         model.eval()
         __snake_case:						Union[str, Any]   =						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 :    Any		):
         __snake_case  ,   __snake_case:						Tuple   =						self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case:						int   =						3
         __snake_case:						Tuple   =						"""multi_label_classification"""
         __snake_case:						List[str]   =						input_dict["""input_ids"""]
         __snake_case:						Optional[Any]   =						input_ids.ne(1		).to(A		)
         __snake_case:						str   =						ids_tensor(
             [self.model_tester.batch_size, config.num_labels]							,				self.model_tester.type_sequence_label_size		).to(torch.float		)
         __snake_case:						int   =						BioGptForSequenceClassification(A		)
         model.to(A		)
         model.eval()
         __snake_case:						int   =						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     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       @slow
       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Optional[int]   =						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""		)
         __snake_case:						int   =						torch.tensor([[2, 4_805, 9, 656, 21]]		)
         __snake_case:						List[Any]   =						model(A		)[0]

         __snake_case:						int   =						42_384

         __snake_case:						Optional[Any]   =						torch.Size((1, 5, vocab_size)		)
         self.assertEqual(output.shape							,				A		)

         __snake_case:						Optional[Any]   =						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 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						Optional[int]   =						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""		)
         __snake_case:						Dict   =						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""		)
         model.to(A		)

         torch.manual_seed(0		)
         __snake_case:						Tuple   =						tokenizer("""COVID-19 is"""							,				return_tensors="""pt"""		).to(A		)
         __snake_case:						Optional[Any]   =						model.generate(
             **A							,				min_length=100							,				max_length=1_024							,				num_beams=5							,				early_stopping=A							,				)
         __snake_case:						Optional[Any]   =						tokenizer.decode(output_ids[0]							,				skip_special_tokens=A		)

         __snake_case:						List[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(A							,				A		)













def 			A__							(    SCREAMING_SNAKE_CASE__ = 200)   ->    int:
  __snake_case:						Optional[int]   =						[1, 2, 5, 10, 20, 50, 100, 200]
  __snake_case:						List[Any]   =						[0] * (pence + 1)
  __snake_case:						int   =						1  # base case: 1 way to make 0 pence

  for coin in coins:
    for i in range(SCREAMING_SNAKE_CASE__					,					pence + 1					,					1):
      number_of_ways[i] += number_of_ways[i - coin]
  return number_of_ways[pence]


if __name__ == "__main__":
      assert solution(200) == 73_682






