

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					:



   def __init__(							self							:					List[str],		__A							:					Union[str, Any],		__A							:					Tuple=1_3,		__A							:					Union[str, Any]=7,		__A							:					str=True,		__A							:					Optional[int]=True,		__A							:					Tuple=False,		__A							:					List[Any]=True,		__A							:					Optional[Any]=9_9,		__A							:					Any=3_2,		__A							:					str=5,		__A							:					Tuple=4,		__A							:					List[Any]=3_7,		__A							:					Dict="gelu",		__A							:					Dict=0.1,		__A							:					List[Any]=0.1,		__A							:					Tuple=5_1_2,		__A							:					Optional[int]=1_6,		__A							:					str=2,		__A							:					Any=0.0_2,		__A							:					int=3,		__A							:					str=4,		__A							:					int=None,		):
     UpperCAmelCase	:							Tuple						=			parent
     UpperCAmelCase	:							List[str]						=			batch_size
     UpperCAmelCase	:							List[Any]						=			seq_length
     UpperCAmelCase	:							Union[str, Any]						=			is_training
     UpperCAmelCase	:							Any						=			use_input_mask
     UpperCAmelCase	:							List[Any]						=			use_token_type_ids
     UpperCAmelCase	:							List[str]						=			use_labels
     UpperCAmelCase	:							Optional[int]						=			vocab_size
     UpperCAmelCase	:							Tuple						=			hidden_size
     UpperCAmelCase	:							Dict						=			num_hidden_layers
     UpperCAmelCase	:							Optional[Any]						=			num_attention_heads
     UpperCAmelCase	:							List[str]						=			intermediate_size
     UpperCAmelCase	:							List[Any]						=			hidden_act
     UpperCAmelCase	:							int						=			hidden_dropout_prob
     UpperCAmelCase	:							List[str]						=			attention_probs_dropout_prob
     UpperCAmelCase	:							int						=			max_position_embeddings
     UpperCAmelCase	:							int						=			type_vocab_size
     UpperCAmelCase	:							Dict						=			type_sequence_label_size
     UpperCAmelCase	:							Optional[Any]						=			initializer_range
     UpperCAmelCase	:							List[Any]						=			num_labels
     UpperCAmelCase	:							int						=			num_choices
     UpperCAmelCase	:							Dict						=			scope



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							List[Any]						=			ids_tensor([self.batch_size, self.seq_length],		self.vocab_size						)

     UpperCAmelCase	:							Tuple						=			None
     if self.use_input_mask:
       UpperCAmelCase	:							Optional[Any]						=			random_attention_mask([self.batch_size, self.seq_length]						)

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

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

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

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



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



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Tuple,		__A							:					Optional[int],		__A							:					List[Any],		__A							:					Optional[int],		__A							:					Optional[int],		__A							:					Optional[Any],		__A							:					str,		__A							:					Dict,		__A							:					int,		):
     UpperCAmelCase	:							Any						=			BioGptForCausalLM(config=__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							List[Any]						=			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 	__magic_name__   (							self							:					List[Any],		__A							:					str,		__A							:					Optional[Any],		__A							:					str,		__A							:					Dict,		__A							:					List[Any],		*__A							:					int						):
     UpperCAmelCase	:							Tuple						=			BioGptModel(config=__A						)
     model.to(__A						)
     model.eval()

     # create attention mask
     UpperCAmelCase	:							str						=			torch.ones(input_ids.shape,		dtype=torch.long,		device=__A						)
     UpperCAmelCase	:							int						=			self.seq_length // 2
     UpperCAmelCase	:							Dict						=			0

     # first forward pass
     UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			model(__A,		attention_mask=__A						).to_tuple()

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

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

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

     # get two different outputs
     UpperCAmelCase	:							Dict						=			model(__A,		attention_mask=__A						)['''last_hidden_state''']
     UpperCAmelCase	:							List[Any]						=			model(__A,		past_key_values=__A,		attention_mask=__A						)['''last_hidden_state''']

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

     UpperCAmelCase	:							Any						=			torch.ones(input_ids.shape,		dtype=torch.long,		device=__A						)

     # first forward pass
     UpperCAmelCase	:							Union[str, Any]						=			model(__A,		attention_mask=__A,		use_cache=__A						)

     UpperCAmelCase			,						UpperCAmelCase	:							int						=			outputs.to_tuple()

     # create hypothetical multiple next token and extent to next_input_ids
     UpperCAmelCase	:							Dict						=			ids_tensor((self.batch_size, 3),		config.vocab_size						)
     UpperCAmelCase	:							Optional[Any]						=			ids_tensor((self.batch_size, 3),		2						)

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

     UpperCAmelCase	:							Any						=			model(__A,		attention_mask=__A						)['''last_hidden_state''']
     UpperCAmelCase	:							Any						=			model(__A,		attention_mask=__A,		past_key_values=__A						)[
         '''last_hidden_state'''
     ]

     # select random slice
     UpperCAmelCase	:							Any						=			ids_tensor((1,),		output_from_past.shape[-1]						).item()
     UpperCAmelCase	:							List[Any]						=			output_from_no_past[:, -3:, random_slice_idx].detach()
     UpperCAmelCase	:							int						=			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 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Union[str, Any],		__A							:					List[str],		__A							:					Optional[Any],		__A							:					int,		__A							:					List[str],		*__A							:					List[Any],		__A							:					Any=False						):
     UpperCAmelCase	:							Optional[Any]						=			BioGptForCausalLM(__A						)
     model.to(__A						)
     if gradient_checkpointing:
       model.gradient_checkpointing_enable()

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






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



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							int						=			BioGptModelTester(self						)
     UpperCAmelCase	:							str						=			ConfigTester(self,		config_class=__A,		hidden_size=3_7						)



   def 	__magic_name__   (							self							:					Dict						):
     self.config_tester.run_common_tests()



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Tuple						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
       UpperCAmelCase	:							List[str]						=			type
       self.model_tester.create_and_check_model(*__A						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							int						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__A						)



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



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							Any						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__A						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Tuple						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_weight_initialization(*__A						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							List[Any]						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_biogpt_for_token_classification(*__A						)



   @slow
   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Optional[Any]						=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''						)
     model.to(__A						)
     UpperCAmelCase	:							Tuple						=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''						)

     UpperCAmelCase	:							int						=			'''left'''

     # Define PAD Token = EOS Token = 50256
     UpperCAmelCase	:							List[Any]						=			tokenizer.eos_token
     UpperCAmelCase	:							Optional[int]						=			model.config.eos_token_id

     # use different length sentences to test batching
     UpperCAmelCase	:							Union[str, Any]						=			[
         '''Hello, my dog is a little''',
         '''Today, I''',
     ]

     UpperCAmelCase	:							Dict						=			tokenizer(__A,		return_tensors='''pt''',		padding=__A						)
     UpperCAmelCase	:							List[Any]						=			inputs['''input_ids'''].to(__A						)

     UpperCAmelCase	:							int						=			model.generate(
         input_ids=__A,		attention_mask=inputs['''attention_mask'''].to(__A						),		)

     UpperCAmelCase	:							Optional[Any]						=			tokenizer(sentences[0],		return_tensors='''pt'''						).input_ids.to(__A						)
     UpperCAmelCase	:							Dict						=			model.generate(input_ids=__A						)

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

     UpperCAmelCase	:							Optional[int]						=			tokenizer.batch_decode(__A,		skip_special_tokens=__A						)
     UpperCAmelCase	:							int						=			tokenizer.decode(output_non_padded[0],		skip_special_tokens=__A						)
     UpperCAmelCase	:							Optional[int]						=			tokenizer.decode(output_padded[0],		skip_special_tokens=__A						)

     UpperCAmelCase	:							str						=			[
         '''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 	__magic_name__   (							self							:					int						):
     for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:							List[str]						=			BioGptModel.from_pretrained(__A						)
       self.assertIsNotNone(__A						)



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase			,						UpperCAmelCase	:							str						=			self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase	:							int						=			3
     UpperCAmelCase	:							Optional[int]						=			input_dict['''input_ids''']
     UpperCAmelCase	:							Any						=			input_ids.ne(1						).to(__A						)
     UpperCAmelCase	:							List[str]						=			ids_tensor([self.model_tester.batch_size],		self.model_tester.type_sequence_label_size						)
     UpperCAmelCase	:							int						=			BioGptForSequenceClassification(__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							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 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase	:							str						=			3
     UpperCAmelCase	:							Tuple						=			'''multi_label_classification'''
     UpperCAmelCase	:							str						=			input_dict['''input_ids''']
     UpperCAmelCase	:							Union[str, Any]						=			input_ids.ne(1						).to(__A						)
     UpperCAmelCase	:							Union[str, Any]						=			ids_tensor(
         [self.model_tester.batch_size, config.num_labels],		self.model_tester.type_sequence_label_size						).to(torch.float						)
     UpperCAmelCase	:							List[Any]						=			BioGptForSequenceClassification(__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							str						=			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						):



   @slow
   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Any						=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''						)
     UpperCAmelCase	:							Optional[int]						=			torch.tensor([[2, 4_8_0_5, 9, 6_5_6, 2_1]]						)
     UpperCAmelCase	:							Dict						=			model(__A						)[0]

     UpperCAmelCase	:							Optional[Any]						=			4_2_3_8_4

     UpperCAmelCase	:							Union[str, Any]						=			torch.Size((1, 5, vocab_size)						)
     self.assertEqual(output.shape,		__A						)

     UpperCAmelCase	:							Dict						=			torch.tensor(
         [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -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],		__A,		atol=1E-4						)						)





   @slow
   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Optional[Any]						=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''						)
     UpperCAmelCase	:							Optional[int]						=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''						)
     model.to(__A						)

     torch.manual_seed(0						)
     UpperCAmelCase	:							List[Any]						=			tokenizer('''COVID-19 is''',		return_tensors='''pt'''						).to(__A						)
     UpperCAmelCase	:							Union[str, Any]						=			model.generate(
         **__A,		min_length=1_0_0,		max_length=1_0_2_4,		num_beams=5,		early_stopping=__A,		)
     UpperCAmelCase	:							Tuple						=			tokenizer.decode(output_ids[0],		skip_special_tokens=__A						)

     UpperCAmelCase	:							Union[str, 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__					(		UpperCAmelCase							:      int      )  ->						bool:
  if not isinstance(UpperCAmelCase    ,			UpperCAmelCase      ):
    UpperCAmelCase	:							List[str]						=			f'''Input value of [number={number}] must be an integer'''
    raise TypeError(UpperCAmelCase      )
  if number < 0:
    return False
  UpperCAmelCase	:							List[str]						=			number * number
  while number > 0:
    if number % 10 != number_square % 10:
      return False
    number //= 10
    number_square //= 10
  return True


if __name__ == "__main__":
     import doctest

     doctest.testmod()





