
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        lowercase      :



		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       ,   )						->      Tuple:
				"""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 )						->      Tuple:
				"""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 )						->      str:
				"""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=__lowerCamelCase       ,   initializer_range=self.initializer_range       ,   )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  BioGptModel(config=__lowerCamelCase )
				model.to(__lowerCamelCase )
				model.eval()
				UpperCamelCase						=  model(__lowerCamelCase       ,   attention_mask=__lowerCamelCase )
				UpperCamelCase						=  model(__lowerCamelCase )
				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_       ,   )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  BioGptForCausalLM(config=__lowerCamelCase )
				model.to(__lowerCamelCase )
				model.eval()
				UpperCamelCase						=  model(__lowerCamelCase       ,   attention_mask=__lowerCamelCase       ,   token_type_ids=__lowerCamelCase       ,   labels=__lowerCamelCase )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.vocab_size) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   *A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  BioGptModel(config=__lowerCamelCase )
				model.to(__lowerCamelCase )
				model.eval()

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

				# first forward pass
				UpperCamelCase						=  model(__lowerCamelCase       ,   attention_mask=__lowerCamelCase ).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,)       ,   __lowerCamelCase ).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=__lowerCamelCase )]       ,   dim=1       ,   )

				# get two different outputs
				UpperCamelCase						=  model(__lowerCamelCase       ,   attention_mask=__lowerCamelCase )['''last_hidden_state''']
				UpperCamelCase						=  model(__lowerCamelCase       ,   past_key_values=__lowerCamelCase       ,   attention_mask=__lowerCamelCase )['''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(__lowerCamelCase       ,   __lowerCamelCase       ,   atol=1e-3 ) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   *A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  BioGptModel(config=__lowerCamelCase ).to(__lowerCamelCase ).eval()

				UpperCamelCase						=  torch.ones(input_ids.shape       ,   dtype=torch.long       ,   device=__lowerCamelCase )

				# first forward pass
				UpperCamelCase						=  model(__lowerCamelCase       ,   attention_mask=__lowerCamelCase       ,   use_cache=__lowerCamelCase )

				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(__lowerCamelCase       ,   attention_mask=__lowerCamelCase )['''last_hidden_state''']
				UpperCamelCase						=  model(__lowerCamelCase       ,   attention_mask=__lowerCamelCase       ,   past_key_values=__lowerCamelCase )[
				    '''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(__lowerCamelCase       ,   __lowerCamelCase       ,   atol=1e-3 ) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   *A_       ,   A_=False )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  BioGptForCausalLM(__lowerCamelCase )
				model.to(__lowerCamelCase )
				if gradient_checkpointing:
						model.gradient_checkpointing_enable()

				UpperCamelCase						=  model(__lowerCamelCase       ,   labels=__lowerCamelCase )
				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_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  BioGptModel(__lowerCamelCase )
				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_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.num_labels
				UpperCamelCase						=  BioGptForTokenClassification(__lowerCamelCase )
				model.to(__lowerCamelCase )
				model.eval()
				UpperCamelCase						=  model(__lowerCamelCase       ,   attention_mask=__lowerCamelCase       ,   token_type_ids=__lowerCamelCase )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.num_labels) )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				(
				    UpperCamelCase	
				)						=  config_and_inputs
				UpperCamelCase						=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
				return config, inputs_dict






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



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  BioGptModelTester(self )
				UpperCamelCase						=  ConfigTester(self       ,   config_class=__lowerCamelCase       ,   hidden_size=37 )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__lowerCamelCase )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""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(*__lowerCamelCase )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__lowerCamelCase )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_forward_and_backwards(*__lowerCamelCase       ,   gradient_checkpointing=__lowerCamelCase )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__lowerCamelCase )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_weight_initialization(*__lowerCamelCase )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_for_token_classification(*__lowerCamelCase )



		@slow
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
				model.to(__lowerCamelCase )
				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(__lowerCamelCase       ,   return_tensors='pt'       ,   padding=__lowerCamelCase )
				UpperCamelCase						=  inputs['''input_ids'''].to(__lowerCamelCase )

				UpperCamelCase						=  model.generate(
				    input_ids=__lowerCamelCase       ,   attention_mask=inputs['attention_mask'].to(__lowerCamelCase )       ,   )

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

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

				UpperCamelCase						=  tokenizer.batch_decode(__lowerCamelCase       ,   skip_special_tokens=__lowerCamelCase )
				UpperCamelCase						=  tokenizer.decode(output_non_padded[0]       ,   skip_special_tokens=__lowerCamelCase )
				UpperCamelCase						=  tokenizer.decode(output_padded[0]       ,   skip_special_tokens=__lowerCamelCase )

				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(__lowerCamelCase       ,   __lowerCamelCase )
				self.assertListEqual(__lowerCamelCase       ,   [non_padded_sentence, padded_sentence] )



		@slow
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""






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



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase						=  3
				UpperCamelCase						=  input_dict['''input_ids''']
				UpperCamelCase						=  input_ids.ne(1 ).to(__lowerCamelCase )
				UpperCamelCase						=  ids_tensor([self.model_tester.batch_size]       ,   self.model_tester.type_sequence_label_size )
				UpperCamelCase						=  BioGptForSequenceClassification(__lowerCamelCase )
				model.to(__lowerCamelCase )
				model.eval()
				UpperCamelCase						=  model(__lowerCamelCase       ,   attention_mask=__lowerCamelCase       ,   labels=__lowerCamelCase )
				self.assertEqual(result.logits.shape       ,   (self.model_tester.batch_size, self.model_tester.num_labels) )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				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(__lowerCamelCase )
				UpperCamelCase						=  ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]       ,   self.model_tester.type_sequence_label_size ).to(torch.float )
				UpperCamelCase						=  BioGptForSequenceClassification(__lowerCamelCase )
				model.to(__lowerCamelCase )
				model.eval()
				UpperCamelCase						=  model(__lowerCamelCase       ,   attention_mask=__lowerCamelCase       ,   labels=__lowerCamelCase )
				self.assertEqual(result.logits.shape       ,   (self.model_tester.batch_size, self.model_tester.num_labels) )






@require_torch
class        lowercase      (	unittest.TestCase     ):



		@slow
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
				UpperCamelCase						=  torch.tensor([[2, 4_805, 9, 656, 21]] )
				UpperCamelCase						=  model(__lowerCamelCase )[0]

				UpperCamelCase						=  42_384

				UpperCamelCase						=  torch.Size((1, 5, vocab_size) )
				self.assertEqual(output.shape       ,   __lowerCamelCase )

				UpperCamelCase						=  torch.tensor(
				    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )

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



		@slow
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  BioGptTokenizer.from_pretrained('microsoft/biogpt' )
				UpperCamelCase						=  BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
				model.to(__lowerCamelCase )

				torch.manual_seed(0 )
				UpperCamelCase						=  tokenizer('COVID-19 is'       ,   return_tensors='pt' ).to(__lowerCamelCase )
				UpperCamelCase						=  model.generate(
				    **__lowerCamelCase       ,   min_length=100       ,   max_length=1_024       ,   num_beams=5       ,   early_stopping=__lowerCamelCase       ,   )
				UpperCamelCase						=  tokenizer.decode(output_ids[0]       ,   skip_special_tokens=__lowerCamelCase )

				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(__lowerCamelCase       ,   __lowerCamelCase )




import re


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		if len(re.findall('[ATCG]'					, lowercase				)				) != len(lowercase				):
				raise ValueError('Invalid Strand'				)

		return dna.translate(dna.maketrans('ATCG'					, 'TAGC'				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
