




"""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   a__					:


			def __init__(   self   :     List[str]   ,	UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     Dict=13   ,	UpperCamelCase_   :     Tuple=7   ,	UpperCamelCase_   :     int=True   ,	UpperCamelCase_   :     Optional[Any]=True   ,	UpperCamelCase_   :     Optional[Any]=False   ,	UpperCamelCase_   :     Any=True   ,	UpperCamelCase_   :     List[Any]=99   ,	UpperCamelCase_   :     List[Any]=32   ,	UpperCamelCase_   :     Dict=5   ,	UpperCamelCase_   :     List[Any]=4   ,	UpperCamelCase_   :     List[str]=37   ,	UpperCamelCase_   :     Any="gelu"   ,	UpperCamelCase_   :     List[Any]=0.1   ,	UpperCamelCase_   :     Tuple=0.1   ,	UpperCamelCase_   :     List[str]=512   ,	UpperCamelCase_   :     Dict=16   ,	UpperCamelCase_   :     Tuple=2   ,	UpperCamelCase_   :     Optional[Any]=0.02   ,	UpperCamelCase_   :     List[str]=3   ,	UpperCamelCase_   :     Tuple=4   ,	UpperCamelCase_   :     List[str]=None   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       parent
						__UpperCAmelCase						:			int												=       batch_size
						__UpperCAmelCase						:			str												=       seq_length
						__UpperCAmelCase						:			Any												=       is_training
						__UpperCAmelCase						:			Dict												=       use_input_mask
						__UpperCAmelCase						:			Tuple												=       use_token_type_ids
						__UpperCAmelCase						:			List[Any]												=       use_labels
						__UpperCAmelCase						:			str												=       vocab_size
						__UpperCAmelCase						:			int												=       hidden_size
						__UpperCAmelCase						:			Any												=       num_hidden_layers
						__UpperCAmelCase						:			Any												=       num_attention_heads
						__UpperCAmelCase						:			Tuple												=       intermediate_size
						__UpperCAmelCase						:			int												=       hidden_act
						__UpperCAmelCase						:			Dict												=       hidden_dropout_prob
						__UpperCAmelCase						:			Union[str, Any]												=       attention_probs_dropout_prob
						__UpperCAmelCase						:			List[str]												=       max_position_embeddings
						__UpperCAmelCase						:			Optional[Any]												=       type_vocab_size
						__UpperCAmelCase						:			Optional[Any]												=       type_sequence_label_size
						__UpperCAmelCase						:			Union[str, Any]												=       initializer_range
						__UpperCAmelCase						:			Optional[int]												=       num_labels
						__UpperCAmelCase						:			int												=       num_choices
						__UpperCAmelCase						:			Dict												=       scope


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size)

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

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

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

						__UpperCAmelCase						:			Any												=       self.get_config()

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


			def    a_  (   self   :     int):


						"""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=UpperCamelCase_   ,	initializer_range=self.initializer_range   ,	)


			def    a_  (   self   :     Any   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     Any   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Optional[int]   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       BioGptModel(config=UpperCamelCase_)
						model.to(UpperCamelCase_)
						model.eval()
						__UpperCAmelCase						:			Optional[int]												=       model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       model(UpperCamelCase_)
						self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size))


			def    a_  (   self   :     Dict   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Tuple   ,	UpperCamelCase_   :     Tuple   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     int   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       BioGptForCausalLM(config=UpperCamelCase_)
						model.to(UpperCamelCase_)
						model.eval()
						__UpperCAmelCase						:			Optional[Any]												=       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    a_  (   self   :     str   ,	UpperCamelCase_   :     Any   ,	UpperCamelCase_   :     Any   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Any   ,	UpperCamelCase_   :     List[str]   ,	*UpperCamelCase_   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       BioGptModel(config=UpperCamelCase_)
						model.to(UpperCamelCase_)
						model.eval()

						# create attention mask
						__UpperCAmelCase						:			List[Any]												=       torch.ones(input_ids.shape   ,	dtype=torch.long   ,	device=UpperCamelCase_)
						__UpperCAmelCase						:			List[Any]												=       self.seq_length // 2
						__UpperCAmelCase						:			Any												=       0

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

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

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

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

						# get two different outputs
						__UpperCAmelCase						:			Optional[Any]												=       model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_)["last_hidden_state"]
						__UpperCAmelCase						:			Any												=       model(UpperCamelCase_   ,	past_key_values=UpperCamelCase_   ,	attention_mask=UpperCamelCase_)["last_hidden_state"]

						# select random slice
						__UpperCAmelCase						:			List[str]												=       ids_tensor((1,)   ,	output_from_past.shape[-1]).item()
						__UpperCAmelCase						:			List[str]												=       output_from_no_past[:, -1, random_slice_idx].detach()
						__UpperCAmelCase						:			Dict												=       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    a_  (   self   :     Union[str, Any]   ,	UpperCamelCase_   :     List[Any]   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Tuple   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Any   ,	*UpperCamelCase_   :     List[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       BioGptModel(config=UpperCamelCase_).to(UpperCamelCase_).eval()

						__UpperCAmelCase						:			Any												=       torch.ones(input_ids.shape   ,	dtype=torch.long   ,	device=UpperCamelCase_)

						# first forward pass
						__UpperCAmelCase						:			int												=       model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_   ,	use_cache=UpperCamelCase_)

						__UpperCAmelCase						,  __UpperCAmelCase						:			Tuple												=       outputs.to_tuple()

						# create hypothetical multiple next token and extent to next_input_ids
						__UpperCAmelCase						:			List[str]												=       ids_tensor((self.batch_size, 3)   ,	config.vocab_size)
						__UpperCAmelCase						:			Optional[int]												=       ids_tensor((self.batch_size, 3)   ,	2)

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

						__UpperCAmelCase						:			Any												=       model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_)["last_hidden_state"]
						__UpperCAmelCase						:			List[str]												=       model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_   ,	past_key_values=UpperCamelCase_)[
						    "last_hidden_state"
						]

						# select random slice
						__UpperCAmelCase						:			List[str]												=       ids_tensor((1,)   ,	output_from_past.shape[-1]).item()
						__UpperCAmelCase						:			List[Any]												=       output_from_no_past[:, -3:, random_slice_idx].detach()
						__UpperCAmelCase						:			Dict												=       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    a_  (   self   :     Union[str, Any]   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     List[Any]   ,	UpperCamelCase_   :     Optional[int]   ,	UpperCamelCase_   :     str   ,	*UpperCamelCase_   :     List[Any]   ,	UpperCamelCase_   :     List[str]=False):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       BioGptForCausalLM(UpperCamelCase_)
						model.to(UpperCamelCase_)
						if gradient_checkpointing:
									model.gradient_checkpointing_enable()

						__UpperCAmelCase						:			List[Any]												=       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    a_  (   self   :     str   ,	UpperCamelCase_   :     int   ,	*UpperCamelCase_   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       BioGptModel(UpperCamelCase_)
						__UpperCAmelCase						:			List[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)   ,	0.001)
												self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0)   ,	0.01)


			def    a_  (   self   :     Optional[Any]   ,	UpperCamelCase_   :     Optional[int]   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     List[Any]   ,	*UpperCamelCase_   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       self.num_labels
						__UpperCAmelCase						:			Any												=       BioGptForTokenClassification(UpperCamelCase_)
						model.to(UpperCamelCase_)
						model.eval()
						__UpperCAmelCase						:			Optional[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    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self.prepare_config_and_inputs()
						(
						    (
						    __UpperCAmelCase
						)						,  (
						    __UpperCAmelCase
						)						,  (
						    __UpperCAmelCase
						)						,  (
						    __UpperCAmelCase
						)						,  (
						    __UpperCAmelCase
						)						,  (
						    __UpperCAmelCase
						)						,  (
						    __UpperCAmelCase
						)						,  
						)						:			Dict												=       config_and_inputs
						__UpperCAmelCase						:			Any												=       {"input_ids": input_ids, "attention_mask": input_mask}
						return config, inputs_dict






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


			def    a_  (   self   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       BioGptModelTester(self)
						__UpperCAmelCase						:			Dict												=       ConfigTester(self   ,	config_class=UpperCamelCase_   ,	hidden_size=37)


			def    a_  (   self   :     List[Any]):


						"""simple docstring"""




						self.config_tester.run_common_tests()


			def    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase_)


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									__UpperCAmelCase						:			Union[str, Any]												=       type
									self.model_tester.create_and_check_model(*UpperCamelCase_)


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCamelCase_)


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_forward_and_backwards(*UpperCamelCase_   ,	gradient_checkpointing=UpperCamelCase_)


			def    a_  (   self   :     List[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			List[str]												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCamelCase_)


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCamelCase_)


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCamelCase_)


			@slow
			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       BioGptForCausalLM.from_pretrained("microsoft/biogpt")
						model.to(UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       BioGptTokenizer.from_pretrained("microsoft/biogpt")

						__UpperCAmelCase						:			Tuple												=       "left"

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

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

						__UpperCAmelCase						:			List[Any]												=       tokenizer(UpperCamelCase_   ,	return_tensors="pt"   ,	padding=UpperCamelCase_)
						__UpperCAmelCase						:			List[Any]												=       inputs["input_ids"].to(UpperCamelCase_)

						__UpperCAmelCase						:			List[Any]												=       model.generate(
						    input_ids=UpperCamelCase_   ,	attention_mask=inputs["attention_mask"].to(UpperCamelCase_)   ,	)

						__UpperCAmelCase						:			Dict												=       tokenizer(sentences[0]   ,	return_tensors="pt").input_ids.to(UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       model.generate(input_ids=UpperCamelCase_)

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

						__UpperCAmelCase						:			Any												=       tokenizer.batch_decode(UpperCamelCase_   ,	skip_special_tokens=UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       tokenizer.decode(output_non_padded[0]   ,	skip_special_tokens=UpperCamelCase_)
						__UpperCAmelCase						:			Dict												=       tokenizer.decode(output_padded[0]   ,	skip_special_tokens=UpperCamelCase_)

						__UpperCAmelCase						:			Union[str, 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    a_  (   self   :     List[Any]):


						"""simple docstring"""




						for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__UpperCAmelCase						:			str												=       BioGptModel.from_pretrained(UpperCamelCase_)
									self.assertIsNotNone(UpperCamelCase_)


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Union[str, Any]												=       self.model_tester.prepare_config_and_inputs_for_common()
						__UpperCAmelCase						:			Any												=       3
						__UpperCAmelCase						:			Union[str, Any]												=       input_dict["input_ids"]
						__UpperCAmelCase						:			Any												=       input_ids.ne(1).to(UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       ids_tensor([self.model_tester.batch_size]   ,	self.model_tester.type_sequence_label_size)
						__UpperCAmelCase						:			Dict												=       BioGptForSequenceClassification(UpperCamelCase_)
						model.to(UpperCamelCase_)
						model.eval()
						__UpperCAmelCase						:			Any												=       model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_   ,	labels=UpperCamelCase_)
						self.assertEqual(result.logits.shape   ,	(self.model_tester.batch_size, self.model_tester.num_labels))


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       self.model_tester.prepare_config_and_inputs_for_common()
						__UpperCAmelCase						:			int												=       3
						__UpperCAmelCase						:			List[Any]												=       "multi_label_classification"
						__UpperCAmelCase						:			str												=       input_dict["input_ids"]
						__UpperCAmelCase						:			Any												=       input_ids.ne(1).to(UpperCamelCase_)
						__UpperCAmelCase						:			List[str]												=       ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]   ,	self.model_tester.type_sequence_label_size).to(torch.float)
						__UpperCAmelCase						:			List[Any]												=       BioGptForSequenceClassification(UpperCamelCase_)
						model.to(UpperCamelCase_)
						model.eval()
						__UpperCAmelCase						:			str												=       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   a__					(						unittest.TestCase ):


			@slow
			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       BioGptForCausalLM.from_pretrained("microsoft/biogpt")
						__UpperCAmelCase						:			Optional[Any]												=       torch.tensor([[2, 4805, 9, 656, 21]])
						__UpperCAmelCase						:			Any												=       model(UpperCamelCase_)[0]

						__UpperCAmelCase						:			Any												=       42384

						__UpperCAmelCase						:			str												=       torch.Size((1, 5, vocab_size))
						self.assertEqual(output.shape   ,	UpperCamelCase_)

						__UpperCAmelCase						:			List[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]   ,	UpperCamelCase_   ,	atol=1e-4))


			@slow
			def    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       BioGptTokenizer.from_pretrained("microsoft/biogpt")
						__UpperCAmelCase						:			Union[str, Any]												=       BioGptForCausalLM.from_pretrained("microsoft/biogpt")
						model.to(UpperCamelCase_)

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

						__UpperCAmelCase						:			Optional[int]												=       (
						    "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_)






from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching





def 						_lowerCAmelCase							(     )    ->						Tuple:


						'''simple docstring'''






						import os as original_os
						from os import path as original_path
						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join

						__snake_case				=		"__test_patch_submodule_mock__"
						with patch_submodule(_test_patching   ,						"os.path.join"   ,						_lowerCAmelCase			):
												# Every way to access os.path.join must be patched, and the rest must stay untouched

												# check os.path.join
												assert isinstance(_test_patching.os   ,						_PatchedModuleObj			)
												assert isinstance(_test_patching.os.path   ,						_PatchedModuleObj			)
												assert _test_patching.os.path.join is mock

												# check path.join
												assert isinstance(_test_patching.path   ,						_PatchedModuleObj			)
												assert _test_patching.path.join is mock

												# check join
												assert _test_patching.join is mock

												# check that the other attributes are untouched
												assert _test_patching.os.rename is original_rename
												assert _test_patching.path.dirname is original_dirname
												assert _test_patching.os.path.dirname is original_dirname

												# Even renamed modules or objects must be patched

												# check renamed_os.path.join
												assert isinstance(_test_patching.renamed_os   ,						_PatchedModuleObj			)
												assert isinstance(_test_patching.renamed_os.path   ,						_PatchedModuleObj			)
												assert _test_patching.renamed_os.path.join is mock

												# check renamed_path.join
												assert isinstance(_test_patching.renamed_path   ,						_PatchedModuleObj			)
												assert _test_patching.renamed_path.join is mock

												# check renamed_join
												assert _test_patching.renamed_join is mock

												# check that the other attributes are untouched
												assert _test_patching.renamed_os.rename is original_rename
												assert _test_patching.renamed_path.dirname is original_dirname
												assert _test_patching.renamed_os.path.dirname is original_dirname

						# check that everthing is back to normal when the patch is over

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join





def 						_lowerCAmelCase							(     )    ->						int:


						'''simple docstring'''






						assert _test_patching.open is open

						__snake_case				=		"__test_patch_submodule_builtin_mock__"
						# _test_patching has "open" in its globals
						assert _test_patching.open is open
						with patch_submodule(_test_patching   ,						"open"   ,						_lowerCAmelCase			):
												assert _test_patching.open is mock

						# check that everthing is back to normal when the patch is over

						assert _test_patching.open is open





def 						_lowerCAmelCase							(     )    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_missing_mock__"
						with patch_submodule(_test_patching   ,						"pandas.read_csv"   ,						_lowerCAmelCase			):
												pass





def 						_lowerCAmelCase							(     )    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_missing_builtin_mock__"
						# _test_patching doesn't have "len" in its globals
						assert getattr(_test_patching   ,						"len"   ,						_lowerCAmelCase			) is None
						with patch_submodule(_test_patching   ,						"len"   ,						_lowerCAmelCase			):
												assert _test_patching.len is mock
						assert _test_patching.len is len





def 						_lowerCAmelCase							(     )    ->						Optional[Any]:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_start_and_stop_mock__"
						__snake_case				=		patch_submodule(_test_patching   ,						"open"   ,						_lowerCAmelCase			)
						assert _test_patching.open is open
						patch.start()
						assert _test_patching.open is mock
						patch.stop()
						assert _test_patching.open is open





def 						_lowerCAmelCase							(     )    ->						Any:


						'''simple docstring'''






						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						__snake_case				=		"__test_patch_submodule_successive_join__"
						__snake_case				=		"__test_patch_submodule_successive_dirname__"
						__snake_case				=		"__test_patch_submodule_successive_rename__"
						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename

						with patch_submodule(_test_patching   ,						"os.path.join"   ,						_lowerCAmelCase			):
												with patch_submodule(_test_patching   ,						"os.rename"   ,						_lowerCAmelCase			):
																		with patch_submodule(_test_patching   ,						"os.path.dirname"   ,						_lowerCAmelCase			):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

    # try another order
						with patch_submodule(_test_patching   ,						"os.rename"   ,						_lowerCAmelCase			):
												with patch_submodule(_test_patching   ,						"os.path.join"   ,						_lowerCAmelCase			):
																		with patch_submodule(_test_patching   ,						"os.path.dirname"   ,						_lowerCAmelCase			):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename





def 						_lowerCAmelCase							(     )    ->						str:


						'''simple docstring'''






						__snake_case				=		"__test_patch_submodule_doesnt_exist_mock__"
						with patch_submodule(_test_patching   ,						"__module_that_doesn_exist__.__attribute_that_doesn_exist__"   ,						_lowerCAmelCase			):
												pass
						with patch_submodule(_test_patching   ,						"os.__attribute_that_doesn_exist__"   ,						_lowerCAmelCase			):
												pass





