



'''simple docstring'''



from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase_     :       Optional[Any]								=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def 					UpperCAmelCase   (						A  :				Optional[Any]				):
							if isinstance(A					,		torch.Tensor				):
														return image
							elif isinstance(A					,		PIL.Image.Image				):
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[image]

							SCREAMING_SNAKE_CASE					:      Dict     					=			[trans(img.convert('''RGB'''				)				) for img in image]
							SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.stack(A				)
							return image






class 	lowerCamelCase_							(					snake_case_			):







						def __init__(  self   :      Dict				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													super().__init__()

													# make sure scheduler can always be converted to DDIM
													SCREAMING_SNAKE_CASE					:      str     					=			DDIMScheduler.from_config(scheduler.config				)

													self.register_modules(unet=lowerCAmelCase__				,    scheduler=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													if strength < 0 or strength > 1:
																				raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""				)







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													# get the original timestep using init_timestep
													SCREAMING_SNAKE_CASE					:      Any     					=			min(int(num_inference_steps * strength				)				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			max(num_inference_steps - init_timestep				,    0				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.scheduler.timesteps[t_start:]

													return timesteps, num_inference_steps - t_start







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      List[str]=None				):

													"""simple docstring"""
													if not isinstance(lowerCAmelCase__				,    (torch.Tensor, PIL.Image.Image, list)				):
																				raise ValueError(
																				    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase__				)}"""				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			image.to(device=lowerCAmelCase__				,    dtype=lowerCAmelCase__				)

													if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				) and len(lowerCAmelCase__				) != batch_size:
																				raise ValueError(
																				    F"""You have passed a list of generators of length {len(lowerCAmelCase__				)}, but requested an effective batch"""
																				    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			init_latents.shape
													SCREAMING_SNAKE_CASE					:      Tuple     					=			randn_tensor(lowerCAmelCase__				,    generator=lowerCAmelCase__				,    device=lowerCAmelCase__				,    dtype=lowerCAmelCase__				)

													# get latents
													print('''add noise to latents at timestep'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			self.scheduler.add_noise(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			init_latents

													return latents







						@torch.no_grad()
						def __call__(  self   :      int				,    lowerCAmelCase__   :      Union[torch.FloatTensor, PIL.Image.Image] = None				,    lowerCAmelCase__   :      float = 0.8				,    lowerCAmelCase__   :      int = 1				,    lowerCAmelCase__   :      Optional[Union[torch.Generator, List[torch.Generator]]] = None				,    lowerCAmelCase__   :      float = 0.0				,    lowerCAmelCase__   :      int = 50				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      Optional[str] = "pil"				,    lowerCAmelCase__   :      bool = True				,    ):

													"""simple docstring"""
													self.check_inputs(lowerCAmelCase__				)

													# 2. Preprocess image
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			preprocess(lowerCAmelCase__				)

													# 3. set timesteps
													self.scheduler.set_timesteps(lowerCAmelCase__				,    device=self.device				)
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			self.get_timesteps(lowerCAmelCase__				,    lowerCAmelCase__				,    self.device				)
													SCREAMING_SNAKE_CASE					:      str     					=			timesteps[:1].repeat(lowerCAmelCase__				)

													# 4. Prepare latent variables
													SCREAMING_SNAKE_CASE					:      int     					=			self.prepare_latents(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    self.unet.dtype				,    self.device				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			latents

													# 5. Denoising loop
													for t in self.progress_bar(lowerCAmelCase__				):
																				# 1. predict noise model_output
																				SCREAMING_SNAKE_CASE					:      Any     					=			self.unet(lowerCAmelCase__				,    lowerCAmelCase__				).sample

																				# 2. predict previous mean of image x_t-1 and add variance depending on eta
																				# eta corresponds to η in paper and should be between [0, 1]
																				# do x_t -> x_t-1
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.scheduler.step(
																				    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    eta=lowerCAmelCase__				,    use_clipped_model_output=lowerCAmelCase__				,    generator=lowerCAmelCase__				,    ).prev_sample

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			(image / 2 + 0.5).clamp(0				,    1				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image.cpu().permute(0				,    2				,    3				,    1				).numpy()
													if output_type == "pil":
																				SCREAMING_SNAKE_CASE					:      Dict     					=			self.numpy_to_pil(lowerCAmelCase__				)

													if not return_dict:
																				return (image, latent_timestep.item())

													return ImagePipelineOutput(images=lowerCAmelCase__				)









'''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 	lowerCamelCase_							:







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      str=13				,    lowerCAmelCase__   :      List[Any]=7				,    lowerCAmelCase__   :      Optional[int]=True				,    lowerCAmelCase__   :      Dict=True				,    lowerCAmelCase__   :      Union[str, Any]=False				,    lowerCAmelCase__   :      Tuple=True				,    lowerCAmelCase__   :      Dict=99				,    lowerCAmelCase__   :      List[str]=32				,    lowerCAmelCase__   :      str=5				,    lowerCAmelCase__   :      str=4				,    lowerCAmelCase__   :      Any=37				,    lowerCAmelCase__   :      Tuple="gelu"				,    lowerCAmelCase__   :      int=0.1				,    lowerCAmelCase__   :      Dict=0.1				,    lowerCAmelCase__   :      str=5_12				,    lowerCAmelCase__   :      List[str]=16				,    lowerCAmelCase__   :      List[str]=2				,    lowerCAmelCase__   :      Union[str, Any]=0.02				,    lowerCAmelCase__   :      int=3				,    lowerCAmelCase__   :      Tuple=4				,    lowerCAmelCase__   :      List[Any]=None				,    ):

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







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

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

													SCREAMING_SNAKE_CASE					:      str     					=			None
													if self.use_token_type_ids:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.type_vocab_size				)

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

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

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







						def 					__lowercase    (  self   :      List[Any]				):

													"""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 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			BioGptModel(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				)
													self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)				)







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Dict				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptForCausalLM(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      str				,    *lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptModel(config=lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()

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

													# first forward pass
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				).to_tuple()

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

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

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

													# get two different outputs
													SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(lowerCAmelCase__				,    past_key_values=lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']

													# select random slice
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ids_tensor((1,)				,    output_from_past.shape[-1]				).item()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			output_from_no_past[:, -1, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      List[Any]				,    *lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			BioGptModel(config=lowerCAmelCase__				).to(lowerCAmelCase__				).eval()

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.ones(input_ids.shape				,    dtype=torch.long				,    device=lowerCAmelCase__				)

													# first forward pass
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    use_cache=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			outputs.to_tuple()

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

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

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']
													SCREAMING_SNAKE_CASE					:      str     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    past_key_values=lowerCAmelCase__				)[
													    '''last_hidden_state'''
													]

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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			BioGptForCausalLM(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													if gradient_checkpointing:
																				model.gradient_checkpointing_enable()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			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 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      str				,    *lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptModel(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			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 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Tuple				,    *lowerCAmelCase__   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Tuple     					=			BioGptForTokenClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			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 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.prepare_config_and_inputs()
													(
													    (
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,(
													    SCREAMING_SNAKE_CASE
													)					,
													)					:      Optional[Any]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Any     					=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
													return config, inputs_dict


@require_torch
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Dict	     =				(
						    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
						    if is_torch_available()
						    else ()
						)
						_lowerCAmelCase     :   Optional[Any]	     =				(BioGptForCausalLM,) if is_torch_available() else ()
						_lowerCAmelCase     :   List[str]	     =				(
						    {
						        'feature-extraction': BioGptModel,
						        'text-classification': BioGptForSequenceClassification,
						        'text-generation': BioGptForCausalLM,
						        'token-classification': BioGptForTokenClassification,
						        'zero-shot': BioGptForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase     :   Dict	     =				False







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Any     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				,    hidden_size=37				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			type
																				self.model_tester.create_and_check_model(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase__				,    gradient_checkpointing=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
													model.to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      int     					=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			'''left'''

													# Define PAD Token = EOS Token = 50256
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.eos_token
													SCREAMING_SNAKE_CASE					:      Dict     					=			model.config.eos_token_id

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

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer(lowerCAmelCase__				,    return_tensors='''pt'''				,    padding=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			inputs['''input_ids'''].to(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.generate(
													    input_ids=lowerCAmelCase__				,    attention_mask=inputs['''attention_mask'''].to(lowerCAmelCase__				)				,    )

													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer(sentences[0]				,    return_tensors='''pt'''				).input_ids.to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Any     					=			model.generate(input_ids=lowerCAmelCase__				)

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

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer.batch_decode(lowerCAmelCase__				,    skip_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.decode(output_non_padded[0]				,    skip_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tokenizer.decode(output_padded[0]				,    skip_special_tokens=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			[
													    '''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 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			BioGptModel.from_pretrained(lowerCAmelCase__				)
																				self.assertIsNotNone(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			3
													SCREAMING_SNAKE_CASE					:      Dict     					=			input_dict['''input_ids''']
													SCREAMING_SNAKE_CASE					:      str     					=			input_ids.ne(1				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor([self.model_tester.batch_size]				,    self.model_tester.type_sequence_label_size				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			BioGptForSequenceClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    labels=lowerCAmelCase__				)
													self.assertEqual(result.logits.shape				,    (self.model_tester.batch_size, self.model_tester.num_labels)				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			3
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''multi_label_classification'''
													SCREAMING_SNAKE_CASE					:      Any     					=			input_dict['''input_ids''']
													SCREAMING_SNAKE_CASE					:      Any     					=			input_ids.ne(1				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ids_tensor(
													    [self.model_tester.batch_size, config.num_labels]				,    self.model_tester.type_sequence_label_size				).to(torch.float				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			BioGptForSequenceClassification(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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 	lowerCamelCase_							(					unittest.TestCase			):







						@slow
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
													SCREAMING_SNAKE_CASE					:      str     					=			torch.tensor([[2, 48_05, 9, 6_56, 21]]				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(lowerCAmelCase__				)[0]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			4_23_84

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.Size((1, 5, vocab_size)				)
													self.assertEqual(output.shape				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, 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]				,    lowerCAmelCase__				,    atol=1e-4				)				)







						@slow
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''				)
													model.to(lowerCAmelCase__				)

													torch.manual_seed(0				)
													SCREAMING_SNAKE_CASE					:      str     					=			tokenizer('''COVID-19 is'''				,    return_tensors='''pt'''				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model.generate(
													    **lowerCAmelCase__				,    min_length=1_00				,    max_length=10_24				,    num_beams=5				,    early_stopping=lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tokenizer.decode(output_ids[0]				,    skip_special_tokens=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      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(lowerCAmelCase__				,    lowerCAmelCase__				)




