




'''simple docstring'''



import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				int        =						StableDiffusionDiffEditPipeline
       A						:				str        =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
       A						:				int        =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
       A						:				str        =						frozenset(
           [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       A						:				Union[str, Any]        =						frozenset([] )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						UNetaDConditionModel(
                 block_out_channels=(32, 64),							layers_per_block=2,							sample_size=32,							in_channels=4,							out_channels=4,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),							cross_attention_dim=32,							attention_head_dim=(2, 4),							use_linear_projection=A,							)
             SCREAMING_SNAKE_CASE   :      int    					=						DDIMScheduler(
                 beta_start=0.0_00_85,							beta_end=0.0_12,							beta_schedule='scaled_linear',							clip_sample=A,							set_alpha_to_one=A,							)
             SCREAMING_SNAKE_CASE   :      str    					=						DDIMInverseScheduler(
                 beta_start=0.0_00_85,							beta_end=0.0_12,							beta_schedule='scaled_linear',							clip_sample=A,							set_alpha_to_zero=A,							)
             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						AutoencoderKL(
                 block_out_channels=[32, 64],							in_channels=3,							out_channels=3,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],							latent_channels=4,							sample_size=128,							)
             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						CLIPTextConfig(
                 bos_token_id=0,							eos_token_id=2,							hidden_size=32,							intermediate_size=37,							layer_norm_eps=1E-05,							num_attention_heads=4,							num_hidden_layers=5,							pad_token_id=1,							vocab_size=1_000,							hidden_act='gelu',							projection_dim=512,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						CLIPTextModel(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

             SCREAMING_SNAKE_CASE   :      int    					=						{
                 'unet': unet,
                 'scheduler': scheduler,
                 'inverse_scheduler': inverse_scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'safety_checker': None,
                 'feature_extractor': None,
             }

             return components







       def        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						floats_tensor((1, 16, 16),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						floats_tensor((1, 2, 4, 16, 16),							rng=random.Random(A  )  ).to(A  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						{
                 'prompt': 'a dog and a newt',
                 'mask_image': mask,
                 'image_latents': latents,
                 'generator': generator,
                 'num_inference_steps': 2,
                 'inpaint_strength': 1.0,
                 'guidance_scale': 6.0,
                 'output_type': 'numpy',
             }

             return inputs







       def        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						image.cpu().permute(0,							2,							3,							1  )[0]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Image.fromarray(np.uinta(A  )  ).convert('RGB'  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						{
                 'image': image,
                 'source_prompt': 'a cat and a frog',
                 'target_prompt': 'a dog and a newt',
                 'generator': generator,
                 'num_inference_steps': 2,
                 'num_maps_per_mask': 2,
                 'mask_encode_strength': 1.0,
                 'guidance_scale': 6.0,
                 'output_type': 'numpy',
             }

             return inputs







       def        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						image.cpu().permute(0,							2,							3,							1  )[0]
             SCREAMING_SNAKE_CASE   :      int    					=						Image.fromarray(np.uinta(A  )  ).convert('RGB'  )
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Any    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						{
                 'image': image,
                 'prompt': 'a cat and a frog',
                 'generator': generator,
                 'num_inference_steps': 2,
                 'inpaint_strength': 1.0,
                 'guidance_scale': 6.0,
                 'decode_latents': True,
                 'output_type': 'numpy',
             }
             return inputs







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if not hasattr(self.pipeline_class,							'_optional_components'  ):
                   return

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             # set all optional components to None and update pipeline config accordingly
             for optional_component in pipe._optional_components:
                   setattr(A,							A,							A  )
             pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						pipe(**A  )[0]

             with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(A  )
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.pipeline_class.from_pretrained(A  )
                   pipe_loaded.to(A  )
                   pipe_loaded.set_progress_bar_config(disable=A  )

             for optional_component in pipe._optional_components:
                   self.assertTrue(
                       getattr(A,							A  ) is None,							F"`{optional_component}` did not stay set to None after loading.",							)

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						pipe_loaded(**A  )[0]

             SCREAMING_SNAKE_CASE   :      List[str]    					=						np.abs(output - output_loaded  ).max()
             self.assertLess(A,							1E-4  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      str    					=						self.get_dummy_mask_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pipe.generate_mask(**A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						mask[0, -3:, -3:]

             self.assertEqual(mask.shape,							(1, 16, 16)  )
             SCREAMING_SNAKE_CASE   :      Any    					=						np.array([0] * 9  )
             SCREAMING_SNAKE_CASE   :      Any    					=						np.abs(mask_slice.flatten() - expected_slice  ).max()
             self.assertLessEqual(A,							1E-3  )
             self.assertEqual(mask[0, -3, -4],							0  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Dict    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Dict    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_inversion_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pipe.invert(**A  ).images
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image[0, -1, -3:, -3:]

             self.assertEqual(image.shape,							(2, 32, 32, 3)  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(
                 [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99],							)
             SCREAMING_SNAKE_CASE   :      Dict    					=						np.abs(image_slice.flatten() - expected_slice  ).max()
             self.assertLessEqual(A,							1E-3  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().test_inference_batch_single_identical(expected_max_diff=5E-3  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						'cpu'

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_dummy_components()

             SCREAMING_SNAKE_CASE   :      Dict    					=						{'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						DPMSolverMultistepScheduler(**A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						DPMSolverMultistepInverseScheduler(**A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.pipeline_class(**A  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.get_dummy_inversion_inputs(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						pipe.invert(**A  ).images
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image[0, -1, -3:, -3:]

             self.assertEqual(image.shape,							(2, 32, 32, 3)  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(
                 [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99],							)
             SCREAMING_SNAKE_CASE   :      Any    					=						np.abs(image_slice.flatten() - expected_slice  ).max()
             self.assertLessEqual(A,							1E-3  )






@require_torch_gpu
@slow
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()







       @classmethod
       def        UpperCamelCase_							(      cls  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						raw_image.convert('RGB'  ).resize((768, 768)  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						raw_image







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(0  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						StableDiffusionDiffEditPipeline.from_pretrained(
                 'stabilityai/stable-diffusion-2-1',							safety_checker=A,							torch_dtype=torch.floataa  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						DDIMScheduler.from_config(pipe.scheduler.config  )
             SCREAMING_SNAKE_CASE   :      int    					=						DDIMInverseScheduler.from_config(pipe.scheduler.config  )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						'a bowl of fruit'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						'a bowl of pears'

             SCREAMING_SNAKE_CASE   :      Dict    					=						pipe.generate_mask(
                 image=self.raw_image,							source_prompt=A,							target_prompt=A,							generator=A,							)

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						pipe.invert(
                 prompt=A,							image=self.raw_image,							inpaint_strength=0.7,							generator=A  ).latents

             SCREAMING_SNAKE_CASE   :      List[str]    					=						pipe(
                 prompt=A,							mask_image=A,							image_latents=A,							generator=A,							negative_prompt=A,							inpaint_strength=0.7,							output_type='numpy',							).images[0]

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						(
                 np.array(
                     load_image(
                         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                         '/diffedit/pears.png'  ).resize((768, 768)  )  )
                 / 255
             )
             assert np.abs((expected_image - image).max()  ) < 5E-1







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						torch.manual_seed(0  )

             SCREAMING_SNAKE_CASE   :      int    					=						StableDiffusionDiffEditPipeline.from_pretrained(
                 'stabilityai/stable-diffusion-2-1',							safety_checker=A,							torch_dtype=torch.floataa  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
             pipe.enable_model_cpu_offload()
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      str    					=						'a bowl of fruit'
             SCREAMING_SNAKE_CASE   :      Tuple    					=						'a bowl of pears'

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						pipe.generate_mask(
                 image=self.raw_image,							source_prompt=A,							target_prompt=A,							generator=A,							)

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pipe.invert(
                 prompt=A,							image=self.raw_image,							inpaint_strength=0.7,							generator=A,							num_inference_steps=25,							).latents

             SCREAMING_SNAKE_CASE   :      str    					=						pipe(
                 prompt=A,							mask_image=A,							image_latents=A,							generator=A,							negative_prompt=A,							inpaint_strength=0.7,							num_inference_steps=25,							output_type='numpy',							).images[0]

             SCREAMING_SNAKE_CASE   :      Tuple    					=						(
                 np.array(
                     load_image(
                         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                         '/diffedit/pears.png'  ).resize((768, 768)  )  )
                 / 255
             )
             assert np.abs((expected_image - image).max()  ) < 5E-1




'''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					,   UpperCAmelCase					,   UpperCAmelCase=13					,   UpperCAmelCase=7					,   UpperCAmelCase=True					,   UpperCAmelCase=True					,   UpperCAmelCase=False					,   UpperCAmelCase=True					,   UpperCAmelCase=99					,   UpperCAmelCase=32					,   UpperCAmelCase=5					,   UpperCAmelCase=4					,   UpperCAmelCase=37					,   UpperCAmelCase="gelu"					,   UpperCAmelCase=0.1					,   UpperCAmelCase=0.1					,   UpperCAmelCase=5_12					,   UpperCAmelCase=16					,   UpperCAmelCase=2					,   UpperCAmelCase=0.02					,   UpperCAmelCase=3					,   UpperCAmelCase=4					,   UpperCAmelCase=None					,   ):
				a_      				=						parent
				a_      				=						batch_size
				a_      				=						seq_length
				a_      				=						is_training
				a_      				=						use_input_mask
				a_      				=						use_token_type_ids
				a_      				=						use_labels
				a_      				=						vocab_size
				a_      				=						hidden_size
				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						intermediate_size
				a_      				=						hidden_act
				a_      				=						hidden_dropout_prob
				a_      				=						attention_probs_dropout_prob
				a_      				=						max_position_embeddings
				a_      				=						type_vocab_size
				a_      				=						type_sequence_label_size
				a_      				=						initializer_range
				a_      				=						num_labels
				a_      				=						num_choices
				a_      				=						scope




			def 					lowerCAmelCase__					(       self	):
				a_      				=						ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

				a_      				=						None
				if self.use_input_mask:
					a_      				=						random_attention_mask([self.batch_size, self.seq_length]	)

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

				a_      				=						None
				a_      				=						None
				a_      				=						None
				if self.use_labels:
					a_      				=						ids_tensor([self.batch_size]					,   self.type_sequence_label_size	)
					a_      				=						ids_tensor([self.batch_size, self.seq_length]					,   self.num_labels	)
					a_      				=						ids_tensor([self.batch_size]					,   self.num_choices	)

				a_      				=						self.get_config()

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




			def 					lowerCAmelCase__					(       self	):
				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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						BioGptModel(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)
				a_      				=						model(UpperCAmelCase	)
				self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   ):
				a_      				=						BioGptForCausalLM(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   *UpperCAmelCase	):
				a_      				=						BioGptModel(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()

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

				# first forward pass
				a_				,  a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	).to_tuple()

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

				# change a random masked slice from input_ids
				a_      				=						ids_tensor((1,)					,   UpperCAmelCase	).item() + 1
				a_      				=						ids_tensor((self.batch_size, 1)					,   config.vocab_size	).squeeze(-1	)
				a_      				=						random_other_next_tokens

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

				# get two different outputs
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)["""last_hidden_state"""]
				a_      				=						model(UpperCAmelCase					,   past_key_values=UpperCAmelCase					,   attention_mask=UpperCAmelCase	)["""last_hidden_state"""]

				# select random slice
				a_      				=						ids_tensor((1,)					,   output_from_past.shape[-1]	).item()
				a_      				=						output_from_no_past[:, -1, random_slice_idx].detach()
				a_      				=						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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   *UpperCAmelCase	):
				a_      				=						BioGptModel(config=UpperCAmelCase	).to(UpperCAmelCase	).eval()

				a_      				=						torch.ones(input_ids.shape					,   dtype=torch.long					,   device=UpperCAmelCase	)

				# first forward pass
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   use_cache=UpperCAmelCase	)

				a_				,  a_      				=						outputs.to_tuple()

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

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

				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)["""last_hidden_state"""]
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   past_key_values=UpperCAmelCase	)[
				    """last_hidden_state"""
				]

				# select random slice
				a_      				=						ids_tensor((1,)					,   output_from_past.shape[-1]	).item()
				a_      				=						output_from_no_past[:, -3:, random_slice_idx].detach()
				a_      				=						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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   *UpperCAmelCase					,   UpperCAmelCase=False	):
				a_      				=						BioGptForCausalLM(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				if gradient_checkpointing:
					model.gradient_checkpointing_enable()

				a_      				=						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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   *UpperCAmelCase	):
				a_      				=						BioGptModel(UpperCAmelCase	)
				a_      				=						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_01	)
						self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]	) - 0.0	)					,   0.01	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   *UpperCAmelCase	):
				a_      				=						self.num_labels
				a_      				=						BioGptForTokenClassification(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						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 					lowerCAmelCase__					(       self	):
				a_      				=						self.prepare_config_and_inputs()
				(
				    (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  
				)      				=						config_and_inputs
				a_      				=						{"""input_ids""": input_ids, """attention_mask""": input_mask}
				return config, inputs_dict







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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						BioGptModelTester(self	)
				a_      				=						ConfigTester(self					,   config_class=UpperCAmelCase					,   hidden_size=37	)




			def 					lowerCAmelCase__					(       self	):
				self.config_tester.run_common_tests()




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					a_      				=						type
					self.model_tester.create_and_check_model(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_attention_mask_past(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_forward_and_backwards(*UpperCAmelCase					,   gradient_checkpointing=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_past_large_inputs(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_weight_initialization(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_for_token_classification(*UpperCAmelCase	)




			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""	)
				model.to(UpperCAmelCase	)
				a_      				=						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""	)

				a_      				=						"""left"""

				# Define PAD Token = EOS Token = 50256
				a_      				=						tokenizer.eos_token
				a_      				=						model.config.eos_token_id

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

				a_      				=						tokenizer(UpperCAmelCase					,   return_tensors="""pt"""					,   padding=UpperCAmelCase	)
				a_      				=						inputs["""input_ids"""].to(UpperCAmelCase	)

				a_      				=						model.generate(
				    input_ids=UpperCAmelCase					,   attention_mask=inputs["""attention_mask"""].to(UpperCAmelCase	)					,   )

				a_      				=						tokenizer(sentences[0]					,   return_tensors="""pt"""	).input_ids.to(UpperCAmelCase	)
				a_      				=						model.generate(input_ids=UpperCAmelCase	)

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

				a_      				=						tokenizer.batch_decode(UpperCAmelCase					,   skip_special_tokens=UpperCAmelCase	)
				a_      				=						tokenizer.decode(output_non_padded[0]					,   skip_special_tokens=UpperCAmelCase	)
				a_      				=						tokenizer.decode(output_padded[0]					,   skip_special_tokens=UpperCAmelCase	)

				a_      				=						[
				    """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 					lowerCAmelCase__					(       self	):
				for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					a_      				=						BioGptModel.from_pretrained(UpperCAmelCase	)
					self.assertIsNotNone(UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()
				a_      				=						3
				a_      				=						input_dict["""input_ids"""]
				a_      				=						input_ids.ne(1	).to(UpperCAmelCase	)
				a_      				=						ids_tensor([self.model_tester.batch_size]					,   self.model_tester.type_sequence_label_size	)
				a_      				=						BioGptForSequenceClassification(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   labels=UpperCAmelCase	)
				self.assertEqual(result.logits.shape					,   (self.model_tester.batch_size, self.model_tester.num_labels)	)






			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()
				a_      				=						3
				a_      				=						"""multi_label_classification"""
				a_      				=						input_dict["""input_ids"""]
				a_      				=						input_ids.ne(1	).to(UpperCAmelCase	)
				a_      				=						ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]					,   self.model_tester.type_sequence_label_size	).to(torch.float	)
				a_      				=						BioGptForSequenceClassification(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						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 					lowerCAmelCase__					(       self	):
				a_      				=						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""	)
				a_      				=						torch.tensor([[2, 48_05, 9, 6_56, 21]]	)
				a_      				=						model(UpperCAmelCase	)[0]

				a_      				=						4_23_84

				a_      				=						torch.Size((1, 5, vocab_size)	)
				self.assertEqual(output.shape					,   UpperCAmelCase	)

				a_      				=						torch.tensor(
				    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]	)

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






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""	)
				a_      				=						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""	)
				model.to(UpperCAmelCase	)

				torch.manual_seed(0	)
				a_      				=						tokenizer("""COVID-19 is"""					,   return_tensors="""pt"""	).to(UpperCAmelCase	)
				a_      				=						model.generate(
				    **UpperCAmelCase					,   min_length=1_00					,   max_length=10_24					,   num_beams=5					,   early_stopping=UpperCAmelCase					,   )
				a_      				=						tokenizer.decode(output_ids[0]					,   skip_special_tokens=UpperCAmelCase	)

				a_      				=						(
				    """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	)
