
'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		__lowercase (     _UpperCamelCase					,   _UpperCamelCase					,   _UpperCamelCase					,   unittest.TestCase			):
			lowerCamelCase	:						List[str]							=	StableDiffusionInstructPixaPixPipeline
			lowerCamelCase	:						Optional[Any]							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
			lowerCamelCase	:						List[Any]							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase	:						Any							=	IMAGE_TO_IMAGE_IMAGE_PARAMS
			lowerCamelCase	:						List[str]							=	IMAGE_TO_IMAGE_IMAGE_PARAMS

			def 				UpperCAmelCase__       (self					):
								torch.manual_seed(0					)
								lowerCamelCase_    :					Tuple   =						UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)	,      layers_per_block=2	,      sample_size=3_2	,      in_channels=8	,      out_channels=4	,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,      cross_attention_dim=3_2	,      )
								lowerCamelCase_    :					Optional[Any]   =						PNDMScheduler(skip_prk_steps=lowerCAmelCase_					)
								torch.manual_seed(0					)
								lowerCamelCase_    :					Any   =						AutoencoderKL(
								    block_out_channels=[3_2, 6_4]	,      in_channels=3	,      out_channels=3	,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']	,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']	,      latent_channels=4	,      )
								torch.manual_seed(0					)
								lowerCamelCase_    :					Dict   =						CLIPTextConfig(
								    bos_token_id=0	,      eos_token_id=2	,      hidden_size=3_2	,      intermediate_size=3_7	,      layer_norm_eps=1E-05	,      num_attention_heads=4	,      num_hidden_layers=5	,      pad_token_id=1	,      vocab_size=1_0_0_0	,      )
								lowerCamelCase_    :					List[str]   =						CLIPTextModel(lowerCAmelCase_					)
								lowerCamelCase_    :					Tuple   =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

								lowerCamelCase_    :					Union[str, Any]   =						{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components

			def 				UpperCAmelCase__       (self	,      A	,      A=0					):
								lowerCamelCase_    :					str   =						floats_tensor((1, 3, 3_2, 3_2)	,      rng=random.Random(lowerCAmelCase_					)					).to(lowerCAmelCase_					)
								lowerCamelCase_    :					Optional[Any]   =						image.cpu().permute(0	,      2	,      3	,      1					)[0]
								lowerCamelCase_    :					str   =						Image.fromarray(np.uinta(lowerCAmelCase_					)					).convert('''RGB'''					)
								if str(lowerCAmelCase_					).startswith('''mps'''					):
													lowerCamelCase_    :					List[str]   =						torch.manual_seed(lowerCAmelCase_					)
								else:
													lowerCamelCase_    :					Dict   =						torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
								lowerCamelCase_    :					Tuple   =						{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''image_guidance_scale''': 1,
								    '''output_type''': '''numpy''',
								}
								return inputs

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					Tuple   =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase_    :					Optional[Any]   =						self.get_dummy_components()
								lowerCamelCase_    :					str   =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_					)
								lowerCamelCase_    :					Tuple   =						sd_pipe.to(lowerCAmelCase_					)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

								lowerCamelCase_    :					Any   =						self.get_dummy_inputs(lowerCAmelCase_					)
								lowerCamelCase_    :					Optional[Any]   =						sd_pipe(**lowerCAmelCase_					).images
								lowerCamelCase_    :					List[str]   =						image[0, -3:, -3:, -1]
								assert image.shape == (1, 3_2, 3_2, 3)
								lowerCamelCase_    :					str   =						np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					Optional[Any]   =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase_    :					Optional[Any]   =						self.get_dummy_components()
								lowerCamelCase_    :					Any   =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_					)
								lowerCamelCase_    :					Optional[Any]   =						sd_pipe.to(lowerCAmelCase_					)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

								lowerCamelCase_    :					int   =						self.get_dummy_inputs(lowerCAmelCase_					)
								lowerCamelCase_    :					int   =						'''french fries'''
								lowerCamelCase_    :					str   =						sd_pipe(**lowerCAmelCase_	,      negative_prompt=lowerCAmelCase_					)
								lowerCamelCase_    :					int   =						output.images
								lowerCamelCase_    :					int   =						image[0, -3:, -3:, -1]

								assert image.shape == (1, 3_2, 3_2, 3)
								lowerCamelCase_    :					Any   =						np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					Any   =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase_    :					Any   =						self.get_dummy_components()
								lowerCamelCase_    :					Tuple   =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_					)
								lowerCamelCase_    :					Tuple   =						sd_pipe.to(lowerCAmelCase_					)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

								lowerCamelCase_    :					Optional[Any]   =						self.get_dummy_inputs(lowerCAmelCase_					)
								lowerCamelCase_    :					List[str]   =						[inputs['''prompt''']] * 2

								lowerCamelCase_    :					Optional[Any]   =						np.array(inputs['''image''']					).astype(np.floataa					) / 2_5_5.0
								lowerCamelCase_    :					List[Any]   =						torch.from_numpy(lowerCAmelCase_					).unsqueeze(0					).to(lowerCAmelCase_					)
								lowerCamelCase_    :					int   =						image / 2 + 0.5
								lowerCamelCase_    :					Tuple   =						image.permute(0	,      3	,      1	,      2					)
								lowerCamelCase_    :					Optional[int]   =						image.repeat(2	,      1	,      1	,      1					)

								lowerCamelCase_    :					Union[str, Any]   =						sd_pipe(**lowerCAmelCase_					).images
								lowerCamelCase_    :					str   =						image[-1, -3:, -3:, -1]

								assert image.shape == (2, 3_2, 3_2, 3)
								lowerCamelCase_    :					str   =						np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					Optional[int]   =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase_    :					Optional[int]   =						self.get_dummy_components()
								lowerCamelCase_    :					int   =						EulerAncestralDiscreteScheduler(
								    beta_start=0.0_00_85	,      beta_end=0.0_12	,      beta_schedule='''scaled_linear'''					)
								lowerCamelCase_    :					Union[str, Any]   =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_					)
								lowerCamelCase_    :					Optional[int]   =						sd_pipe.to(lowerCAmelCase_					)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

								lowerCamelCase_    :					Optional[Any]   =						self.get_dummy_inputs(lowerCAmelCase_					)
								lowerCamelCase_    :					List[Any]   =						sd_pipe(**lowerCAmelCase_					).images
								lowerCamelCase_    :					str   =						image[0, -3:, -3:, -1]

								lowerCamelCase_    :					Optional[Any]   =						[round(lowerCAmelCase_	,      4					) for x in image_slice.flatten().tolist()]
								print(''','''.join([str(lowerCAmelCase_					) for x in slice]					)					)

								assert image.shape == (1, 3_2, 3_2, 3)
								lowerCamelCase_    :					str   =						np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3

			def 				UpperCAmelCase__       (self					):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					str   =						self.get_dummy_components()
								lowerCamelCase_    :					Any   =						StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_					)
								lowerCamelCase_    :					Optional[Any]   =						VaeImageProcessor(do_resize=lowerCAmelCase_	,      do_normalize=lowerCAmelCase_					)
								lowerCamelCase_    :					Any   =						pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

								lowerCamelCase_    :					int   =						pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_	,      input_image_type='''pt'''					)					)[0]

								lowerCamelCase_    :					Union[str, Any]   =						components['''vae''']
								lowerCamelCase_    :					Any   =						self.get_dummy_inputs_by_type(lowerCAmelCase_	,      input_image_type='''pt'''					)

								for image_param in self.image_latents_params:
													if image_param in inputs.keys():
																		lowerCamelCase_    :					List[Any]   =						vae.encode(inputs[image_param]					).latent_dist.mode()

								lowerCamelCase_    :					List[Any]   =						pipe(**lowerCAmelCase_					)[0]

								lowerCamelCase_    :					int   =						np.abs(out - out_latents_inputs					).max()
								self.assertLess(lowerCAmelCase_	,      1E-4	,      '''passing latents as image input generate different result from passing image'''					)

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

			def 				UpperCAmelCase__       (self					):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()

			def 				UpperCAmelCase__       (self	,      A=0					):
								lowerCamelCase_    :					Any   =						torch.manual_seed(lowerCAmelCase_					)
								lowerCamelCase_    :					int   =						load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''					)
								lowerCamelCase_    :					str   =						{
								    '''prompt''': '''turn him into a cyborg''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''image_guidance_scale''': 1.0,
								    '''output_type''': '''numpy''',
								}
								return inputs

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					Union[str, Any]   =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''	,      safety_checker=lowerCAmelCase_					)
								pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
								pipe.enable_attention_slicing()

								lowerCamelCase_    :					Union[str, Any]   =						self.get_inputs()
								lowerCamelCase_    :					int   =						pipe(**lowerCAmelCase_					).images
								lowerCamelCase_    :					str   =						image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								lowerCamelCase_    :					Dict   =						np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]					)

								assert np.abs(expected_slice - image_slice					).max() < 1E-3

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					Optional[Any]   =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''	,      safety_checker=lowerCAmelCase_					)
								lowerCamelCase_    :					str   =						LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
								pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
								pipe.enable_attention_slicing()

								lowerCamelCase_    :					str   =						self.get_inputs()
								lowerCamelCase_    :					List[Any]   =						pipe(**lowerCAmelCase_					).images
								lowerCamelCase_    :					Optional[Any]   =						image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								lowerCamelCase_    :					Tuple   =						np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]					)

								assert np.abs(expected_slice - image_slice					).max() < 1E-3

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					List[Any]   =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''	,      safety_checker=lowerCAmelCase_					)
								lowerCamelCase_    :					str   =						DDIMScheduler.from_config(pipe.scheduler.config					)
								pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
								pipe.enable_attention_slicing()

								lowerCamelCase_    :					Any   =						self.get_inputs()
								lowerCamelCase_    :					List[str]   =						pipe(**lowerCAmelCase_					).images
								lowerCamelCase_    :					Any   =						image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								lowerCamelCase_    :					Dict   =						np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]					)

								assert np.abs(expected_slice - image_slice					).max() < 1E-3

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					List[Any]   =						0

								def callback_fn(A	,      A	,      A					) -> None:
													lowerCamelCase_    :					Any   =						True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																		lowerCamelCase_    :					Optional[Any]   =						latents.detach().cpu().numpy()
																		assert latents.shape == (1, 4, 6_4, 6_4)
																		lowerCamelCase_    :					str   =						latents[0, -3:, -3:, -1]
																		lowerCamelCase_    :					List[str]   =						np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]					)

																		assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2
													elif step == 2:
																		lowerCamelCase_    :					Dict   =						latents.detach().cpu().numpy()
																		assert latents.shape == (1, 4, 6_4, 6_4)
																		lowerCamelCase_    :					str   =						latents[0, -3:, -3:, -1]
																		lowerCamelCase_    :					int   =						np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]					)

																		assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2

								lowerCamelCase_    :					str   =						False

								lowerCamelCase_    :					Dict   =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''	,      safety_checker=lowerCAmelCase_	,      torch_dtype=torch.floataa					)
								lowerCamelCase_    :					Tuple   =						pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
								pipe.enable_attention_slicing()

								lowerCamelCase_    :					Tuple   =						self.get_inputs()
								pipe(**lowerCAmelCase_	,      callback=lowerCAmelCase_	,      callback_steps=1					)
								assert callback_fn.has_been_called
								assert number_of_steps == 3

			def 				UpperCAmelCase__       (self					):
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								lowerCamelCase_    :					int   =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''	,      safety_checker=lowerCAmelCase_	,      torch_dtype=torch.floataa					)
								lowerCamelCase_    :					Any   =						pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
								pipe.enable_attention_slicing(1					)
								pipe.enable_sequential_cpu_offload()

								lowerCamelCase_    :					Dict   =						self.get_inputs()
								lowerCamelCase_    :					Any   =						pipe(**lowerCAmelCase_					)

								lowerCamelCase_    :					List[str]   =						torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 1_0**9






			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					str   =						self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								lowerCamelCase_    :					List[str]   =						inputs['''image'''].resize((5_0_4, 5_0_4)					)

								lowerCamelCase_    :					str   =						'''timbrooks/instruct-pix2pix'''
								lowerCamelCase_    :					int   =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    lowerCAmelCase_	,      safety_checker=lowerCAmelCase_	,      )
								pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
								pipe.enable_attention_slicing()

								lowerCamelCase_    :					Tuple   =						pipe(**lowerCAmelCase_					)
								lowerCamelCase_    :					Union[str, Any]   =						output.images[0]

								lowerCamelCase_    :					Optional[Any]   =						image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

								assert image.shape == (5_0_4, 5_0_4, 3)
								lowerCamelCase_    :					Union[str, Any]   =						np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-3









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case  :    List[Any]						    =  logging.get_logger(__name__)

_snake_case  :    Any						    =  {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """pegasus"""
  a_        = ["""past_key_values"""]
  a_        = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






  def __init__(				self :		List[str]					,  lowerCAmelCase_ :		Union[str, Any]=5_0_2_6_5					,  lowerCAmelCase_ :		Union[str, Any]=1_0_2_4					,  lowerCAmelCase_ :		Union[str, Any]=1_2					,  lowerCAmelCase_ :		Dict=4_0_9_6					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		List[Any]=1_2					,  lowerCAmelCase_ :		Union[str, Any]=4_0_9_6					,  lowerCAmelCase_ :		Union[str, Any]=1_6					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Optional[int]=0.0					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		Any=True					,  lowerCAmelCase_ :		Dict="gelu"					,  lowerCAmelCase_ :		Optional[int]=1_0_2_4					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		str=0.0					,  lowerCAmelCase_ :		Union[str, Any]=0.0					,  lowerCAmelCase_ :		Optional[int]=0.02					,  lowerCAmelCase_ :		Tuple=0					,  lowerCAmelCase_ :		Tuple=False					,  lowerCAmelCase_ :		Union[str, Any]=0					,  lowerCAmelCase_ :		int=1					,  lowerCAmelCase_ :		Tuple=1					,  **lowerCAmelCase_ :		Tuple					,  )							->						List[str]:
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	max_position_embeddings
     __lowerCAmelCase								=	d_model
     __lowerCAmelCase								=	encoder_ffn_dim
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	encoder_attention_heads
     __lowerCAmelCase								=	decoder_ffn_dim
     __lowerCAmelCase								=	decoder_layers
     __lowerCAmelCase								=	decoder_attention_heads
     __lowerCAmelCase								=	dropout
     __lowerCAmelCase								=	attention_dropout
     __lowerCAmelCase								=	activation_dropout
     __lowerCAmelCase								=	activation_function
     __lowerCAmelCase								=	init_std
     __lowerCAmelCase								=	encoder_layerdrop
     __lowerCAmelCase								=	decoder_layerdrop
     __lowerCAmelCase								=	use_cache
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=lowerCAmelCase_					,  eos_token_id=lowerCAmelCase_					,  is_encoder_decoder=lowerCAmelCase_					,  decoder_start_token_id=lowerCAmelCase_					,  forced_eos_token_id=lowerCAmelCase_					,  **lowerCAmelCase_					,  )






  @property
  def 				lowercase		(				self :		List[Any]     )							->						int:
     return self.encoder_attention_heads






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						int:
     return self.d_model

