"""simple docstring"""


from typing import Callable, Optional, Union

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


lowerCAmelCase_:       Tuple			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Optional[int]			     =       {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}






class  a__     ( A_						):
   snake_case_				=   "prophetnet"
   snake_case_				=   ["past_key_values"]
   snake_case_				=   {
       "num_attention_heads": "num_encoder_attention_heads",
   }


   def __init__(  self,					_UpperCAmelCase = 0.1,					_UpperCAmelCase = "gelu",					_UpperCAmelCase = 3_0522,					_UpperCAmelCase = 1024,					_UpperCAmelCase = 4096,					_UpperCAmelCase = 12,					_UpperCAmelCase = 16,					_UpperCAmelCase = 4096,					_UpperCAmelCase = 12,					_UpperCAmelCase = 16,					_UpperCAmelCase = 0.1,					_UpperCAmelCase = 0.1,					_UpperCAmelCase = 512,					_UpperCAmelCase = 0.02,					_UpperCAmelCase = True,					_UpperCAmelCase = True,					_UpperCAmelCase = 0,					_UpperCAmelCase = 2,					_UpperCAmelCase = 32,					_UpperCAmelCase = 128,					_UpperCAmelCase = False,					_UpperCAmelCase = 0.0,					_UpperCAmelCase = True,					_UpperCAmelCase = 0,					_UpperCAmelCase = 1,					_UpperCAmelCase = 2,					**_UpperCAmelCase,					):
        '''simple docstring'''



        lowercase__								=				vocab_size
        lowercase__								=				hidden_size
        lowercase__								=				encoder_ffn_dim
        lowercase__								=				num_encoder_layers
        lowercase__								=				num_encoder_attention_heads
        lowercase__								=				decoder_ffn_dim
        lowercase__								=				num_decoder_layers
        lowercase__								=				num_decoder_attention_heads
        lowercase__								=				max_position_embeddings
        lowercase__								=				init_std  # Normal(0, this parameter)
        lowercase__								=				activation_function

        # parameters for prophetnet
        lowercase__								=				ngram
        lowercase__								=				num_buckets
        lowercase__								=				relative_max_distance
        lowercase__								=				disable_ngram_loss
        lowercase__								=				eps

        # 3 Types of Dropout
        lowercase__								=				attention_dropout
        lowercase__								=				activation_dropout
        lowercase__								=				dropout

        lowercase__								=				use_cache

        super().__init__(
            pad_token_id=__lowerCamelCase,					bos_token_id=__lowerCamelCase,					eos_token_id=__lowerCamelCase,					is_encoder_decoder=__lowerCamelCase,					add_cross_attention=__lowerCamelCase,					decoder_start_token_id=__lowerCamelCase,					**__lowerCamelCase,					)


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.num_encoder_layers + self.num_decoder_layers


   @num_hidden_layers.setter
   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        raise NotImplementedError(
            "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
            " `num_decoder_layers`."	)





"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         __A					:					str								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_optional_components()
    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()					,       reason='XFormers attention is only available with CUDA and `xformers` installed'					,       )
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2					,       )



