"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCamelCase      : Optional[Any]       =logging.get_logger(__name__)

lowerCamelCase      : str       ={
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			"plbart"
    _UpperCAmelCase       =			["past_key_values"]
    _UpperCAmelCase       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
    def __init__(  self					,       __lowerCamelCase=50005					,       __lowerCamelCase=1024					,       __lowerCamelCase=6					,       __lowerCamelCase=3072					,       __lowerCamelCase=12					,       __lowerCamelCase=6					,       __lowerCamelCase=3072					,       __lowerCamelCase=12					,       __lowerCamelCase=0.0					,       __lowerCamelCase=0.0					,       __lowerCamelCase=True					,       __lowerCamelCase=True					,       __lowerCamelCase="gelu"					,       __lowerCamelCase=768					,       __lowerCamelCase=0.1					,       __lowerCamelCase=0.1					,       __lowerCamelCase=0.0					,       __lowerCamelCase=0.02					,       __lowerCamelCase=0.0					,       __lowerCamelCase=True					,       __lowerCamelCase=1					,       __lowerCamelCase=0					,       __lowerCamelCase=2					,       __lowerCamelCase=2					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



         __A					:					Any								=		vocab_size
         __A					:					Optional[int]								=		max_position_embeddings
         __A					:					List[Any]								=		d_model
         __A					:					Tuple								=		encoder_ffn_dim
         __A					:					List[str]								=		encoder_layers
         __A					:					Dict								=		encoder_attention_heads
         __A					:					str								=		decoder_ffn_dim
         __A					:					Dict								=		decoder_layers
         __A					:					List[str]								=		decoder_attention_heads
         __A					:					Optional[Any]								=		dropout
         __A					:					List[str]								=		attention_dropout
         __A					:					str								=		activation_dropout
         __A					:					Tuple								=		activation_function
         __A					:					Optional[Any]								=		init_std
         __A					:					Dict								=		encoder_layerdrop
         __A					:					Tuple								=		decoder_layerdrop
         __A					:					Optional[int]								=		classifier_dropout
         __A					:					Dict								=		use_cache
         __A					:					Tuple								=		encoder_layers
         __A					:					List[str]								=		scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=__lowerCamelCase					,       bos_token_id=__lowerCamelCase					,       eos_token_id=__lowerCamelCase					,       is_encoder_decoder=__lowerCamelCase					,       forced_eos_token_id=__lowerCamelCase					,       **__lowerCamelCase					,       )







class 							__snake_case(					A_	):


    '''simple docstring'''





    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return OrderedDict(
             [
                 ('input_ids', {0: 'batch', 1: 'sequence'}),
                 ('attention_mask', {0: 'batch', 1: 'sequence'}),
             ]	)

    @property
    def 						_a							(  self	):




         '''simple docstring'''



         if self.use_past:
              return OrderedDict(
                  [
                      ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                      ('past_keys', {0: 'batch', 2: 'sequence'}),
                      ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
                  ]	)
         else:
              return OrderedDict(
                  [
                      ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                      ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
                  ]	)




"""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					,       )



