





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:List[str]						    =   KandinskyInpaintPipeline
      _UpperCAmelCase	:List[str]						    =   ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      _UpperCAmelCase	:Dict						    =   [
          'prompt',
          'negative_prompt',
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      _UpperCAmelCase	:Optional[int]						    =   [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase	:int						    =   False





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




       return 32





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




       return 32





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




       return self.time_input_dim





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




       return self.time_input_dim * 4





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




       return 100





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




       UpperCamelCase	:					Any       	=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
       return tokenizer





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




       torch.manual_seed(0   )
       UpperCamelCase	:					Optional[int]       	=   MCLIPConfig(
           numDims=self.cross_attention_dim       ,							transformerDimensions=self.text_embedder_hidden_size       ,							hidden_size=self.text_embedder_hidden_size       ,							intermediate_size=37       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							vocab_size=1005       ,							)

       UpperCamelCase	:					Optional[int]       	=   MultilingualCLIP(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   text_encoder.eval()

       return text_encoder





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




       torch.manual_seed(0   )

       UpperCamelCase	:					Optional[int]       	=   {
           "in_channels": 9,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "text_image",
           "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
           "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
           "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "layers_per_block": 1,
           "encoder_hid_dim": self.text_embedder_hidden_size,
           "encoder_hid_dim_type": "text_image_proj",
           "cross_attention_dim": self.cross_attention_dim,
           "attention_head_dim": 4,
           "resnet_time_scale_shift": "scale_shift",
           "class_embed_type": None,
       }

       UpperCamelCase	:					List[Any]       	=   UNetaDConditionModel(**A_   )
       return model





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




       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }





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




       torch.manual_seed(0   )
       UpperCamelCase	:					List[str]       	=   VQModel(**self.dummy_movq_kwargs   )
       return model





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.dummy_text_encoder
       UpperCamelCase	:					str       	=   self.dummy_tokenizer
       UpperCamelCase	:					List[Any]       	=   self.dummy_unet
       UpperCamelCase	:					Optional[Any]       	=   self.dummy_movq

       UpperCamelCase	:					Union[str, Any]       	=   DDIMScheduler(
           num_train_timesteps=1000       ,							beta_schedule="linear"       ,							beta_start=0.0_00_85       ,							beta_end=0.0_12       ,							clip_sample=A_       ,							set_alpha_to_one=A_       ,							steps_offset=1       ,							prediction_type="epsilon"       ,							thresholding=A_       ,							)

       UpperCamelCase	:					Optional[Any]       	=   {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components





      def        __UpperCamelCase( self       ,							A_       ,							A_=0   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   floats_tensor((1, self.cross_attention_dim)       ,							rng=random.Random(A_   )   ).to(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   floats_tensor((1, self.cross_attention_dim)       ,							rng=random.Random(seed + 1   )   ).to(A_   )
       # create init_image
       UpperCamelCase	:					Union[str, Any]       	=   floats_tensor((1, 3, 64, 64)       ,							rng=random.Random(A_   )   ).to(A_   )
       UpperCamelCase	:					str       	=   image.cpu().permute(0       ,							2       ,							3       ,							1   )[0]
       UpperCamelCase	:					List[Any]       	=   Image.fromarray(np.uinta(A_   )   ).convert("RGB"   ).resize((256, 256)   )
       # create mask
       UpperCamelCase	:					str       	=   np.ones((64, 64)       ,							dtype=np.floataa   )
       UpperCamelCase	:					str       	=   0

       if str(A_   ).startswith("mps"   ):
        UpperCamelCase	:					int       	=   torch.manual_seed(A_   )
       else:
        UpperCamelCase	:					Tuple       	=   torch.Generator(device=A_   ).manual_seed(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   {
           "prompt": "horse",
           "image": init_image,
           "mask_image": mask,
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 64,
           "width": 64,
           "num_inference_steps": 2,
           "guidance_scale": 4.0,
           "output_type": "np",
       }
       return inputs





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   "cpu"

       UpperCamelCase	:					Tuple       	=   self.get_dummy_components()

       UpperCamelCase	:					str       	=   self.pipeline_class(**A_   )
       UpperCamelCase	:					Tuple       	=   pipe.to(A_   )

       pipe.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					Any       	=   pipe(**self.get_dummy_inputs(A_   )   )
       UpperCamelCase	:					List[Any]       	=   output.images

       UpperCamelCase	:					List[Any]       	=   pipe(
           **self.get_dummy_inputs(A_   )       ,							return_dict=A_       ,							)[0]

       UpperCamelCase	:					List[Any]       	=   image[0, -3:, -3:, -1]
       UpperCamelCase	:					Any       	=   image_from_tuple[0, -3:, -3:, -1]

       print(F"""image.shape {image.shape}"""   )

       assert image.shape == (1, 64, 64, 3)

       UpperCamelCase	:					Union[str, Any]       	=   np.array(
           [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

       assert (
           np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
       ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"   )

       UpperCamelCase	:					List[str]       	=   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"   )
       UpperCamelCase	:					Dict       	=   np.ones((768, 768)       ,							dtype=np.floataa   )
       UpperCamelCase	:					str       	=   0

       UpperCamelCase	:					List[Any]       	=   "a hat"

       UpperCamelCase	:					Tuple       	=   KandinskyPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-prior"       ,							torch_dtype=torch.floataa   )
       pipe_prior.to(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   KandinskyInpaintPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-inpaint"       ,							torch_dtype=torch.floataa   )
       UpperCamelCase	:					Optional[Any]       	=   pipeline.to(A_   )
       pipeline.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					List[Any]       	=   torch.Generator(device="cpu"   ).manual_seed(0   )
       UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   pipe_prior(
           A_       ,							generator=A_       ,							num_inference_steps=5       ,							negative_prompt=""       ,							).to_tuple()

       UpperCamelCase	:					Dict       	=   pipeline(
           A_       ,							image=A_       ,							mask_image=A_       ,							image_embeds=A_       ,							negative_image_embeds=A_       ,							generator=A_       ,							num_inference_steps=100       ,							height=768       ,							width=768       ,							output_type="np"       ,							)

       UpperCamelCase	:					List[str]       	=   output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(A_       ,							A_   )







import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowerCamelCase       :     Union[str, Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     Any   =     {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Dict						    =   'bart'
      _UpperCAmelCase	:str						    =   ['past_key_values']
      _UpperCAmelCase	:Any						    =   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__( self       ,							A_=5_0265       ,							A_=1024       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=0.0       ,							A_=0.0       ,							A_="gelu"       ,							A_=1024       ,							A_=0.1       ,							A_=0.0       ,							A_=0.0       ,							A_=0.02       ,							A_=0.0       ,							A_=False       ,							A_=True       ,							A_=3       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_=True       ,							A_=2       ,							A_=2       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   vocab_size
       UpperCamelCase	:					List[Any]       	=   max_position_embeddings
       UpperCamelCase	:					Any       	=   d_model
       UpperCamelCase	:					Optional[Any]       	=   encoder_ffn_dim
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   encoder_attention_heads
       UpperCamelCase	:					Optional[int]       	=   decoder_ffn_dim
       UpperCamelCase	:					List[str]       	=   decoder_layers
       UpperCamelCase	:					Optional[int]       	=   decoder_attention_heads
       UpperCamelCase	:					int       	=   dropout
       UpperCamelCase	:					int       	=   attention_dropout
       UpperCamelCase	:					Tuple       	=   activation_dropout
       UpperCamelCase	:					Tuple       	=   activation_function
       UpperCamelCase	:					int       	=   init_std
       UpperCamelCase	:					List[Any]       	=   encoder_layerdrop
       UpperCamelCase	:					List[str]       	=   decoder_layerdrop
       UpperCamelCase	:					Dict       	=   classifier_dropout
       UpperCamelCase	:					Optional[int]       	=   use_cache
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           num_labels=A_       ,							pad_token_id=A_       ,							bos_token_id=A_       ,							eos_token_id=A_       ,							is_encoder_decoder=A_       ,							decoder_start_token_id=A_       ,							forced_eos_token_id=A_       ,							**A_       ,							)

       # ensure backward compatibility for BART CNN models
       if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"       ,							A_   ):
        UpperCamelCase	:					int       	=   self.bos_token_id
        warnings.warn(
            F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
            "The config can simply be saved and uploaded again to be fixed."   )



class 					A__   (       __snake_case			):





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




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )

        if self.use_past:
         UpperCamelCase	:					List[str]       	=   {0: "batch"}
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "decoder_sequence"}
         UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
         self.fill_with_past_key_values_(A_       ,							direction="inputs"   )
       elif self.task == "causal-lm":
        # TODO: figure this case out.
        UpperCamelCase	:					Any       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					Optional[Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
       else:
        UpperCamelCase	:					Optional[Any]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
            ]   )

       return common_inputs





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




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Tuple       	=   super().outputs
       else:
        UpperCamelCase	:					Dict       	=   super(A_       ,							self   ).outputs
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					int       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					int       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Tuple       	=   {0: "batch", 2: "past_sequence + sequence"}
       return common_outputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       # Generate decoder inputs
       UpperCamelCase	:					List[Any]       	=   seq_length if not self.use_past else 1
       UpperCamelCase	:					Tuple       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )
       UpperCamelCase	:					Optional[int]       	=   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase	:					List[Any]       	=   dict(**A_       ,							**A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   common_inputs["input_ids"].shape
        UpperCamelCase	:					List[Any]       	=   common_inputs["decoder_input_ids"].shape[1]
        UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   self.num_attention_heads
        UpperCamelCase	:					int       	=   (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        UpperCamelCase	:					List[Any]       	=   decoder_seq_length + 3
        UpperCamelCase	:					str       	=   (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["decoder_attention_mask"], torch.ones(A_       ,							A_   )]       ,							dim=1   )

        UpperCamelCase	:					int       	=   []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   self.num_layers
        UpperCamelCase	:					Any       	=   min(A_       ,							A_   )
        UpperCamelCase	:					List[str]       	=   max(A_       ,							A_   ) - min_num_layers
        UpperCamelCase	:					Dict       	=   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        for _ in range(A_   ):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
             )   )
        # TODO: test this.
        UpperCamelCase	:					Optional[Any]       	=   encoder_shape if remaining_side_name == "encoder" else decoder_shape
        for _ in range(A_       ,							A_   ):
         common_inputs["past_key_values"].append((torch.zeros(A_   ), torch.zeros(A_   ))   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   common_inputs["input_ids"].shape
        # Not using the same length for past_key_values
        UpperCamelCase	:					Optional[Any]       	=   seqlen + 2
        UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.num_layers
        UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_attention_heads
        UpperCamelCase	:					str       	=   (
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        UpperCamelCase	:					Optional[Any]       	=   common_inputs["attention_mask"].dtype
        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["attention_mask"], torch.ones(A_       ,							A_       ,							dtype=A_   )]       ,							dim=1   )
        UpperCamelCase	:					Optional[Any]       	=   [
            (torch.zeros(A_   ), torch.zeros(A_   )) for _ in range(A_   )
        ]
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_batch       ,							num_token_to_add=0   )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase	:					Union[str, Any]       	=   tokenizer.num_special_tokens_to_add(A_   )
       UpperCamelCase	:					int       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_sequence       ,							num_token_to_add=A_   )

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase	:					int       	=   [" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
       UpperCamelCase	:					Dict       	=   dict(tokenizer(A_       ,							return_tensors=A_   )   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       elif self.task == "causal-lm":
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_causal_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )
       else:
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[Any]       	=   super()._flatten_past_key_values_(A_       ,							A_       ,							A_       ,							A_   )
       else:
        UpperCamelCase	:					Optional[Any]       	=   super(A_       ,							self   )._flatten_past_key_values_(
            A_       ,							A_       ,							A_       ,							A_   )
