

'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    KandinskyVaaInpaintPipeline
						__UpperCAmelCase			     =    ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						__UpperCAmelCase			     =    [
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						__UpperCAmelCase			     =    [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						__UpperCAmelCase			     =    False







						@property
						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								return 3_2







						@property
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								return 3_2







						@property
						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								return self.time_input_dim







						@property
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								return self.time_input_dim * 4







						@property
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								return 1_0_0







						@property
						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								torch.manual_seed(0  )

								snake_case__						:       int       						=  {
								    '''in_channels''': 9,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''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''': '''image_proj''',
								    '''cross_attention_dim''': self.cross_attention_dim,
								    '''attention_head_dim''': 4,
								    '''resnet_time_scale_shift''': '''scale_shift''',
								    '''class_embed_type''': None,
								}

								snake_case__						:       Any       						=  UNetaDConditionModel(**snake_case_  )
								return model







						@property
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								return {
								    "block_out_channels": [3_2, 6_4],
								    "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": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}







						@property
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								torch.manual_seed(0  )
								snake_case__						:       Optional[int]       						=  VQModel(**self.dummy_movq_kwargs  )
								return model







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  self.dummy_unet
								snake_case__						:       Tuple       						=  self.dummy_movq

								snake_case__						:       Union[str, Any]       						=  DDIMScheduler(
								    num_train_timesteps=1_0_0_0			,			beta_schedule='''linear'''			,			beta_start=0.0_0_0_8_5			,			beta_end=0.0_1_2			,			clip_sample=snake_case_			,			set_alpha_to_one=snake_case_			,			steps_offset=1			,			prediction_type='''epsilon'''			,			thresholding=snake_case_			,			)

								snake_case__						:       Union[str, Any]       						=  {
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      List[str]=0  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  floats_tensor((1, self.text_embedder_hidden_size)			,			rng=random.Random(snake_case_  )  ).to(snake_case_  )
								snake_case__						:       str       						=  floats_tensor((1, self.text_embedder_hidden_size)			,			rng=random.Random(seed + 1  )  ).to(
								    snake_case_  )
								# create init_image
								snake_case__						:       Optional[int]       						=  floats_tensor((1, 3, 6_4, 6_4)			,			rng=random.Random(snake_case_  )  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  image.cpu().permute(0			,			2			,			3			,			1  )[0]
								snake_case__						:       Dict       						=  Image.fromarray(np.uinta(snake_case_  )  ).convert('''RGB'''  ).resize((2_5_6, 2_5_6)  )
								# create mask
								snake_case__						:       Tuple       						=  np.ones((6_4, 6_4)			,			dtype=np.floataa  )
								snake_case__						:       Optional[Any]       						=  0

								if str(snake_case_  ).startswith('''mps'''  ):
										snake_case__						:       Optional[int]       						=  torch.manual_seed(snake_case_  )
								else:
										snake_case__						:       Any       						=  torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )
								snake_case__						:       List[str]       						=  {
								    '''image''': init_image,
								    '''mask_image''': mask,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 4.0,
								    '''output_type''': '''np''',
								}
								return inputs







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  '''cpu'''

								snake_case__						:       int       						=  self.get_dummy_components()

								snake_case__						:       Any       						=  self.pipeline_class(**snake_case_  )
								snake_case__						:       List[str]       						=  pipe.to(snake_case_  )

								pipe.set_progress_bar_config(disable=snake_case_  )

								snake_case__						:       Optional[Any]       						=  pipe(**self.get_dummy_inputs(snake_case_  )  )
								snake_case__						:       int       						=  output.images

								snake_case__						:       Optional[Any]       						=  pipe(
								    **self.get_dummy_inputs(snake_case_  )			,			return_dict=snake_case_			,			)[0]

								snake_case__						:       Tuple       						=  image[0, -3:, -3:, -1]
								snake_case__						:       Any       						=  image_from_tuple[0, -3:, -3:, -1]

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

								assert image.shape == (1, 6_4, 6_4, 3)

								snake_case__						:       Tuple       						=  np.array(
								    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]  )

								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       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



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





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






						"""simple docstring"""




						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''  )

								snake_case__						:       Any       						=  load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''  )
								snake_case__						:       Optional[Any]       						=  np.ones((7_6_8, 7_6_8)			,			dtype=np.floataa  )
								snake_case__						:       Tuple       						=  0

								snake_case__						:       Tuple       						=  '''a hat'''

								snake_case__						:       str       						=  KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''			,			torch_dtype=torch.floataa  )
								pipe_prior.to(snake_case_  )

								snake_case__						:       Any       						=  KandinskyVaaInpaintPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''			,			torch_dtype=torch.floataa  )
								snake_case__						:       Dict       						=  pipeline.to(snake_case_  )
								pipeline.set_progress_bar_config(disable=snake_case_  )

								snake_case__						:       List[Any]       						=  torch.Generator(device='''cpu'''  ).manual_seed(0  )
								snake_case__						:       List[str]       						=  pipe_prior(
								    snake_case_			,			generator=snake_case_			,			num_inference_steps=5			,			negative_prompt=''''''			,			).to_tuple()

								snake_case__						:       Optional[Any]       						=  pipeline(
								    image=snake_case_			,			mask_image=snake_case_			,			image_embeds=snake_case_			,			negative_image_embeds=snake_case_			,			generator=snake_case_			,			num_inference_steps=1_0_0			,			height=7_6_8			,			width=7_6_8			,			output_type='''np'''			,			)

								snake_case__						:       Dict       						=  output.images[0]

								assert image.shape == (7_6_8, 7_6_8, 3)

								assert_mean_pixel_difference(snake_case_			,			snake_case_  )






'''simple docstring'''


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

lowerCAmelCase__						:       Optional[int]										=						{
    """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			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """bart"""
						__UpperCAmelCase			     =    ["""past_key_values"""]
						__UpperCAmelCase			     =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







						def __init__(							self					:      Optional[Any]			,			snake_case_					:      Tuple=5_0_2_6_5			,			snake_case_					:      Dict=1_0_2_4			,			snake_case_					:      int=1_2			,			snake_case_					:      int=4_0_9_6			,			snake_case_					:      str=1_6			,			snake_case_					:      List[Any]=1_2			,			snake_case_					:      List[Any]=4_0_9_6			,			snake_case_					:      Any=1_6			,			snake_case_					:      str=0.0			,			snake_case_					:      Optional[int]=0.0			,			snake_case_					:      List[Any]="gelu"			,			snake_case_					:      List[Any]=1_0_2_4			,			snake_case_					:      Union[str, Any]=0.1			,			snake_case_					:      int=0.0			,			snake_case_					:      Tuple=0.0			,			snake_case_					:      Optional[Any]=0.0_2			,			snake_case_					:      Dict=0.0			,			snake_case_					:      str=False			,			snake_case_					:      Optional[int]=True			,			snake_case_					:      Any=3			,			snake_case_					:      int=1			,			snake_case_					:      int=0			,			snake_case_					:      Optional[Any]=2			,			snake_case_					:      str=True			,			snake_case_					:      int=2			,			snake_case_					:      Union[str, Any]=2			,			**snake_case_					:      int			,			):



								'''simple docstring'''



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

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

								# ensure backward compatibility for BART CNN models
								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''			,			snake_case_  ):
										snake_case__						:       Union[str, Any]       						=  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			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						@property
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



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

										if self.use_past:
												snake_case__						:       List[str]       						=  {0: '''batch'''}
												snake_case__						:       int       						=  {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										else:
												snake_case__						:       List[Any]       						=  {0: '''batch''', 1: '''decoder_sequence'''}
												snake_case__						:       Optional[int]       						=  {0: '''batch''', 1: '''decoder_sequence'''}

										if self.use_past:
												self.fill_with_past_key_values_(snake_case_			,			direction='''inputs'''  )
								elif self.task == "causal-lm":
										# TODO: figure this case out.
										snake_case__						:       int       						=  OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]  )
										if self.use_past:
												snake_case__						,       snake_case__						:       Tuple       						=  self.num_layers
												for i in range(snake_case_  ):
														snake_case__						:       List[str]       						=  {0: '''batch''', 2: '''past_sequence + sequence'''}
														snake_case__						:       Tuple       						=  {0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
										snake_case__						:       List[str]       						=  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       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
										snake_case__						:       str       						=  super().outputs
								else:
										snake_case__						:       List[Any]       						=  super(snake_case_			,			self  ).outputs
										if self.use_past:
												snake_case__						,       snake_case__						:       Dict       						=  self.num_layers
												for i in range(snake_case_  ):
														snake_case__						:       Any       						=  {0: '''batch''', 2: '''past_sequence + sequence'''}
														snake_case__						:       Optional[int]       						=  {0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      PreTrainedTokenizer			,			snake_case_					:      int = -1			,			snake_case_					:      int = -1			,			snake_case_					:      bool = False			,			snake_case_					:      Optional[TensorType] = None			,			):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    snake_case_			,			snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )

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

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

										snake_case__						:       Optional[Any]       						=  torch.cat(
										    [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case_			,			snake_case_  )]			,			dim=1  )

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

										for _ in range(snake_case_  ):
												common_inputs["past_key_values"].append(
												    (
												        torch.zeros(snake_case_  ),
												        torch.zeros(snake_case_  ),
												        torch.zeros(snake_case_  ),
												        torch.zeros(snake_case_  ),
												    )  )
										# TODO: test this.
										snake_case__						:       int       						=  encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
										for _ in range(snake_case_			,			snake_case_  ):
												common_inputs["past_key_values"].append((torch.zeros(snake_case_  ), torch.zeros(snake_case_  ))  )
								return common_inputs







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      PreTrainedTokenizer			,			snake_case_					:      int = -1			,			snake_case_					:      int = -1			,			snake_case_					:      bool = False			,			snake_case_					:      Optional[TensorType] = None			,			):



								'''simple docstring'''



								snake_case__						:       Any       						=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    snake_case_			,			snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )

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

										snake_case__						:       List[Any]       						=  common_inputs['''attention_mask'''].dtype
										snake_case__						:       List[Any]       						=  torch.cat(
										    [common_inputs['''attention_mask'''], torch.ones(snake_case_			,			snake_case_			,			dtype=snake_case_  )]			,			dim=1  )
										snake_case__						:       Optional[Any]       						=  [
										    (torch.zeros(snake_case_  ), torch.zeros(snake_case_  )) for _ in range(snake_case_  )
										]
								return common_inputs







						def       __magic_name__							(							self					:      int			,			snake_case_					:      PreTrainedTokenizer			,			snake_case_					:      int = -1			,			snake_case_					:      int = -1			,			snake_case_					:      bool = False			,			snake_case_					:      Optional[TensorType] = None			,			):



								'''simple docstring'''



								snake_case__						:       str       						=  compute_effective_axis_dimension(
								    snake_case_			,			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
								snake_case__						:       List[Any]       						=  tokenizer.num_special_tokens_to_add(snake_case_  )
								snake_case__						:       List[str]       						=  compute_effective_axis_dimension(
								    snake_case_			,			fixed_dimension=OnnxConfig.default_fixed_sequence			,			num_token_to_add=snake_case_  )

								# Generate dummy inputs according to compute batch and sequence
								snake_case__						:       Any       						=  [''' '''.join([tokenizer.unk_token]  ) * seq_length] * batch_size
								snake_case__						:       Any       						=  dict(tokenizer(snake_case_			,			return_tensors=snake_case_  )  )
								return common_inputs







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      PreTrainedTokenizer			,			snake_case_					:      int = -1			,			snake_case_					:      int = -1			,			snake_case_					:      bool = False			,			snake_case_					:      Optional[TensorType] = None			,			):



								'''simple docstring'''



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

								elif self.task == "causal-lm":
										snake_case__						:       Optional[Any]       						=  self._generate_dummy_inputs_for_causal_lm(
										    snake_case_			,			batch_size=snake_case_			,			seq_length=snake_case_			,			is_pair=snake_case_			,			framework=snake_case_  )
								else:
										snake_case__						:       Optional[int]       						=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    snake_case_			,			batch_size=snake_case_			,			seq_length=snake_case_			,			is_pair=snake_case_			,			framework=snake_case_  )

								return common_inputs







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      List[str]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Any			,			snake_case_					:      Any  ):



								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
										snake_case__						:       Optional[Any]       						=  super()._flatten_past_key_values_(snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )
								else:
										snake_case__						:       Union[str, Any]       						=  super(snake_case_			,			self  )._flatten_past_key_values_(
										    snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )



