


import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		_snake_case  (      _snake_case						,    _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       StableDiffusionDiffEditPipeline
 SCREAMING_SNAKE_CASE__      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
 SCREAMING_SNAKE_CASE__      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
 SCREAMING_SNAKE_CASE__      =       frozenset(
     []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 SCREAMING_SNAKE_CASE__      =       frozenset([]							)




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )
       a						:Dict 							=       UNetaDConditionModel(
           block_out_channels=(32, 64)					,  layers_per_block=2					,  sample_size=32					,  in_channels=4					,  out_channels=4					,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,  cross_attention_dim=32					,  attention_head_dim=(2, 4)					,  use_linear_projection=_lowerCamelCase					,  )
       a						:int 							=       DDIMScheduler(
           beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='''scaled_linear'''					,  clip_sample=_lowerCamelCase					,  set_alpha_to_one=_lowerCamelCase					,  )
       a						:Tuple 							=       DDIMInverseScheduler(
           beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='''scaled_linear'''					,  clip_sample=_lowerCamelCase					,  set_alpha_to_zero=_lowerCamelCase					,  )
       torch.manual_seed(0    )
       a						:Dict 							=       AutoencoderKL(
           block_out_channels=[32, 64]					,  in_channels=3					,  out_channels=3					,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,  latent_channels=4					,  sample_size=128					,  )
       torch.manual_seed(0    )
       a						:int 							=       CLIPTextConfig(
           bos_token_id=0					,  eos_token_id=2					,  hidden_size=32					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1000					,  hidden_act='''gelu'''					,  projection_dim=512					,  )
       a						:Optional[int] 							=       CLIPTextModel(_lowerCamelCase    )
       a						:str 							=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

       a						:Optional[Any] 							=       {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''inverse_scheduler''': inverse_scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }

       return components




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:Optional[Any] 							=       floats_tensor((1, 16, 16)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       a						:Tuple 							=       floats_tensor((1, 2, 4, 16, 16)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:int 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:int 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:Dict 							=       {
           '''prompt''': '''a dog and a newt''',
           '''mask_image''': mask,
           '''image_latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''inpaint_strength''': 1.0,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }

       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:str 							=       floats_tensor((1, 3, 32, 32)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       a						:str 							=       image.cpu().permute(0					,  2					,  3					,  1    )[0]
       a						:Optional[Any] 							=       Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    )
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:Dict 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:Any 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:Union[str, Any] 							=       {
           '''image''': image,
           '''source_prompt''': '''a cat and a frog''',
           '''target_prompt''': '''a dog and a newt''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''num_maps_per_mask''': 2,
           '''mask_encode_strength''': 1.0,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }

       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:List[str] 							=       floats_tensor((1, 3, 32, 32)					,  rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
       a						:int 							=       image.cpu().permute(0					,  2					,  3					,  1    )[0]
       a						:Optional[Any] 							=       Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('''RGB'''    )
       if str(_lowerCamelCase    ).startswith('''mps'''    ):
             a						:Dict 							=       torch.manual_seed(_lowerCamelCase    )
       else:
             a						:Any 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
       a						:List[Any] 							=       {
           '''image''': image,
           '''prompt''': '''a cat and a frog''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''inpaint_strength''': 1.0,
           '''guidance_scale''': 6.0,
           '''decode_latents''': True,
           '''output_type''': '''numpy''',
       }
       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if not hasattr(self.pipeline_class					,  '''_optional_components'''    ):
             return

       a						:Any 							=       self.get_dummy_components()
       a						:str 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       # set all optional components to None and update pipeline config accordingly
       for optional_component in pipe._optional_components:
             setattr(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

       a						:Union[str, Any] 							=       self.get_dummy_inputs(_lowerCamelCase    )
       a						:List[Any] 							=       pipe(**_lowerCamelCase    )[0]

       with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(_lowerCamelCase    )
             a						:int 							=       self.pipeline_class.from_pretrained(_lowerCamelCase    )
             pipe_loaded.to(_lowerCamelCase    )
             pipe_loaded.set_progress_bar_config(disable=_lowerCamelCase    )

       for optional_component in pipe._optional_components:
             self.assertTrue(
                 getattr(_lowerCamelCase					,  _lowerCamelCase    ) is None					,  F'''`{optional_component}` did not stay set to None after loading.'''					,  )

       a						:Dict 							=       self.get_dummy_inputs(_lowerCamelCase    )
       a						:Tuple 							=       pipe_loaded(**_lowerCamelCase    )[0]

       a						:List[str] 							=       np.abs(output - output_loaded    ).max()
       self.assertLess(_lowerCamelCase					,  1e-4    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       '''cpu'''

       a						:Optional[int] 							=       self.get_dummy_components()
       a						:List[str] 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Any 							=       self.get_dummy_mask_inputs(_lowerCamelCase    )
       a						:str 							=       pipe.generate_mask(**_lowerCamelCase    )
       a						:List[str] 							=       mask[0, -3:, -3:]

       self.assertEqual(mask.shape					,  (1, 16, 16)    )
       a						:List[Any] 							=       np.array([0] * 9    )
       a						:str 							=       np.abs(mask_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )
       self.assertEqual(mask[0, -3, -4]					,  0    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       '''cpu'''

       a						:List[str] 							=       self.get_dummy_components()
       a						:List[Any] 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:str 							=       self.get_dummy_inversion_inputs(_lowerCamelCase    )
       a						:Any 							=       pipe.invert(**_lowerCamelCase    ).images
       a						:Union[str, Any] 							=       image[0, -1, -3:, -3:]

       self.assertEqual(image.shape					,  (2, 32, 32, 3)    )
       a						:str 							=       np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]					,  )
       a						:str 							=       np.abs(image_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       super().test_inference_batch_single_identical(expected_max_diff=5e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       '''cpu'''

       a						:str 							=       self.get_dummy_components()

       a						:Union[str, Any] 							=       {'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
       a						:Dict 							=       DPMSolverMultistepScheduler(**_lowerCamelCase    )
       a						:List[str] 							=       DPMSolverMultistepInverseScheduler(**_lowerCamelCase    )

       a						:int 							=       self.pipeline_class(**_lowerCamelCase    )
       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:str 							=       self.get_dummy_inversion_inputs(_lowerCamelCase    )
       a						:Tuple 							=       pipe.invert(**_lowerCamelCase    ).images
       a						:Any 							=       image[0, -1, -3:, -3:]

       self.assertEqual(image.shape					,  (2, 32, 32, 3)    )
       a						:int 							=       np.array(
           [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]					,  )
       a						:Tuple 							=       np.abs(image_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(_lowerCamelCase					,  1e-3    )







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




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




 @classmethod
 def        SCREAMING_SNAKE_CASE__				(		cls    ):
       a						:Tuple 							=       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''    )

       a						:Union[str, Any] 							=       raw_image.convert('''RGB'''    ).resize((768, 768)    )

       a						:List[Any] 							=       raw_image




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       torch.manual_seed(0    )

       a						:int 							=       StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1'''					,  safety_checker=_lowerCamelCase					,  torch_dtype=torch.floataa    )
       a						:Optional[int] 							=       DDIMScheduler.from_config(pipe.scheduler.config    )
       a						:Optional[int] 							=       DDIMInverseScheduler.from_config(pipe.scheduler.config    )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Optional[Any] 							=       '''a bowl of fruit'''
       a						:Any 							=       '''a bowl of pears'''

       a						:Any 							=       pipe.generate_mask(
           image=self.raw_image					,  source_prompt=_lowerCamelCase					,  target_prompt=_lowerCamelCase					,  generator=_lowerCamelCase					,  )

       a						:Dict 							=       pipe.invert(
           prompt=_lowerCamelCase					,  image=self.raw_image					,  inpaint_strength=0.7					,  generator=_lowerCamelCase    ).latents

       a						:List[str] 							=       pipe(
           prompt=_lowerCamelCase					,  mask_image=_lowerCamelCase					,  image_latents=_lowerCamelCase					,  generator=_lowerCamelCase					,  negative_prompt=_lowerCamelCase					,  inpaint_strength=0.7					,  output_type='''numpy'''					,  ).images[0]

       a						:List[str] 							=       (
           np.array(
               load_image(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
           / 255
       )
       assert np.abs((expected_image - image).max()    ) < 5e-1




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       torch.manual_seed(0    )

       a						:List[Any] 							=       StableDiffusionDiffEditPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-1'''					,  safety_checker=_lowerCamelCase					,  torch_dtype=torch.floataa    )
       a						:Any 							=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
       a						:Union[str, Any] 							=       DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Dict 							=       '''a bowl of fruit'''
       a						:Optional[Any] 							=       '''a bowl of pears'''

       a						:Tuple 							=       pipe.generate_mask(
           image=self.raw_image					,  source_prompt=_lowerCamelCase					,  target_prompt=_lowerCamelCase					,  generator=_lowerCamelCase					,  )

       a						:Dict 							=       pipe.invert(
           prompt=_lowerCamelCase					,  image=self.raw_image					,  inpaint_strength=0.7					,  generator=_lowerCamelCase					,  num_inference_steps=25					,  ).latents

       a						:str 							=       pipe(
           prompt=_lowerCamelCase					,  mask_image=_lowerCamelCase					,  image_latents=_lowerCamelCase					,  generator=_lowerCamelCase					,  negative_prompt=_lowerCamelCase					,  inpaint_strength=0.7					,  num_inference_steps=25					,  output_type='''numpy'''					,  ).images[0]

       a						:List[Any] 							=       (
           np.array(
               load_image(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   '''/diffedit/pears.png'''    ).resize((768, 768)    )    )
           / 255
       )
       assert np.abs((expected_image - image).max()    ) < 5e-1









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

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


snake_case				:    List[str]       				=     logging.get_logger(__name__)

snake_case				:    int       				=     {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'blenderbot-small'
 SCREAMING_SNAKE_CASE__      =       ['past_key_values']
 SCREAMING_SNAKE_CASE__      =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




 def __init__(		self					,  _lowerCamelCase=5_0265					,  _lowerCamelCase=512					,  _lowerCamelCase=8					,  _lowerCamelCase=2048					,  _lowerCamelCase=16					,  _lowerCamelCase=8					,  _lowerCamelCase=2048					,  _lowerCamelCase=16					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.0					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=512					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1					,  _lowerCamelCase=False					,  _lowerCamelCase=0					,  _lowerCamelCase=1					,  _lowerCamelCase=2					,  _lowerCamelCase=2					,  **_lowerCamelCase					,  ):
       a						:Dict 							=       vocab_size
       a						:Optional[Any] 							=       max_position_embeddings
       a						:str 							=       d_model
       a						:Any 							=       encoder_ffn_dim
       a						:Optional[int] 							=       encoder_layers
       a						:List[str] 							=       encoder_attention_heads
       a						:List[str] 							=       decoder_ffn_dim
       a						:Optional[int] 							=       decoder_layers
       a						:str 							=       decoder_attention_heads
       a						:List[str] 							=       dropout
       a						:Optional[int] 							=       attention_dropout
       a						:Dict 							=       activation_dropout
       a						:List[str] 							=       activation_function
       a						:List[Any] 							=       init_std
       a						:Optional[int] 							=       encoder_layerdrop
       a						:Tuple 							=       decoder_layerdrop
       a						:List[str] 							=       use_cache
       a						:int 							=       encoder_layers
       a						:Union[str, Any] 							=       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					,  decoder_start_token_id=_lowerCamelCase					,  forced_eos_token_id=_lowerCamelCase					,  **_lowerCamelCase					,  )







class 		_snake_case  (      _snake_case							):




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Optional[Any] 							=       OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]    )

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

             if self.use_past:
                   self.fill_with_past_key_values_(_lowerCamelCase					,  direction='''inputs'''    )
       elif self.task == "causal-lm":
             # TODO: figure this case out.
             a						:Optional[int] 							=       OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]    )
             if self.use_past:
                   a							,				a						:str 							=       self.num_layers
                   for i in range(_lowerCamelCase    ):
                         a						:List[Any] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                         a						:List[str] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
             a						:Optional[int] 							=       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        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:List[Any] 							=       super().outputs
       else:
             a						:Union[str, Any] 							=       super(_lowerCamelCase					,  self    ).outputs
             if self.use_past:
                   a							,				a						:int 							=       self.num_layers
                   for i in range(_lowerCamelCase    ):
                         a						:int 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                         a						:Optional[Any] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       a						:Tuple 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

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

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

             a						:Union[str, Any] 							=       torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase					,  _lowerCamelCase    )]					,  dim=1    )

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

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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       a						:Tuple 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

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

             a						:Tuple 							=       common_inputs['''attention_mask'''].dtype
             a						:Any 							=       torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(_lowerCamelCase					,  _lowerCamelCase					,  dtype=_lowerCamelCase    )]					,  dim=1    )
             a						:Any 							=       [
                 (torch.zeros(_lowerCamelCase    ), torch.zeros(_lowerCamelCase    )) for _ in range(_lowerCamelCase    )
             ]
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       a						:Optional[Any] 							=       compute_effective_axis_dimension(
           _lowerCamelCase					,  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
       a						:Optional[int] 							=       tokenizer.num_special_tokens_to_add(_lowerCamelCase    )
       a						:Tuple 							=       compute_effective_axis_dimension(
           _lowerCamelCase					,  fixed_dimension=OnnxConfig.default_fixed_sequence					,  num_token_to_add=_lowerCamelCase    )

       # Generate dummy inputs according to compute batch and sequence
       a						:List[str] 							=       [''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
       a						:Dict 							=       dict(tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase    )    )
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Tuple 							=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )

       elif self.task == "causal-lm":
             a						:Dict 							=       self._generate_dummy_inputs_for_causal_lm(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )
       else:
             a						:Dict 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )

       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Optional[int] 							=       super()._flatten_past_key_values_(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       else:
             a						:Any 							=       super(_lowerCamelCase					,  self    )._flatten_past_key_values_(
                 _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )





