'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   UnCLIPImageVariationPipeline
   __lowerCAmelCase         =   IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
   __lowerCAmelCase         =   IMAGE_VARIATION_BATCH_PARAMS

   __lowerCAmelCase         =   [
       "generator",
       "return_dict",
       "decoder_num_inference_steps",
       "super_res_num_inference_steps",
   ]
   __lowerCAmelCase         =   False
   @property
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Union[str, Any]:
        return 32
   @property
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[str]:
        return 32
   @property
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Dict:
        return self.time_input_dim
   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        return self.time_input_dim * 4
   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        return 1_00
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[Any]:
        _A    		=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
        return tokenizer
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Dict:
        torch.manual_seed(0     )
        _A    		=  CLIPTextConfig(
            bos_token_id=0, eos_token_id=2, hidden_size=self.text_embedder_hidden_size, projection_dim=self.text_embedder_hidden_size, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=10_00, )
        return CLIPTextModelWithProjection(UpperCamelCase__     )
   @property
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[str]:
        torch.manual_seed(0     )
        _A    		=  CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size, projection_dim=self.text_embedder_hidden_size, num_hidden_layers=5, num_attention_heads=4, image_size=32, intermediate_size=37, patch_size=1, )
        return CLIPVisionModelWithProjection(UpperCamelCase__     )
   @property
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Dict:
        torch.manual_seed(0     )

        _A    		=  {
            'clip_embeddings_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'cross_attention_dim': self.cross_attention_dim,
        }

        _A    		=  UnCLIPTextProjModel(**UpperCamelCase__     )
        return model
   @property
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        torch.manual_seed(0     )

        _A    		=  {
            'sample_size': 32,
            # RGB in channels
            'in_channels': 3,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 6,
            '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,
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': 'identity',
        }

        _A    		=  UNetaDConditionModel(**UpperCamelCase__     )
        return model
   @property
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Any:
        return {
            "sample_size": 64,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }
   @property
   def 	__UpperCAmelCase   (      self      :			int     )  ->			int:
        torch.manual_seed(0     )

        _A    		=  UNetaDModel(**self.dummy_super_res_kwargs     )
        return model
   @property
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Any:
        # seeded differently to get different unet than `self.dummy_super_res_first`
        torch.manual_seed(1     )

        _A    		=  UNetaDModel(**self.dummy_super_res_kwargs     )
        return model
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[Any]:
        _A    		=  self.dummy_decoder
        _A    		=  self.dummy_text_proj
        _A    		=  self.dummy_text_encoder
        _A    		=  self.dummy_tokenizer
        _A    		=  self.dummy_super_res_first
        _A    		=  self.dummy_super_res_last

        _A    		=  UnCLIPScheduler(
            variance_type='learned_range', prediction_type='epsilon', num_train_timesteps=10_00, )

        _A    		=  UnCLIPScheduler(
            variance_type='fixed_small_log', prediction_type='epsilon', num_train_timesteps=10_00, )

        _A    		=  CLIPImageProcessor(crop_size=32, size=32     )

        _A    		=  self.dummy_image_encoder

        return {
            "decoder": decoder,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_proj": text_proj,
            "feature_extractor": feature_extractor,
            "image_encoder": image_encoder,
            "super_res_first": super_res_first,
            "super_res_last": super_res_last,
            "decoder_scheduler": decoder_scheduler,
            "super_res_scheduler": super_res_scheduler,
        }
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Any=0, UpperCamelCase__      :			int=True     )  ->			str:
        _A    		=  floats_tensor((1, 3, 32, 32), rng=random.Random(UpperCamelCase__     )     ).to(UpperCamelCase__     )
        if str(UpperCamelCase__     ).startswith('mps'     ):
             _A    		=  torch.manual_seed(UpperCamelCase__     )
        else:
             _A    		=  torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )

        if pil_image:
             _A    		=  input_image * 0.5 + 0.5
             _A    		=  input_image.clamp(0, 1     )
             _A    		=  input_image.cpu().permute(0, 2, 3, 1     ).float().numpy()
             _A    		=  DiffusionPipeline.numpy_to_pil(UpperCamelCase__     )[0]

        return {
            "image": input_image,
            "generator": generator,
            "decoder_num_inference_steps": 2,
            "super_res_num_inference_steps": 2,
            "output_type": "np",
        }
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        _A    		=  'cpu'

        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(**UpperCamelCase__     )
        _A    		=  output.images

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(
            **UpperCamelCase__, return_dict=UpperCamelCase__, )[0]

        _A    		=  image[0, -3:, -3:, -1]
        _A    		=  image_from_tuple[0, -3:, -3:, -1]

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

        _A    		=  np.array(
            [
                0.9_997,
                0.0_002,
                0.9_997,
                0.9_997,
                0.9_969,
                0.0_023,
                0.9_997,
                0.9_969,
                0.9_970,
            ]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Any:
        _A    		=  'cpu'

        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(**UpperCamelCase__     )
        _A    		=  output.images

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(
            **UpperCamelCase__, return_dict=UpperCamelCase__, )[0]

        _A    		=  image[0, -3:, -3:, -1]
        _A    		=  image_from_tuple[0, -3:, -3:, -1]

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

        _A    		=  np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Any:
        _A    		=  'cpu'

        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )
        _A    		=  [
            pipeline_inputs['image'],
            pipeline_inputs['image'],
        ]

        _A    		=  pipe(**UpperCamelCase__     )
        _A    		=  output.images

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )
        _A    		=  [
            tuple_pipeline_inputs['image'],
            tuple_pipeline_inputs['image'],
        ]

        _A    		=  pipe(
            **UpperCamelCase__, return_dict=UpperCamelCase__, )[0]

        _A    		=  image[0, -3:, -3:, -1]
        _A    		=  image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)

        _A    		=  np.array(
            [
                0.9_997,
                0.9_989,
                0.0_008,
                0.0_021,
                0.9_960,
                0.0_018,
                0.0_014,
                0.0_002,
                0.9_933,
            ]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[Any]:
        _A    		=  torch.device('cpu'     )







        class lowercase_       :







           """simple docstring"""

           __lowerCAmelCase         =   1






        _A    		=  self.get_dummy_components()

        _A    		=  self.pipeline_class(**UpperCamelCase__     )
        _A    		=  pipe.to(UpperCamelCase__     )

        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  torch.Generator(device=UpperCamelCase__     ).manual_seed(0     )
        _A    		=  pipe.decoder.dtype
        _A    		=  1

        _A    		=  (
            batch_size,
            pipe.decoder.config.in_channels,
            pipe.decoder.config.sample_size,
            pipe.decoder.config.sample_size,
        )
        _A    		=  pipe.prepare_latents(
            UpperCamelCase__, dtype=UpperCamelCase__, device=UpperCamelCase__, generator=UpperCamelCase__, latents=UpperCamelCase__, scheduler=DummyScheduler()     )

        _A    		=  (
            batch_size,
            pipe.super_res_first.config.in_channels // 2,
            pipe.super_res_first.config.sample_size,
            pipe.super_res_first.config.sample_size,
        )
        _A    		=  pipe.prepare_latents(
            UpperCamelCase__, dtype=UpperCamelCase__, device=UpperCamelCase__, generator=UpperCamelCase__, latents=UpperCamelCase__, scheduler=DummyScheduler()     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )

        _A    		=  pipe(
            **UpperCamelCase__, decoder_latents=UpperCamelCase__, super_res_latents=UpperCamelCase__     ).images

        _A    		=  self.get_dummy_inputs(UpperCamelCase__, pil_image=UpperCamelCase__     )
        # Don't pass image, instead pass embedding
        _A    		=  pipeline_inputs.pop('image'     )
        _A    		=  pipe.image_encoder(UpperCamelCase__     ).image_embeds

        _A    		=  pipe(
            **UpperCamelCase__, decoder_latents=UpperCamelCase__, super_res_latents=UpperCamelCase__, image_embeddings=UpperCamelCase__, ).images

        # make sure passing text embeddings manually is identical
        assert np.abs(img_out_a - img_out_a     ).max() < 1e-4
   @skip_mps
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Tuple:
        _A    		=  torch_device == 'cpu'

        # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
        _A    		=  1e-2

        self._test_attention_slicing_forward_pass(
            test_max_difference=UpperCamelCase__, expected_max_diff=UpperCamelCase__     )
   @skip_mps
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        _A    		=  torch_device == 'cpu'
        _A    		=  True
        _A    		=  [
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=UpperCamelCase__, relax_max_difference=UpperCamelCase__, additional_params_copy_to_batched_inputs=UpperCamelCase__, )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Dict:
        _A    		=  [
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
             # TODO: MPS errors with larger batch sizes
             _A    		=  [2, 3]
             self._test_inference_batch_consistent(
                 batch_sizes=UpperCamelCase__, additional_params_copy_to_batched_inputs=UpperCamelCase__, )
        else:
             self._test_inference_batch_consistent(
                 additional_params_copy_to_batched_inputs=UpperCamelCase__     )
   @skip_mps
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Tuple:
        return super().test_dict_tuple_outputs_equivalent()
   @skip_mps
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Optional[Any]:
        return super().test_save_load_local()

   @skip_mps
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			str:
        return super().test_save_load_optional_components()







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







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			str     )  ->			Any:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        _A    		=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'     )
        _A    		=  load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'     )

        _A    		=  UnCLIPImageVariationPipeline.from_pretrained(
            'kakaobrain/karlo-v1-alpha-image-variations', torch_dtype=torch.floataa     )
        _A    		=  pipeline.to(UpperCamelCase__     )
        pipeline.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  torch.Generator(device='cpu'     ).manual_seed(0     )
        _A    		=  pipeline(
            UpperCamelCase__, generator=UpperCamelCase__, output_type='np', )

        _A    		=  output.images[0]

        assert image.shape == (2_56, 2_56, 3)

        assert_mean_pixel_difference(UpperCamelCase__, UpperCamelCase__, 15     )










"""simple docstring"""





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


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							Tuple									=		"""marian"""
				__snake_case :							Any									=		["""past_key_values"""]
				__snake_case :							Optional[Any]									=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self	:List[Any]    ,     __lowercase	:Any=5_8101    ,     __lowercase	:Tuple=None    ,     __lowercase	:Union[str, Any]=1024    ,     __lowercase	:Dict=12    ,     __lowercase	:int=4096    ,     __lowercase	:int=16    ,     __lowercase	:List[Any]=12    ,     __lowercase	:Dict=4096    ,     __lowercase	:Dict=16    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:Tuple=0.0    ,     __lowercase	:List[Any]=True    ,     __lowercase	:int=True    ,     __lowercase	:Tuple="gelu"    ,     __lowercase	:str=1024    ,     __lowercase	:Optional[int]=0.1    ,     __lowercase	:List[str]=0.0    ,     __lowercase	:Union[str, Any]=0.0    ,     __lowercase	:Dict=0.02    ,     __lowercase	:Tuple=5_8100    ,     __lowercase	:Optional[Any]=False    ,     __lowercase	:int=5_8100    ,     __lowercase	:Any=0    ,     __lowercase	:str=0    ,     __lowercase	:str=True    ,     **__lowercase	:Any    ,     ):
											__lowerCamelCase   :   List[Any]       =vocab_size
											__lowerCamelCase   :   Optional[int]       =decoder_vocab_size or vocab_size
											__lowerCamelCase   :   Tuple       =max_position_embeddings
											__lowerCamelCase   :   List[Any]       =d_model
											__lowerCamelCase   :   Any       =encoder_ffn_dim
											__lowerCamelCase   :   str       =encoder_layers
											__lowerCamelCase   :   List[str]       =encoder_attention_heads
											__lowerCamelCase   :   str       =decoder_ffn_dim
											__lowerCamelCase   :   Tuple       =decoder_layers
											__lowerCamelCase   :   Any       =decoder_attention_heads
											__lowerCamelCase   :   List[Any]       =dropout
											__lowerCamelCase   :   Any       =attention_dropout
											__lowerCamelCase   :   Union[str, Any]       =activation_dropout
											__lowerCamelCase   :   Optional[int]       =activation_function
											__lowerCamelCase   :   Dict       =init_std
											__lowerCamelCase   :   List[Any]       =encoder_layerdrop
											__lowerCamelCase   :   Optional[Any]       =decoder_layerdrop
											__lowerCamelCase   :   Any       =use_cache
											__lowerCamelCase   :   Any       =encoder_layers
											__lowerCamelCase   :   Optional[int]       =scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCamelCase   :   int       =share_encoder_decoder_embeddings
											super().__init__(
											    pad_token_id=__lowercase    ,     eos_token_id=__lowercase    ,     is_encoder_decoder=__lowercase    ,     decoder_start_token_id=__lowercase    ,     forced_eos_token_id=__lowercase    ,     **__lowercase    ,     )

class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def        __lowercase	(  self	:int      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )

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

																		if self.use_past:
																									self.fill_with_past_key_values_(__lowercase    ,     direction='''inputs'''      )
											elif self.task == "causal-lm":
																		# TODO: figure this case out.
																		__lowerCamelCase   :   Any       =OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]      )
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   str       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   List[str]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											else:
																		__lowerCamelCase   :   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
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def        __lowercase	(  self	:Optional[int]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   Tuple       =super().outputs
											else:
																		__lowerCamelCase   :   List[str]       =super(__lowercase    ,     self      ).outputs
																		if self.use_past:
																									__lowerCamelCase     ,    __lowerCamelCase   :   int       =self.num_layers
																									for i in range(__lowercase      ):
																																__lowerCamelCase   :   Any       ={0: '''batch''', 2: '''past_sequence + sequence'''}
																																__lowerCamelCase   :   Optional[int]       ={0: '''batch''', 2: '''past_sequence + sequence'''}
											return common_outputs




				def        __lowercase	(  self	:str    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

											# Generate decoder inputs
											__lowerCamelCase   :   Optional[Any]       =seq_length if not self.use_past else 1
											__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											__lowerCamelCase   :   Dict       ={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
											__lowerCamelCase   :   str       =dict(**__lowercase    ,     **__lowercase      )

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

																		__lowerCamelCase   :   Optional[int]       =torch.cat(
																		    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowercase    ,     __lowercase      )]    ,     dim=1      )

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

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




				def        __lowercase	(  self	:Dict    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											__lowerCamelCase   :   List[str]       =self._generate_dummy_inputs_for_encoder_and_decoder(
											    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )

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

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




				def        __lowercase	(  self	:int    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = 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
											__lowerCamelCase   :   Union[str, Any]       =compute_effective_axis_dimension(
											    __lowercase    ,     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
											__lowerCamelCase   :   Optional[int]       =tokenizer.num_special_tokens_to_add(__lowercase      )
											__lowerCamelCase   :   Optional[int]       =compute_effective_axis_dimension(
											    __lowercase    ,     fixed_dimension=OnnxConfig.default_fixed_sequence    ,     num_token_to_add=__lowercase      )

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




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:PreTrainedTokenizer    ,     __lowercase	:int = -1    ,     __lowercase	:int = -1    ,     __lowercase	:bool = False    ,     __lowercase	:Optional[TensorType] = None    ,     ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   int       =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											else:
																		__lowerCamelCase   :   List[Any]       =self._generate_dummy_inputs_for_causal_lm(
																		    __lowercase    ,     batch_size=__lowercase    ,     seq_length=__lowercase    ,     is_pair=__lowercase    ,     framework=__lowercase      )

											return common_inputs




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Tuple    ,     __lowercase	:Tuple    ,     __lowercase	:Dict    ,     __lowercase	:List[str]      ):
											if self.task in ["default", "seq2seq-lm"]:
																		__lowerCamelCase   :   str       =super()._flatten_past_key_values_(__lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )
											else:
																		__lowerCamelCase   :   Optional[Any]       =super(__lowercase    ,     self      )._flatten_past_key_values_(
																		    __lowercase    ,     __lowercase    ,     __lowercase    ,     __lowercase      )




				@property
				def        __lowercase	(  self	:List[str]      ):
											return 1e-4




