


'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							ShapEImgaImgPipeline
  a_  					=							["""image"""]
  a_  					=							["""image"""]
  a_  					=							[
      """num_images_per_prompt""",
      """num_inference_steps""",
      """generator""",
      """latents""",
      """guidance_scale""",
      """frame_size""",
      """output_type""",
      """return_dict""",
  ]
  a_  					=							False






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return 32






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        return self.time_input_dim * 4






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

        '''simple docstring'''




        return 8






  @property
  def        _a							(							self :       Dict ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  Tuple          =				CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size	,image_size=64	,projection_dim=self.text_embedder_hidden_size	,intermediate_size=37	,num_attention_heads=4	,num_channels=3	,num_hidden_layers=5	,patch_size=1	,)

        A_						:  Optional[Any]          =				CLIPVisionModel(_a )
        return model






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  Tuple          =				CLIPImageProcessor(
            crop_size=224	,do_center_crop=_a	,do_normalize=_a	,do_resize=_a	,image_mean=[0.48145466, 0.4578275, 0.40821073]	,image_std=[0.26862954, 0.26130258, 0.27577711]	,resample=3	,size=224	,)

        return image_processor






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  Tuple          =				{
            """num_attention_heads""": 2,
            """attention_head_dim""": 16,
            """embedding_dim""": self.time_input_dim,
            """num_embeddings""": 32,
            """embedding_proj_dim""": self.text_embedder_hidden_size,
            """time_embed_dim""": self.time_embed_dim,
            """num_layers""": 1,
            """clip_embed_dim""": self.time_input_dim * 2,
            """additional_embeddings""": 0,
            """time_embed_act_fn""": """gelu""",
            """norm_in_type""": """layer""",
            """embedding_proj_norm_type""": """layer""",
            """encoder_hid_proj_type""": None,
            """added_emb_type""": None,
        }

        A_						:  Optional[Any]          =				PriorTransformer(**_a )
        return model






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

        '''simple docstring'''




        torch.manual_seed(0 )

        A_						:  List[str]          =				{
            """param_shapes""": (
                (self.renderer_dim, 93),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
            ),
            """d_latent""": self.time_input_dim,
            """d_hidden""": self.renderer_dim,
            """n_output""": 12,
            """background""": (
                0.1,
                0.1,
                0.1,
            ),
        }
        A_						:  List[Any]          =				ShapERenderer(**_a )
        return model






  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  str          =				self.dummy_prior
        A_						:  Union[str, Any]          =				self.dummy_image_encoder
        A_						:  List[Any]          =				self.dummy_image_processor
        A_						:  Union[str, Any]          =				self.dummy_renderer

        A_						:  List[Any]          =				HeunDiscreteScheduler(
            beta_schedule="""exp"""	,num_train_timesteps=1024	,prediction_type="""sample"""	,use_karras_sigmas=_a	,clip_sample=_a	,clip_sample_range=1.0	,)
        A_						:  Tuple          =				{
            """prior""": prior,
            """image_encoder""": image_encoder,
            """image_processor""": image_processor,
            """renderer""": renderer,
            """scheduler""": scheduler,
        }

        return components






  def        _a							(							self :       int	,_a :       Dict	,_a :       List[str]=0 ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				floats_tensor((1, 3, 64, 64)	,rng=random.Random(_a ) ).to(_a )

        if str(_a ).startswith("""mps""" ):
              A_						:  Optional[int]          =				torch.manual_seed(_a )
        else:
              A_						:  List[str]          =				torch.Generator(device=_a ).manual_seed(_a )
        A_						:  Tuple          =				{
            """image""": input_image,
            """generator""": generator,
            """num_inference_steps""": 1,
            """frame_size""": 32,
            """output_type""": """np""",
        }
        return inputs






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				"""cpu"""

        A_						:  Tuple          =				self.get_dummy_components()

        A_						:  Optional[int]          =				self.pipeline_class(**_a )
        A_						:  Optional[int]          =				pipe.to(_a )

        pipe.set_progress_bar_config(disable=_a )

        A_						:  Optional[Any]          =				pipe(**self.get_dummy_inputs(_a ) )
        A_						:  Tuple          =				output.images[0]
        A_						:  List[Any]          =				image[0, -3:, -3:, -1]

        assert image.shape == (20, 32, 32, 3)

        A_						:  Dict          =				np.array(
            [
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
                0.00039216,
            ] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        self._test_inference_batch_consistent(batch_sizes=[1, 2] )






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  int          =				torch_device == """cpu"""
        A_						:  List[Any]          =				True
        self._test_inference_batch_single_identical(
            batch_size=2	,test_max_difference=_a	,relax_max_difference=_a	,)






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




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

        A_						:  str          =				1
        A_						:  str          =				2

        A_						:  List[Any]          =				self.get_dummy_inputs(_a )

        for key in inputs.keys():
              if key in self.batch_params:
                    A_						:  str          =				batch_size * [inputs[key]]

        A_						:  Dict          =				pipe(**_a	,num_images_per_prompt=_a )[0]

        assert images.shape[0] == batch_size * num_images_per_prompt







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


  '''simple docstring'''







  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




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






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Any          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
        A_						:  int          =				load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/shap_e/test_shap_e_img2img_out.npy""" )
        A_						:  Tuple          =				ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
        A_						:  Union[str, Any]          =				pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )

        A_						:  Dict          =				torch.Generator(device=_a ).manual_seed(0 )

        A_						:  Optional[Any]          =				pipe(
            _a	,generator=_a	,guidance_scale=3.0	,num_inference_steps=64	,frame_size=64	,output_type="""np"""	,).images[0]

        assert images.shape == (20, 64, 64, 3)

        assert_mean_pixel_difference(_a	,_a )




'''simple docstring'''


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

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


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""deberta-v2"""






  def __init__(							self :       Optional[Any]	,_a :       Union[str, Any]=128100	,_a :       Optional[int]=1536	,_a :       Dict=24	,_a :       int=24	,_a :       Tuple=6144	,_a :       Union[str, Any]="gelu"	,_a :       List[Any]=0.1	,_a :       Dict=0.1	,_a :       int=512	,_a :       int=0	,_a :       int=0.02	,_a :       int=1e-7	,_a :       List[str]=False	,_a :       Union[str, Any]=-1	,_a :       List[Any]=0	,_a :       Optional[Any]=True	,_a :       Tuple=None	,_a :       Any=0	,_a :       int="gelu"	,**_a :       Any	,):

        '''simple docstring'''




        super().__init__(**_a )

        A_						:  Union[str, Any]          =				hidden_size
        A_						:  Dict          =				num_hidden_layers
        A_						:  Union[str, Any]          =				num_attention_heads
        A_						:  List[Any]          =				intermediate_size
        A_						:  List[Any]          =				hidden_act
        A_						:  Optional[int]          =				hidden_dropout_prob
        A_						:  Dict          =				attention_probs_dropout_prob
        A_						:  int          =				max_position_embeddings
        A_						:  Any          =				type_vocab_size
        A_						:  List[Any]          =				initializer_range
        A_						:  int          =				relative_attention
        A_						:  Tuple          =				max_relative_positions
        A_						:  int          =				pad_token_id
        A_						:  Tuple          =				position_biased_input

        # Backwards compatibility
        if type(_a ) == str:
              A_						:  str          =				[x.strip() for x in pos_att_type.lower().split("""|""" )]

        A_						:  Any          =				pos_att_type
        A_						:  Optional[int]          =				vocab_size
        A_						:  Tuple          =				layer_norm_eps

        A_						:  Any          =				kwargs.get("""pooler_hidden_size"""	,_a )
        A_						:  Union[str, Any]          =				pooler_dropout
        A_						:  List[Any]          =				pooler_hidden_act







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        if self.task == "multiple-choice":
              A_						:  Any          =				{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
              A_						:  Any          =				{0: """batch""", 1: """sequence"""}
        if self._config.type_vocab_size > 0:
              return OrderedDict(
                  [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
        else:
              return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return 12






  def        _a							(							self :       int	,_a :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,_a :       int = -1	,_a :       int = -1	,_a :       int = -1	,_a :       bool = False	,_a :       Optional["TensorType"] = None	,_a :       int = 3	,_a :       int = 40	,_a :       int = 40	,_a :       "PreTrainedTokenizerBase" = None	,):

        '''simple docstring'''




        A_						:  Any          =				super().generate_dummy_inputs(preprocessor=_a	,framework=_a )
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
              del dummy_inputs["token_type_ids"]
        return dummy_inputs
