




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 						__lowercase		(_a     ,		unittest.TestCase							):




      """simple docstring"""





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

      @property
      def 	UpperCAmelCase      (      self	)		->				int:
         return 3_2

      @property
      def 	UpperCAmelCase      (      self	)		->				str:
         return 3_2

      @property
      def 	UpperCAmelCase      (      self	)		->				Tuple:
         return self.time_input_dim * 4

      @property
      def 	UpperCAmelCase      (      self	)		->				Tuple:
         return 8

      @property
      def 	UpperCAmelCase      (      self	)		->				Any:
         torch.manual_seed(0	)
         snake_case      :				Union[str, Any]				=       CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size				,      image_size=6_4				,      projection_dim=self.text_embedder_hidden_size				,      intermediate_size=3_7				,      num_attention_heads=4				,      num_channels=3				,      num_hidden_layers=5				,      patch_size=1				,      )

         snake_case      :				Optional[int]				=       CLIPVisionModel(__lowerCamelCase	)
         return model

      @property
      def 	UpperCAmelCase      (      self	)		->				Optional[Any]:
         snake_case      :				Optional[int]				=       CLIPImageProcessor(
             crop_size=2_2_4				,      do_center_crop=__lowerCamelCase				,      do_normalize=__lowerCamelCase				,      do_resize=__lowerCamelCase				,      image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,      image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,      resample=3				,      size=2_2_4				,      )

         return image_processor

      @property
      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         torch.manual_seed(0	)

         snake_case      :				Dict				=       {
             """num_attention_heads""": 2,
             """attention_head_dim""": 1_6,
             """embedding_dim""": self.time_input_dim,
             """num_embeddings""": 3_2,
             """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,
         }

         snake_case      :				int				=       PriorTransformer(**__lowerCamelCase	)
         return model

      @property
      def 	UpperCAmelCase      (      self	)		->				Any:
         torch.manual_seed(0	)

         snake_case      :				str				=       {
             """param_shapes""": (
                 (self.renderer_dim, 9_3),
                 (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""": 1_2,
             """background""": (
                 0.1,
                 0.1,
                 0.1,
             ),
         }
         snake_case      :				List[str]				=       ShapERenderer(**__lowerCamelCase	)
         return model

      def 	UpperCAmelCase      (      self	)		->				List[str]:
         snake_case      :				int				=       self.dummy_prior
         snake_case      :				Any				=       self.dummy_image_encoder
         snake_case      :				Dict				=       self.dummy_image_processor
         snake_case      :				List[Any]				=       self.dummy_renderer

         snake_case      :				int				=       HeunDiscreteScheduler(
             beta_schedule="""exp"""				,      num_train_timesteps=1_0_2_4				,      prediction_type="""sample"""				,      use_karras_sigmas=__lowerCamelCase				,      clip_sample=__lowerCamelCase				,      clip_sample_range=1.0				,      )
         snake_case      :				Optional[Any]				=       {
             """prior""": prior,
             """image_encoder""": image_encoder,
             """image_processor""": image_processor,
             """renderer""": renderer,
             """scheduler""": scheduler,
         }

         return components

      def 	UpperCAmelCase      (      self				,      A				,      A=0	)		->				Optional[int]:
         snake_case      :				Any				=       floats_tensor((1, 3, 6_4, 6_4)				,      rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         if str(__lowerCamelCase	).startswith("""mps"""	):
            snake_case      :				List[Any]				=       torch.manual_seed(__lowerCamelCase	)
         else:
            snake_case      :				Optional[int]				=       torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)
         snake_case      :				Optional[Any]				=       {
             """image""": input_image,
             """generator""": generator,
             """num_inference_steps""": 1,
             """frame_size""": 3_2,
             """output_type""": """np""",
         }
         return inputs

      def 	UpperCAmelCase      (      self	)		->				Any:
         snake_case      :				Dict				=       """cpu"""

         snake_case      :				List[Any]				=       self.get_dummy_components()

         snake_case      :				Optional[int]				=       self.pipeline_class(**__lowerCamelCase	)
         snake_case      :				int				=       pipe.to(__lowerCamelCase	)

         pipe.set_progress_bar_config(disable=__lowerCamelCase	)

         snake_case      :				Optional[Any]				=       pipe(**self.get_dummy_inputs(__lowerCamelCase	)	)
         snake_case      :				Dict				=       output.images[0]
         snake_case      :				List[Any]				=       image[0, -3:, -3:, -1]

         assert image.shape == (2_0, 3_2, 3_2, 3)

         snake_case      :				Dict				=       np.array(
             [
                 0.00_03_92_16,
                 0.00_03_92_16,
                 0.00_03_92_16,
                 0.00_03_92_16,
                 0.00_03_92_16,
                 0.00_03_92_16,
                 0.00_03_92_16,
                 0.00_03_92_16,
                 0.00_03_92_16,
             ]	)

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

      def 	UpperCAmelCase      (      self	)		->				int:
         # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
         self._test_inference_batch_consistent(batch_sizes=[1, 2]	)

      def 	UpperCAmelCase      (      self	)		->				int:
         snake_case      :				str				=       torch_device == """cpu"""
         snake_case      :				int				=       True
         self._test_inference_batch_single_identical(
             batch_size=2				,      test_max_difference=__lowerCamelCase				,      relax_max_difference=__lowerCamelCase				,      )

      def 	UpperCAmelCase      (      self	)		->				str:
         snake_case      :				List[Any]				=       self.get_dummy_components()
         snake_case      :				Optional[int]				=       self.pipeline_class(**__lowerCamelCase	)
         snake_case      :				List[Any]				=       pipe.to(__lowerCamelCase	)
         pipe.set_progress_bar_config(disable=__lowerCamelCase	)

         snake_case      :				Any				=       1
         snake_case      :				int				=       2

         snake_case      :				Union[str, Any]				=       self.get_dummy_inputs(__lowerCamelCase	)

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

         snake_case      :				Optional[int]				=       pipe(**__lowerCamelCase				,      num_images_per_prompt=__lowerCamelCase	)[0]

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




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




      """simple docstring"""





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

      def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
         snake_case      :				Optional[Any]				=       load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""	)
         snake_case      :				Any				=       load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/shap_e/test_shap_e_img2img_out.npy"""	)
         snake_case      :				Union[str, Any]				=       ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""	)
         snake_case      :				List[str]				=       pipe.to(__lowerCamelCase	)
         pipe.set_progress_bar_config(disable=__lowerCamelCase	)

         snake_case      :				Optional[Any]				=       torch.Generator(device=__lowerCamelCase	).manual_seed(0	)

         snake_case      :				Optional[int]				=       pipe(
             __lowerCamelCase				,      generator=__lowerCamelCase				,      guidance_scale=3.0				,      num_inference_steps=6_4				,      frame_size=6_4				,      output_type="""np"""				,      ).images[0]

         assert images.shape == (2_0, 6_4, 6_4, 3)

         assert_mean_pixel_difference(__lowerCamelCase				,      __lowerCamelCase	)













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

from packaging import version

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


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


UpperCAmelCase_ :						Dict      =      logging.get_logger(__name__)

UpperCAmelCase_ :						str      =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Optional[int]     	=  """layoutlmv3"""





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Optional[Any]=50_265 ,  __lowerCamelCase				:					Dict=768 ,  __lowerCamelCase				:					Any=12 ,  __lowerCamelCase				:					int=12 ,  __lowerCamelCase				:					str=3_072 ,  __lowerCamelCase				:					List[Any]="gelu" ,  __lowerCamelCase				:					List[str]=0.1 ,  __lowerCamelCase				:					Optional[int]=0.1 ,  __lowerCamelCase				:					Optional[Any]=512 ,  __lowerCamelCase				:					Optional[int]=2 ,  __lowerCamelCase				:					Union[str, Any]=0.02 ,  __lowerCamelCase				:					Union[str, Any]=1E-5 ,  __lowerCamelCase				:					Any=1 ,  __lowerCamelCase				:					Optional[int]=0 ,  __lowerCamelCase				:					List[Any]=2 ,  __lowerCamelCase				:					Dict=1_024 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=128 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					str=32 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=64 ,  __lowerCamelCase				:					List[str]=256 ,  __lowerCamelCase				:					Dict=True ,  __lowerCamelCase				:					List[Any]=True ,  __lowerCamelCase				:					Tuple=True ,  __lowerCamelCase				:					Tuple=224 ,  __lowerCamelCase				:					Tuple=3 ,  __lowerCamelCase				:					Dict=16 ,  __lowerCamelCase				:					Union[str, Any]=None ,  **__lowerCamelCase				:					Optional[Any] ,  ):
		super().__init__(
		    vocab_size=__lowerCamelCase ,  hidden_size=__lowerCamelCase ,  num_hidden_layers=__lowerCamelCase ,  num_attention_heads=__lowerCamelCase ,  intermediate_size=__lowerCamelCase ,  hidden_act=__lowerCamelCase ,  hidden_dropout_prob=__lowerCamelCase ,  attention_probs_dropout_prob=__lowerCamelCase ,  max_position_embeddings=__lowerCamelCase ,  type_vocab_size=__lowerCamelCase ,  initializer_range=__lowerCamelCase ,  layer_norm_eps=__lowerCamelCase ,  pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :int          =	max_ad_position_embeddings
		UpperCamelCase     :Tuple          =	coordinate_size
		UpperCamelCase     :List[Any]          =	shape_size
		UpperCamelCase     :Union[str, Any]          =	has_relative_attention_bias
		UpperCamelCase     :Any          =	rel_pos_bins
		UpperCamelCase     :Optional[Any]          =	max_rel_pos
		UpperCamelCase     :str          =	has_spatial_attention_bias
		UpperCamelCase     :Tuple          =	rel_ad_pos_bins
		UpperCamelCase     :Optional[int]          =	max_rel_ad_pos
		UpperCamelCase     :Tuple          =	text_embed
		UpperCamelCase     :str          =	visual_embed
		UpperCamelCase     :Optional[Any]          =	input_size
		UpperCamelCase     :str          =	num_channels
		UpperCamelCase     :List[Any]          =	patch_size
		UpperCamelCase     :Optional[Any]          =	classifier_dropout

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     int     	=  version.parse("""1.12"""      )





	@property
	def 						_A (		self				:					Optional[int]	):
		# The order of inputs is different for question answering and sequence classification
		if self.task in ["question-answering", "sequence-classification"]:
			return OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
			        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
			    ]	)
		else:
			return OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
			        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
			    ]	)





	@property
	def 						_A (		self				:					str	):
		return 1E-5





	@property
	def 						_A (		self				:					Dict	):
		return 12





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					"ProcessorMixin" ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional["TensorType"] = None ,  __lowerCamelCase				:					int = 3 ,  __lowerCamelCase				:					int = 40 ,  __lowerCamelCase				:					int = 40 ,  ):
		setattr(processor.image_processor ,  """apply_ocr""" ,  __lowerCamelCase	)

		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		UpperCamelCase     :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
		UpperCamelCase     :Optional[int]          =	processor.tokenizer.num_special_tokens_to_add(__lowerCamelCase	)
		UpperCamelCase     :int          =	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
		UpperCamelCase     :Any          =	[[""" """.join([processor.tokenizer.unk_token]	) * seq_length]] * batch_size

		# Generate dummy bounding boxes
		UpperCamelCase     :Optional[Any]          =	[[[48, 84, 73, 128]]] * batch_size

		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
		UpperCamelCase     :List[str]          =	self._generate_dummy_images(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)

		UpperCamelCase     :Any          =	dict(
		    processor(
		        __lowerCamelCase ,  text=__lowerCamelCase ,  boxes=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  )	)

		return inputs
