"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Any					=	ShapEPipeline
				UpperCAmelCase       :   Union[str, Any]					=	["""prompt"""]
				UpperCAmelCase       :   str					=	["""prompt"""]
				UpperCAmelCase       :   List[str]					=	[
				    """num_images_per_prompt""",
				    """num_inference_steps""",
				    """generator""",
				    """latents""",
				    """guidance_scale""",
				    """frame_size""",
				    """output_type""",
				    """return_dict""",
				]
				UpperCAmelCase       :   Dict					=	False



				@property
				def       _lowercase  (self       :   Tuple)					->			str:
					return 32



				@property
				def       _lowercase  (self       :   Tuple)					->			Optional[int]:
					return 32



				@property
				def       _lowercase  (self       :   Any)					->			str:
					return self.time_input_dim * 4



				@property
				def       _lowercase  (self       :   Any)					->			Any:
					return 8



				@property
				def       _lowercase  (self       :   List[str])					->			Dict:
					__snake_case    :							Union[str, Any]   	=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
					return tokenizer



				@property
				def       _lowercase  (self       :   Union[str, Any])					->			List[Any]:
					torch.manual_seed(0)
					__snake_case    :							Optional[Any]   	=				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(_A)



				@property
				def       _lowercase  (self       :   int)					->			Dict:
					torch.manual_seed(0)

					__snake_case    :							str   	=				{
					    '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',
					    'encoder_hid_proj_type': None,
					    'added_emb_type': None,
					}

					__snake_case    :							str   	=				PriorTransformer(**_A)
					return model



				@property
				def       _lowercase  (self       :   Optional[Any])					->			Union[str, Any]:
					torch.manual_seed(0)

					__snake_case    :							Any   	=				{
					    '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,
					    ),
					}
					__snake_case    :							int   	=				ShapERenderer(**_A)
					return model



				def       _lowercase  (self       :   List[str])					->			Optional[Any]:
					__snake_case    :							Union[str, Any]   	=				self.dummy_prior
					__snake_case    :							Tuple   	=				self.dummy_text_encoder
					__snake_case    :							Optional[int]   	=				self.dummy_tokenizer
					__snake_case    :							Optional[int]   	=				self.dummy_renderer

					__snake_case    :							Optional[int]   	=				HeunDiscreteScheduler(
					    beta_schedule='exp'			,						num_train_timesteps=10_24			,						prediction_type='sample'			,						use_karras_sigmas=_A			,						clip_sample=_A			,						clip_sample_range=1.0			,						)
					__snake_case    :							Optional[int]   	=				{
					    'prior': prior,
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'renderer': renderer,
					    'scheduler': scheduler,
					}

					return components



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   List[str]			,						_A       :   Dict=0)					->			Union[str, Any]:
					if str(_A).startswith('mps'):
						__snake_case    :							List[Any]   	=				torch.manual_seed(_A)
					else:
						__snake_case    :							Any   	=				torch.Generator(device=_A).manual_seed(_A)
					__snake_case    :							List[str]   	=				{
					    'prompt': 'horse',
					    'generator': generator,
					    'num_inference_steps': 1,
					    'frame_size': 32,
					    'output_type': 'np',
					}
					return inputs



				def       _lowercase  (self       :   Union[str, Any])					->			str:
					__snake_case    :							List[Any]   	=				'cpu'

					__snake_case    :							Optional[int]   	=				self.get_dummy_components()

					__snake_case    :							List[Any]   	=				self.pipeline_class(**_A)
					__snake_case    :							Tuple   	=				pipe.to(_A)

					pipe.set_progress_bar_config(disable=_A)

					__snake_case    :							Any   	=				pipe(**self.get_dummy_inputs(_A))
					__snake_case    :							List[str]   	=				output.images[0]
					__snake_case    :							Any   	=				image[0, -3:, -3:, -1]

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

					__snake_case    :							Dict   	=				np.array(
					    [
					        0.00_039_216,
					        0.00_039_216,
					        0.00_039_216,
					        0.00_039_216,
					        0.00_039_216,
					        0.00_039_216,
					        0.00_039_216,
					        0.00_039_216,
					        0.00_039_216,
					    ])

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



				def       _lowercase  (self       :   Tuple)					->			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       _lowercase  (self       :   int)					->			Tuple:
					__snake_case    :							List[str]   	=				torch_device == 'cpu'
					__snake_case    :							Optional[int]   	=				True

					self._test_inference_batch_single_identical(
					    batch_size=2			,						test_max_difference=_A			,						relax_max_difference=_A			,						)






				def       _lowercase  (self       :   Optional[int])					->			str:
					__snake_case    :							Dict   	=				self.get_dummy_components()
					__snake_case    :							List[Any]   	=				self.pipeline_class(**_A)
					__snake_case    :							Optional[int]   	=				pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case    :							Union[str, Any]   	=				1
					__snake_case    :							List[Any]   	=				2

					__snake_case    :							Dict   	=				self.get_dummy_inputs(_A)

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

					__snake_case    :							str   	=				pipe(**_A			,						num_images_per_prompt=_A)[0]

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







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



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






				def       _lowercase  (self       :   List[str])					->			Union[str, Any]:
					__snake_case    :							Optional[int]   	=				load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/shap_e/test_shap_e_np_out.npy')
					__snake_case    :							List[str]   	=				ShapEPipeline.from_pretrained('openai/shap-e')
					__snake_case    :							Any   	=				pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case    :							Optional[int]   	=				torch.Generator(device=_A).manual_seed(0)

					__snake_case    :							List[Any]   	=				pipe(
					    'a shark'			,						generator=_A			,						guidance_scale=15.0			,						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

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


_a  :  Optional[int]=				logging.get_logger(__name__)

_a  :  Tuple=				{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Dict					=	"""layoutlmv3"""



				def __init__(self       :   List[str]			,						_A       :   Optional[int]=5_02_65			,						_A       :   List[str]=7_68			,						_A       :   List[Any]=12			,						_A       :   List[str]=12			,						_A       :   Optional[int]=30_72			,						_A       :   str="gelu"			,						_A       :   int=0.1			,						_A       :   Tuple=0.1			,						_A       :   List[Any]=5_12			,						_A       :   List[str]=2			,						_A       :   List[Any]=0.02			,						_A       :   Tuple=1E-5			,						_A       :   Dict=1			,						_A       :   str=0			,						_A       :   str=2			,						_A       :   List[str]=10_24			,						_A       :   Optional[Any]=1_28			,						_A       :   Union[str, Any]=1_28			,						_A       :   Union[str, Any]=True			,						_A       :   Union[str, Any]=32			,						_A       :   Any=1_28			,						_A       :   Optional[Any]=64			,						_A       :   List[Any]=2_56			,						_A       :   str=True			,						_A       :   List[Any]=True			,						_A       :   Tuple=True			,						_A       :   Tuple=2_24			,						_A       :   Tuple=3			,						_A       :   Optional[Any]=16			,						_A       :   Tuple=None			,						**_A       :   Optional[int]			,						)					->			Optional[int]:
					super().__init__(
					    vocab_size=_A			,						hidden_size=_A			,						num_hidden_layers=_A			,						num_attention_heads=_A			,						intermediate_size=_A			,						hidden_act=_A			,						hidden_dropout_prob=_A			,						attention_probs_dropout_prob=_A			,						max_position_embeddings=_A			,						type_vocab_size=_A			,						initializer_range=_A			,						layer_norm_eps=_A			,						pad_token_id=_A			,						bos_token_id=_A			,						eos_token_id=_A			,						**_A			,						)
					__snake_case    :							List[Any]   	=				max_ad_position_embeddings
					__snake_case    :							List[str]   	=				coordinate_size
					__snake_case    :							int   	=				shape_size
					__snake_case    :							List[str]   	=				has_relative_attention_bias
					__snake_case    :							Union[str, Any]   	=				rel_pos_bins
					__snake_case    :							Tuple   	=				max_rel_pos
					__snake_case    :							Optional[Any]   	=				has_spatial_attention_bias
					__snake_case    :							Union[str, Any]   	=				rel_ad_pos_bins
					__snake_case    :							Any   	=				max_rel_ad_pos
					__snake_case    :							Tuple   	=				text_embed
					__snake_case    :							str   	=				visual_embed
					__snake_case    :							List[str]   	=				input_size
					__snake_case    :							List[Any]   	=				num_channels
					__snake_case    :							Union[str, Any]   	=				patch_size
					__snake_case    :							str   	=				classifier_dropout







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Optional[int]					=	version.parse("""1.12"""		)



				@property
				def       _lowercase  (self       :   Optional[int])					->			Mapping[str, Mapping[int, str]]:
					# 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       _lowercase  (self       :   Tuple)					->			float:
					return 1E-5



				@property
				def       _lowercase  (self       :   Optional[int])					->			int:
					return 12






				def       _lowercase  (self       :   str			,						_A       :   "ProcessorMixin"			,						_A       :   int = -1			,						_A       :   int = -1			,						_A       :   bool = False			,						_A       :   Optional["TensorType"] = None			,						_A       :   int = 3			,						_A       :   int = 40			,						_A       :   int = 40			,						)					->			Mapping[str, Any]:
					setattr(processor.image_processor			,						'apply_ocr'			,						_A)

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					__snake_case    :							Union[str, Any]   	=				compute_effective_axis_dimension(
					    _A			,						fixed_dimension=OnnxConfig.default_fixed_batch			,						num_token_to_add=0)
					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					__snake_case    :							Any   	=				processor.tokenizer.num_special_tokens_to_add(_A)
					__snake_case    :							Optional[int]   	=				compute_effective_axis_dimension(
					    _A			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=_A)
					# Generate dummy inputs according to compute batch and sequence
					__snake_case    :							Optional[int]   	=				[[' '.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					__snake_case    :							Dict   	=				[[[48, 84, 73, 1_28]]] * 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)
					__snake_case    :							Optional[Any]   	=				self._generate_dummy_images(_A			,						_A			,						_A			,						_A)

					__snake_case    :							Dict   	=				dict(
					    processor(
					        _A			,						text=_A			,						boxes=_A			,						return_tensors=_A			,						))

					return inputs





