






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


lowerCamelCase       :       int        =				logging.get_logger(__name__)

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

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					'''layoutlmv3'''



					def __init__(     self :		Union[str, Any]						,   __a :		Dict=50265						,   __a :		Dict=768						,   __a :		Tuple=12						,   __a :		Optional[Any]=12						,   __a :		Tuple=3072						,   __a :		List[str]="gelu"						,   __a :		Dict=0.1						,   __a :		Tuple=0.1						,   __a :		Optional[Any]=512						,   __a :		int=2						,   __a :		Union[str, Any]=0.02						,   __a :		Optional[int]=1E-5						,   __a :		Tuple=1						,   __a :		List[Any]=0						,   __a :		List[str]=2						,   __a :		int=1024						,   __a :		Union[str, Any]=128						,   __a :		List[str]=128						,   __a :		List[str]=True						,   __a :		Optional[Any]=32						,   __a :		Union[str, Any]=128						,   __a :		Dict=64						,   __a :		Dict=256						,   __a :		List[Any]=True						,   __a :		Tuple=True						,   __a :		List[Any]=True						,   __a :		Any=224						,   __a :		List[str]=3						,   __a :		Optional[int]=16						,   __a :		Dict=None						,   **__a :		List[str]						,   )	->     int:


												"""simple docstring"""

												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						,   )
												__lowercase					:							Tuple			=   max_ad_position_embeddings
												__lowercase					:							List[str]			=   coordinate_size
												__lowercase					:							Any			=   shape_size
												__lowercase					:							Tuple			=   has_relative_attention_bias
												__lowercase					:							Optional[Any]			=   rel_pos_bins
												__lowercase					:							Union[str, Any]			=   max_rel_pos
												__lowercase					:							int			=   has_spatial_attention_bias
												__lowercase					:							Optional[Any]			=   rel_ad_pos_bins
												__lowercase					:							Optional[int]			=   max_rel_ad_pos
												__lowercase					:							Dict			=   text_embed
												__lowercase					:							Tuple			=   visual_embed
												__lowercase					:							str			=   input_size
												__lowercase					:							int			=   num_channels
												__lowercase					:							Optional[int]			=   patch_size
												__lowercase					:							Tuple			=   classifier_dropout







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Any     =					version.parse('''1.12'''	)



					@property
					def 					lowerCAmelCase (     self :		str							)	->     Mapping[str, Mapping[int, str]]:


												"""simple docstring"""

												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 					lowerCAmelCase (     self :		int							)	->     float:


												"""simple docstring"""

												return 1E-5



					@property
					def 					lowerCAmelCase (     self :		int							)	->     int:


												"""simple docstring"""

												return 12



					def 					lowerCAmelCase (     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]:


												"""simple docstring"""

												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
												__lowercase					:							Dict			=   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
												__lowercase					:							Tuple			=   processor.tokenizer.num_special_tokens_to_add(__a							)
												__lowercase					:							List[Any]			=   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
												__lowercase					:							List[str]			=   [[""" """.join([processor.tokenizer.unk_token]							) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												__lowercase					:							Union[str, 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)
												__lowercase					:							Union[str, Any]			=   self._generate_dummy_images(__a						,   __a						,   __a						,   __a							)

												__lowercase					:							Optional[Any]			=   dict(
												    processor(
												        __a						,   text=__a						,   boxes=__a						,   return_tensors=__a						,   )							)

												return inputs







import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort

class 						lowerCAmelCase   (       __a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



					def 					lowerCAmelCase (     self :		int						,   __a :		List[Any]=0							)	->     Optional[int]:


												"""simple docstring"""

												__lowercase					:							Any			=   floats_tensor((1, 3, 128, 128)						,   rng=random.Random(__a							)							)
												__lowercase					:							Any			=   np.random.RandomState(__a							)
												__lowercase					:							Union[str, Any]			=   {
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """image""": image,
												    """generator""": generator,
												    """num_inference_steps""": 3,
												    """strength""": 0.75,
												    """guidance_scale""": 7.5,
												    """output_type""": """numpy""",
												}
												return inputs



					def 					lowerCAmelCase (     self :		Union[str, Any]							)	->     Any:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Optional[Any]			=   self.get_dummy_inputs()
												__lowercase					:							str			=   pipe(**__a							).images
												__lowercase					:							Union[str, Any]			=   image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							List[Any]			=   np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]							)
												assert np.abs(image_slice - expected_slice							).max() < 1E-1



					def 					lowerCAmelCase (     self :		Any							)	->     Any:


												"""simple docstring"""

												__lowercase					:							str			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Union[str, Any]			=   PNDMScheduler.from_config(pipe.scheduler.config						,   skip_prk_steps=__a							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Dict			=   self.get_dummy_inputs()
												__lowercase					:							Optional[int]			=   pipe(**__a							).images
												__lowercase					:							Union[str, Any]			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Any			=   np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]							)

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



					def 					lowerCAmelCase (     self :		int							)	->     str:


												"""simple docstring"""

												__lowercase					:							int			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Union[str, Any]			=   LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												# warmup pass to apply optimizations
												__lowercase					:							Optional[int]			=   pipe(**self.get_dummy_inputs()							)

												__lowercase					:							Dict			=   self.get_dummy_inputs()
												__lowercase					:							str			=   pipe(**__a							).images
												__lowercase					:							Optional[Any]			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Any			=   np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]							)

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



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							List[str]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							List[Any]			=   EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Any			=   self.get_dummy_inputs()
												__lowercase					:							int			=   pipe(**__a							).images
												__lowercase					:							Dict			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Optional[int]			=   np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]							)

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



					def 					lowerCAmelCase (     self :		List[Any]							)	->     str:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Dict			=   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Tuple			=   self.get_dummy_inputs()
												__lowercase					:							Union[str, Any]			=   pipe(**__a							).images
												__lowercase					:							int			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Union[str, Any]			=   np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]							)

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



					def 					lowerCAmelCase (     self :		Dict							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,   provider="""CPUExecutionProvider"""							)
												__lowercase					:							Tuple			=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							str			=   self.get_dummy_inputs()
												__lowercase					:							Dict			=   pipe(**__a							).images
												__lowercase					:							int			=   image[0, -3:, -3:, -1]

												assert image.shape == (1, 128, 128, 3)
												__lowercase					:							Dict			=   np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]							)

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







@nightly
@require_onnxruntime
@require_torch_gpu
class 						lowerCAmelCase   (       unittest.TestCase	):

					'''simple docstring'''

					@property
					def 					lowerCAmelCase (     self :		Tuple							)	->     Optional[int]:


												"""simple docstring"""

												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)



					@property
					def 					lowerCAmelCase (     self :		str							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   ort.SessionOptions()
												__lowercase					:							Dict			=   False
												return options



					def 					lowerCAmelCase (     self :		int							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""							)
												__lowercase					:							Union[str, Any]			=   init_image.resize((768, 512)							)
												# using the PNDM scheduler by default
												__lowercase					:							Optional[int]			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    """CompVis/stable-diffusion-v1-4"""						,   revision="""onnx"""						,   safety_checker=__a						,   feature_extractor=__a						,   provider=self.gpu_provider						,   sess_options=self.gpu_options						,   )
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							int			=   """A fantasy landscape, trending on artstation"""

												__lowercase					:							int			=   np.random.RandomState(0							)
												__lowercase					:							List[str]			=   pipe(
												    prompt=__a						,   image=__a						,   strength=0.75						,   guidance_scale=7.5						,   num_inference_steps=10						,   generator=__a						,   output_type="""np"""						,   )
												__lowercase					:							Any			=   output.images
												__lowercase					:							Optional[int]			=   images[0, 255:258, 383:386, -1]

												assert images.shape == (1, 512, 768, 3)
												__lowercase					:							Optional[int]			=   np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]							)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



					def 					lowerCAmelCase (     self :		List[Any]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg"""							)
												__lowercase					:							Any			=   init_image.resize((768, 512)							)
												__lowercase					:							int			=   LMSDiscreteScheduler.from_pretrained(
												    """runwayml/stable-diffusion-v1-5"""						,   subfolder="""scheduler"""						,   revision="""onnx"""							)
												__lowercase					:							str			=   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    """runwayml/stable-diffusion-v1-5"""						,   revision="""onnx"""						,   scheduler=__a						,   safety_checker=__a						,   feature_extractor=__a						,   provider=self.gpu_provider						,   sess_options=self.gpu_options						,   )
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase					:							Union[str, Any]			=   """A fantasy landscape, trending on artstation"""

												__lowercase					:							Any			=   np.random.RandomState(0							)
												__lowercase					:							Optional[Any]			=   pipe(
												    prompt=__a						,   image=__a						,   strength=0.75						,   guidance_scale=7.5						,   num_inference_steps=20						,   generator=__a						,   output_type="""np"""						,   )
												__lowercase					:							str			=   output.images
												__lowercase					:							Dict			=   images[0, 255:258, 383:386, -1]

												assert images.shape == (1, 512, 768, 3)
												__lowercase					:							Union[str, Any]			=   np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]							)
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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