


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			=				logging.get_logger(__name__)

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


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     List[str]     =					'''layoutlmv3'''

							def __init__(				self:      Optional[Any]       ,		UpperCamelCase_:      Union[str, Any]=50_265       ,		UpperCamelCase_:      Tuple=768       ,		UpperCamelCase_:      int=12       ,		UpperCamelCase_:      List[str]=12       ,		UpperCamelCase_:      List[Any]=3_072       ,		UpperCamelCase_:      Any="gelu"       ,		UpperCamelCase_:      Any=0.1       ,		UpperCamelCase_:      str=0.1       ,		UpperCamelCase_:      str=512       ,		UpperCamelCase_:      Optional[Any]=2       ,		UpperCamelCase_:      str=0.02       ,		UpperCamelCase_:      int=1E-5       ,		UpperCamelCase_:      List[Any]=1       ,		UpperCamelCase_:      List[str]=0       ,		UpperCamelCase_:      str=2       ,		UpperCamelCase_:      Any=1_024       ,		UpperCamelCase_:      List[str]=128       ,		UpperCamelCase_:      List[str]=128       ,		UpperCamelCase_:      Tuple=True       ,		UpperCamelCase_:      Any=32       ,		UpperCamelCase_:      Any=128       ,		UpperCamelCase_:      Optional[int]=64       ,		UpperCamelCase_:      str=256       ,		UpperCamelCase_:      Dict=True       ,		UpperCamelCase_:      Optional[int]=True       ,		UpperCamelCase_:      Optional[Any]=True       ,		UpperCamelCase_:      Optional[int]=224       ,		UpperCamelCase_:      Any=3       ,		UpperCamelCase_:      int=16       ,		UpperCamelCase_:      List[Any]=None       ,		**UpperCamelCase_:      List[str]       ,		)     ->  Optional[int]:





									"""simple docstring"""







									super().__init__(
									    vocab_size=UpperCamelCase_       ,		hidden_size=UpperCamelCase_       ,		num_hidden_layers=UpperCamelCase_       ,		num_attention_heads=UpperCamelCase_       ,		intermediate_size=UpperCamelCase_       ,		hidden_act=UpperCamelCase_       ,		hidden_dropout_prob=UpperCamelCase_       ,		attention_probs_dropout_prob=UpperCamelCase_       ,		max_position_embeddings=UpperCamelCase_       ,		type_vocab_size=UpperCamelCase_       ,		initializer_range=UpperCamelCase_       ,		layer_norm_eps=UpperCamelCase_       ,		pad_token_id=UpperCamelCase_       ,		bos_token_id=UpperCamelCase_       ,		eos_token_id=UpperCamelCase_       ,		**UpperCamelCase_       ,		)
									lowercase__				= max_ad_position_embeddings
									lowercase__				= coordinate_size
									lowercase__				= shape_size
									lowercase__				= has_relative_attention_bias
									lowercase__				= rel_pos_bins
									lowercase__				= max_rel_pos
									lowercase__				= has_spatial_attention_bias
									lowercase__				= rel_ad_pos_bins
									lowercase__				= max_rel_ad_pos
									lowercase__				= text_embed
									lowercase__				= visual_embed
									lowercase__				= input_size
									lowercase__				= num_channels
									lowercase__				= patch_size
									lowercase__				= classifier_dropout


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     List[str]     =					version.parse('''1.12'''     )

							@property
							def  lowerCamelCase_					(				self:      int				)     ->  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:      Any				)     ->  float:





									"""simple docstring"""







									return 1E-5

							@property
							def  lowerCamelCase_					(				self:      str				)     ->  int:





									"""simple docstring"""







									return 12


							def  lowerCamelCase_					(				self:      List[str]       ,		UpperCamelCase_:      "ProcessorMixin"       ,		UpperCamelCase_:      int = -1       ,		UpperCamelCase_:      int = -1       ,		UpperCamelCase_:      bool = False       ,		UpperCamelCase_:      Optional["TensorType"] = None       ,		UpperCamelCase_:      int = 3       ,		UpperCamelCase_:      int = 40       ,		UpperCamelCase_:      int = 40       ,		)     ->  Mapping[str, Any]:





									"""simple docstring"""







									setattr(processor.image_processor       ,		'''apply_ocr'''       ,		UpperCamelCase_				)

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									lowercase__				= compute_effective_axis_dimension(
									    UpperCamelCase_       ,		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__				= processor.tokenizer.num_special_tokens_to_add(UpperCamelCase_				)
									lowercase__				= compute_effective_axis_dimension(
									    UpperCamelCase_       ,		fixed_dimension=OnnxConfig.default_fixed_sequence       ,		num_token_to_add=UpperCamelCase_				)
									# Generate dummy inputs according to compute batch and sequence
									lowercase__				= [[''' '''.join([processor.tokenizer.unk_token]				) * seq_length]] * batch_size

									# Generate dummy bounding boxes
									lowercase__				= [[[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__				= self._generate_dummy_images(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_				)

									lowercase__				= dict(
									    processor(
									        UpperCamelCase_       ,		text=UpperCamelCase_       ,		boxes=UpperCamelCase_       ,		return_tensors=UpperCamelCase_       ,		)				)

									return inputs







'''simple docstring'''





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard


@slow
@require_flax
class 					snake_case			(unittest.TestCase ):



       def 				_a   (					self   )	->				Tuple:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()



       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-canny"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "bird"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"   )
           lowercase__									=    pipe.prepare_image_inputs([canny_image] * num_samples   )

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    jnp.array(
               [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]   )
           print(F'''output_slice: {output_slice}'''   )
           assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2







       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-openpose"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "Chef in the kitchen"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"   )
           lowercase__									=    pipe.prepare_image_inputs([pose_image] * num_samples   )

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    jnp.array(
               [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]   )
           print(F'''output_slice: {output_slice}'''   )
           assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2




