'''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


lowerCAmelCase_ : Any						  =      logging.get_logger(__name__)

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





class 							lowercase						(						__UpperCAmelCase ):
   lowerCamelCase_    					='''layoutlmv3'''


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	str=5_0265   ,		__lowerCAmelCase				:	List[Any]=768   ,		__lowerCAmelCase				:	Any=12   ,		__lowerCAmelCase				:	Dict=12   ,		__lowerCAmelCase				:	Union[str, Any]=3072   ,		__lowerCAmelCase				:	Optional[Any]="gelu"   ,		__lowerCAmelCase				:	Union[str, Any]=0.1   ,		__lowerCAmelCase				:	Optional[Any]=0.1   ,		__lowerCAmelCase				:	Tuple=512   ,		__lowerCAmelCase				:	Tuple=2   ,		__lowerCAmelCase				:	Union[str, Any]=0.02   ,		__lowerCAmelCase				:	List[Any]=1e-5   ,		__lowerCAmelCase				:	Any=1   ,		__lowerCAmelCase				:	Optional[Any]=0   ,		__lowerCAmelCase				:	List[Any]=2   ,		__lowerCAmelCase				:	Tuple=1024   ,		__lowerCAmelCase				:	Dict=128   ,		__lowerCAmelCase				:	Union[str, Any]=128   ,		__lowerCAmelCase				:	List[Any]=True   ,		__lowerCAmelCase				:	Dict=32   ,		__lowerCAmelCase				:	int=128   ,		__lowerCAmelCase				:	Union[str, Any]=64   ,		__lowerCAmelCase				:	Optional[int]=256   ,		__lowerCAmelCase				:	Dict=True   ,		__lowerCAmelCase				:	Dict=True   ,		__lowerCAmelCase				:	Any=True   ,		__lowerCAmelCase				:	Tuple=224   ,		__lowerCAmelCase				:	Dict=3   ,		__lowerCAmelCase				:	Dict=16   ,		__lowerCAmelCase				:	List[Any]=None   ,		**__lowerCAmelCase				:	str   ,		)      ->					Any:
         super().__init__(
             vocab_size=__SCREAMING_SNAKE_CASE   ,		hidden_size=__SCREAMING_SNAKE_CASE   ,		num_hidden_layers=__SCREAMING_SNAKE_CASE   ,		num_attention_heads=__SCREAMING_SNAKE_CASE   ,		intermediate_size=__SCREAMING_SNAKE_CASE   ,		hidden_act=__SCREAMING_SNAKE_CASE   ,		hidden_dropout_prob=__SCREAMING_SNAKE_CASE   ,		attention_probs_dropout_prob=__SCREAMING_SNAKE_CASE   ,		max_position_embeddings=__SCREAMING_SNAKE_CASE   ,		type_vocab_size=__SCREAMING_SNAKE_CASE   ,		initializer_range=__SCREAMING_SNAKE_CASE   ,		layer_norm_eps=__SCREAMING_SNAKE_CASE   ,		pad_token_id=__SCREAMING_SNAKE_CASE   ,		bos_token_id=__SCREAMING_SNAKE_CASE   ,		eos_token_id=__SCREAMING_SNAKE_CASE   ,		**__SCREAMING_SNAKE_CASE   ,		)
         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 							lowercase						(						__UpperCAmelCase ):
   lowerCamelCase_    					=version.parse('1.12' )


   @property
   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Union[str, Any]:

         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 __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[Any]:
         return 1e-5


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					List[Any]:
         return 12


   def __UpperCAmelCase       (   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   ,		)      ->					Tuple:
         setattr(processor.image_processor   ,		"apply_ocr"   ,		__SCREAMING_SNAKE_CASE)

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         lowercase_     =      compute_effective_axis_dimension(
             __SCREAMING_SNAKE_CASE   ,		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(__SCREAMING_SNAKE_CASE)
         lowercase_     =      compute_effective_axis_dimension(
             __SCREAMING_SNAKE_CASE   ,		fixed_dimension=OnnxConfig.default_fixed_sequence   ,		num_token_to_add=__SCREAMING_SNAKE_CASE)
         # 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(__SCREAMING_SNAKE_CASE   ,		__SCREAMING_SNAKE_CASE   ,		__SCREAMING_SNAKE_CASE   ,		__SCREAMING_SNAKE_CASE)

         lowercase_     =      dict(
             processor(
                 __SCREAMING_SNAKE_CASE   ,		text=__SCREAMING_SNAKE_CASE   ,		boxes=__SCREAMING_SNAKE_CASE   ,		return_tensors=__SCREAMING_SNAKE_CASE   ,		))

         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 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


   def __UpperCAmelCase       (   self				:	Dict)      ->					Any:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		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(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).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_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2


   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		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(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).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_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2






