





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase				:   str						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   Optional[int]						       =						{
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''table-transformer'''
     __UpperCamelCase            =  ['''past_key_values''']
     __UpperCamelCase            =  {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }



     def __init__(				self							:Tuple      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:int=None      ,   snake_case							:List[str]=3      ,   snake_case							:List[str]=100      ,   snake_case							:Tuple=6      ,   snake_case							:List[str]=2_048      ,   snake_case							:Tuple=8      ,   snake_case							:List[Any]=6      ,   snake_case							:Any=2_048      ,   snake_case							:Dict=8      ,   snake_case							:Tuple=0.0      ,   snake_case							:Optional[Any]=0.0      ,   snake_case							:Dict=True      ,   snake_case							:Dict="relu"      ,   snake_case							:List[Any]=256      ,   snake_case							:int=0.1      ,   snake_case							:Optional[int]=0.0      ,   snake_case							:Any=0.0      ,   snake_case							:Optional[Any]=0.02      ,   snake_case							:Dict=1.0      ,   snake_case							:List[Any]=False      ,   snake_case							:int="sine"      ,   snake_case							:Tuple="resnet50"      ,   snake_case							:Tuple=True      ,   snake_case							:str=False      ,   snake_case							:Union[str, Any]=1      ,   snake_case							:Dict=5      ,   snake_case							:Union[str, Any]=2      ,   snake_case							:Tuple=1      ,   snake_case							:Union[str, Any]=1      ,   snake_case							:Union[str, Any]=5      ,   snake_case							:Dict=2      ,   snake_case							:List[Any]=0.1      ,   **snake_case							:Union[str, Any]      ,   ):





        '''simple docstring'''




        if backbone_config is not None and use_timm_backbone:
           raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."	)

        if not use_timm_backbone:
           if backbone_config is None:
              logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
              A_       :					List[Any]        =      CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
           elif isinstance(snake_case      ,   snake_case	):
              A_       :					List[str]        =      backbone_config.get("model_type"	)
              A_       :					Any        =      CONFIG_MAPPING[backbone_model_type]
              A_       :					int        =      config_class.from_dict(snake_case	)
           # set timm attributes to None
           A_   ,    A_   ,    A_       :					int        =      None, None, None

        A_       :					Optional[int]        =      use_timm_backbone
        A_       :					int        =      backbone_config
        A_       :					str        =      num_channels
        A_       :					Optional[int]        =      num_queries
        A_       :					Any        =      d_model
        A_       :					str        =      encoder_ffn_dim
        A_       :					List[str]        =      encoder_layers
        A_       :					Optional[int]        =      encoder_attention_heads
        A_       :					List[Any]        =      decoder_ffn_dim
        A_       :					List[str]        =      decoder_layers
        A_       :					Optional[Any]        =      decoder_attention_heads
        A_       :					Optional[Any]        =      dropout
        A_       :					str        =      attention_dropout
        A_       :					Any        =      activation_dropout
        A_       :					int        =      activation_function
        A_       :					Any        =      init_std
        A_       :					Union[str, Any]        =      init_xavier_std
        A_       :					Tuple        =      encoder_layerdrop
        A_       :					Optional[Any]        =      decoder_layerdrop
        A_       :					Any        =      encoder_layers
        A_       :					Any        =      auxiliary_loss
        A_       :					Tuple        =      position_embedding_type
        A_       :					str        =      backbone
        A_       :					Dict        =      use_pretrained_backbone
        A_       :					Any        =      dilation
        # Hungarian matcher
        A_       :					Any        =      class_cost
        A_       :					Optional[Any]        =      bbox_cost
        A_       :					Optional[int]        =      giou_cost
        # Loss coefficients
        A_       :					Any        =      mask_loss_coefficient
        A_       :					List[Any]        =      dice_loss_coefficient
        A_       :					List[Any]        =      bbox_loss_coefficient
        A_       :					List[str]        =      giou_loss_coefficient
        A_       :					int        =      eos_coefficient
        super().__init__(is_encoder_decoder=snake_case      ,   **snake_case	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        return self.encoder_attention_heads





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        return self.d_model




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  version.parse('''1.11'''				)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                ("pixel_mask", {0: "batch"}),
            ]	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return 1e-5





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        return 12







import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        super().tearDown()
        gc.collect()



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_   ,    A_       :					Optional[Any]        =      FlaxStableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2"      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )

        A_       :					List[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					int        =      jax.device_count()
        A_       :					Union[str, Any]        =      num_samples * [prompt]
        A_       :					Dict        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					List[Any]        =      replicate(snake_case	)
        A_       :					str        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Dict        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					int        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[str]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Dict        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					List[str]        =      jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					str        =      "stabilityai/stable-diffusion-2"
        A_   ,    A_       :					Dict        =      FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case      ,   subfolder="scheduler"	)
        A_   ,    A_       :					Any        =      FlaxStableDiffusionPipeline.from_pretrained(
            snake_case      ,   scheduler=snake_case      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )
        A_       :					Any        =      scheduler_params

        A_       :					Optional[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					Dict        =      jax.device_count()
        A_       :					List[str]        =      num_samples * [prompt]
        A_       :					int        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					Optional[Any]        =      replicate(snake_case	)
        A_       :					Dict        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Union[str, Any]        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					Union[str, Any]        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[Any]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Tuple        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					str        =      jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2
