

'''simple docstring'''
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 							A__    (       unittest.TestCase  ):





  @property
  def 						A  (		self      :	Any )	->    List[Any]:





       '''simple docstring'''






       torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =UNetaDModel(
           block_out_channels=(32, 64)							,					layers_per_block=2							,					sample_size=32							,					in_channels=3							,					out_channels=3							,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,					)
       return model





  @property
  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =VQModel(
           block_out_channels=[32, 64]							,					in_channels=3							,					out_channels=3							,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,					latent_channels=3							,					)
       return model





  @property
  def 						A  (		self      :	Any )	->    List[Any]:





       '''simple docstring'''






       torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =CLIPTextConfig(
           bos_token_id=0							,					eos_token_id=2							,					hidden_size=32							,					intermediate_size=37							,					layer_norm_eps=1e-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					pad_token_id=1							,					vocab_size=1000							,					)
       return CLIPTextModel(_a )





  def 						A  (		self      :	List[str] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.dummy_uncond_unet
       _SCREAMING_SNAKE_CASE							    =DDIMScheduler()
       _SCREAMING_SNAKE_CASE							    =self.dummy_vq_model

       _SCREAMING_SNAKE_CASE							    =LDMPipeline(unet=_a							,					vqvae=_a							,					scheduler=_a )
       ldm.to(_a )
       ldm.set_progress_bar_config(disable=_a )

       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =ldm(generator=_a							,					num_inference_steps=2							,					output_type='numpy' ).images

       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =ldm(generator=_a							,					num_inference_steps=2							,					output_type='numpy'							,					return_dict=_a )[0]

       _SCREAMING_SNAKE_CASE							    =image[0, -3:, -3:, -1]
       _SCREAMING_SNAKE_CASE							    =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       _SCREAMING_SNAKE_CASE							    =np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72] )
       _SCREAMING_SNAKE_CASE							    =1e-2 if torch_device != 'mps' else 3e-2

       assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance







@slow
@require_torch
class 							A__    (       unittest.TestCase  ):





  def 						A  (		self      :	str )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
       ldm.to(_a )
       ldm.set_progress_bar_config(disable=_a )

       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =ldm(generator=_a							,					num_inference_steps=5							,					output_type='numpy' ).images

       _SCREAMING_SNAKE_CASE							    =image[0, -3:, -3:, -1]

       assert image.shape == (1, 256, 256, 3)
       _SCREAMING_SNAKE_CASE							    =np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47] )
       _SCREAMING_SNAKE_CASE							    =1e-2 if torch_device != 'mps' else 3e-2

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



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


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """table-transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(							self      ,							A_=True      ,							A_=None      ,							A_=3      ,							A_=100      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=0.0      ,							A_=0.0      ,							A_=True      ,							A_="relu"      ,							A_=256      ,							A_=0.1      ,							A_=0.0      ,							A_=0.0      ,							A_=0.02      ,							A_=1.0      ,							A_=False      ,							A_="sine"      ,							A_="resnet50"      ,							A_=True      ,							A_=False      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=1      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=0.1      ,							**A_      ,							)					->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.'''					)
                   __lowerCAmelCase					:						Optional[Any]					=					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
             elif isinstance(A_      ,							A_					):
                   __lowerCAmelCase					:						int					=					backbone_config.get('''model_type'''					)
                   __lowerCAmelCase					:						List[str]					=					CONFIG_MAPPING[backbone_model_type]
                   __lowerCAmelCase					:						Any					=					config_class.from_dict(A_					)
             # set timm attributes to None
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					None, None, None

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



       '''simple docstring'''





       return self.encoder_attention_heads





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.d_model







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  version.parse("""1.11""" )
 @property
 def 	UpperCamelCase__  (							self					)					->Mapping[str, Mapping[int, str]]:



       '''simple docstring'''





       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]					)
 @property
 def 	UpperCamelCase__  (							self					)					->float:



       '''simple docstring'''





       return 1e-5





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return 12


