import copy
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


_a					:					Any=			logging.get_logger(__name__)

_a					:					Any=			{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				List[str]   =		"""conditional_detr"""
				UpperCAmelCase    :				Tuple   =		["""past_key_values"""]
				UpperCAmelCase    :				Optional[int]   =		{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}



				def __init__(self    :				int     ,  _A    :				str=True     ,  _A    :				Optional[Any]=None     ,  _A    :				Dict=3     ,  _A    :				str=3_00     ,  _A    :				str=6     ,  _A    :				List[Any]=20_48     ,  _A    :				Union[str, Any]=8     ,  _A    :				Optional[Any]=6     ,  _A    :				List[str]=20_48     ,  _A    :				List[Any]=8     ,  _A    :				Tuple=0.0     ,  _A    :				str=0.0     ,  _A    :				Optional[Any]=True     ,  _A    :				str="relu"     ,  _A    :				Optional[Any]=2_56     ,  _A    :				List[Any]=0.1     ,  _A    :				Union[str, Any]=0.0     ,  _A    :				Optional[Any]=0.0     ,  _A    :				Union[str, Any]=0.02     ,  _A    :				Any=1.0     ,  _A    :				Optional[Any]=False     ,  _A    :				Optional[Any]="sine"     ,  _A    :				int="resnet50"     ,  _A    :				Dict=True     ,  _A    :				int=False     ,  _A    :				Dict=2     ,  _A    :				Dict=5     ,  _A    :				Tuple=2     ,  _A    :				Union[str, Any]=1     ,  _A    :				Union[str, Any]=1     ,  _A    :				List[Any]=2     ,  _A    :				Tuple=5     ,  _A    :				Union[str, Any]=2     ,  _A    :				List[str]=0.25     ,  **_A    :				List[Any]     ,  )   ->     Union[str, Any]:
					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.')
							__snake_case      :							str						=			CONFIG_MAPPING['resnet'](out_features=['stage4'])
						elif isinstance(_A     ,  _A):
							__snake_case      :							Tuple						=			backbone_config.get('model_type')
							__snake_case      :							Dict						=			CONFIG_MAPPING[backbone_model_type]
							__snake_case      :							List[Any]						=			config_class.from_dict(_A)

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



				@property
				def  _lowercase					(self    :				int)   ->     int:
					return self.encoder_attention_heads



				@property
				def  _lowercase					(self    :				Tuple)   ->     int:
					return self.d_model






				def  _lowercase					(self    :				Optional[Any])   ->     Optional[Any]:
					__snake_case      :							Tuple						=			copy.deepcopy(self.__dict__)
					if self.backbone_config is not None:
						__snake_case      :							Any						=			self.backbone_config.to_dict()
					__snake_case      :							str						=			self.__class__.model_type
					return output







class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Dict   =		version.parse("""1.11"""						)



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



				@property
				def  _lowercase					(self    :				Optional[int])   ->     float:
					return 1E-5






				@property
				def  _lowercase					(self    :				int)   ->     int:
					return 12






"""simple docstring"""







import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_a					:					List[str]=			False

class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				@property
				def  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				Dict)   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				int)   ->     Tuple:
					return 32



				@property
				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					torch.manual_seed(0)
					__snake_case      :							Any						=			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     ,  num_vq_embeddings=self.num_embed     ,  vq_embed_dim=3     ,  )
					return model



				@property
				def  _lowercase					(self    :				List[Any])   ->     Optional[int]:
					__snake_case      :							Dict						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
					return tokenizer



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					torch.manual_seed(0)
					__snake_case      :							Any						=			CLIPTextConfig(
					    bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=self.text_embedder_hidden_size     ,  intermediate_size=37     ,  layer_norm_eps=1E-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=10_00     ,  )
					return CLIPTextModel(_A)



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					torch.manual_seed(0)

					__snake_case      :							Any						=			12
					__snake_case      :							int						=			12

					__snake_case      :							List[Any]						=			{
					    'attention_bias': True,
					    'cross_attention_dim': 32,
					    'attention_head_dim': height * width,
					    'num_attention_heads': 1,
					    'num_vector_embeds': self.num_embed,
					    'num_embeds_ada_norm': self.num_embeds_ada_norm,
					    'norm_num_groups': 32,
					    'sample_size': width,
					    'activation_fn': 'geglu-approximate',
					}

					__snake_case      :							Union[str, Any]						=			TransformeraDModel(**_A)
					return model



				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					__snake_case      :							Tuple						=			'cpu'

					__snake_case      :							List[str]						=			self.dummy_vqvae
					__snake_case      :							str						=			self.dummy_text_encoder
					__snake_case      :							Optional[Any]						=			self.dummy_tokenizer
					__snake_case      :							Dict						=			self.dummy_transformer
					__snake_case      :							Optional[int]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_A)

					__snake_case      :							List[Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							List[Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Optional[Any]						=			'teddy bear playing in the pool'

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Union[str, Any]						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[int]						=			output.images

					__snake_case      :							int						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							str						=			image[0, -3:, -3:, -1]
					__snake_case      :							Union[str, Any]						=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					__snake_case      :							str						=			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			'cpu'

					__snake_case      :							Optional[int]						=			self.dummy_vqvae
					__snake_case      :							List[str]						=			self.dummy_text_encoder
					__snake_case      :							Optional[int]						=			self.dummy_tokenizer
					__snake_case      :							Optional[Any]						=			self.dummy_transformer
					__snake_case      :							Union[str, Any]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							Optional[int]						=			LearnedClassifierFreeSamplingEmbeddings(
					    learnable=_A     ,  hidden_size=self.text_embedder_hidden_size     ,  length=tokenizer.model_max_length)

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							Union[str, Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Union[str, Any]						=			'teddy bear playing in the pool'

					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[Any]						=			output.images

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Dict						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							int						=			image[0, -3:, -3:, -1]
					__snake_case      :							Tuple						=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					__snake_case      :							Optional[Any]						=			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







@slow
@require_torch_gpu
class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Any)   ->     Union[str, Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							List[Any]						=			load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
					__snake_case      :							Tuple						=			pipeline.to(_A)
					pipeline.set_progress_bar_config(disable=_A)

					# requires GPU generator for gumbel softmax
					# don't use GPU generator in tests though
					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							int						=			pipeline(
					    'teddy bear playing in the pool'     ,  num_images_per_prompt=1     ,  generator=_A     ,  output_type='np'     ,  )

					__snake_case      :							int						=			output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image).max() < 2.0





