



'''simple docstring'''






import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


UpperCamelCase__:   str								=			logging.get_logger(__name__)

UpperCamelCase__:   List[Any]								=			{
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       """detr"""
	lowerCamelCase__			 =       ["""past_key_values"""]
	lowerCamelCase__			 =       {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}







	def __init__(						self			:     Tuple		,			__snake_case			:     Any=True		,			__snake_case			:     int=None		,			__snake_case			:     Dict=3		,			__snake_case			:     Optional[Any]=100		,			__snake_case			:     str=6		,			__snake_case			:     Tuple=2048		,			__snake_case			:     int=8		,			__snake_case			:     List[Any]=6		,			__snake_case			:     Optional[int]=2048		,			__snake_case			:     Tuple=8		,			__snake_case			:     Tuple=0.0		,			__snake_case			:     Union[str, Any]=0.0		,			__snake_case			:     str=True		,			__snake_case			:     Tuple="relu"		,			__snake_case			:     Optional[Any]=256		,			__snake_case			:     Optional[Any]=0.1		,			__snake_case			:     Dict=0.0		,			__snake_case			:     Any=0.0		,			__snake_case			:     Union[str, Any]=0.02		,			__snake_case			:     Tuple=1.0		,			__snake_case			:     Optional[int]=False		,			__snake_case			:     Union[str, Any]="sine"		,			__snake_case			:     Optional[Any]="resnet50"		,			__snake_case			:     str=True		,			__snake_case			:     List[Any]=False		,			__snake_case			:     Tuple=1		,			__snake_case			:     Union[str, Any]=5		,			__snake_case			:     Optional[int]=2		,			__snake_case			:     int=1		,			__snake_case			:     Optional[int]=1		,			__snake_case			:     Union[str, Any]=5		,			__snake_case			:     Any=2		,			__snake_case			:     Optional[int]=0.1		,			**__snake_case			:     Any		,			)			->		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.'''		)
																						UpperCAmelCase :       Optional[Any] 							=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']		)
															elif isinstance(__snake_case		,			__snake_case		):
																						UpperCAmelCase :       Union[str, Any] 							=		backbone_config.get('''model_type'''		)
																						UpperCAmelCase :       List[Any] 							=		CONFIG_MAPPING[backbone_model_type]
																						UpperCAmelCase :       int 							=		config_class.from_dict(__snake_case		)
															# set timm attributes to None
															UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		None, None, None

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







	@property
	def      A	(						self			:     Optional[Any]		)			->		int:
								return self.encoder_attention_heads







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







	@classmethod
	def      A	(						cls			:     List[Any]		,			__snake_case			:     PretrainedConfig		,			**__snake_case			:     str		)			->		str:
								return cls(backbone_config=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Union[str, Any]		)			->		Dict[str, any]:
								UpperCAmelCase :       List[str] 							=		copy.deepcopy(self.__dict__		)
								if output["backbone_config"] is not None:
															UpperCAmelCase :       Any 							=		self.backbone_config.to_dict()
								UpperCAmelCase :       int 							=		self.__class__.model_type
								return output

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       version.parse("""1.11"""			)







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







	@property
	def      A	(						self			:     List[str]		)			->		float:
								return 1E-5







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










'''simple docstring'''






import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


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

						from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline

@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     str		)			->		int:
								with tempfile.TemporaryDirectory() as tmpdirname:
															# pipeline has Flax weights
															UpperCAmelCase :       Optional[Any] 							=		FlaxDiffusionPipeline.from_pretrained(
															    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		,			cache_dir=__snake_case		)

															UpperCAmelCase :       str 							=		[t[-1] for t in os.walk(os.path.join(__snake_case		,			os.listdir(__snake_case		)[0]		,			'''snapshots'''		)		)]
															UpperCAmelCase :       str 							=		[item for sublist in all_root_files for item in sublist]

															# None of the downloaded files should be a PyTorch file even if we have some here:
															# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
															assert not any(f.endswith('''.bin'''		) for f in files		)

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







	"""simple docstring"""






	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       str 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		)

								UpperCAmelCase :       List[Any] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       List[str] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Optional[Any] 							=		4

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Tuple 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 64, 64, 3)
								if jax.device_count() == 8:
															assert np.abs(np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 4.1_51_47_45		) < 1E-3
															assert np.abs(np.abs(__snake_case		,			dtype=np.floataa		).sum() - 4_99_47.8_75		) < 5E-1

								UpperCAmelCase :       Union[str, Any] 							=		pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
								assert len(__snake_case		) == num_samples







	def      A	(						self			:     List[Any]		)			->		List[str]:
								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''flax'''		,			safety_checker=__snake_case		)

								UpperCAmelCase :       Dict 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Optional[Any] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Any 							=		50

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       int 							=		num_samples * [prompt]
								UpperCAmelCase :       Union[str, Any] 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Dict 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Tuple 							=		shard(__snake_case		)

								UpperCAmelCase :       Tuple 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.05_65_24_01)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_38_38_08.2)		) < 5E-1







	def      A	(						self			:     int		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		)

								UpperCAmelCase :       Dict 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Union[str, Any] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       List[str] 							=		50

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       List[Any] 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.04_00_39_06)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     int		)			->		Any:
								UpperCAmelCase							,		UpperCAmelCase :       Dict 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		)

								UpperCAmelCase :       List[str] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       List[str] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Union[str, Any] 							=		50

								UpperCAmelCase :       Optional[int] 							=		jax.device_count()
								UpperCAmelCase :       List[str] 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       str 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.04_00_39_06)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     Tuple		)			->		Optional[Any]:
								UpperCAmelCase :       int 							=		FlaxDDIMScheduler(
								    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule='''scaled_linear'''		,			set_alpha_to_one=__snake_case		,			steps_offset=1		,			)

								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			scheduler=__snake_case		,			safety_checker=__snake_case		,			)
								UpperCAmelCase :       Tuple 							=		scheduler.create_state()

								UpperCAmelCase :       Dict 							=		scheduler_state

								UpperCAmelCase :       str 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       int 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Union[str, Any] 							=		50

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Any 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       str 							=		replicate(__snake_case		)
								UpperCAmelCase :       List[str] 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		shard(__snake_case		)

								UpperCAmelCase :       Dict 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.0_45_04_39_45)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_34_76_93.5)		) < 5E-1







	def      A	(						self			:     Any		)			->		Tuple:
								UpperCAmelCase :       List[Any] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       List[Any] 							=		num_samples * [prompt]
								UpperCAmelCase :       str 							=		jax.random.split(jax.random.PRNGKey(0		)		,			__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		,			)

								UpperCAmelCase :       Dict 							=		replicate(__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		pipeline.prepare_inputs(__snake_case		)
								UpperCAmelCase :       List[str] 							=		shard(__snake_case		)
								UpperCAmelCase :       Any 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images
								assert images.shape == (num_samples, 1, 512, 512, 3)
								UpperCAmelCase :       Optional[int] 							=		images[2, 0, 256, 10:17, 1]

								# With memory efficient attention
								UpperCAmelCase							,		UpperCAmelCase :       Any 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		,			use_memory_efficient_attention=__snake_case		,			)

								UpperCAmelCase :       int 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		shard(__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images
								assert images_eff.shape == (num_samples, 1, 512, 512, 3)
								UpperCAmelCase :       int 							=		images[2, 0, 256, 10:17, 1]

								# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
								# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
								assert abs(slice_eff - slice		).max() < 1E-2





