




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 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

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

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						Union[str, Any]										=FlaxControlNetModel.from_pretrained(
										    """lllyasviel/sd-controlnet-canny"""     ,			from_pt=__snake_case     ,			dtype=jnp.bfloataa			)
										__magic_name__					,			__magic_name__						:						Union[str, Any]										=FlaxStableDiffusionControlNetPipeline.from_pretrained(
										    """runwayml/stable-diffusion-v1-5"""     ,			controlnet=__snake_case     ,			from_pt=__snake_case     ,			dtype=jnp.bfloataa			)
										__magic_name__						:						Union[str, Any]										=controlnet_params

										__magic_name__						:						int										="""bird"""
										__magic_name__						:						Optional[int]										=jax.device_count()
										__magic_name__						:						Optional[int]										=pipe.prepare_text_inputs([prompts] * num_samples			)

										__magic_name__						:						List[str]										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""			)
										__magic_name__						:						Union[str, Any]										=pipe.prepare_image_inputs([canny_image] * num_samples			)

										__magic_name__						:						Union[str, Any]										=jax.random.PRNGKey(0			)
										__magic_name__						:						List[Any]										=jax.random.split(__snake_case     ,			jax.device_count()			)

										__magic_name__						:						Dict										=replicate(__snake_case			)
										__magic_name__						:						Tuple										=shard(__snake_case			)
										__magic_name__						:						int										=shard(__snake_case			)

										__magic_name__						:						Optional[Any]										=pipe(
										    prompt_ids=__snake_case     ,			image=__snake_case     ,			params=__snake_case     ,			prng_seed=__snake_case     ,			num_inference_steps=50     ,			jit=__snake_case     ,			).images
										assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

										__magic_name__						:						Any										=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
										__magic_name__						:						List[Any]										=images[0, 2_53:2_56, 2_53:2_56, -1]

										__magic_name__						:						Dict										=jnp.asarray(jax.device_get(image_slice.flatten()			)			)
										__magic_name__						:						Optional[int]										=jnp.array(
										    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]			)
										print(f"output_slice: {output_slice}"			)
										assert jnp.abs(output_slice - expected_slice			).max() < 1E-2


						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						Optional[int]										=FlaxControlNetModel.from_pretrained(
										    """lllyasviel/sd-controlnet-openpose"""     ,			from_pt=__snake_case     ,			dtype=jnp.bfloataa			)
										__magic_name__					,			__magic_name__						:						Tuple										=FlaxStableDiffusionControlNetPipeline.from_pretrained(
										    """runwayml/stable-diffusion-v1-5"""     ,			controlnet=__snake_case     ,			from_pt=__snake_case     ,			dtype=jnp.bfloataa			)
										__magic_name__						:						List[Any]										=controlnet_params

										__magic_name__						:						Optional[int]										="""Chef in the kitchen"""
										__magic_name__						:						Dict										=jax.device_count()
										__magic_name__						:						Optional[int]										=pipe.prepare_text_inputs([prompts] * num_samples			)

										__magic_name__						:						Union[str, Any]										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""			)
										__magic_name__						:						Optional[Any]										=pipe.prepare_image_inputs([pose_image] * num_samples			)

										__magic_name__						:						Any										=jax.random.PRNGKey(0			)
										__magic_name__						:						Optional[Any]										=jax.random.split(__snake_case     ,			jax.device_count()			)

										__magic_name__						:						str										=replicate(__snake_case			)
										__magic_name__						:						Optional[int]										=shard(__snake_case			)
										__magic_name__						:						List[str]										=shard(__snake_case			)

										__magic_name__						:						List[str]										=pipe(
										    prompt_ids=__snake_case     ,			image=__snake_case     ,			params=__snake_case     ,			prng_seed=__snake_case     ,			num_inference_steps=50     ,			jit=__snake_case     ,			).images
										assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

										__magic_name__						:						Union[str, Any]										=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
										__magic_name__						:						Dict										=images[0, 2_53:2_56, 2_53:2_56, -1]

										__magic_name__						:						Union[str, Any]										=jnp.asarray(jax.device_get(image_slice.flatten()			)			)
										__magic_name__						:						str										=jnp.array(
										    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]			)
										print(f"output_slice: {output_slice}"			)
										assert jnp.abs(output_slice - expected_slice			).max() < 1E-2










import warnings
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


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """segformer"""

						def __init__(		self :List[str]     ,			__snake_case :str=3     ,			__snake_case :Optional[Any]=4     ,			__snake_case :List[Any]=[2, 2, 2, 2]     ,			__snake_case :Dict=[8, 4, 2, 1]     ,			__snake_case :Optional[int]=[32, 64, 1_60, 2_56]     ,			__snake_case :Union[str, Any]=[7, 3, 3, 3]     ,			__snake_case :Optional[Any]=[4, 2, 2, 2]     ,			__snake_case :Tuple=[1, 2, 5, 8]     ,			__snake_case :List[Any]=[4, 4, 4, 4]     ,			__snake_case :Optional[Any]="gelu"     ,			__snake_case :Tuple=0.0     ,			__snake_case :Dict=0.0     ,			__snake_case :Optional[int]=0.1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :Tuple=0.1     ,			__snake_case :Union[str, Any]=1E-6     ,			__snake_case :int=2_56     ,			__snake_case :Optional[int]=2_55     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

										if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
														warnings.warn(
														    """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
														    """ removed, as the behaviour will default to that of reshape_last_stage = True."""     ,			__snake_case     ,			)

										__magic_name__						:						Dict										=num_channels
										__magic_name__						:						str										=num_encoder_blocks
										__magic_name__						:						List[Any]										=depths
										__magic_name__						:						Optional[Any]										=sr_ratios
										__magic_name__						:						List[str]										=hidden_sizes
										__magic_name__						:						List[str]										=patch_sizes
										__magic_name__						:						Any										=strides
										__magic_name__						:						Optional[Any]										=mlp_ratios
										__magic_name__						:						str										=num_attention_heads
										__magic_name__						:						int										=hidden_act
										__magic_name__						:						List[Any]										=hidden_dropout_prob
										__magic_name__						:						Optional[Any]										=attention_probs_dropout_prob
										__magic_name__						:						Optional[Any]										=classifier_dropout_prob
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						List[str]										=drop_path_rate
										__magic_name__						:						List[Any]										=layer_norm_eps
										__magic_name__						:						List[str]										=decoder_hidden_size
										__magic_name__						:						Union[str, Any]										=kwargs.get("""reshape_last_stage"""     ,			__snake_case			)
										__magic_name__						:						Dict										=semantic_loss_ignore_index




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       version.parse("""1.11"""      )

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

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

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										return 1E-4


						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										return 12




