



"""simple docstring"""






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        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					def 		snake_case       (       self							:  int     )->					List[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 		snake_case       (       self							:  int     )->					str:
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa     )
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa     )
								lowerCamelCase__		:		Optional[Any]			=controlnet_params

								lowerCamelCase__		:		List[str]			='''bird'''
								lowerCamelCase__		:		str			=jax.device_count()
								lowerCamelCase__		:		Any			=pipe.prepare_text_inputs([prompts] * num_samples     )

								lowerCamelCase__		:		Any			=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''     )
								lowerCamelCase__		:		Optional[int]			=pipe.prepare_image_inputs([canny_image] * num_samples     )

								lowerCamelCase__		:		Optional[Any]			=jax.random.PRNGKey(0     )
								lowerCamelCase__		:		List[str]			=jax.random.split(lowerCamelCase,  jax.device_count()     )

								lowerCamelCase__		:		Optional[Any]			=replicate(lowerCamelCase     )
								lowerCamelCase__		:		List[Any]			=shard(lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=shard(lowerCamelCase     )

								lowerCamelCase__		:		Any			=pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

								lowerCamelCase__		:		List[Any]			=jnp.asarray(jax.device_get(image_slice.flatten()     )     )
								lowerCamelCase__		:		List[str]			=jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]     )
								print(F'''output_slice: {output_slice}'''     )
								assert jnp.abs(output_slice - expected_slice     ).max() < 1E-2




					def 		snake_case       (       self							:  str     )->					List[Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',  from_pt=lowerCamelCase,  dtype=jnp.bfloataa     )
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[Any]			=FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',  controlnet=lowerCamelCase,  from_pt=lowerCamelCase,  dtype=jnp.bfloataa     )
								lowerCamelCase__		:		Union[str, Any]			=controlnet_params

								lowerCamelCase__		:		List[str]			='''Chef in the kitchen'''
								lowerCamelCase__		:		Any			=jax.device_count()
								lowerCamelCase__		:		List[Any]			=pipe.prepare_text_inputs([prompts] * num_samples     )

								lowerCamelCase__		:		str			=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''     )
								lowerCamelCase__		:		str			=pipe.prepare_image_inputs([pose_image] * num_samples     )

								lowerCamelCase__		:		Optional[Any]			=jax.random.PRNGKey(0     )
								lowerCamelCase__		:		Dict			=jax.random.split(lowerCamelCase,  jax.device_count()     )

								lowerCamelCase__		:		Any			=replicate(lowerCamelCase     )
								lowerCamelCase__		:		List[Any]			=shard(lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=shard(lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=pipe(
								    prompt_ids=lowerCamelCase,  image=lowerCamelCase,  params=lowerCamelCase,  prng_seed=lowerCamelCase,  num_inference_steps=50,  jit=lowerCamelCase,  ).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

								lowerCamelCase__		:		Optional[int]			=jnp.asarray(jax.device_get(image_slice.flatten()     )     )
								lowerCamelCase__		:		Optional[Any]			=jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]     )
								print(F'''output_slice: {output_slice}'''     )
								assert jnp.abs(output_slice - expected_slice     ).max() < 1E-2









"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase : List[str]      						=						logging.get_logger(__name__)

_lowercase : int      						=						{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							'deberta-v2'



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  Optional[int]=12_8100,  lowerCamelCase							:  List[Any]=1536,  lowerCamelCase							:  Dict=24,  lowerCamelCase							:  Any=24,  lowerCamelCase							:  Union[str, Any]=6144,  lowerCamelCase							:  List[Any]="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  Union[str, Any]=512,  lowerCamelCase							:  Optional[Any]=0,  lowerCamelCase							:  Any=0.02,  lowerCamelCase							:  int=1E-7,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Union[str, Any]=-1,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  int=None,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Tuple="gelu",  **lowerCamelCase							:  Optional[int],  )->					Union[str, Any]:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		str			=hidden_size
								lowerCamelCase__		:		Optional[int]			=num_hidden_layers
								lowerCamelCase__		:		Optional[Any]			=num_attention_heads
								lowerCamelCase__		:		List[Any]			=intermediate_size
								lowerCamelCase__		:		int			=hidden_act
								lowerCamelCase__		:		Tuple			=hidden_dropout_prob
								lowerCamelCase__		:		Union[str, Any]			=attention_probs_dropout_prob
								lowerCamelCase__		:		Optional[Any]			=max_position_embeddings
								lowerCamelCase__		:		int			=type_vocab_size
								lowerCamelCase__		:		Tuple			=initializer_range
								lowerCamelCase__		:		Tuple			=relative_attention
								lowerCamelCase__		:		Optional[Any]			=max_relative_positions
								lowerCamelCase__		:		List[Any]			=pad_token_id
								lowerCamelCase__		:		int			=position_biased_input

								# Backwards compatibility
								if type(lowerCamelCase     ) == str:
											lowerCamelCase__		:		Union[str, Any]			=[x.strip() for x in pos_att_type.lower().split('''|'''     )]

								lowerCamelCase__		:		Tuple			=pos_att_type
								lowerCamelCase__		:		Union[str, Any]			=vocab_size
								lowerCamelCase__		:		Optional[int]			=layer_norm_eps

								lowerCamelCase__		:		Dict			=kwargs.get('''pooler_hidden_size''',  lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=pooler_dropout
								lowerCamelCase__		:		List[Any]			=pooler_hidden_act





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  List[str]     )->					Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
											lowerCamelCase__		:		Union[str, Any]			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											lowerCamelCase__		:		Any			={0: '''batch''', 1: '''sequence'''}
								if self._config.type_vocab_size > 0:
											return OrderedDict(
											    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]     )
								else:
											return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]     )



					@property
					def 		snake_case       (       self							:  List[str]     )->					int:
								return 12




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional["TensorType"] = None,  lowerCamelCase							:  int = 3,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  int = 40,  lowerCamelCase							:  "PreTrainedTokenizerBase" = None,  )->					Mapping[str, Any]:
								lowerCamelCase__		:		List[Any]			=super().generate_dummy_inputs(preprocessor=lowerCamelCase,  framework=lowerCamelCase     )
								if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
											del dummy_inputs["token_type_ids"]
								return dummy_inputs




