





"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase    	=      logging.get_logger(__name__)

_lowerCamelCase    	=      {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class      snake_case						(  a_  ):
      lowerCamelCase__         =					'''xlm-roberta-xl'''

      def __init__(     self  :Dict		,				_lowerCamelCase  :int=2_5_0_8_8_0		,				_lowerCamelCase  :Tuple=2_5_6_0		,				_lowerCamelCase  :Any=3_6		,				_lowerCamelCase  :Optional[int]=3_2		,				_lowerCamelCase  :Tuple=1_0_2_4_0		,				_lowerCamelCase  :List[str]="gelu"		,				_lowerCamelCase  :int=0.1		,				_lowerCamelCase  :Dict=0.1		,				_lowerCamelCase  :Union[str, Any]=5_1_4		,				_lowerCamelCase  :List[Any]=1		,				_lowerCamelCase  :List[Any]=0.0_2		,				_lowerCamelCase  :List[str]=1e-05		,				_lowerCamelCase  :Tuple=1		,				_lowerCamelCase  :Dict=0		,				_lowerCamelCase  :List[Any]=2		,				_lowerCamelCase  :Tuple="absolute"		,				_lowerCamelCase  :List[str]=True		,				_lowerCamelCase  :List[Any]=None		,				**_lowerCamelCase  :Optional[Any]		,				):
       super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		,				bos_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		)
       __SCREAMING_SNAKE_CASE					:     Dict   				=			vocab_size
       __SCREAMING_SNAKE_CASE					:     Dict   				=			hidden_size
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			num_hidden_layers
       __SCREAMING_SNAKE_CASE					:     Dict   				=			num_attention_heads
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			hidden_act
       __SCREAMING_SNAKE_CASE					:     str   				=			intermediate_size
       __SCREAMING_SNAKE_CASE					:     Any   				=			hidden_dropout_prob
       __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			max_position_embeddings
       __SCREAMING_SNAKE_CASE					:     int   				=			type_vocab_size
       __SCREAMING_SNAKE_CASE					:     Dict   				=			initializer_range
       __SCREAMING_SNAKE_CASE					:     Any   				=			layer_norm_eps
       __SCREAMING_SNAKE_CASE					:     List[Any]   				=			position_embedding_type
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			use_cache
       __SCREAMING_SNAKE_CASE					:     Tuple   				=			classifier_dropout







class      snake_case						(  a_  ):

      @property
      def 		SCREAMING_SNAKE_CASE_     (     self  :Dict		):

       if self.task == "multiple-choice":
        __SCREAMING_SNAKE_CASE					:     List[Any]   				=			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
        __SCREAMING_SNAKE_CASE					:     int   				=			{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]		)




'''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 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-canny'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'bird'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

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

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

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

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxControlNetModel.from_pretrained(
          'lllyasviel/sd-controlnet-openpose'      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase						,   lowerCAmelCase 						=							FlaxStableDiffusionControlNetPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'      , controlnet=_SCREAMING_SNAKE_CASE      , from_pt=_SCREAMING_SNAKE_CASE      , dtype=jnp.bfloataa	)
      lowerCAmelCase 						=							controlnet_params

      lowerCAmelCase 						=							'Chef in the kitchen'
      lowerCAmelCase 						=							jax.device_count()
      lowerCAmelCase 						=							pipe.prepare_text_inputs([prompts] * num_samples	)

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

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

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

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							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





