





from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}

class 		SCREAMING_SNAKE_CASE__    (	A__  ):
						A_						:    Union[str, Any]							=   'transfo-xl'
						A_						:    str							=   ['mems']
						A_						:    Union[str, Any]							=   {
						    'n_token': 'vocab_size',
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}


						def __init__(self	:				Optional[Any]    ,      a__	:				Union[str, Any]=26_7735    ,      a__	:				Tuple=[2_0000, 4_0000, 20_0000]    ,      a__	:				List[Any]=1024    ,      a__	:				Tuple=1024    ,      a__	:				int=16    ,      a__	:				Optional[int]=64    ,      a__	:				Any=4096    ,      a__	:				Union[str, Any]=4    ,      a__	:				List[str]=False    ,      a__	:				Optional[Any]=18    ,      a__	:				str=1600    ,      a__	:				Union[str, Any]=1000    ,      a__	:				Optional[Any]=True    ,      a__	:				List[Any]=True    ,      a__	:				List[str]=0    ,      a__	:				Optional[Any]=-1    ,      a__	:				Optional[int]=True    ,      a__	:				Tuple=0.1    ,      a__	:				List[str]=0.0    ,      a__	:				Tuple=True    ,      a__	:				int="normal"    ,      a__	:				Union[str, Any]=0.0_1    ,      a__	:				List[str]=0.0_1    ,      a__	:				Optional[int]=0.0_2    ,      a__	:				Tuple=1E-5    ,      a__	:				Optional[Any]=0    ,      **a__	:				Union[str, Any]    ,      ):





									"""simple docstring"""



									__snake_case								=       vocab_size
									__snake_case								=       []
									self.cutoffs.extend(lowerCamelCase__				)
									if proj_share_all_but_first:
												__snake_case								=       [False] + [True] * len(self.cutoffs				)
									else:
												__snake_case								=       [False] + [False] * len(self.cutoffs				)
									__snake_case								=       d_model
									__snake_case								=       d_embed
									__snake_case								=       d_head
									__snake_case								=       d_inner
									__snake_case								=       div_val
									__snake_case								=       pre_lnorm
									__snake_case								=       n_layer
									__snake_case								=       n_head
									__snake_case								=       mem_len
									__snake_case								=       same_length
									__snake_case								=       attn_type
									__snake_case								=       clamp_len
									__snake_case								=       sample_softmax
									__snake_case								=       adaptive
									__snake_case								=       dropout
									__snake_case								=       dropatt
									__snake_case								=       untie_r
									__snake_case								=       init
									__snake_case								=       init_range
									__snake_case								=       proj_init_std
									__snake_case								=       init_std
									__snake_case								=       layer_norm_epsilon
									super().__init__(eos_token_id=lowerCamelCase__    ,      **lowerCamelCase__				)


						@property
						def        a			(self	:				List[str]				):





									"""simple docstring"""



									logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""				)
									return -1


						@max_position_embeddings.setter
						def        a			(self	:				List[Any]    ,      a__	:				Dict				):





									"""simple docstring"""



									raise NotImplementedError(
									    f"""The model {self.model_type} is one of the few models that has no sequence length limit."""				)








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  ):


      def        a			(self	:				int				):





         """simple docstring"""



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


      def        a			(self	:				Dict				):





         """simple docstring"""



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

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

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

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       pipe(
             prompt_ids=a__    ,      image=a__    ,      params=a__    ,      prng_seed=a__    ,      num_inference_steps=50    ,      jit=a__    ,      ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __snake_case								=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         __snake_case								=       images[0, 253:256, 253:256, -1]

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2


      def        a			(self	:				Dict				):





         """simple docstring"""



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

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

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

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       pipe(
             prompt_ids=a__    ,      image=a__    ,      params=a__    ,      prng_seed=a__    ,      num_inference_steps=50    ,      jit=a__    ,      ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __snake_case								=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         __snake_case								=       images[0, 253:256, 253:256, -1]

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2

