

"""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 	_lowercase       (    unittest.TestCase		):




	def       _UpperCamelCase   (			self )		->							Dict:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()




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

			lowerCamelCase					:      Optional[int]          =   'bird'
			lowerCamelCase					:      int          =   jax.device_count()
			lowerCamelCase					:      Tuple          =   pipe.prepare_text_inputs([prompts] * num_samples )

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

			lowerCamelCase					:      str          =   replicate(a__ )
			lowerCamelCase					:      Any          =   shard(a__ )
			lowerCamelCase					:      int          =   shard(a__ )

			lowerCamelCase					:      Optional[int]          =   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)

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

			lowerCamelCase					:      List[Any]          =   jnp.asarray(jax.device_get(image_slice.flatten() ) )
			lowerCamelCase					:      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       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase     ,   lowerCamelCase					:      int          =   FlaxControlNetModel.from_pretrained(
			    'lllyasviel/sd-controlnet-openpose'		,			from_pt=a__		,			dtype=jnp.bfloataa )
			lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
			    'runwayml/stable-diffusion-v1-5'		,			controlnet=a__		,			from_pt=a__		,			dtype=jnp.bfloataa )
			lowerCamelCase					:      Tuple          =   controlnet_params

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

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

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

			lowerCamelCase					:      List[Any]          =   replicate(a__ )
			lowerCamelCase					:      Optional[Any]          =   shard(a__ )
			lowerCamelCase					:      str          =   shard(a__ )

			lowerCamelCase					:      List[str]          =   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)

			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					:      Any          =   jnp.asarray(jax.device_get(image_slice.flatten() ) )
			lowerCamelCase					:      Any          =   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








"""simple docstring"""

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


_A						=			logging.get_logger(__name__)

_A						=			{
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'trajectory_transformer'
	lowercase_				=     ['past_key_values']
	lowercase_				=     {
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}




	def __init__(			self		,			UpperCAmelCase_=100		,			UpperCAmelCase_=5		,			UpperCAmelCase_=1		,			UpperCAmelCase_=1		,			UpperCAmelCase_=249		,			UpperCAmelCase_=6		,			UpperCAmelCase_=17		,			UpperCAmelCase_=25		,			UpperCAmelCase_=4		,			UpperCAmelCase_=4		,			UpperCAmelCase_=128		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0006		,			UpperCAmelCase_=512		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=1		,			UpperCAmelCase_=True		,			UpperCAmelCase_=1		,			UpperCAmelCase_=50256		,			UpperCAmelCase_=50256		,			**UpperCAmelCase_		,			)		->							List[Any]:
			lowerCamelCase					:      int          =   vocab_size
			lowerCamelCase					:      List[str]          =   action_weight
			lowerCamelCase					:      List[Any]          =   reward_weight
			lowerCamelCase					:      List[str]          =   value_weight
			lowerCamelCase					:      Tuple          =   max_position_embeddings
			lowerCamelCase					:      List[str]          =   block_size
			lowerCamelCase					:      Any          =   action_dim
			lowerCamelCase					:      List[Any]          =   observation_dim
			lowerCamelCase					:      Any          =   transition_dim
			lowerCamelCase					:      int          =   learning_rate
			lowerCamelCase					:      Union[str, Any]          =   n_layer
			lowerCamelCase					:      Tuple          =   n_head
			lowerCamelCase					:      Any          =   n_embd
			lowerCamelCase					:      Union[str, Any]          =   embd_pdrop
			lowerCamelCase					:      Optional[int]          =   attn_pdrop
			lowerCamelCase					:      int          =   resid_pdrop
			lowerCamelCase					:      Optional[int]          =   initializer_range
			lowerCamelCase					:      List[str]          =   layer_norm_eps
			lowerCamelCase					:      Any          =   kaiming_initializer_range
			lowerCamelCase					:      str          =   use_cache
			super().__init__(pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			**UpperCAmelCase_ )






