

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 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  List[str]										=				'''trajectory_transformer'''
							_UpperCAmelCase   :  Union[str, Any]										=				['''past_key_values''']
							_UpperCAmelCase   :  Any										=				{
							    '''hidden_size''': '''n_embd''',
							    '''num_attention_heads''': '''n_head''',
							    '''num_hidden_layers''': '''n_layer''',
							}
							def __init__(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_0_0       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=5       ,SCREAMING_SNAKE_CASE__		:      List[str]=1       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1       ,SCREAMING_SNAKE_CASE__		:      str=2_4_9       ,SCREAMING_SNAKE_CASE__		:      int=6       ,SCREAMING_SNAKE_CASE__		:      Any=1_7       ,SCREAMING_SNAKE_CASE__		:      Tuple=2_5       ,SCREAMING_SNAKE_CASE__		:      Any=4       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=4       ,SCREAMING_SNAKE_CASE__		:      str=1_2_8       ,SCREAMING_SNAKE_CASE__		:      Any=0.1       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=0.1       ,SCREAMING_SNAKE_CASE__		:      str=0.1       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=0.0006       ,SCREAMING_SNAKE_CASE__		:      Tuple=5_1_2       ,SCREAMING_SNAKE_CASE__		:      int=0.02       ,SCREAMING_SNAKE_CASE__		:      List[Any]=1E-12       ,SCREAMING_SNAKE_CASE__		:      int=1       ,SCREAMING_SNAKE_CASE__		:      Any=True       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=5_0_2_5_6       ,SCREAMING_SNAKE_CASE__		:      int=5_0_2_5_6       ,**SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,):
								__lowerCamelCase    :    Dict 	=			vocab_size
								__lowerCamelCase    :    Optional[int] 	=			action_weight
								__lowerCamelCase    :    Tuple 	=			reward_weight
								__lowerCamelCase    :    Optional[int] 	=			value_weight
								__lowerCamelCase    :    Any 	=			max_position_embeddings
								__lowerCamelCase    :    Union[str, Any] 	=			block_size
								__lowerCamelCase    :    Any 	=			action_dim
								__lowerCamelCase    :    Optional[int] 	=			observation_dim
								__lowerCamelCase    :    Optional[int] 	=			transition_dim
								__lowerCamelCase    :    int 	=			learning_rate
								__lowerCamelCase    :    int 	=			n_layer
								__lowerCamelCase    :    int 	=			n_head
								__lowerCamelCase    :    List[str] 	=			n_embd
								__lowerCamelCase    :    Optional[int] 	=			embd_pdrop
								__lowerCamelCase    :    List[Any] 	=			attn_pdrop
								__lowerCamelCase    :    int 	=			resid_pdrop
								__lowerCamelCase    :    Union[str, Any] 	=			initializer_range
								__lowerCamelCase    :    Optional[Any] 	=			layer_norm_eps
								__lowerCamelCase    :    Optional[Any] 	=			kaiming_initializer_range
								__lowerCamelCase    :    Tuple 	=			use_cache
								super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__       ,bos_token_id=SCREAMING_SNAKE_CASE__       ,eos_token_id=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)







import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
					import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 	A_		(	unittest.TestCase    ):
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)
							@property
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			ort.SessionOptions()
								__lowerCamelCase    :    Tuple 	=			False
								return options


							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Tuple 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png')
								__lowerCamelCase    :    List[str] 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
								__lowerCamelCase    :    Optional[int] 	=			load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy')

								# using the PNDM scheduler by default
								__lowerCamelCase    :    Dict 	=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'       ,revision='onnx'       ,safety_checker=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			'A red cat sitting on a park bench'

								__lowerCamelCase    :    Any 	=			np.random.RandomState(0)
								__lowerCamelCase    :    List[Any] 	=			pipe(
								    prompt=SCREAMING_SNAKE_CASE__       ,image=SCREAMING_SNAKE_CASE__       ,mask_image=SCREAMING_SNAKE_CASE__       ,strength=0.75       ,guidance_scale=7.5       ,num_inference_steps=1_5       ,generator=SCREAMING_SNAKE_CASE__       ,output_type='np'       ,)

								__lowerCamelCase    :    Union[str, Any] 	=			output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image).max() < 1E-2




