


import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort







class lowerCAmelCase_		(				lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 pass







@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 @property
 def 		UpperCamelCase__  (			self      ):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ort.SessionOptions()
       snake_case_ =   False
       return options



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''      )
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''      )
       snake_case_ =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   revision='''onnx'''	,   safety_checker=_UpperCAmelCase	,   feature_extractor=_UpperCAmelCase	,   provider=self.gpu_provider	,   sess_options=self.gpu_options	,   )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   '''A red cat sitting on a park bench'''

       snake_case_ =   np.random.RandomState(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   mask_image=_UpperCAmelCase	,   guidance_scale=7.5	,   num_inference_steps=10	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images
       snake_case_ =   images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''      )
       snake_case_ =   load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''      )
       snake_case_ =   LMSDiscreteScheduler.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   subfolder='''scheduler'''	,   revision='''onnx'''      )
       snake_case_ =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''	,   revision='''onnx'''	,   scheduler=_UpperCAmelCase	,   safety_checker=_UpperCAmelCase	,   feature_extractor=_UpperCAmelCase	,   provider=self.gpu_provider	,   sess_options=self.gpu_options	,   )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

       snake_case_ =   '''A red cat sitting on a park bench'''

       snake_case_ =   np.random.RandomState(0      )
       snake_case_ =   pipe(
           prompt=_UpperCAmelCase	,   image=_UpperCAmelCase	,   mask_image=_UpperCAmelCase	,   guidance_scale=7.5	,   num_inference_steps=20	,   generator=_UpperCAmelCase	,   output_type='''np'''	,   )
       snake_case_ =   output.images
       snake_case_ =   images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_ =   np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3




from typing import List, Optional, Union

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


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"informer"
 __snake_case				=						{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }



 def __init__(			self	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = "student_t"	,   _UpperCAmelCase = "nll"	,   _UpperCAmelCase = 1	,   _UpperCAmelCase = None	,   _UpperCAmelCase = "mean"	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = 64	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = True	,   _UpperCAmelCase = "gelu"	,   _UpperCAmelCase = 0.05	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 1_00	,   _UpperCAmelCase = 0.02	,   _UpperCAmelCase=True	,   _UpperCAmelCase = "prob"	,   _UpperCAmelCase = 5	,   _UpperCAmelCase = True	,   **_UpperCAmelCase	,   ):
       # time series specific configuration
       snake_case_ =   prediction_length
       snake_case_ =   context_length or prediction_length
       snake_case_ =   distribution_output
       snake_case_ =   loss
       snake_case_ =   input_size
       snake_case_ =   num_time_features
       snake_case_ =   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       snake_case_ =   scaling
       snake_case_ =   num_dynamic_real_features
       snake_case_ =   num_static_real_features
       snake_case_ =   num_static_categorical_features

       # set cardinality
       if cardinality and num_static_categorical_features > 0:
             if len(_UpperCAmelCase      ) != num_static_categorical_features:
                   raise ValueError(
                       '''The cardinality should be a list of the same length as `num_static_categorical_features`'''      )
             snake_case_ =   cardinality
       else:
             snake_case_ =   [0]

       # set embedding_dimension
       if embedding_dimension and num_static_categorical_features > 0:
             if len(_UpperCAmelCase      ) != num_static_categorical_features:
                   raise ValueError(
                       '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''      )
             snake_case_ =   embedding_dimension
       else:
             snake_case_ =   [min(50	,   (cat + 1) // 2      ) for cat in self.cardinality]

       snake_case_ =   num_parallel_samples

       # Transformer architecture configuration
       snake_case_ =   input_size * len(self.lags_sequence      ) + self._number_of_features
       snake_case_ =   d_model
       snake_case_ =   encoder_attention_heads
       snake_case_ =   decoder_attention_heads
       snake_case_ =   encoder_ffn_dim
       snake_case_ =   decoder_ffn_dim
       snake_case_ =   encoder_layers
       snake_case_ =   decoder_layers

       snake_case_ =   dropout
       snake_case_ =   attention_dropout
       snake_case_ =   activation_dropout
       snake_case_ =   encoder_layerdrop
       snake_case_ =   decoder_layerdrop

       snake_case_ =   activation_function
       snake_case_ =   init_std

       snake_case_ =   use_cache

       # Informer
       snake_case_ =   attention_type
       snake_case_ =   sampling_factor
       snake_case_ =   distil

       super().__init__(is_encoder_decoder=_UpperCAmelCase	,   **_UpperCAmelCase      )





 @property
 def 		UpperCamelCase__  (			self      ):
       return (
           sum(self.embedding_dimension      )
           + self.num_dynamic_real_features
           + self.num_time_features
           + self.num_static_real_features
           + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
       )