

'''simple docstring'''



import os
import time

import numpy as np
import onnxruntime as ort


__snake_case			:     List[str]										=      '1'
__snake_case			:     Union[str, Any]										=      '0'
__snake_case			:     Union[str, Any]										=      '1'

__snake_case			:     Any										=      ort.SessionOptions()
__snake_case			:     List[Any]										=      ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
__snake_case			:     List[Any]										=      ['TensorrtExecutionProvider', 'CUDAExecutionProvider']
__snake_case			:     int										=      ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
__snake_case			:     Any										=      ort.RunOptions()

__snake_case			:     Dict										=      128
__snake_case			:     Tuple										=      1
__snake_case			:     Any										=      np.ones((batch, sequence), dtype=np.intaa)
__snake_case			:     Any										=      np.ones((batch, sequence), dtype=np.intaa)
__snake_case			:     str										=      np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
__snake_case			:     Tuple										=      time.time()
__snake_case			:     Union[str, Any]										=      2000
__snake_case			:     int										=      {}
for iter in range(max_iters):
		__snake_case			:     Optional[Any]										=      sess.run(
		    None,
		    {
		        sess.get_inputs()[0].name: input_ids,
		        sess.get_inputs()[1].name: attention_mask,
		        sess.get_inputs()[2].name: token_type_ids,
		    },
		    run_options=run_opt,
		)
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))




'''simple docstring'''



import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp



@slow
@require_flax
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						List[str]			)      ->		List[str]:





									'''simple docstring'''
									return f"gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase_			) for s in shape]			)}.npy"


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						str=0		,      lowerCAmelCase_	:						List[str]=(4, 4, 64, 64)		,      lowerCAmelCase_	:						Optional[Any]=False			)      ->		str:





									'''simple docstring'''
									A__					:       Union[str, Any]     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       Any     =jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_		,      lowerCAmelCase_			)			)		,      dtype=lowerCAmelCase_			)
									return image


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						Optional[int]=False		,      lowerCAmelCase_	:						Dict="CompVis/stable-diffusion-v1-4"			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Any     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       int     ="""bf16""" if fpaa else None

									A__   ,						A__					:       Any     =FlaxUNetaDConditionModel.from_pretrained(
									    lowerCAmelCase_		,      subfolder="""unet"""		,      dtype=lowerCAmelCase_		,      revision=lowerCAmelCase_			)
									return model, params


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						int=0		,      lowerCAmelCase_	:						Optional[Any]=(4, 77, 7_68)		,      lowerCAmelCase_	:						List[str]=False			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Union[str, Any]     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       Optional[int]     =jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_		,      lowerCAmelCase_			)			)		,      dtype=lowerCAmelCase_			)
									return hidden_states


			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
			        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
			        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
			        [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						str			)      ->		int:





									'''simple docstring'''
									A__   ,						A__					:       Tuple     =self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""		,      fpaa=lowerCAmelCase_			)
									A__					:       List[Any]     =self.get_latents(lowerCAmelCase_		,      fpaa=lowerCAmelCase_			)
									A__					:       Optional[int]     =self.get_encoder_hidden_states(lowerCAmelCase_		,      fpaa=lowerCAmelCase_			)

									A__					:       Union[str, Any]     =model.apply(
									    {"""params""": params}		,      lowerCAmelCase_		,      jnp.array(lowerCAmelCase_		,      dtype=jnp.intaa			)		,      encoder_hidden_states=lowerCAmelCase_		,      ).sample

									assert sample.shape == latents.shape

									A__					:       Optional[int]     =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)		,      dtype=jnp.floataa			)
									A__					:       Tuple     =jnp.array(lowerCAmelCase_		,      dtype=jnp.floataa			)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(lowerCAmelCase_		,      lowerCAmelCase_		,      atol=1e-2			)



			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
			        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
			        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
			        [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Optional[int]			)      ->		List[str]:





									'''simple docstring'''
									A__   ,						A__					:       List[str]     =self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""		,      fpaa=lowerCAmelCase_			)
									A__					:       Union[str, Any]     =self.get_latents(lowerCAmelCase_		,      shape=(4, 4, 96, 96)		,      fpaa=lowerCAmelCase_			)
									A__					:       Dict     =self.get_encoder_hidden_states(lowerCAmelCase_		,      shape=(4, 77, 10_24)		,      fpaa=lowerCAmelCase_			)

									A__					:       Optional[Any]     =model.apply(
									    {"""params""": params}		,      lowerCAmelCase_		,      jnp.array(lowerCAmelCase_		,      dtype=jnp.intaa			)		,      encoder_hidden_states=lowerCAmelCase_		,      ).sample

									assert sample.shape == latents.shape

									A__					:       Optional[Any]     =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)		,      dtype=jnp.floataa			)
									A__					:       List[Any]     =jnp.array(lowerCAmelCase_		,      dtype=jnp.floataa			)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(lowerCAmelCase_		,      lowerCAmelCase_		,      atol=1e-2			)

