'''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 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       return F'''gaussian_noise_s={seed}_shape={'_'.join([str(snake_case_						) for s in shape]						)}.npy'''


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		snake_case_=0				,		snake_case_=(4, 4, 6_4, 6_4)				,		snake_case_=False						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				jnp.bfloataa if fpaa else jnp.floataa
       UpperCAmelCase_							:       Optional[int]       =				jnp.array(load_hf_numpy(self.get_file_format(snake_case_				,		snake_case_						)						)				,		dtype=snake_case_						)
       return image


  def _UpperCamelCase					(			self				,		snake_case_=False				,		snake_case_="CompVis/stable-diffusion-v1-4"						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				jnp.bfloataa if fpaa else jnp.floataa
       UpperCAmelCase_							:       List[Any]       =				'bf16' if fpaa else None

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[Any]       =				FlaxUNetaDConditionModel.from_pretrained(
           snake_case_				,		subfolder='unet'				,		dtype=snake_case_				,		revision=snake_case_						)
       return model, params


  def _UpperCamelCase					(			self				,		snake_case_=0				,		snake_case_=(4, 7_7, 7_6_8)				,		snake_case_=False						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				jnp.bfloataa if fpaa else jnp.floataa
       UpperCAmelCase_							:       Dict       =				jnp.array(load_hf_numpy(self.get_file_format(snake_case_				,		snake_case_						)						)				,		dtype=snake_case_						)
       return hidden_states


  @parameterized.expand(
      [
          # fmt: off
          [8_3, 4, [-0.23_23, -0.13_04, 0.08_13, -0.30_93, -0.09_19, -0.15_71, -0.11_25, -0.58_06]],
          [1_7, 0.55, [-0.08_31, -0.24_43, 0.09_01, -0.09_19, 0.33_96, 0.01_03, -0.37_43, 0.07_01]],
          [8, 0.89, [-0.48_63, 0.08_59, 0.08_75, -0.16_58, 0.91_99, -0.01_14, 0.48_39, 0.46_39]],
          [3, 1_0_0_0, [-0.56_49, 0.24_02, -0.55_18, 0.12_48, 1.13_28, -0.24_43, -0.03_25, -1.00_78]],
          # fmt: on
      ]						)
  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[Any]       =				self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'				,		fpaa=snake_case_						)
       UpperCAmelCase_							:       int       =				self.get_latents(snake_case_				,		fpaa=snake_case_						)
       UpperCAmelCase_							:       Any       =				self.get_encoder_hidden_states(snake_case_				,		fpaa=snake_case_						)

       UpperCAmelCase_							:       List[Any]       =				model.apply(
           {'params': params}				,		snake_case_				,		jnp.array(snake_case_				,		dtype=jnp.intaa						)				,		encoder_hidden_states=snake_case_				,		).sample

       assert sample.shape == latents.shape

       UpperCAmelCase_							:       List[Any]       =				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)				,		dtype=jnp.floataa						)
       UpperCAmelCase_							:       int       =				jnp.array(snake_case_				,		dtype=jnp.floataa						)

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






  @parameterized.expand(
      [
          # fmt: off
          [8_3, 4, [0.15_14, 0.08_07, 0.16_24, 0.10_16, -0.18_96, 0.02_63, 0.06_77, 0.23_10]],
          [1_7, 0.55, [0.11_64, -0.02_16, 0.01_70, 0.15_89, -0.31_20, 0.10_05, -0.05_81, -0.14_58]],
          [8, 0.89, [-0.17_58, -0.01_69, 0.10_04, -0.14_11, 0.13_12, 0.11_03, -0.19_96, 0.21_39]],
          [3, 1_0_0_0, [0.12_14, 0.03_52, -0.07_31, -0.15_62, -0.09_94, -0.09_06, -0.23_40, -0.05_39]],
          # fmt: on
      ]						)
  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				self.get_unet_model(model_id='stabilityai/stable-diffusion-2'				,		fpaa=snake_case_						)
       UpperCAmelCase_							:       List[str]       =				self.get_latents(snake_case_				,		shape=(4, 4, 9_6, 9_6)				,		fpaa=snake_case_						)
       UpperCAmelCase_							:       str       =				self.get_encoder_hidden_states(snake_case_				,		shape=(4, 7_7, 1_0_2_4)				,		fpaa=snake_case_						)

       UpperCAmelCase_							:       int       =				model.apply(
           {'params': params}				,		snake_case_				,		jnp.array(snake_case_				,		dtype=jnp.intaa						)				,		encoder_hidden_states=snake_case_				,		).sample

       assert sample.shape == latents.shape

       UpperCAmelCase_							:       Union[str, Any]       =				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())						)				,		dtype=jnp.floataa						)
       UpperCAmelCase_							:       List[Any]       =				jnp.array(snake_case_				,		dtype=jnp.floataa						)

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







'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


snake_case__					:      Optional[int]				      =  '''1'''
snake_case__					:      str				      =  '''0'''
snake_case__					:      List[str]				      =  '''1'''

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

snake_case__					:      List[Any]				      =  128
snake_case__					:      Union[str, Any]				      =  1
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Union[str, Any]				      =  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__					:      Union[str, Any]				      =  time.time()
snake_case__					:      str				      =  2000
snake_case__					:      Tuple				      =  {}
for iter in range(max_iters):
 snake_case__					:      str				      =  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))






