

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= XLMRobertaModel.from_pretrained('xlm-roberta-base'							)
				snake_case_      		= torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]							)
				# The dog is cute and lives in the garden house

				snake_case_      		= torch.Size((1, 12, 7_68)							)  # batch_size, sequence_length, embedding_vector_dim
				snake_case_      		= torch.tensor(
				    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
					snake_case_      		= model(__UpperCamelCase							)['last_hidden_state'].detach()
				self.assertEqual(output.shape ,				__UpperCamelCase							)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1] ,				__UpperCamelCase ,				atol=1E-3							)							)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= XLMRobertaModel.from_pretrained('xlm-roberta-large'							)
				snake_case_      		= torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]							)
				# The dog is cute and lives in the garden house

				snake_case_      		= torch.Size((1, 12, 10_24)							)  # batch_size, sequence_length, embedding_vector_dim
				snake_case_      		= torch.tensor(
				    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
					snake_case_      		= model(__UpperCamelCase							)['last_hidden_state'].detach()
				self.assertEqual(output.shape ,				__UpperCamelCase							)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1] ,				__UpperCamelCase ,				atol=1E-3							)							)



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A											=     argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A											=     parser.parse_args()


A											=     'cpu'
A											=     'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

A											=     'path-to-your-trained-model'
A											=     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		A											=     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A											=     pipe.to(device)

# to channels last
A											=     pipe.unet.to(memory_format=torch.channels_last)
A											=     pipe.vae.to(memory_format=torch.channels_last)
A											=     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		A											=     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
A											=     torch.randn(2, 4, 64, 64)
A											=     torch.rand(1) * 999
A											=     torch.randn(2, 77, 768)
A											=     (sample, timestep, encoder_hidden_status)
try:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		A											=     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A											=     666
A											=     torch.Generator(device).manual_seed(seed)
A											=     {'generator': generator}
if args.steps is not None:
		A											=     args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
		A											=     pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')
