
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
			import jax
			import jax.numpy as jnp
			from flax.jax_utils import replicate
			from flax.training.common_utils import shard




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





	'''simple docstring'''



	def  A							(  self	:    List[str]	):


				'''simple docstring'''


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



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			FlaxStableDiffusionPipeline.from_pretrained(
				    'stabilityai/stable-diffusion-2'			,  revision='bf16'			,  dtype=jnp.bfloataa			,  )

				_snake_case		=			'A painting of a squirrel eating a burger'
				_snake_case		=			jax.device_count()
				_snake_case		=			num_samples * [prompt]
				_snake_case		=			sd_pipe.prepare_inputs(lowercase	)

				_snake_case		=			replicate(lowercase	)
				_snake_case		=			shard(lowercase	)

				_snake_case		=			jax.random.PRNGKey(0	)
				_snake_case		=			jax.random.split(lowercase			,  jax.device_count()	)

				_snake_case		=			sd_pipe(lowercase			,  lowercase			,  lowercase			,  num_inference_steps=25			,  jit=lowercase	)[0]
				assert images.shape == (jax.device_count(), 1, 768, 768, 3)

				_snake_case		=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
				_snake_case		=			images[0, 253:256, 253:256, -1]

				_snake_case		=			jnp.asarray(jax.device_get(image_slice.flatten()	)	)
				_snake_case		=			jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]	)
				print(f'''output_slice: {output_slice}'''	)
				assert jnp.abs(output_slice - expected_slice	).max() < 1E-2





	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'stabilityai/stable-diffusion-2'
				_snake_case	,					_snake_case		=			FlaxDPMSolverMultistepScheduler.from_pretrained(lowercase			,  subfolder='scheduler'	)
				_snake_case	,					_snake_case		=			FlaxStableDiffusionPipeline.from_pretrained(
				    lowercase			,  scheduler=lowercase			,  revision='bf16'			,  dtype=jnp.bfloataa			,  )
				_snake_case		=			scheduler_params

				_snake_case		=			'A painting of a squirrel eating a burger'
				_snake_case		=			jax.device_count()
				_snake_case		=			num_samples * [prompt]
				_snake_case		=			sd_pipe.prepare_inputs(lowercase	)

				_snake_case		=			replicate(lowercase	)
				_snake_case		=			shard(lowercase	)

				_snake_case		=			jax.random.PRNGKey(0	)
				_snake_case		=			jax.random.split(lowercase			,  jax.device_count()	)

				_snake_case		=			sd_pipe(lowercase			,  lowercase			,  lowercase			,  num_inference_steps=25			,  jit=lowercase	)[0]
				assert images.shape == (jax.device_count(), 1, 768, 768, 3)

				_snake_case		=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
				_snake_case		=			images[0, 253:256, 253:256, -1]

				_snake_case		=			jnp.asarray(jax.device_get(image_slice.flatten()	)	)
				_snake_case		=			jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]	)
				print(f'''output_slice: {output_slice}'''	)
				assert jnp.abs(output_slice - expected_slice	).max() < 1E-2

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
			import torch




@require_torch
@require_optimum
@slow
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoTokenizer.from_pretrained(lowercase	)
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			tokenizer('This is me'			,  return_tensors='pt'	)

				_snake_case		=			model.to_bettertransformer()

				self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

				_snake_case		=			model.generate(**lowercase	)

				_snake_case		=			model.reverse_bettertransformer()

				self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase	)

							_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

							self.assertFalse(
							    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

							_snake_case		=			model_reloaded.generate(**lowercase	)
							self.assertTrue(torch.allclose(lowercase			,  lowercase	)	)





	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase	):
										model.save_pretrained(lowercase	)

							_snake_case		=			model.reverse_bettertransformer()
							model.save_pretrained(lowercase	)