






'''simple docstring'''

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      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_     ,      UpperCamelCase_					: int            =				FlaxStableDiffusionPipeline.from_pretrained(
           """stabilityai/stable-diffusion-2"""      ,  revision="""bf16"""      ,  dtype=jnp.bfloataa      ,  )

       UpperCamelCase_					: Any            =				"""A painting of a squirrel eating a burger"""
       UpperCamelCase_					: int            =				jax.device_count()
       UpperCamelCase_					: Dict            =				num_samples * [prompt]
       UpperCamelCase_					: str            =				sd_pipe.prepare_inputs(__lowerCAmelCase			)

       UpperCamelCase_					: List[Any]            =				replicate(__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				shard(__lowerCAmelCase			)

       UpperCamelCase_					: List[Any]            =				jax.random.PRNGKey(0			)
       UpperCamelCase_					: List[Any]            =				jax.random.split(__lowerCAmelCase      ,  jax.device_count()			)

       UpperCamelCase_					: Optional[Any]            =				sd_pipe(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  num_inference_steps=25      ,  jit=__lowerCAmelCase			)[0]
       assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

       UpperCamelCase_					: Any            =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
       UpperCamelCase_					: int            =				images[0, 2_53:2_56, 2_53:2_56, -1]

       UpperCamelCase_					: Dict            =				jnp.asarray(jax.device_get(image_slice.flatten()			)			)
       UpperCamelCase_					: Dict            =				jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12]			)
       print(F"output_slice: {output_slice}"			)
       assert jnp.abs(output_slice - expected_slice			).max() < 1E-2





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[str]            =				"""stabilityai/stable-diffusion-2"""
       UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				FlaxDPMSolverMultistepScheduler.from_pretrained(__lowerCAmelCase      ,  subfolder="""scheduler"""			)
       UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				FlaxStableDiffusionPipeline.from_pretrained(
           __lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  revision="""bf16"""      ,  dtype=jnp.bfloataa      ,  )
       UpperCamelCase_					: int            =				scheduler_params

       UpperCamelCase_					: List[Any]            =				"""A painting of a squirrel eating a burger"""
       UpperCamelCase_					: Optional[Any]            =				jax.device_count()
       UpperCamelCase_					: List[str]            =				num_samples * [prompt]
       UpperCamelCase_					: List[Any]            =				sd_pipe.prepare_inputs(__lowerCAmelCase			)

       UpperCamelCase_					: Optional[int]            =				replicate(__lowerCAmelCase			)
       UpperCamelCase_					: str            =				shard(__lowerCAmelCase			)

       UpperCamelCase_					: Any            =				jax.random.PRNGKey(0			)
       UpperCamelCase_					: List[str]            =				jax.random.split(__lowerCAmelCase      ,  jax.device_count()			)

       UpperCamelCase_					: Dict            =				sd_pipe(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  num_inference_steps=25      ,  jit=__lowerCAmelCase			)[0]
       assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

       UpperCamelCase_					: Any            =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
       UpperCamelCase_					: int            =				images[0, 2_53:2_56, 2_53:2_56, -1]

       UpperCamelCase_					: Optional[Any]            =				jnp.asarray(jax.device_get(image_slice.flatten()			)			)
       UpperCamelCase_					: int            =				jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97]			)
       print(F"output_slice: {output_slice}"			)
       assert jnp.abs(output_slice - expected_slice			).max() < 1E-2














'''simple docstring'''

from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Optional[int]			  =    DistilBertTokenizer
 __a      :    Union[str, Any]			  =    DistilBertTokenizerFast
 __a      :    Any			  =    True
 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""			)

       UpperCamelCase_					: Dict            =				tokenizer.encode("""sequence builders"""      ,  add_special_tokens=__lowerCAmelCase			)
       UpperCamelCase_					: int            =				tokenizer.encode("""multi-sequence build"""      ,  add_special_tokens=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
       UpperCamelCase_					: Dict            =				tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase      ,  __lowerCAmelCase			)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]






