




'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_SCREAMING_SNAKE_CASE							:   Union[str, Any]      		= False




class  _snake_case		(	unittest.TestCase							):




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




			@property
			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										return 12




			@property
			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										return 12




			@property
			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										return 32




			@property
			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										torch.manual_seed(0							)
										snake_case_						=      VQModel(
										    block_out_channels=[32, 64]				,							in_channels=3				,							out_channels=3				,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,							latent_channels=3				,							num_vq_embeddings=self.num_embed				,							vq_embed_dim=3				,							)
										return model




			@property
			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										snake_case_						=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
										return tokenizer




			@property
			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										torch.manual_seed(0							)
										snake_case_						=      CLIPTextConfig(
										    bos_token_id=0				,							eos_token_id=2				,							hidden_size=self.text_embedder_hidden_size				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
										return CLIPTextModel(a__							)




			@property
			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										torch.manual_seed(0							)

										snake_case_						=      12
										snake_case_						=      12

										snake_case_						=      {
										    "attention_bias": True,
										    "cross_attention_dim": 32,
										    "attention_head_dim": height * width,
										    "num_attention_heads": 1,
										    "num_vector_embeds": self.num_embed,
										    "num_embeds_ada_norm": self.num_embeds_ada_norm,
										    "norm_num_groups": 32,
										    "sample_size": width,
										    "activation_fn": "geglu-approximate",
										}

										snake_case_						=      TransformeraDModel(**a__							)
										return model




			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      "cpu"

										snake_case_						=      self.dummy_vqvae
										snake_case_						=      self.dummy_text_encoder
										snake_case_						=      self.dummy_tokenizer
										snake_case_						=      self.dummy_transformer
										snake_case_						=      VQDiffusionScheduler(self.num_embed							)
										snake_case_						=      LearnedClassifierFreeSamplingEmbeddings(learnable=a__							)

										snake_case_						=      VQDiffusionPipeline(
										    vqvae=a__				,							text_encoder=a__				,							tokenizer=a__				,							transformer=a__				,							scheduler=a__				,							learned_classifier_free_sampling_embeddings=a__				,							)
										snake_case_						=      pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

										snake_case_						=      "teddy bear playing in the pool"

										snake_case_						=      torch.Generator(device=a__							).manual_seed(0							)
										snake_case_						=      pipe([prompt]				,							generator=a__				,							num_inference_steps=2				,							output_type="np"							)
										snake_case_						=      output.images

										snake_case_						=      torch.Generator(device=a__							).manual_seed(0							)
										snake_case_						=      pipe(
										    [prompt]				,							generator=a__				,							output_type="np"				,							return_dict=a__				,							num_inference_steps=2							)[0]

										snake_case_						=      image[0, -3:, -3:, -1]
										snake_case_						=      image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										snake_case_						=      np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      "cpu"

										snake_case_						=      self.dummy_vqvae
										snake_case_						=      self.dummy_text_encoder
										snake_case_						=      self.dummy_tokenizer
										snake_case_						=      self.dummy_transformer
										snake_case_						=      VQDiffusionScheduler(self.num_embed							)
										snake_case_						=      LearnedClassifierFreeSamplingEmbeddings(
										    learnable=a__				,							hidden_size=self.text_embedder_hidden_size				,							length=tokenizer.model_max_length							)

										snake_case_						=      VQDiffusionPipeline(
										    vqvae=a__				,							text_encoder=a__				,							tokenizer=a__				,							transformer=a__				,							scheduler=a__				,							learned_classifier_free_sampling_embeddings=a__				,							)
										snake_case_						=      pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

										snake_case_						=      "teddy bear playing in the pool"

										snake_case_						=      torch.Generator(device=a__							).manual_seed(0							)
										snake_case_						=      pipe([prompt]				,							generator=a__				,							num_inference_steps=2				,							output_type="np"							)
										snake_case_						=      output.images

										snake_case_						=      torch.Generator(device=a__							).manual_seed(0							)
										snake_case_						=      pipe(
										    [prompt]				,							generator=a__				,							output_type="np"				,							return_dict=a__				,							num_inference_steps=2							)[0]

										snake_case_						=      image[0, -3:, -3:, -1]
										snake_case_						=      image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										snake_case_						=      np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2




@slow
@require_torch_gpu
class  _snake_case		(	unittest.TestCase							):




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"							)

										snake_case_						=      VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"							)
										snake_case_						=      pipeline.to(a__							)
										pipeline.set_progress_bar_config(disable=a__							)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										snake_case_						=      torch.Generator(device=a__							).manual_seed(0							)
										snake_case_						=      pipeline(
										    "teddy bear playing in the pool"				,							num_images_per_prompt=1				,							generator=a__				,							output_type="np"				,							)

										snake_case_						=      output.images[0]

										assert image.shape == (256, 256, 3)
										assert np.abs(expected_image - image							).max() < 2.0








"""simple docstring"""





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )



class 						snake_case_(				unittest.TestCase	):






     def __init__(		self     :    List[Any]    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    List[Any]=1_3    , UpperCamelCase_     :    Tuple=7    , UpperCamelCase_     :    List[Any]=True    , UpperCamelCase_     :    int=True    , UpperCamelCase_     :    Union[str, Any]=True    , UpperCamelCase_     :    Optional[Any]=True    , UpperCamelCase_     :    List[str]=9_9    , UpperCamelCase_     :    str=3_2    , UpperCamelCase_     :    Union[str, Any]=5    , UpperCamelCase_     :    int=4    , UpperCamelCase_     :    Optional[Any]=3_7    , UpperCamelCase_     :    Optional[int]="gelu"    , UpperCamelCase_     :    Any=0.1    , UpperCamelCase_     :    List[str]=0.1    , UpperCamelCase_     :    str=5_1_2    , UpperCamelCase_     :    Optional[Any]=1_6    , UpperCamelCase_     :    Union[str, Any]=2    , UpperCamelCase_     :    Any=0.02    , UpperCamelCase_     :    Union[str, Any]=4    , ):
           lowerCAmelCase    :		str				=      parent
           lowerCAmelCase    :		List[str]				=      batch_size
           lowerCAmelCase    :		int				=      seq_length
           lowerCAmelCase    :		str				=      is_training
           lowerCAmelCase    :		Tuple				=      use_attention_mask
           lowerCAmelCase    :		Dict				=      use_token_type_ids
           lowerCAmelCase    :		Optional[int]				=      use_labels
           lowerCAmelCase    :		Optional[Any]				=      vocab_size
           lowerCAmelCase    :		Optional[int]				=      hidden_size
           lowerCAmelCase    :		Optional[Any]				=      num_hidden_layers
           lowerCAmelCase    :		str				=      num_attention_heads
           lowerCAmelCase    :		Optional[Any]				=      intermediate_size
           lowerCAmelCase    :		int				=      hidden_act
           lowerCAmelCase    :		int				=      hidden_dropout_prob
           lowerCAmelCase    :		Tuple				=      attention_probs_dropout_prob
           lowerCAmelCase    :		str				=      max_position_embeddings
           lowerCAmelCase    :		str				=      type_vocab_size
           lowerCAmelCase    :		str				=      type_sequence_label_size
           lowerCAmelCase    :		Any				=      initializer_range
           lowerCAmelCase    :		int				=      num_choices






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Tuple				=      ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

           lowerCAmelCase    :		Optional[int]				=      None
           if self.use_attention_mask:
                 lowerCAmelCase    :		Union[str, Any]				=      random_attention_mask([self.batch_size, self.seq_length] )

           lowerCAmelCase    :		Union[str, Any]				=      None
           if self.use_token_type_ids:
                 lowerCAmelCase    :		Dict				=      ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size )

           lowerCAmelCase    :		Union[str, Any]				=      RobertaPreLayerNormConfig(
               vocab_size=self.vocab_size    , hidden_size=self.hidden_size    , num_hidden_layers=self.num_hidden_layers    , num_attention_heads=self.num_attention_heads    , intermediate_size=self.intermediate_size    , hidden_act=self.hidden_act    , hidden_dropout_prob=self.hidden_dropout_prob    , attention_probs_dropout_prob=self.attention_probs_dropout_prob    , max_position_embeddings=self.max_position_embeddings    , type_vocab_size=self.type_vocab_size    , is_decoder=UpperCamelCase_    , initializer_range=self.initializer_range    , )

           return config, input_ids, token_type_ids, attention_mask






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		List[str]				=      self.prepare_config_and_inputs()
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Optional[Any]				=      config_and_inputs
           lowerCAmelCase    :		Optional[Any]				=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		int				=      self.prepare_config_and_inputs()
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      config_and_inputs

           lowerCAmelCase    :		str				=      True
           lowerCAmelCase    :		Optional[Any]				=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
           lowerCAmelCase    :		str				=      ids_tensor([self.batch_size, self.seq_length]    , vocab_size=2 )

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    True

     __UpperCamelCase     =    (
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		Any				=      FlaxRobertaPreLayerNormModelTester(self )






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase    :		Optional[int]				=      model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''    , from_pt=UpperCamelCase_ )
                 lowerCAmelCase    :		int				=      model(np.ones((1, 1) ) )
                 self.assertIsNotNone(UpperCamelCase_ )



@require_flax
class 						snake_case_(				unittest.TestCase	):






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		str				=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''    , from_pt=UpperCamelCase_ )

           lowerCAmelCase    :		Any				=      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    , dtype=jnp.intaa )
           lowerCAmelCase    :		Union[str, Any]				=      model(UpperCamelCase_ )[0]
           lowerCAmelCase    :		str				=      [1, 1_1, 5_0_2_6_5]
           self.assertEqual(list(output.shape )    , UpperCamelCase_ )
           # compare the actual values for a slice.
           lowerCAmelCase    :		Optional[Any]				=      np.array(
               [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]    , dtype=np.floataa )
           self.assertTrue(np.allclose(output[:, :3, :3]    , UpperCamelCase_    , atol=1E-4 ) )






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Dict				=      FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''    , from_pt=UpperCamelCase_ )

           lowerCAmelCase    :		str				=      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    , dtype=jnp.intaa )
           lowerCAmelCase    :		str				=      model(UpperCamelCase_ )[0]
           # compare the actual values for a slice.
           lowerCAmelCase    :		str				=      np.array(
               [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]    , dtype=np.floataa )
           self.assertTrue(np.allclose(output[:, :3, :3]    , UpperCamelCase_    , atol=1E-4 ) )


