"""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       ):




     """simple docstring"""

     def __init__(       self     :			Optional[Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			List[Any]=13					,lowerCamelCase__     :			Optional[Any]=7					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			List[str]=True					,lowerCamelCase__     :			Any=True					,lowerCamelCase__     :			Union[str, Any]=99					,lowerCamelCase__     :			Dict=32					,lowerCamelCase__     :			List[Any]=5					,lowerCamelCase__     :			Tuple=4					,lowerCamelCase__     :			Any=37					,lowerCamelCase__     :			List[str]="gelu"					,lowerCamelCase__     :			List[Any]=0.1					,lowerCamelCase__     :			int=0.1					,lowerCamelCase__     :			str=512					,lowerCamelCase__     :			Optional[int]=16					,lowerCamelCase__     :			Optional[Any]=2					,lowerCamelCase__     :			Dict=0.0_2					,lowerCamelCase__     :			List[str]=4					,):
         UpperCAmelCase__    =							parent
         UpperCAmelCase__    =							batch_size
         UpperCAmelCase__    =							seq_length
         UpperCAmelCase__    =							is_training
         UpperCAmelCase__    =							use_attention_mask
         UpperCAmelCase__    =							use_token_type_ids
         UpperCAmelCase__    =							use_labels
         UpperCAmelCase__    =							vocab_size
         UpperCAmelCase__    =							hidden_size
         UpperCAmelCase__    =							num_hidden_layers
         UpperCAmelCase__    =							num_attention_heads
         UpperCAmelCase__    =							intermediate_size
         UpperCAmelCase__    =							hidden_act
         UpperCAmelCase__    =							hidden_dropout_prob
         UpperCAmelCase__    =							attention_probs_dropout_prob
         UpperCAmelCase__    =							max_position_embeddings
         UpperCAmelCase__    =							type_vocab_size
         UpperCAmelCase__    =							type_sequence_label_size
         UpperCAmelCase__    =							initializer_range
         UpperCAmelCase__    =							num_choices







     def 	__lowerCAmelCase		(       self     :			str    ):
         UpperCAmelCase__    =							ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size    )

         UpperCAmelCase__    =							None
         if self.use_attention_mask:
             UpperCAmelCase__    =							random_attention_mask([self.batch_size, self.seq_length]    )

         UpperCAmelCase__    =							None
         if self.use_token_type_ids:
             UpperCAmelCase__    =							ids_tensor([self.batch_size, self.seq_length]					,self.type_vocab_size    )

         UpperCAmelCase__    =							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=lowerCamelCase__					,initializer_range=self.initializer_range					,)

         return config, input_ids, token_type_ids, attention_mask







     def 	__lowerCAmelCase		(       self     :			Dict    ):
         UpperCAmelCase__    =							self.prepare_config_and_inputs()
         UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							config_and_inputs
         UpperCAmelCase__    =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







     def 	__lowerCAmelCase		(       self     :			str    ):
         UpperCAmelCase__    =							self.prepare_config_and_inputs()
         UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							config_and_inputs

         UpperCAmelCase__    =							True
         UpperCAmelCase__    =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
         UpperCAmelCase__    =							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 (				__UpperCAmelCase   ,					unittest.TestCase       ):




     """simple docstring"""

     snake_case__       					=				True

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







     def 	__lowerCAmelCase		(       self     :			List[str]    ):
         UpperCAmelCase__    =							FlaxRobertaPreLayerNormModelTester(self    )







     @slow
     def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
         for model_class_name in self.all_model_classes:
             UpperCAmelCase__    =							model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,from_pt=lowerCamelCase__    )
             UpperCAmelCase__    =							model(np.ones((1, 1)    )    )
             self.assertIsNotNone(lowerCamelCase__    )






@require_flax
class        snake_case (				unittest.TestCase       ):




     """simple docstring"""

     @slow
     def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
         UpperCAmelCase__    =							FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,from_pt=lowerCamelCase__    )

         UpperCAmelCase__    =							np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					,dtype=jnp.intaa    )
         UpperCAmelCase__    =							model(lowerCamelCase__    )[0]
         UpperCAmelCase__    =							[1, 11, 50_265]
         self.assertEqual(list(output.shape    )					,lowerCamelCase__    )
         # compare the actual values for a slice.
         UpperCAmelCase__    =							np.array(
             [[[40.4_880, 18.0_199, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 10.7_085], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]					,dtype=np.floataa    )
         self.assertTrue(np.allclose(output[:, :3, :3]					,lowerCamelCase__					,atol=1e-4    )    )







     @slow
     def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
         UpperCAmelCase__    =							FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'					,from_pt=lowerCamelCase__    )

         UpperCAmelCase__    =							np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					,dtype=jnp.intaa    )
         UpperCAmelCase__    =							model(lowerCamelCase__    )[0]
         # compare the actual values for a slice.
         UpperCAmelCase__    =							np.array(
             [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]					,dtype=np.floataa    )
         self.assertTrue(np.allclose(output[:, :3, :3]					,lowerCamelCase__					,atol=1e-4    )    )




"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__						:						Union[str, Any]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






@nightly
@require_torch_gpu
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Any    ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 	__lowerCAmelCase		(       self     :			Any    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )

									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(lowerCamelCase__    )
													UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained(lowerCamelCase__					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							generator.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt='first prompt'					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=2					,output_type='numpy'					,).images

									assert np.abs(image - new_image    ).sum() < 1e-5, "Models don't have the same forward pass"







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'					,torch_dtype=torch.floataa    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							'cyberpunk 2077'
									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.dual_guided(
									    prompt=lowerCamelCase__					,image=lowerCamelCase__					,text_to_image_strength=0.7_5					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]    )

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

									UpperCAmelCase__    =							'A painting of a squirrel eating a burger '
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe.text_to_image(
									    prompt=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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

									UpperCAmelCase__    =							pipe.image_variation(lowerCamelCase__					,generator=lowerCamelCase__					,output_type='numpy'    ).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]    )

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



