


"""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   __a      (   unittest.TestCase				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=13    ,			lowerCAmelCase__=7    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=99    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=5    ,			lowerCAmelCase__=4    ,			lowerCAmelCase__=37    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=512    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=4    ,			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       parent
        lowercase__:       List[str]        =       batch_size
        lowercase__:       List[Any]        =       seq_length
        lowercase__:       Any        =       is_training
        lowercase__:       Any        =       use_attention_mask
        lowercase__:       Any        =       use_token_type_ids
        lowercase__:       List[Any]        =       use_labels
        lowercase__:       int        =       vocab_size
        lowercase__:       int        =       hidden_size
        lowercase__:       List[str]        =       num_hidden_layers
        lowercase__:       Dict        =       num_attention_heads
        lowercase__:       int        =       intermediate_size
        lowercase__:       List[Any]        =       hidden_act
        lowercase__:       int        =       hidden_dropout_prob
        lowercase__:       Tuple        =       attention_probs_dropout_prob
        lowercase__:       List[Any]        =       max_position_embeddings
        lowercase__:       Tuple        =       type_vocab_size
        lowercase__:       Optional[int]        =       type_sequence_label_size
        lowercase__:       Any        =       initializer_range
        lowercase__:       int        =       num_choices
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size			)

        lowercase__:       Optional[Any]        =       None
        if self.use_attention_mask:
               lowercase__:       Optional[Any]        =       random_attention_mask([self.batch_size, self.seq_length]			)

        lowercase__:       List[Any]        =       None
        if self.use_token_type_ids:
               lowercase__:       Dict        =       ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size			)

        lowercase__:       List[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=_a    ,			initializer_range=self.initializer_range    ,			)

        return config, input_ids, token_type_ids, attention_mask
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.prepare_config_and_inputs()
        lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       Dict        =       config_and_inputs
        lowercase__:       Any        =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
        return config, inputs_dict
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.prepare_config_and_inputs()
        lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__:       Optional[int]        =       config_and_inputs

        lowercase__:       Optional[Any]        =       True
        lowercase__:       Optional[Any]        =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
        lowercase__:       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   __a      (   __SCREAMING_SNAKE_CASE	,	unittest.TestCase				):
 __lowercase			:							int       					=			True

 __lowercase			:							List[Any]       					=			(
     (
         FlaxRobertaPreLayerNormModel,
         FlaxRobertaPreLayerNormForCausalLM,
         FlaxRobertaPreLayerNormForMaskedLM,
         FlaxRobertaPreLayerNormForSequenceClassification,
         FlaxRobertaPreLayerNormForTokenClassification,
         FlaxRobertaPreLayerNormForMultipleChoice,
         FlaxRobertaPreLayerNormForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Dict        =       FlaxRobertaPreLayerNormModelTester(self			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        for model_class_name in self.all_model_classes:
               lowercase__:       Tuple        =       model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,			from_pt=_a			)
               lowercase__:       Optional[Any]        =       model(np.ones((1, 1)			)			)
               self.assertIsNotNone(_a			)





@require_flax
class   __a      (   unittest.TestCase				):
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,			from_pt=_a			)

        lowercase__:       Dict        =       np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    ,			dtype=jnp.intaa			)
        lowercase__:       Optional[int]        =       model(_a			)[0]
        lowercase__:       Optional[int]        =       [1, 11, 50_265]
        self.assertEqual(list(output.shape			)    ,			_a			)
        # compare the actual values for a slice.
        lowercase__:       int        =       np.array(
            [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-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]    ,			_a    ,			atol=1E-4			)			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,			from_pt=_a			)

        lowercase__:       int        =       np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    ,			dtype=jnp.intaa			)
        lowercase__:       Union[str, Any]        =       model(_a			)[0]
        # compare the actual values for a slice.
        lowercase__:       Optional[int]        =       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]    ,			_a    ,			atol=1E-4			)			)






import torch

from diffusers import StableDiffusionPipeline


__lowerCAmelCase			=							'''path-to-your-trained-model'''
__lowerCAmelCase			=							StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

__lowerCAmelCase			=							'''A photo of sks dog in a bucket'''
__lowerCAmelCase			=							pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


