

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      lowerCamelCase_      (	unittest.TestCase	):




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




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			List[Any]		    =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

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

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

		__A :			Dict		    =			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        lowerCAmelCase_       (     self						:     int							):
		__A :			Optional[int]		    =			self.prepare_config_and_inputs()
		__A    ,				__A    ,				__A    ,				__A :			Tuple		    =			config_and_inputs
		__A :			str		    =			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
		return config, inputs_dict




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			Tuple		    =			self.prepare_config_and_inputs()
		__A    ,				__A    ,				__A    ,				__A :			List[Any]		    =			config_and_inputs

		__A :			Optional[int]		    =			True
		__A :			int		    =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
		__A :			List[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      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   str     					=				True

	_lowercase		:   Any     					=				(
	    (
	        FlaxRobertaPreLayerNormModel,
	        FlaxRobertaPreLayerNormForCausalLM,
	        FlaxRobertaPreLayerNormForMaskedLM,
	        FlaxRobertaPreLayerNormForSequenceClassification,
	        FlaxRobertaPreLayerNormForTokenClassification,
	        FlaxRobertaPreLayerNormForMultipleChoice,
	        FlaxRobertaPreLayerNormForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			List[Any]		    =			FlaxRobertaPreLayerNormModelTester(self							)




	@slow
	def        lowerCAmelCase_       (     self						:     Any							):
		for model_class_name in self.all_model_classes:
			__A :			List[Any]		    =			model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""					,      from_pt=__A							)
			__A :			Dict		    =			model(np.ones((1, 1)							)							)
			self.assertIsNotNone(__A							)






@require_flax
class      lowerCamelCase_      (	unittest.TestCase	):




	@slow
	def        lowerCAmelCase_       (     self						:     int							):
		__A :			Dict		    =			FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""					,      from_pt=__A							)

		__A :			str		    =			np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					,      dtype=jnp.intaa							)
		__A :			List[Any]		    =			model(__A							)[0]
		__A :			str		    =			[1, 11, 5_0265]
		self.assertEqual(list(output.shape							)					,      __A							)
		# compare the actual values for a slice.
		__A :			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        lowerCAmelCase_       (     self						:     Any							):
		__A :			Union[str, Any]		    =			FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""					,      from_pt=__A							)

		__A :			Dict		    =			np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					,      dtype=jnp.intaa							)
		__A :			Optional[int]		    =			model(__A							)[0]
		# compare the actual values for a slice.
		__A :			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 warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)





