
import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    MobileBertForMaskedLM,
		    MobileBertForMultipleChoice,
		    MobileBertForNextSentencePrediction,
		    MobileBertForPreTraining,
		    MobileBertForQuestionAnswering,
		    MobileBertForSequenceClassification,
		    MobileBertForTokenClassification,
		    MobileBertModel,
		)




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:Optional[int]  ,     a	:Optional[int]  ,     a	:List[Any]=1_3  ,     a	:str=7  ,     a	:int=True  ,     a	:Union[str, Any]=True  ,     a	:Any=True  ,     a	:Tuple=True  ,     a	:List[str]=9_9  ,     a	:str=6_4  ,     a	:List[Any]=3_2  ,     a	:List[Any]=5  ,     a	:Optional[Any]=4  ,     a	:Optional[int]=3_7  ,     a	:Union[str, Any]="gelu"  ,     a	:str=0.1  ,     a	:List[str]=0.1  ,     a	:Any=5_1_2  ,     a	:Dict=1_6  ,     a	:int=2  ,     a	:List[str]=0.02  ,     a	:Optional[Any]=3  ,     a	:Dict=4  ,     a	:List[str]=None  ,     )	->				Union[str, Any]:
								__UpperCamelCase			:     List[Any]         =      parent
								__UpperCamelCase			:     List[Any]         =      batch_size
								__UpperCamelCase			:     Union[str, Any]         =      seq_length
								__UpperCamelCase			:     Optional[int]         =      is_training
								__UpperCamelCase			:     List[str]         =      use_input_mask
								__UpperCamelCase			:     List[str]         =      use_token_type_ids
								__UpperCamelCase			:     Tuple         =      use_labels
								__UpperCamelCase			:     Optional[Any]         =      vocab_size
								__UpperCamelCase			:     int         =      hidden_size
								__UpperCamelCase			:     Any         =      embedding_size
								__UpperCamelCase			:     Any         =      num_hidden_layers
								__UpperCamelCase			:     str         =      num_attention_heads
								__UpperCamelCase			:     Tuple         =      intermediate_size
								__UpperCamelCase			:     Optional[Any]         =      hidden_act
								__UpperCamelCase			:     List[Any]         =      hidden_dropout_prob
								__UpperCamelCase			:     Optional[Any]         =      attention_probs_dropout_prob
								__UpperCamelCase			:     int         =      max_position_embeddings
								__UpperCamelCase			:     Any         =      type_vocab_size
								__UpperCamelCase			:     Dict         =      type_sequence_label_size
								__UpperCamelCase			:     Union[str, Any]         =      initializer_range
								__UpperCamelCase			:     str         =      num_labels
								__UpperCamelCase			:     Union[str, Any]         =      num_choices
								__UpperCamelCase			:     Dict         =      scope







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Optional[Any]:
								__UpperCamelCase			:     List[Any]         =      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size		)

								__UpperCamelCase			:     Union[str, Any]         =      None
								if self.use_input_mask:
															__UpperCamelCase			:     str         =      random_attention_mask([self.batch_size, self.seq_length]		)

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

								__UpperCamelCase			:     List[Any]         =      None
								__UpperCamelCase			:     Any         =      None
								__UpperCamelCase			:     Optional[Any]         =      None
								if self.use_labels:
															__UpperCamelCase			:     str         =      ids_tensor([self.batch_size]  ,     self.type_sequence_label_size		)
															__UpperCamelCase			:     Optional[int]         =      ids_tensor([self.batch_size, self.seq_length]  ,     self.num_labels		)
															__UpperCamelCase			:     Dict         =      ids_tensor([self.batch_size]  ,     self.num_choices		)

								__UpperCamelCase			:     str         =      self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[int]:
								return MobileBertConfig(
								    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  ,     embedding_size=self.embedding_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  ,     )







	def      _lowerCamelCase       (      self	:int  ,     a	:str  ,     a	:Union[str, Any]  ,     a	:Optional[int]  ,     a	:int  ,     a	:Optional[Any]  ,     a	:List[Any]  ,     a	:str		)	->				Tuple:
								__UpperCamelCase			:     Union[str, Any]         =      MobileBertModel(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     Optional[int]         =      model(a  ,     attention_mask=a  ,     token_type_ids=a		)
								__UpperCamelCase			:     List[Any]         =      model(a  ,     token_type_ids=a		)
								__UpperCamelCase			:     int         =      model(a		)

								self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)		)
								self.parent.assertEqual(result.pooler_output.shape  ,     (self.batch_size, self.hidden_size)		)







	def      _lowerCamelCase       (      self	:Any  ,     a	:List[Any]  ,     a	:Tuple  ,     a	:List[Any]  ,     a	:List[Any]  ,     a	:int  ,     a	:int  ,     a	:Union[str, Any]		)	->				Dict:
								__UpperCamelCase			:     Any         =      MobileBertForMaskedLM(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     List[Any]         =      model(a  ,     attention_mask=a  ,     token_type_ids=a  ,     labels=a		)
								self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)		)







	def      _lowerCamelCase       (      self	:Tuple  ,     a	:int  ,     a	:List[Any]  ,     a	:List[str]  ,     a	:List[Any]  ,     a	:Tuple  ,     a	:List[str]  ,     a	:Optional[Any]		)	->				Tuple:
								__UpperCamelCase			:     int         =      MobileBertForNextSentencePrediction(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     List[str]         =      model(
								    a  ,     attention_mask=a  ,     token_type_ids=a  ,     labels=a  ,     )
								self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, 2)		)







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[int]  ,     a	:str  ,     a	:Optional[Any]  ,     a	:List[str]  ,     a	:Union[str, Any]  ,     a	:Tuple  ,     a	:Tuple		)	->				List[str]:
								__UpperCamelCase			:     int         =      MobileBertForPreTraining(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     Tuple         =      model(
								    a  ,     attention_mask=a  ,     token_type_ids=a  ,     labels=a  ,     next_sentence_label=a  ,     )
								self.parent.assertEqual(result.prediction_logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)		)
								self.parent.assertEqual(result.seq_relationship_logits.shape  ,     (self.batch_size, 2)		)







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:List[str]  ,     a	:str  ,     a	:Tuple  ,     a	:List[Any]  ,     a	:Any  ,     a	:str  ,     a	:int		)	->				Optional[Any]:
								__UpperCamelCase			:     Union[str, Any]         =      MobileBertForQuestionAnswering(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     str         =      model(
								    a  ,     attention_mask=a  ,     token_type_ids=a  ,     start_positions=a  ,     end_positions=a  ,     )
								self.parent.assertEqual(result.start_logits.shape  ,     (self.batch_size, self.seq_length)		)
								self.parent.assertEqual(result.end_logits.shape  ,     (self.batch_size, self.seq_length)		)







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:List[Any]  ,     a	:str  ,     a	:List[str]  ,     a	:Tuple  ,     a	:Any  ,     a	:List[Any]  ,     a	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Optional[Any]         =      self.num_labels
								__UpperCamelCase			:     Optional[int]         =      MobileBertForSequenceClassification(a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     Dict         =      model(a  ,     attention_mask=a  ,     token_type_ids=a  ,     labels=a		)
								self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)		)







	def      _lowerCamelCase       (      self	:int  ,     a	:Dict  ,     a	:List[Any]  ,     a	:int  ,     a	:List[str]  ,     a	:Dict  ,     a	:Tuple  ,     a	:List[Any]		)	->				Dict:
								__UpperCamelCase			:     str         =      self.num_labels
								__UpperCamelCase			:     Optional[int]         =      MobileBertForTokenClassification(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     Any         =      model(a  ,     attention_mask=a  ,     token_type_ids=a  ,     labels=a		)
								self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.num_labels)		)







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:List[str]  ,     a	:Tuple  ,     a	:List[Any]  ,     a	:Tuple  ,     a	:int  ,     a	:List[Any]  ,     a	:List[str]		)	->				Tuple:
								__UpperCamelCase			:     Tuple         =      self.num_choices
								__UpperCamelCase			:     Any         =      MobileBertForMultipleChoice(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     Any         =      input_ids.unsqueeze(1		).expand(-1  ,     self.num_choices  ,     -1		).contiguous()
								__UpperCamelCase			:     int         =      token_type_ids.unsqueeze(1		).expand(-1  ,     self.num_choices  ,     -1		).contiguous()
								__UpperCamelCase			:     List[str]         =      input_mask.unsqueeze(1		).expand(-1  ,     self.num_choices  ,     -1		).contiguous()
								__UpperCamelCase			:     Dict         =      model(
								    a  ,     attention_mask=a  ,     token_type_ids=a  ,     labels=a  ,     )
								self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_choices)		)







	def      _lowerCamelCase       (      self	:List[str]		)	->				List[Any]:
								__UpperCamelCase			:     Tuple         =      self.prepare_config_and_inputs()
								(
								    (
								    __UpperCamelCase
								)     ,		(
								    __UpperCamelCase
								)     ,		(
								    __UpperCamelCase
								)     ,		(
								    __UpperCamelCase
								)     ,		(
								    __UpperCamelCase
								)     ,		(
								    __UpperCamelCase
								)     ,		(
								    __UpperCamelCase
								)     ,		
								)			:     List[str]         =      config_and_inputs
								__UpperCamelCase			:     Optional[Any]         =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
								return config, inputs_dict




@require_torch
class 				lowerCamelCase__							(       __lowercase  ,		__lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  (
	    (
	        MobileBertModel,
	        MobileBertForMaskedLM,
	        MobileBertForMultipleChoice,
	        MobileBertForNextSentencePrediction,
	        MobileBertForPreTraining,
	        MobileBertForQuestionAnswering,
	        MobileBertForSequenceClassification,
	        MobileBertForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	_A			=  (
	    {
	        'feature-extraction': MobileBertModel,
	        'fill-mask': MobileBertForMaskedLM,
	        'question-answering': MobileBertForQuestionAnswering,
	        'text-classification': MobileBertForSequenceClassification,
	        'token-classification': MobileBertForTokenClassification,
	        'zero-shot': MobileBertForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_A			=  True







	def      _lowerCamelCase       (      self	:Any  ,     a	:Union[str, Any]  ,     a	:Tuple  ,     a	:Tuple=False		)	->				List[Any]:
								__UpperCamelCase			:     str         =      super()._prepare_for_class(a  ,     a  ,     return_labels=a		)

								if return_labels:
															if model_class in get_values(a		):
																						__UpperCamelCase			:     List[str]         =      torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.seq_length)  ,     dtype=torch.long  ,     device=a		)
																						__UpperCamelCase			:     int         =      torch.zeros(
																						    self.model_tester.batch_size  ,     dtype=torch.long  ,     device=a		)
								return inputs_dict







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Optional[Any]:
								__UpperCamelCase			:     str         =      MobileBertModelTester(self		)
								__UpperCamelCase			:     str         =      ConfigTester(self  ,     config_class=a  ,     hidden_size=3_7		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Tuple:
								self.config_tester.run_common_tests()







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				str:
								__UpperCamelCase			:     Optional[Any]         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*a		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				List[str]:
								__UpperCamelCase			:     int         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*a		)







	def      _lowerCamelCase       (      self	:Tuple		)	->				int:
								__UpperCamelCase			:     Dict         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*a		)







	def      _lowerCamelCase       (      self	:Any		)	->				Optional[int]:
								__UpperCamelCase			:     str         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*a		)







	def      _lowerCamelCase       (      self	:Any		)	->				Optional[Any]:
								__UpperCamelCase			:     Optional[int]         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*a		)







	def      _lowerCamelCase       (      self	:Any		)	->				Optional[Any]:
								__UpperCamelCase			:     Optional[Any]         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*a		)







	def      _lowerCamelCase       (      self	:int		)	->				int:
								__UpperCamelCase			:     Dict         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*a		)







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Dict:
								__UpperCamelCase			:     List[Any]         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*a		)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple)      ->					str:






							'''simple docstring'''





							return torch.tensor(
							    _lowerCamelCase		,						dtype=torch.long		,						device=_lowerCamelCase		,						)


lowercase						:      List[str]									= 1E-3




@require_torch
@require_sentencepiece
@require_tokenizers
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	@slow
	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				List[Any]:
								__UpperCamelCase			:     str         =      MobileBertModel.from_pretrained("google/mobilebert-uncased"		).to(a		)
								__UpperCamelCase			:     List[str]         =      _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]		)
								with torch.no_grad():
															__UpperCamelCase			:     Optional[int]         =      model(a		)[0]
								__UpperCamelCase			:     Optional[int]         =      torch.Size((1, 9, 5_1_2)		)
								self.assertEqual(output.shape  ,     a		)
								__UpperCamelCase			:     Any         =      torch.tensor(
								    [
								        [
								            [-2.4_7_3_6_5_2_6E0_7, 8.2_6_9_1_6_5_6E0_4, 1.6_5_2_1_8_3_8E0_5],
								            [-5.7_5_4_1_7_0_4E-0_1, 3.9_0_5_6_0_2_2E0_0, 4.4_0_1_1_5_0_7E0_0],
								            [2.6_0_4_7_3_5_9E0_0, 1.5_6_7_7_6_5_2E0_0, -1.7_3_2_4_1_8_8E-0_1],
								        ]
								    ]  ,     device=a  ,     )

								# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
								# ~1 difference, it's therefore not a good idea to measure using addition.
								# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
								# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
								__UpperCamelCase			:     Union[str, Any]         =      torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE		)
								__UpperCamelCase			:     List[Any]         =      torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE		)

								self.assertTrue(lower_bound and upper_bound		)

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase						:      Any									= argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
lowercase						:      Tuple									= parser.parse_args()


lowercase						:      str									= 'cpu'
lowercase						:      List[Any]									= 'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase						:      Any									= 'path-to-your-trained-model'
lowercase						:      List[Any]									= StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		lowercase						:      Any									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase						:      Dict									= pipe.to(device)

# to channels last
lowercase						:      int									= pipe.unet.to(memory_format=torch.channels_last)
lowercase						:      int									= pipe.vae.to(memory_format=torch.channels_last)
lowercase						:      Dict									= pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		lowercase						:      List[str]									= pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase						:      Any									= torch.randn(2, 4, 64, 64)
lowercase						:      Union[str, Any]									= torch.rand(1) * 999
lowercase						:      List[Any]									= torch.randn(2, 77, 768)
lowercase						:      List[Any]									= (sample, timestep, encoder_hidden_status)
try:
		lowercase						:      List[Any]									= ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		lowercase						:      str									= ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      Dict									= ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase						:      str									= ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		lowercase						:      Dict									= ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase						:      Any									= 666
lowercase						:      int									= torch.Generator(device).manual_seed(seed)
lowercase						:      List[str]									= {'generator': generator}
if args.steps is not None:
		lowercase						:      List[str]									= args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
		lowercase						:      Dict									= pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')