import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import 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 (
        MPNetForMaskedLM,
        MPNetForMultipleChoice,
        MPNetForQuestionAnswering,
        MPNetForSequenceClassification,
        MPNetForTokenClassification,
        MPNetModel,
    )



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Tuple=13      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=7      ,	SCREAMING_SNAKE_CASE__    :					str=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					str=False      ,	SCREAMING_SNAKE_CASE__    :					Any=True      ,	SCREAMING_SNAKE_CASE__    :					int=99      ,	SCREAMING_SNAKE_CASE__    :					Tuple=64      ,	SCREAMING_SNAKE_CASE__    :					List[str]=5      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=4      ,	SCREAMING_SNAKE_CASE__    :					Any=64      ,	SCREAMING_SNAKE_CASE__    :					str="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Dict=5_12      ,	SCREAMING_SNAKE_CASE__    :					int=16      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					Dict=3      ,	SCREAMING_SNAKE_CASE__    :					int=4      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=None      ,	)    ->				Any:
         A						:     Tuple         =parent
         A						:     int         =batch_size
         A						:     Any         =seq_length
         A						:     Union[str, Any]         =is_training
         A						:     List[str]         =use_input_mask
         A						:     List[Any]         =use_token_type_ids
         A						:     Any         =use_labels
         A						:     Tuple         =vocab_size
         A						:     List[Any]         =hidden_size
         A						:     List[Any]         =num_hidden_layers
         A						:     Optional[int]         =num_attention_heads
         A						:     List[str]         =intermediate_size
         A						:     Optional[int]         =hidden_act
         A						:     List[Any]         =hidden_dropout_prob
         A						:     Optional[int]         =attention_probs_dropout_prob
         A						:     Any         =max_position_embeddings
         A						:     str         =type_vocab_size
         A						:     Tuple         =type_sequence_label_size
         A						:     Dict         =initializer_range
         A						:     List[str]         =num_labels
         A						:     Tuple         =num_choices
         A						:     Dict         =scope







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[str]:
         return MPNetConfig.from_pretrained('microsoft/mpnet-base'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				List[str]:
         A						:     Any         =ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size     )

         A						:     Tuple         =None
         if self.use_input_mask:
                A						:     List[Any]         =random_attention_mask([self.batch_size, self.seq_length]     )

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

         A						:     Tuple         =self.get_config()
         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				int:
         return MPNetConfig(
             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      ,	initializer_range=self.initializer_range      ,	)







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				str:
         A						:     Any         =MPNetModel(config=SCREAMING_SNAKE_CASE__     )
         model.to(SCREAMING_SNAKE_CASE__     )
         model.eval()
         A						:     int         =model(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         =model(SCREAMING_SNAKE_CASE__     )
         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 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				Tuple:
         A						:     Optional[Any]         =MPNetForQuestionAnswering(config=SCREAMING_SNAKE_CASE__     )
         model.to(SCREAMING_SNAKE_CASE__     )
         model.eval()
         A						:     List[str]         =model(
             SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	start_positions=SCREAMING_SNAKE_CASE__      ,	end_positions=SCREAMING_SNAKE_CASE__      ,	)
         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 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				Dict:
         A						:     Optional[Any]         =self.num_labels
         A						:     Optional[int]         =MPNetForSequenceClassification(SCREAMING_SNAKE_CASE__     )
         model.to(SCREAMING_SNAKE_CASE__     )
         model.eval()
         A						:     Optional[Any]         =model(SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	labels=SCREAMING_SNAKE_CASE__     )
         self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_labels)     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				Optional[int]:
         A						:     Optional[int]         =self.num_choices
         A						:     Dict         =MPNetForMultipleChoice(config=SCREAMING_SNAKE_CASE__     )
         model.to(SCREAMING_SNAKE_CASE__     )
         model.eval()
         A						:     int         =input_ids.unsqueeze(1     ).expand(-1      ,	self.num_choices      ,	-1     ).contiguous()
         A						:     Dict         =input_mask.unsqueeze(1     ).expand(-1      ,	self.num_choices      ,	-1     ).contiguous()
         A						:     int         =model(
             SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	labels=SCREAMING_SNAKE_CASE__      ,	)
         self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_choices)     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Any:
         A						:     Optional[int]         =self.num_labels
         A						:     Optional[Any]         =MPNetForTokenClassification(config=SCREAMING_SNAKE_CASE__     )
         model.to(SCREAMING_SNAKE_CASE__     )
         model.eval()
         A						:     Union[str, Any]         =model(SCREAMING_SNAKE_CASE__      ,	attention_mask=SCREAMING_SNAKE_CASE__      ,	labels=SCREAMING_SNAKE_CASE__     )
         self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.seq_length, self.num_labels)     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Any:
         A						:     List[str]         =self.prepare_config_and_inputs()
         ((A)  ,  (A)  ,  (A)  ,  (A)  ,  (A)  ,  (A))						:     str         =config_and_inputs
         A						:     int         ={'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_torch
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     int            =				(
      (
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )
      if is_torch_available()
      else ()
  )
  lowercase		:     Any            =				(
      {
          "feature-extraction": MPNetModel,
          "fill-mask": MPNetForMaskedLM,
          "question-answering": MPNetForQuestionAnswering,
          "text-classification": MPNetForSequenceClassification,
          "token-classification": MPNetForTokenClassification,
          "zero-shot": MPNetForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowercase		:     Dict            =				False
  lowercase		:     List[str]            =				True







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				str:
         A						:     Dict         =MPNetModelTester(self     )
         A						:     Union[str, Any]         =ConfigTester(self      ,	config_class=SCREAMING_SNAKE_CASE__      ,	hidden_size=37     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Union[str, Any]:
         self.config_tester.run_common_tests()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				str:
         A						:     List[Any]         =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Optional[Any]:
         A						:     Tuple         =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Union[str, Any]:
         A						:     Any         =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Optional[int]:
         A						:     Optional[int]         =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				int:
         A						:     List[Any]         =self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*SCREAMING_SNAKE_CASE__     )







@require_torch
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				int:
         A						:     List[Any]         =MPNetModel.from_pretrained('microsoft/mpnet-base'     )
         A						:     Optional[int]         =torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]     )
         A						:     List[Any]         =model(SCREAMING_SNAKE_CASE__     )[0]
         A						:     str         =torch.Size((1, 11, 7_68)     )
         self.assertEqual(output.shape      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =torch.tensor(
             [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]     )
         # compare the actual values for a slice.
         self.assertTrue(torch.allclose(output[:, :3, :3]      ,	SCREAMING_SNAKE_CASE__      ,	atol=1e-4     )     )







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				DDIMPipeline
  lowercase		:     int            =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  lowercase		:     Optional[Any]            =				PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "latents",
      "callback",
      "callback_steps",
  }
  lowercase		:     Optional[Any]            =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  lowercase		:     Union[str, Any]            =				False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Optional[int]:
         torch.manual_seed(0     )
         A						:     str         =UNetaDModel(
             block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=3      ,	out_channels=3      ,	down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,	up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,	)
         A						:     Optional[int]         =DDIMScheduler()
         A						:     Optional[Any]         ={'unet': unet, 'scheduler': scheduler}
         return components







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0     )    ->				Any:
         if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                A						:     List[Any]         =torch.manual_seed(SCREAMING_SNAKE_CASE__     )
         else:
                A						:     Union[str, Any]         =torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         ={
             'batch_size': 1,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[Any]:
         A						:     Union[str, Any]         ='cpu'

         A						:     Tuple         =self.get_dummy_components()
         A						:     Union[str, Any]         =self.pipeline_class(**SCREAMING_SNAKE_CASE__     )
         pipe.to(SCREAMING_SNAKE_CASE__     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     str         =self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )
         A						:     str         =pipe(**SCREAMING_SNAKE_CASE__     ).images
         A						:     Optional[Any]         =image[0, -3:, -3:, -1]

         self.assertEqual(image.shape      ,	(1, 32, 32, 3)     )
         A						:     Optional[Any]         =np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]     )
         A						:     str         =np.abs(image_slice.flatten() - expected_slice     ).max()
         self.assertLessEqual(SCREAMING_SNAKE_CASE__      ,	1e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Dict:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[Any]:
         super().test_save_load_local(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         super().test_save_load_optional_components(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Tuple:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     Any         ='google/ddpm-cifar10-32'

         A						:     Optional[int]         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =DDIMScheduler()

         A						:     int         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddim.to(SCREAMING_SNAKE_CASE__     )
         ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =torch.manual_seed(0     )
         A						:     Optional[Any]         =ddim(generator=SCREAMING_SNAKE_CASE__      ,	eta=0.0      ,	output_type='numpy'     ).images

         A						:     str         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A						:     Tuple         =np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         A						:     Optional[int]         ='google/ddpm-ema-bedroom-256'

         A						:     str         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     str         =DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__     )

         A						:     Tuple         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddpm.to(SCREAMING_SNAKE_CASE__     )
         ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Any         =torch.manual_seed(0     )
         A						:     Optional[int]         =ddpm(generator=SCREAMING_SNAKE_CASE__      ,	output_type='numpy'     ).images

         A						:     List[Any]         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)
         A						:     Optional[int]         =np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]     )

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






