"""simple docstring"""




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 						__A							:






     def __init__(		self				,     a__				,     a__=13				,     a__=7				,     a__=True				,     a__=True				,     a__=False				,     a__=True				,     a__=99				,     a__=64				,     a__=5				,     a__=4				,     a__=64				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=512				,     a__=16				,     a__=2				,     a__=0.0_2				,     a__=3				,     a__=4				,     a__=None				,     ):
       _lowerCAmelCase						:       Optional[int]  			=     parent
       _lowerCAmelCase						:       Optional[Any]  			=     batch_size
       _lowerCAmelCase						:       Optional[int]  			=     seq_length
       _lowerCAmelCase						:       str  			=     is_training
       _lowerCAmelCase						:       List[str]  			=     use_input_mask
       _lowerCAmelCase						:       Union[str, Any]  			=     use_token_type_ids
       _lowerCAmelCase						:       Any  			=     use_labels
       _lowerCAmelCase						:       Any  			=     vocab_size
       _lowerCAmelCase						:       str  			=     hidden_size
       _lowerCAmelCase						:       str  			=     num_hidden_layers
       _lowerCAmelCase						:       Union[str, Any]  			=     num_attention_heads
       _lowerCAmelCase						:       Optional[int]  			=     intermediate_size
       _lowerCAmelCase						:       Tuple  			=     hidden_act
       _lowerCAmelCase						:       int  			=     hidden_dropout_prob
       _lowerCAmelCase						:       List[Any]  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       str  			=     max_position_embeddings
       _lowerCAmelCase						:       int  			=     type_vocab_size
       _lowerCAmelCase						:       Any  			=     type_sequence_label_size
       _lowerCAmelCase						:       List[str]  			=     initializer_range
       _lowerCAmelCase						:       List[Any]  			=     num_labels
       _lowerCAmelCase						:       Optional[Any]  			=     num_choices
       _lowerCAmelCase						:       Optional[Any]  			=     scope






     def     __A  (		self  ):
       return MPNetConfig.from_pretrained("""microsoft/mpnet-base"""  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )

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

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

       _lowerCAmelCase						:       int  			=     self.get_config()
       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






     def     __A  (		self  ):
       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     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[Any]  			=     MPNetModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Union[str, Any]  			=     model(a__				,     a__  )
       _lowerCAmelCase						:       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     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     MPNetForQuestionAnswering(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       str  			=     model(
           a__				,     attention_mask=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     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.num_labels
       _lowerCAmelCase						:       Union[str, Any]  			=     MPNetForSequenceClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[str]  			=     model(a__				,     attention_mask=a__				,     labels=a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)  )






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






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.num_labels
       _lowerCAmelCase						:       List[str]  			=     MPNetForTokenClassification(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Dict  			=     model(a__				,     attention_mask=a__				,     labels=a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.prepare_config_and_inputs()
       ((_lowerCAmelCase)			,	(_lowerCAmelCase)			,	(_lowerCAmelCase)			,	(_lowerCAmelCase)			,	(_lowerCAmelCase)			,	(_lowerCAmelCase))						:       int  			=     config_and_inputs
       _lowerCAmelCase						:       str  			=     {"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		(
         (
             MPNetForMaskedLM,
             MPNetForMultipleChoice,
             MPNetForQuestionAnswering,
             MPNetForSequenceClassification,
             MPNetForTokenClassification,
             MPNetModel,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			Optional[int]							=		(
         {
             "feature-extraction": MPNetModel,
             "fill-mask": MPNetForMaskedLM,
             "question-answering": MPNetForQuestionAnswering,
             "text-classification": MPNetForSequenceClassification,
             "token-classification": MPNetForTokenClassification,
             "zero-shot": MPNetForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _UpperCamelCase							:			Dict							=		False
     _UpperCamelCase							:			List[str]							=		True






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     MPNetModelTester(self  )
       _lowerCAmelCase						:       Any  			=     ConfigTester(self				,     config_class=a__				,     hidden_size=37  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_model(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_sequence_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_multiple_choice(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_token_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_question_answering(*a__  )







@require_torch
class 						__A							( unittest.TestCase		):






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     MPNetModel.from_pretrained("""microsoft/mpnet-base"""  )
       _lowerCAmelCase						:       str  			=     torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]  )
       _lowerCAmelCase						:       Optional[int]  			=     model(a__  )[0]
       _lowerCAmelCase						:       List[Any]  			=     torch.Size((1, 11, 768)  )
       self.assertEqual(output.shape				,     a__  )
       _lowerCAmelCase						:       str  			=     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]				,     a__				,     atol=1e-4  )  )





"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		KandinskyImgaImgPipeline
     _UpperCamelCase							:			Optional[Any]							=		["prompt", "image_embeds", "negative_image_embeds", "image"]
     _UpperCamelCase							:			List[Any]							=		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     _UpperCamelCase							:			Dict							=		[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     _UpperCamelCase							:			Union[str, Any]							=		False






     @property
     def     __A  (		self  ):
       return 32






     @property
     def     __A  (		self  ):
       return 32






     @property
     def     __A  (		self  ):
       return self.time_input_dim






     @property
     def     __A  (		self  ):
       return self.time_input_dim * 4






     @property
     def     __A  (		self  ):
       return 100






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
       return tokenizer






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       List[Any]  			=     MCLIPConfig(
           numDims=self.cross_attention_dim				,     transformerDimensions=self.text_embedder_hidden_size				,     hidden_size=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_hidden_layers=5				,     vocab_size=1005				,     )

       _lowerCAmelCase						:       int  			=     MultilingualCLIP(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     text_encoder.eval()

       return text_encoder






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )

       _lowerCAmelCase						:       str  			=     {
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_image""",
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """encoder_hid_dim""": self.text_embedder_hidden_size,
           """encoder_hid_dim_type""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     UNetaDConditionModel(**a__  )
       return model






     @property
     def     __A  (		self  ):
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       str  			=     VQModel(**self.dummy_movq_kwargs  )
       return model






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       List[Any]  			=     self.dummy_tokenizer
       _lowerCAmelCase						:       int  			=     self.dummy_unet
       _lowerCAmelCase						:       Dict  			=     self.dummy_movq

       _lowerCAmelCase						:       Tuple  			=     {
           """num_train_timesteps""": 1000,
           """beta_schedule""": """linear""",
           """beta_start""": 0.0_0_0_8_5,
           """beta_end""": 0.0_1_2,
           """clip_sample""": False,
           """set_alpha_to_one""": False,
           """steps_offset""": 0,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     DDIMScheduler(**a__  )

       _lowerCAmelCase						:       List[Any]  			=     {
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components






     def     __A  (		self				,     a__				,     a__=0  ):
       _lowerCAmelCase						:       Optional[int]  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(seed + 1  )  ).to(a__  )
       # create init_image
       _lowerCAmelCase						:       List[Any]  			=     floats_tensor((1, 3, 64, 64)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     image.cpu().permute(0				,     2				,     3				,     1  )[0]
       _lowerCAmelCase						:       List[Any]  			=     Image.fromarray(np.uinta(a__  )  ).convert("""RGB"""  ).resize((256, 256)  )

       if str(a__  ).startswith("""mps"""  ):
         _lowerCAmelCase						:       List[Any]  			=     torch.manual_seed(a__  )
       else:
         _lowerCAmelCase						:       Tuple  			=     torch.Generator(device=a__  ).manual_seed(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     {
           """prompt""": """horse""",
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 64,
           """width""": 64,
           """num_inference_steps""": 10,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     """cpu"""

       _lowerCAmelCase						:       int  			=     self.get_dummy_components()

       _lowerCAmelCase						:       int  			=     self.pipeline_class(**a__  )
       _lowerCAmelCase						:       Optional[int]  			=     pipe.to(a__  )

       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Tuple  			=     pipe(**self.get_dummy_inputs(a__  )  )
       _lowerCAmelCase						:       List[Any]  			=     output.images

       _lowerCAmelCase						:       Tuple  			=     pipe(
           **self.get_dummy_inputs(a__  )				,     return_dict=a__				,     )[0]

       _lowerCAmelCase						:       Dict  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       List[Any]  			=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       _lowerCAmelCase						:       str  			=     np.array(
           [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]  )
       assert (
           np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@require_torch_gpu
class 						__A							( unittest.TestCase		):






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_img2img_frog.npy"""  )

       _lowerCAmelCase						:       List[str]  			=     load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     """A red cartoon frog, 4k"""

       _lowerCAmelCase						:       int  			=     KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""				,     torch_dtype=torch.floataa  )
       pipe_prior.to(a__  )

       _lowerCAmelCase						:       Tuple  			=     KandinskyImgaImgPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1"""				,     torch_dtype=torch.floataa  )
       _lowerCAmelCase						:       Any  			=     pipeline.to(a__  )

       pipeline.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Any  			=     torch.Generator(device="""cpu"""  ).manual_seed(0  )
       _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     pipe_prior(
           a__				,     generator=a__				,     num_inference_steps=5				,     negative_prompt=""""""				,     ).to_tuple()

       _lowerCAmelCase						:       Union[str, Any]  			=     pipeline(
           a__				,     image=a__				,     image_embeds=a__				,     negative_image_embeds=a__				,     generator=a__				,     num_inference_steps=100				,     height=768				,     width=768				,     strength=0.2				,     output_type="""np"""				,     )

       _lowerCAmelCase						:       Dict  			=     output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(a__				,     a__  )




