

import unittest

import numpy as np

from transformers import BertConfig, 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():
  from transformers.models.bert.modeling_flax_bert import (
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
  )






class 							A_ (		unittest.TestCase					):


     def __init__(							self			:		Optional[int]			,  UpperCAmelCase			:		List[Any]			,  UpperCAmelCase			:		Tuple=1_3			,  UpperCAmelCase			:		Optional[int]=7			,  UpperCAmelCase			:		Optional[int]=True			,  UpperCAmelCase			:		Optional[Any]=True			,  UpperCAmelCase			:		int=True			,  UpperCAmelCase			:		Dict=True			,  UpperCAmelCase			:		Any=9_9			,  UpperCAmelCase			:		Dict=3_2			,  UpperCAmelCase			:		Optional[Any]=5			,  UpperCAmelCase			:		Tuple=4			,  UpperCAmelCase			:		List[Any]=3_7			,  UpperCAmelCase			:		Dict="gelu"			,  UpperCAmelCase			:		Tuple=0.1			,  UpperCAmelCase			:		str=0.1			,  UpperCAmelCase			:		str=5_1_2			,  UpperCAmelCase			:		List[str]=1_6			,  UpperCAmelCase			:		Union[str, Any]=2			,  UpperCAmelCase			:		Optional[int]=0.02			,  UpperCAmelCase			:		int=4			,  )							->	List[Any]:
      __lowerCAmelCase:   int   	=      parent
      __lowerCAmelCase:   Optional[int]   	=      batch_size
      __lowerCAmelCase:   str   	=      seq_length
      __lowerCAmelCase:   Union[str, Any]   	=      is_training
      __lowerCAmelCase:   Tuple   	=      use_attention_mask
      __lowerCAmelCase:   Any   	=      use_token_type_ids
      __lowerCAmelCase:   Tuple   	=      use_labels
      __lowerCAmelCase:   int   	=      vocab_size
      __lowerCAmelCase:   Any   	=      hidden_size
      __lowerCAmelCase:   Tuple   	=      num_hidden_layers
      __lowerCAmelCase:   Union[str, Any]   	=      num_attention_heads
      __lowerCAmelCase:   Union[str, Any]   	=      intermediate_size
      __lowerCAmelCase:   Tuple   	=      hidden_act
      __lowerCAmelCase:   List[str]   	=      hidden_dropout_prob
      __lowerCAmelCase:   Union[str, Any]   	=      attention_probs_dropout_prob
      __lowerCAmelCase:   Union[str, Any]   	=      max_position_embeddings
      __lowerCAmelCase:   Optional[int]   	=      type_vocab_size
      __lowerCAmelCase:   Tuple   	=      type_sequence_label_size
      __lowerCAmelCase:   Optional[Any]   	=      initializer_range
      __lowerCAmelCase:   Any   	=      num_choices


     def   UpperCAmelCase  (							self			:		Tuple					)							->	Optional[int]:
      __lowerCAmelCase:   Tuple   	=      ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size					)

      __lowerCAmelCase:   List[Any]   	=      None
      if self.use_attention_mask:
       __lowerCAmelCase:   Union[str, Any]   	=      random_attention_mask([self.batch_size, self.seq_length]					)

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

      __lowerCAmelCase:   Tuple   	=      BertConfig(
          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=UpperCAmelCase			,  initializer_range=self.initializer_range			,  )

      return config, input_ids, token_type_ids, attention_mask


     def   UpperCAmelCase  (							self			:		List[str]					)							->	str:
      __lowerCAmelCase:   Optional[int]   	=      self.prepare_config_and_inputs()
      __lowerCAmelCase:   Optional[int]   	=      config_and_inputs
      __lowerCAmelCase:   List[str]   	=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict


     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	Any:
      __lowerCAmelCase:   Optional[Any]   	=      self.prepare_config_and_inputs()
      __lowerCAmelCase:   List[str]   	=      config_and_inputs

      __lowerCAmelCase:   Dict   	=      True
      __lowerCAmelCase:   Optional[Any]   	=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
      __lowerCAmelCase:   Any   	=      ids_tensor([self.batch_size, self.seq_length]			,  vocab_size=2					)

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )







@require_flax
class 							A_ (		__lowerCAmelCase							,	unittest.TestCase					):
     _lowercase			:     Optional[Any]								=	True

     _lowercase			:     str								=	(
         (
             FlaxBertModel,
             FlaxBertForPreTraining,
             FlaxBertForMaskedLM,
             FlaxBertForMultipleChoice,
             FlaxBertForQuestionAnswering,
             FlaxBertForNextSentencePrediction,
             FlaxBertForSequenceClassification,
             FlaxBertForTokenClassification,
             FlaxBertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )


     def   UpperCAmelCase  (							self			:		Dict					)							->	int:
      __lowerCAmelCase:   Tuple   	=      FlaxBertModelTester(self					)


     @slow
     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	Union[str, Any]:
      __lowerCAmelCase:   Any   	=      FlaxBertModel.from_pretrained('bert-base-cased'					)
      __lowerCAmelCase:   str   	=      model(np.ones((1, 1)					)					)
      self.assertIsNotNone(UpperCAmelCase					)






"""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, KandinskyInpaintPipeline, 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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		KandinskyInpaintPipeline
      a__        =		["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
      a__        =		[
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
          """mask_image""",
      ]
      a__        =		[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      a__        =		False



      @property
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim * 4



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        return 1_00



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
        return tokenizer



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Dict										=	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=10_05    ,			)

        a__:     Optional[Any]										=	MultilingualCLIP(lowercase)
        a__:     int										=	text_encoder.eval()

        return text_encoder



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Any										=	{
            'in_channels': 9,
            # 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,
        }

        a__:     str										=	UNetaDConditionModel(**lowercase)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        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   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Any										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.dummy_text_encoder
        a__:     int										=	self.dummy_tokenizer
        a__:     str										=	self.dummy_unet
        a__:     Any										=	self.dummy_movq

        a__:     Tuple										=	DDIMScheduler(
            num_train_timesteps=10_00    ,			beta_schedule='linear'    ,			beta_start=0.00085    ,			beta_end=0.012    ,			clip_sample=lowercase    ,			set_alpha_to_one=lowercase    ,			steps_offset=1    ,			prediction_type='epsilon'    ,			thresholding=lowercase    ,			)

        a__:     Tuple										=	{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(seed + 1)).to(lowercase)
        # create init_image
        a__:     Optional[int]										=	floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
        a__:     Optional[int]										=	Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
        # create mask
        a__:     Tuple										=	np.ones((64, 64)    ,			dtype=np.floataa)
        a__:     Optional[Any]										=	0

        if str(lowercase).startswith('mps'):
          a__:     str										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Optional[int]										=	{
            'prompt': 'horse',
            'image': init_image,
            'mask_image': mask,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 2,
            'guidance_scale': 4.0,
            'output_type': 'np',
        }
        return inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

        a__:     List[Any]										=	self.get_dummy_components()

        a__:     Optional[Any]										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images

        a__:     int										=	pipe(
            **self.get_dummy_inputs(lowercase)    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     List[Any]										=	image_from_tuple[0, -3:, -3:, -1]

        print(f'image.shape {image.shape}')

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

        a__:     str										=	np.array(
            [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])

        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()}'



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

        a__:     int										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
        a__:     Union[str, Any]										=	np.ones((7_68, 7_68)    ,			dtype=np.floataa)
        a__:     int										=	0

        a__:     Optional[int]										=	'a hat'

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

        a__:     Any										=	KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__							,    a__:     Optional[Any]										=	pipe_prior(
            lowercase    ,			generator=lowercase    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     List[str]										=	pipeline(
            lowercase    ,			image=lowercase    ,			mask_image=lowercase    ,			image_embeds=lowercase    ,			negative_image_embeds=lowercase    ,			generator=lowercase    ,			num_inference_steps=1_00    ,			height=7_68    ,			width=7_68    ,			output_type='np'    ,			)

        a__:     str										=	output.images[0]

        assert image.shape == (7_68, 7_68, 3)

        assert_mean_pixel_difference(lowercase    ,			lowercase)


