'''simple docstring'''

import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax

  from transformers.models.big_bird.modeling_flax_big_bird import (
      FlaxBigBirdForCausalLM,
      FlaxBigBirdForMaskedLM,
      FlaxBigBirdForMultipleChoice,
      FlaxBigBirdForPreTraining,
      FlaxBigBirdForQuestionAnswering,
      FlaxBigBirdForSequenceClassification,
      FlaxBigBirdForTokenClassification,
      FlaxBigBirdModel,
  )


class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=2       ,lowerCamelCase_=56       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=99       ,lowerCamelCase_=32       ,lowerCamelCase_=2       ,lowerCamelCase_=2       ,lowerCamelCase_=7       ,lowerCamelCase_="gelu_new"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=512       ,lowerCamelCase_=16       ,lowerCamelCase_=2       ,lowerCamelCase_=0.02       ,lowerCamelCase_=4       ,lowerCamelCase_="block_sparse"       ,lowerCamelCase_=True       ,lowerCamelCase_=False       ,lowerCamelCase_=2       ,lowerCamelCase_=3       ,)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	parent
         UpperCAmelCase__				:					Tuple 					=	batch_size
         UpperCAmelCase__				:					Optional[Any] 					=	seq_length
         UpperCAmelCase__				:					Dict 					=	is_training
         UpperCAmelCase__				:					List[str] 					=	use_attention_mask
         UpperCAmelCase__				:					int 					=	use_token_type_ids
         UpperCAmelCase__				:					Union[str, Any] 					=	use_labels
         UpperCAmelCase__				:					List[Any] 					=	vocab_size
         UpperCAmelCase__				:					List[Any] 					=	hidden_size
         UpperCAmelCase__				:					Dict 					=	num_hidden_layers
         UpperCAmelCase__				:					List[str] 					=	num_attention_heads
         UpperCAmelCase__				:					str 					=	intermediate_size
         UpperCAmelCase__				:					int 					=	hidden_act
         UpperCAmelCase__				:					Optional[int] 					=	hidden_dropout_prob
         UpperCAmelCase__				:					Dict 					=	attention_probs_dropout_prob
         UpperCAmelCase__				:					Tuple 					=	max_position_embeddings
         UpperCAmelCase__				:					Optional[int] 					=	type_vocab_size
         UpperCAmelCase__				:					Optional[int] 					=	type_sequence_label_size
         UpperCAmelCase__				:					List[Any] 					=	initializer_range
         UpperCAmelCase__				:					List[Any] 					=	num_choices

         UpperCAmelCase__				:					Tuple 					=	rescale_embeddings
         UpperCAmelCase__				:					Optional[Any] 					=	attention_type
         UpperCAmelCase__				:					Union[str, Any] 					=	use_bias
         UpperCAmelCase__				:					Tuple 					=	block_size
         UpperCAmelCase__				:					Optional[Any] 					=	num_random_blocks




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size				)

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

         UpperCAmelCase__				:					int 					=	None
         if self.use_token_type_ids:
            UpperCAmelCase__				:					Tuple 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.type_vocab_size				)

         UpperCAmelCase__				:					List[str] 					=	BigBirdConfig(
             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=lowerCamelCase_       ,initializer_range=self.initializer_range       ,attention_type=self.attention_type       ,block_size=self.block_size       ,num_random_blocks=self.num_random_blocks       ,use_bias=self.use_bias       ,rescale_embeddings=self.rescale_embeddings       ,)

         return config, input_ids, token_type_ids, attention_mask




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



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.prepare_config_and_inputs()
         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	config_and_inputs
         UpperCAmelCase__				:					List[Any] 					=	{
             '''input_ids''': input_ids,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': attention_mask,
         }
         return config, inputs_dict




@require_flax
class 					_lowercase						(   lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Optional[int]   				= (
          (
              FlaxBigBirdForCausalLM,
              FlaxBigBirdModel,
              FlaxBigBirdForPreTraining,
              FlaxBigBirdForMaskedLM,
              FlaxBigBirdForMultipleChoice,
              FlaxBigBirdForQuestionAnswering,
              FlaxBigBirdForSequenceClassification,
              FlaxBigBirdForTokenClassification,
          )
          if is_flax_available()
          else ()
      )

      UpperCAmelCase_   :   int   				= False
      UpperCAmelCase_   :   Any   				= False




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	FlaxBigBirdModelTester(self				)




      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         super().test_from_pretrained_save_pretrained()




      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         super().test_from_pretrained_with_no_automatic_init()




      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         super().test_no_automatic_init()




      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         super().test_hidden_states_output()




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         for model_class_name in self.all_model_classes:
            UpperCAmelCase__				:					int 					=	model_class_name.from_pretrained('''google/bigbird-roberta-base'''				)
            self.assertIsNotNone(lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         if self.test_attn_probs:
            super().test_attention_outputs()




      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Dict 					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__				):
               UpperCAmelCase__				:					List[str] 					=	self._prepare_for_class(lowerCamelCase_       ,lowerCamelCase_				)
               UpperCAmelCase__				:					str 					=	model_class(lowerCamelCase_				)

               @jax.jit
               def model_jitted(lowerCamelCase_       ,lowerCamelCase_=None       ,**lowerCamelCase_				):
                  return model(input_ids=lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,**lowerCamelCase_				)

               with self.subTest('''JIT Enabled'''				):
                  UpperCAmelCase__				:					Optional[int] 					=	model_jitted(**lowerCamelCase_				).to_tuple()

               with self.subTest('''JIT Disabled'''				):
                  with jax.disable_jit():
                     UpperCAmelCase__				:					Dict 					=	model_jitted(**lowerCamelCase_				).to_tuple()

               self.assertEqual(len(lowerCamelCase_				)       ,len(lowerCamelCase_				)				)
               for jitted_output, output in zip(lowerCamelCase_       ,lowerCamelCase_				):
                  self.assertEqual(jitted_output.shape       ,output.shape				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_=1e-5       ,lowerCamelCase_="outputs"       ,lowerCamelCase_=None				)      ->      str:



         '''simple docstring'''



         if name.startswith('''outputs.attentions'''				):
            return
         else:
            super().check_pt_flax_outputs(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)





'''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 					_lowercase						(   lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Any   				= KandinskyInpaintPipeline
      UpperCAmelCase_   :   str   				= ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''prompt''',
          '''negative_prompt''',
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      UpperCAmelCase_   :   Union[str, Any]   				= [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''negative_prompt''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCAmelCase_   :   Optional[int]   				= False




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         return 32




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return self.time_input_dim




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



         '''simple docstring'''



         return self.time_input_dim * 4




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         return 100




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



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




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					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       ,)

         UpperCAmelCase__				:					Any 					=	MultilingualCLIP(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	text_encoder.eval()

         return text_encoder




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



         '''simple docstring'''



         torch.manual_seed(0				)

         UpperCAmelCase__				:					Tuple 					=	{
             '''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,
         }

         UpperCAmelCase__				:					List[str] 					=	UNetaDConditionModel(**lowerCamelCase_				)
         return model




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



         '''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				)      ->      Tuple:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Union[str, Any] 					=	VQModel(**self.dummy_movq_kwargs				)
         return model




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.dummy_text_encoder
         UpperCAmelCase__				:					Optional[int] 					=	self.dummy_tokenizer
         UpperCAmelCase__				:					Optional[Any] 					=	self.dummy_unet
         UpperCAmelCase__				:					Any 					=	self.dummy_movq

         UpperCAmelCase__				:					Tuple 					=	DDIMScheduler(
             num_train_timesteps=1000       ,beta_schedule='''linear'''       ,beta_start=0.0_0085       ,beta_end=0.012       ,clip_sample=lowerCamelCase_       ,set_alpha_to_one=lowerCamelCase_       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=lowerCamelCase_       ,)

         UpperCAmelCase__				:					Dict 					=	{
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=0				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	floats_tensor((1, self.cross_attention_dim)       ,rng=random.Random(seed + 1				)				).to(lowerCamelCase_				)
         # create init_image
         UpperCAmelCase__				:					str 					=	floats_tensor((1, 3, 64, 64)       ,rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	image.cpu().permute(0       ,2       ,3       ,1				)[0]
         UpperCAmelCase__				:					Union[str, Any] 					=	Image.fromarray(np.uinta(lowerCamelCase_				)				).convert('''RGB'''				).resize((256, 256)				)
         # create mask
         UpperCAmelCase__				:					List[str] 					=	np.ones((64, 64)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					Optional[Any] 					=	0

         if str(lowerCamelCase_				).startswith('''mps'''				):
            UpperCAmelCase__				:					Any 					=	torch.manual_seed(lowerCamelCase_				)
         else:
            UpperCAmelCase__				:					Dict 					=	torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	{
             '''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				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	'''cpu'''

         UpperCAmelCase__				:					List[str] 					=	self.get_dummy_components()

         UpperCAmelCase__				:					Tuple 					=	self.pipeline_class(**lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	pipe.to(lowerCamelCase_				)

         pipe.set_progress_bar_config(disable=lowerCamelCase_				)

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

         UpperCAmelCase__				:					Union[str, Any] 					=	pipe(
             **self.get_dummy_inputs(lowerCamelCase_				)       ,return_dict=lowerCamelCase_       ,)[0]

         UpperCAmelCase__				:					Tuple 					=	image[0, -3:, -3:, -1]
         UpperCAmelCase__				:					int 					=	image_from_tuple[0, -3:, -3:, -1]

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

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

         UpperCAmelCase__				:					List[Any] 					=	np.array(
             [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]				)

         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				)      ->      Dict:



         '''simple docstring'''



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




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


      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''				)

         UpperCAmelCase__				:					str 					=	load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
         UpperCAmelCase__				:					Union[str, Any] 					=	np.ones((768, 768)       ,dtype=np.floataa				)
         UpperCAmelCase__				:					int 					=	0

         UpperCAmelCase__				:					List[Any] 					=	'''a hat'''

         UpperCAmelCase__				:					str 					=	KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''       ,torch_dtype=torch.floataa				)
         pipe_prior.to(lowerCamelCase_				)

         UpperCAmelCase__				:					List[str] 					=	KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''       ,torch_dtype=torch.floataa				)
         UpperCAmelCase__				:					Tuple 					=	pipeline.to(lowerCamelCase_				)
         pipeline.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	torch.Generator(device='''cpu'''				).manual_seed(0				)
         UpperCAmelCase__					,   UpperCAmelCase__				:					int 					=	pipe_prior(
             lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

         UpperCAmelCase__				:					Optional[Any] 					=	pipeline(
             lowerCamelCase_       ,image=lowerCamelCase_       ,mask_image=lowerCamelCase_       ,image_embeds=lowerCamelCase_       ,negative_image_embeds=lowerCamelCase_       ,generator=lowerCamelCase_       ,num_inference_steps=100       ,height=768       ,width=768       ,output_type='''np'''       ,)

         UpperCAmelCase__				:					str 					=	output.images[0]

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

         assert_mean_pixel_difference(lowerCamelCase_       ,lowerCamelCase_				)




