



import unittest

import numpy as np

from transformers import AlbertConfig, 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.numpy as jnp

    from transformers.models.albert.modeling_flax_albert import (
        FlaxAlbertForMaskedLM,
        FlaxAlbertForMultipleChoice,
        FlaxAlbertForPreTraining,
        FlaxAlbertForQuestionAnswering,
        FlaxAlbertForSequenceClassification,
        FlaxAlbertForTokenClassification,
        FlaxAlbertModel,
    )



class 							lowerCAmelCase_						(    unittest.TestCase				):
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=13,     SCREAMING_SNAKE_CASE_=7,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=99,     SCREAMING_SNAKE_CASE_=32,     SCREAMING_SNAKE_CASE_=5,     SCREAMING_SNAKE_CASE_=4,     SCREAMING_SNAKE_CASE_=37,     SCREAMING_SNAKE_CASE_="gelu",     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=512,     SCREAMING_SNAKE_CASE_=16,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_=4,     )	->						Optional[int]:
          UpperCamelCase       :    Any		      =      parent
          UpperCamelCase       :    Union[str, Any]		      =      batch_size
          UpperCamelCase       :    Union[str, Any]		      =      seq_length
          UpperCamelCase       :    Union[str, Any]		      =      is_training
          UpperCamelCase       :    List[Any]		      =      use_attention_mask
          UpperCamelCase       :    Tuple		      =      use_token_type_ids
          UpperCamelCase       :    Optional[int]		      =      use_labels
          UpperCamelCase       :    Tuple		      =      vocab_size
          UpperCamelCase       :    str		      =      hidden_size
          UpperCamelCase       :    Optional[Any]		      =      num_hidden_layers
          UpperCamelCase       :    List[Any]		      =      num_attention_heads
          UpperCamelCase       :    int		      =      intermediate_size
          UpperCamelCase       :    Any		      =      hidden_act
          UpperCamelCase       :    int		      =      hidden_dropout_prob
          UpperCamelCase       :    Optional[Any]		      =      attention_probs_dropout_prob
          UpperCamelCase       :    Optional[Any]		      =      max_position_embeddings
          UpperCamelCase       :    Dict		      =      type_vocab_size
          UpperCamelCase       :    str		      =      type_sequence_label_size
          UpperCamelCase       :    Union[str, Any]		      =      initializer_range
          UpperCamelCase       :    Optional[Any]		      =      num_choices
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase       :    Optional[Any]		      =      ids_tensor([self.batch_size, self.seq_length],     self.vocab_size			)

          UpperCamelCase       :    Optional[int]		      =      None
          if self.use_attention_mask:
             UpperCamelCase       :    Union[str, Any]		      =      random_attention_mask([self.batch_size, self.seq_length]			)

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

          UpperCamelCase       :    int		      =      AlbertConfig(
              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,     )

          return config, input_ids, token_type_ids, attention_mask






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






@require_flax
class 							lowerCAmelCase_						(    lowerCAmelCase__			,       unittest.TestCase				):
       UpperCAmelCase__							:   int  	=    (
           (
               FlaxAlbertModel,
               FlaxAlbertForPreTraining,
               FlaxAlbertForMaskedLM,
               FlaxAlbertForMultipleChoice,
               FlaxAlbertForQuestionAnswering,
               FlaxAlbertForSequenceClassification,
               FlaxAlbertForTokenClassification,
               FlaxAlbertForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    Tuple		      =      FlaxAlbertModelTester(self			)






       @slow
       def    snake_case_			(     self			)	->						Optional[int]:


          for model_class_name in self.all_model_classes:
             UpperCamelCase       :    int		      =      model_class_name.from_pretrained('albert-base-v2'			)
             UpperCamelCase       :    str		      =      model(np.ones((1, 1)			)			)
             self.assertIsNotNone(__lowerCAmelCase			)






@require_flax
class 							lowerCAmelCase_						(    unittest.TestCase				):
       @slow
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    Union[str, Any]		      =      FlaxAlbertModel.from_pretrained('albert-base-v2'			)
          UpperCamelCase       :    str		      =      np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]			)
          UpperCamelCase       :    List[Any]		      =      np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
          UpperCamelCase       :    str		      =      model(__lowerCAmelCase,     attention_mask=__lowerCAmelCase			)[0]
          UpperCamelCase       :    Optional[Any]		      =      (1, 11, 768)
          self.assertEqual(output.shape,     __lowerCAmelCase			)
          UpperCamelCase       :    str		      =      np.array(
              [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]			)

          self.assertTrue(jnp.allclose(output[:, 1:4, 1:4],     __lowerCAmelCase,     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__(   lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			KandinskyImgaImgPipeline
			UpperCAmelCase_     :	Union[str, Any]						=			['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
			UpperCAmelCase_     :	List[str]						=			[
			    '''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_     :	str						=			False



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim * 4



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 100



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
					return tokenizer



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    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     =    MultilingualCLIP(__lowerCAmelCase)
					lowerCAmelCase     =    text_encoder.eval()

					return text_encoder



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)

					lowerCAmelCase     =    {
					    """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     =    UNetaDConditionModel(**__lowerCAmelCase)
					return model



			@property
			def 				a_			(  self):




					"""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 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    VQModel(**self.dummy_movq_kwargs)
					return model



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.dummy_text_encoder
					lowerCAmelCase     =    self.dummy_tokenizer
					lowerCAmelCase     =    self.dummy_unet
					lowerCAmelCase     =    self.dummy_movq

					lowerCAmelCase     =    {
					    """num_train_timesteps""": 1000,
					    """beta_schedule""": """linear""",
					    """beta_start""": 0.00085,
					    """beta_end""": 0.012,
					    """clip_sample""": False,
					    """set_alpha_to_one""": False,
					    """steps_offset""": 0,
					    """prediction_type""": """epsilon""",
					    """thresholding""": False,
					}

					lowerCAmelCase     =    DDIMScheduler(**__lowerCAmelCase)

					lowerCAmelCase     =    {
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=0):




					"""simple docstring"""


					lowerCAmelCase     =    floats_tensor((1, self.cross_attention_dim)			,       rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase     =    floats_tensor((1, self.cross_attention_dim)			,       rng=random.Random(seed + 1)).to(__lowerCAmelCase)
					# create init_image
					lowerCAmelCase     =    floats_tensor((1, 3, 64, 64)			,       rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase     =    image.cpu().permute(0			,       2			,       3			,       1)[0]
					lowerCAmelCase     =    Image.fromarray(np.uinta(__lowerCAmelCase)).convert("""RGB""").resize((256, 256))

					if str(__lowerCAmelCase).startswith("""mps"""):
							lowerCAmelCase     =    torch.manual_seed(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
					lowerCAmelCase     =    {
					    """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):




					"""simple docstring"""


					lowerCAmelCase     =    """cpu"""

					lowerCAmelCase     =    self.get_dummy_components()

					lowerCAmelCase     =    self.pipeline_class(**__lowerCAmelCase)
					lowerCAmelCase     =    pipe.to(__lowerCAmelCase)

					pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    pipe(**self.get_dummy_inputs(__lowerCAmelCase))
					lowerCAmelCase     =    output.images

					lowerCAmelCase     =    pipe(
					    **self.get_dummy_inputs(__lowerCAmelCase)			,       return_dict=__lowerCAmelCase			,       )[0]

					lowerCAmelCase     =    image[0, -3:, -3:, -1]
					lowerCAmelCase     =    image_from_tuple[0, -3:, -3:, -1]

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

					lowerCAmelCase     =    np.array(
					    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233])
					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      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


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







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinsky/kandinsky_img2img_frog.npy""")

					lowerCAmelCase     =    load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
					lowerCAmelCase     =    """A red cartoon frog, 4k"""

					lowerCAmelCase     =    KandinskyPriorPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1-prior"""			,       torch_dtype=torch.floataa)
					pipe_prior.to(__lowerCAmelCase)

					lowerCAmelCase     =    KandinskyImgaImgPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1"""			,       torch_dtype=torch.floataa)
					lowerCAmelCase     =    pipeline.to(__lowerCAmelCase)

					pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase     =    torch.Generator(device="""cpu""").manual_seed(0)
					lowerCAmelCase   ,	lowerCAmelCase     =    pipe_prior(
					    __lowerCAmelCase			,       generator=__lowerCAmelCase			,       num_inference_steps=5			,       negative_prompt=""""""			,       ).to_tuple()

					lowerCAmelCase     =    pipeline(
					    __lowerCAmelCase			,       image=__lowerCAmelCase			,       image_embeds=__lowerCAmelCase			,       negative_image_embeds=__lowerCAmelCase			,       generator=__lowerCAmelCase			,       num_inference_steps=100			,       height=768			,       width=768			,       strength=0.2			,       output_type="""np"""			,       )

					lowerCAmelCase     =    output.images[0]

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

					assert_mean_pixel_difference(__lowerCAmelCase			,       __lowerCAmelCase)




