





import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import BertGenerationDecoder, BertGenerationEncoder



class       A__     :





      def __init__(				self ,							A_ ,							A_=13 ,							A_=7 ,							A_=True ,							A_=True ,							A_=99 ,							A_=32 ,							A_=5 ,							A_=4 ,							A_=37 ,							A_="gelu" ,							A_=0.1 ,							A_=0.1 ,							A_=50 ,							A_=0.02 ,							A_=True ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     parent
       UpperCamelCase			:      List[str] 			=     batch_size
       UpperCamelCase			:      Optional[int] 			=     seq_length
       UpperCamelCase			:      Union[str, Any] 			=     is_training
       UpperCamelCase			:      List[Any] 			=     use_input_mask
       UpperCamelCase			:      str 			=     vocab_size
       UpperCamelCase			:      Union[str, Any] 			=     hidden_size
       UpperCamelCase			:      Union[str, Any] 			=     num_hidden_layers
       UpperCamelCase			:      Union[str, Any] 			=     num_attention_heads
       UpperCamelCase			:      List[str] 			=     intermediate_size
       UpperCamelCase			:      int 			=     hidden_act
       UpperCamelCase			:      Union[str, Any] 			=     hidden_dropout_prob
       UpperCamelCase			:      List[str] 			=     attention_probs_dropout_prob
       UpperCamelCase			:      Tuple 			=     max_position_embeddings
       UpperCamelCase			:      List[Any] 			=     initializer_range
       UpperCamelCase			:      Union[str, Any] 			=     use_labels
       UpperCamelCase			:      str 			=     scope





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size     )

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

       if self.use_labels:
        UpperCamelCase			:      List[str] 			=     ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size     )

       UpperCamelCase			:      Optional[int] 			=     self.get_config()

       return config, input_ids, input_mask, token_labels





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return BertGenerationConfig(
           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 ,							is_decoder=A_ ,							initializer_range=self.initializer_range ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       (
           (
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					
       )			:      Dict 			=     self.prepare_config_and_inputs()

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

       return (
           config,
           input_ids,
           input_mask,
           token_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     BertGenerationEncoder(config=A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      List[str] 			=     model(A_ ,							attention_mask=A_     )
       UpperCamelCase			:      int 			=     model(A_     )
       self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     True
       UpperCamelCase			:      Tuple 			=     BertGenerationEncoder(config=A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      Dict 			=     model(
           A_ ,							attention_mask=A_ ,							encoder_hidden_states=A_ ,							encoder_attention_mask=A_ ,							)
       UpperCamelCase			:      Optional[int] 			=     model(
           A_ ,							attention_mask=A_ ,							encoder_hidden_states=A_ ,							)
       self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     True
       UpperCamelCase			:      Tuple 			=     True
       UpperCamelCase			:      Optional[int] 			=     BertGenerationDecoder(config=A_     ).to(A_     ).eval()

       # first forward pass
       UpperCamelCase			:      str 			=     model(
           A_ ,							attention_mask=A_ ,							encoder_hidden_states=A_ ,							encoder_attention_mask=A_ ,							use_cache=A_ ,							)
       UpperCamelCase			:      Dict 			=     outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       UpperCamelCase			:      int 			=     ids_tensor((self.batch_size, 3) ,							config.vocab_size     )
       UpperCamelCase			:      Dict 			=     ids_tensor((self.batch_size, 3) ,							vocab_size=2     )

       # append to next input_ids and
       UpperCamelCase			:      Optional[int] 			=     torch.cat([input_ids, next_tokens] ,							dim=-1     )
       UpperCamelCase			:      Dict 			=     torch.cat([input_mask, next_mask] ,							dim=-1     )

       UpperCamelCase			:      List[str] 			=     model(
           A_ ,							attention_mask=A_ ,							encoder_hidden_states=A_ ,							encoder_attention_mask=A_ ,							output_hidden_states=A_ ,							)["hidden_states"][0]
       UpperCamelCase			:      Union[str, Any] 			=     model(
           A_ ,							attention_mask=A_ ,							encoder_hidden_states=A_ ,							encoder_attention_mask=A_ ,							past_key_values=A_ ,							output_hidden_states=A_ ,							)["hidden_states"][0]

       # select random slice
       UpperCamelCase			:      List[Any] 			=     ids_tensor((1,) ,							output_from_past.shape[-1]     ).item()
       UpperCamelCase			:      Dict 			=     output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCamelCase			:      Optional[Any] 			=     output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(A_ ,							A_ ,							atol=1e-3     )     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							*A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     BertGenerationDecoder(A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      Union[str, Any] 			=     model(A_ ,							attention_mask=A_ ,							labels=A_     )
       self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Optional[Any] 			=     self.prepare_config_and_inputs()
       UpperCamelCase			:      Dict 			=     {"input_ids": input_ids, "attention_mask": input_mask}
       return config, inputs_dict



@require_torch
class       A__     ( __snake_case ,     __snake_case ,     __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:str							=     (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
      _UpperCAmelCase			:Optional[int]							=     (BertGenerationDecoder,) if is_torch_available() else ()
      _UpperCAmelCase			:List[str]							=     (
          {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
          if is_torch_available()
          else {}
      )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     BertGenerationEncoderTester(self     )
       UpperCamelCase			:      Optional[Any] 			=     ConfigTester(self ,							config_class=A_ ,							hidden_size=37     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     self.model_tester.prepare_config_and_inputs()
       UpperCamelCase			:      Dict 			=     "bert"
       self.model_tester.create_and_check_model(A_ ,							A_ ,							A_ ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(*A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       (
           (
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					
       )			:      Tuple 			=     self.model_tester.prepare_config_and_inputs_for_decoder()

       UpperCamelCase			:      List[str] 			=     None

       self.model_tester.create_and_check_model_as_decoder(
           A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_for_causal_lm(*A_     )





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"     )
       self.assertIsNotNone(A_     )



@require_torch
class       A__     ( unittest.TestCase    ):





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"     )
       UpperCamelCase			:      Tuple 			=     torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]     )
       with torch.no_grad():
        UpperCamelCase			:      Union[str, Any] 			=     model(A_     )[0]
       UpperCamelCase			:      List[str] 			=     torch.Size([1, 8, 1024]     )
       self.assertEqual(output.shape ,							A_     )
       UpperCamelCase			:      int 			=     torch.tensor(
           [[[0.17_75, 0.00_83, -0.03_21], [1.60_02, 0.12_87, 0.39_12], [2.14_73, 0.57_91, 0.60_66]]]     )
       self.assertTrue(torch.allclose(output[:, :3, :3] ,							A_ ,							atol=1e-4     )     )



@require_torch
class       A__     ( unittest.TestCase    ):





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"     )
       UpperCamelCase			:      List[Any] 			=     torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]     )
       with torch.no_grad():
        UpperCamelCase			:      int 			=     model(A_     )[0]
       UpperCamelCase			:      Optional[int] 			=     torch.Size([1, 8, 5_0358]     )
       self.assertEqual(output.shape ,							A_     )
       UpperCamelCase			:      List[Any] 			=     torch.tensor(
           [[[-0.57_88, -2.59_94, -3.70_54], [0.04_38, 4.79_97, 1.87_95], [1.58_62, 6.64_09, 4.46_38]]]     )
       self.assertTrue(torch.allclose(output[:, :3, :3] ,							A_ ,							atol=1e-4     )     )







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:str							=     KandinskyVaaPipeline
      _UpperCAmelCase			:str							=     [
          'image_embeds',
          'negative_image_embeds',
      ]
      _UpperCAmelCase			:str							=     ['image_embeds', 'negative_image_embeds']
      _UpperCAmelCase			:List[str]							=     [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase			:List[str]							=     False





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim * 4





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 100





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )

       UpperCamelCase			:      List[str] 			=     {
           "in_channels": 4,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "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": "image_proj",
           "cross_attention_dim": self.cross_attention_dim,
           "attention_head_dim": 4,
           "resnet_time_scale_shift": "scale_shift",
           "class_embed_type": None,
       }

       UpperCamelCase			:      Dict 			=     UNetaDConditionModel(**A_     )
       return model





      @property
      def  __UpperCamelCase(				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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )
       UpperCamelCase			:      Optional[Any] 			=     VQModel(**self.dummy_movq_kwargs     )
       return model





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.dummy_unet
       UpperCamelCase			:      Optional[Any] 			=     self.dummy_movq

       UpperCamelCase			:      Dict 			=     DDIMScheduler(
           num_train_timesteps=1000 ,							beta_schedule="linear" ,							beta_start=0.0_00_85 ,							beta_end=0.0_12 ,							clip_sample=A_ ,							set_alpha_to_one=A_ ,							steps_offset=1 ,							prediction_type="epsilon" ,							thresholding=A_ ,							)

       UpperCamelCase			:      Tuple 			=     {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }
       return components





      def  __UpperCamelCase(				self ,							A_ ,							A_=0     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(A_     )     ).to(A_     )
       UpperCamelCase			:      Optional[Any] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(seed + 1     )     ).to(
           A_     )
       if str(A_     ).startswith("mps"     ):
        UpperCamelCase			:      Optional[Any] 			=     torch.manual_seed(A_     )
       else:
        UpperCamelCase			:      List[Any] 			=     torch.Generator(device=A_     ).manual_seed(A_     )
       UpperCamelCase			:      Optional[int] 			=     {
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 64,
           "width": 64,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     "cpu"

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

       UpperCamelCase			:      Tuple 			=     self.pipeline_class(**A_     )
       UpperCamelCase			:      List[str] 			=     pipe.to(A_     )

       pipe.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      Dict 			=     pipe(**self.get_dummy_inputs(A_     )     )
       UpperCamelCase			:      Optional[int] 			=     output.images

       UpperCamelCase			:      int 			=     pipe(
           **self.get_dummy_inputs(A_     ) ,							return_dict=A_ ,							)[0]

       UpperCamelCase			:      Tuple 			=     image[0, -3:, -3:, -1]
       UpperCamelCase			:      List[Any] 			=     image_from_tuple[0, -3:, -3:, -1]

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

       UpperCamelCase			:      int 			=     np.array(
           [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]     )

       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  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"     )

       UpperCamelCase			:      Dict 			=     KandinskyVaaPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior" ,							torch_dtype=torch.floataa     )
       pipe_prior.to(A_     )

       UpperCamelCase			:      Dict 			=     KandinskyVaaPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-decoder" ,							torch_dtype=torch.floataa     )
       UpperCamelCase			:      Tuple 			=     pipeline.to(A_     )
       pipeline.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      str 			=     "red cat, 4k photo"

       UpperCamelCase			:      str 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			,					UpperCamelCase			:      Tuple 			=     pipe_prior(
           A_ ,							generator=A_ ,							num_inference_steps=5 ,							negative_prompt="" ,							).to_tuple()

       UpperCamelCase			:      int 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			:      Tuple 			=     pipeline(
           image_embeds=A_ ,							negative_image_embeds=A_ ,							generator=A_ ,							num_inference_steps=100 ,							output_type="np" ,							)

       UpperCamelCase			:      Union[str, Any] 			=     output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(A_ ,							A_     )
