from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
      import tensorflow as tf

      from transformers import TFBlipTextModel
      from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class      lowerCamelCase    :
  """simple docstring"""







  def __init__(							self       :				List[Any]     ,   __magic_name__       :				Dict     ,   __magic_name__       :				Optional[int]=12     ,   __magic_name__       :				str=7     ,   __magic_name__       :				str=True     ,   __magic_name__       :				Tuple=True     ,   __magic_name__       :				Optional[Any]=True     ,   __magic_name__       :				str=99     ,   __magic_name__       :				List[str]=32     ,   __magic_name__       :				Tuple=32     ,   __magic_name__       :				Tuple=2     ,   __magic_name__       :				List[Any]=4     ,   __magic_name__       :				Optional[int]=37     ,   __magic_name__       :				Dict=0.1     ,   __magic_name__       :				Dict=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Any=0.02     ,   __magic_name__       :				int=0     ,   __magic_name__       :				int=None     ,   )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     parent
        SCREAMING_SNAKE_CASE_      =     batch_size
        SCREAMING_SNAKE_CASE_      =     seq_length
        SCREAMING_SNAKE_CASE_      =     is_training
        SCREAMING_SNAKE_CASE_      =     use_input_mask
        SCREAMING_SNAKE_CASE_      =     use_labels
        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     projection_dim
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     dropout
        SCREAMING_SNAKE_CASE_      =     attention_dropout
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     scope
        SCREAMING_SNAKE_CASE_      =     bos_token_id







  def       __A   (							self       :				Optional[int]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size      )

        SCREAMING_SNAKE_CASE_      =     None
        if self.use_input_mask:
              SCREAMING_SNAKE_CASE_      =     random_attention_mask([self.batch_size, self.seq_length]      )

        if input_mask is not None:
              SCREAMING_SNAKE_CASE_      =     input_mask.numpy()
              SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     input_mask.shape
              SCREAMING_SNAKE_CASE_      =     np.random.randint(1     ,   seq_length - 1     ,   size=(batch_size,)      )
              for batch_idx, start_index in enumerate(__magic_name__      ):
                    SCREAMING_SNAKE_CASE_      =     1
                    SCREAMING_SNAKE_CASE_      =     0

        SCREAMING_SNAKE_CASE_      =     self.get_config()

        return config, input_ids, tf.convert_to_tensor(__magic_name__      )







  def       __A   (							self       :				Tuple      )     ->		Optional[int]:
        return BlipTextConfig(
            vocab_size=self.vocab_size     ,   hidden_size=self.hidden_size     ,   projection_dim=self.projection_dim     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   dropout=self.dropout     ,   attention_dropout=self.attention_dropout     ,   max_position_embeddings=self.max_position_embeddings     ,   initializer_range=self.initializer_range     ,   bos_token_id=self.bos_token_id     ,   )







  def       __A   (							self       :				Dict     ,   __magic_name__       :				int     ,   __magic_name__       :				int     ,   __magic_name__       :				Dict      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     TFBlipTextModel(config=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     model(__magic_name__     ,   attention_mask=__magic_name__     ,   training=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     model(__magic_name__     ,   training=__magic_name__      )
        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       :				str      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     config_and_inputs
        SCREAMING_SNAKE_CASE_      =     {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict



@require_tf
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  (TFBlipTextModel,) if is_tf_available() else ()
  lowerCamelCase__   =  False
  lowerCamelCase__   =  False
  lowerCamelCase__   =  False







  def       __A   (							self       :				Union[str, Any]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     BlipTextModelTester(self      )
        SCREAMING_SNAKE_CASE_      =     ConfigTester(self     ,   config_class=__magic_name__     ,   hidden_size=37      )







  def       __A   (							self       :				Union[str, Any]      )     ->		int:
        self.config_tester.run_common_tests()







  def       __A   (							self       :				List[str]      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__magic_name__      )







  def       __A   (							self       :				str      )     ->		int:
        pass







  def       __A   (							self       :				str      )     ->		List[str]:
        pass







  @unittest.skip(reason="Blip does not use inputs_embeds"      )
  def       __A   (							self       :				Optional[int]      )     ->		Optional[Any]:
        pass







  @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"      )
  def       __A   (							self       :				str      )     ->		Optional[int]:
        pass







  @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"      )
  def       __A   (							self       :				List[Any]      )     ->		Optional[Any]:
        pass







  @slow
  def       __A   (							self       :				str      )     ->		str:
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      =     TFBlipTextModel.from_pretrained(__magic_name__      )
              self.assertIsNotNone(__magic_name__      )







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				List[Any]=True      )     ->		Tuple:
        super().test_pt_tf_model_equivalence(allow_missing_keys=__magic_name__      )



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  KandinskyVaaControlnetPipeline
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  [
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowerCamelCase__   =  False







  @property
  def       __A   (							self       :				str      )     ->		Optional[int]:
        return 32







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		Any:
        return 32







  @property
  def       __A   (							self       :				Any      )     ->		List[Any]:
        return self.time_input_dim







  @property
  def       __A   (							self       :				Tuple      )     ->		int:
        return self.time_input_dim * 4







  @property
  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        return 100







  @property
  def       __A   (							self       :				int      )     ->		Tuple:
        torch.manual_seed(0      )

        SCREAMING_SNAKE_CASE_      =     {
            "in_channels": 8,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "image_hint",
            "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,
        }

        SCREAMING_SNAKE_CASE_      =     UNetaDConditionModel(**__magic_name__      )
        return model







  @property
  def       __A   (							self       :				Optional[int]      )     ->		str:
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }







  @property
  def       __A   (							self       :				List[str]      )     ->		Dict:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     VQModel(**self.dummy_movq_kwargs      )
        return model







  def       __A   (							self       :				Dict      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.dummy_unet
        SCREAMING_SNAKE_CASE_      =     self.dummy_movq

        SCREAMING_SNAKE_CASE_      =     DDIMScheduler(
            num_train_timesteps=1_000     ,   beta_schedule="linear"     ,   beta_start=0.0_0085     ,   beta_end=0.012     ,   clip_sample=__magic_name__     ,   set_alpha_to_one=__magic_name__     ,   steps_offset=1     ,   prediction_type="epsilon"     ,   thresholding=__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     {
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components







  def       __A   (							self       :				int     ,   __magic_name__       :				Any     ,   __magic_name__       :				Optional[int]=0      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(seed + 1      )      ).to(
            __magic_name__      )

        # create hint
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, 3, 64, 64)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )

        if str(__magic_name__      ).startswith("mps"      ):
              SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        else:
              SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "hint": hint,
            "generator": generator,
            "height": 64,
            "width": 64,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs







  def       __A   (							self       :				Optional[int]      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     "cpu"

        SCREAMING_SNAKE_CASE_      =     self.get_dummy_components()

        SCREAMING_SNAKE_CASE_      =     self.pipeline_class(**__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     pipe.to(__magic_name__      )

        pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     pipe(**self.get_dummy_inputs(__magic_name__      )      )
        SCREAMING_SNAKE_CASE_      =     output.images

        SCREAMING_SNAKE_CASE_      =     pipe(
            **self.get_dummy_inputs(__magic_name__      )     ,   return_dict=__magic_name__     ,   )[0]

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

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

        SCREAMING_SNAKE_CASE_      =     np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]      )

        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      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







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







  def       __A   (							self       :				List[Any]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"      )

        SCREAMING_SNAKE_CASE_      =     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/hint_image_cat.png"      )
        SCREAMING_SNAKE_CASE_      =     torch.from_numpy(np.array(__magic_name__      )      ).float() / 255.0
        SCREAMING_SNAKE_CASE_      =     hint.permute(2     ,   0     ,   1      ).unsqueeze(0      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-prior"     ,   torch_dtype=torch.floataa      )
        pipe_prior.to(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaControlnetPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,   torch_dtype=torch.floataa      )
        SCREAMING_SNAKE_CASE_      =     pipeline.to(__magic_name__      )
        pipeline.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "A robot, 4k photo"

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pipe_prior(
            __magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=5     ,   negative_prompt=""     ,   ).to_tuple()

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     pipeline(
            image_embeds=__magic_name__     ,   negative_image_embeds=__magic_name__     ,   hint=__magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=100     ,   output_type="np"     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images[0]

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

        assert_mean_pixel_difference(__magic_name__     ,   __magic_name__      )


