


'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				KandinskyVaaImgaImgPipeline
     UpperCAmelCase 					=				['''image_embeds''', '''negative_image_embeds''', '''image''']
     UpperCAmelCase 					=				[
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
     ]
     UpperCAmelCase 					=				[
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     UpperCAmelCase 					=				False






     @property
     def  a_					(			self		)       ->    Tuple:
          return 32






     @property
     def  a_					(			self		)       ->    List[Any]:
          return 32






     @property
     def  a_					(			self		)       ->    str:
          return self.time_input_dim






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






     @property
     def  a_					(			self		)       ->    List[str]:
          return 100






     @property
     def  a_					(			self		)       ->    Optional[Any]:
          torch.manual_seed(0		)

          _a						=		{
              '''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,
          }

          _a						=		UNetaDConditionModel(**__lowerCamelCase		)
          return model






     @property
     def  a_					(			self		)       ->    int:
          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		)       ->    Union[str, Any]:
          torch.manual_seed(0		)
          _a						=		VQModel(**self.dummy_movq_kwargs		)
          return model






     def  a_					(			self		)       ->    Any:
          _a						=		self.dummy_unet
          _a						=		self.dummy_movq

          _a						=		{
              '''num_train_timesteps''': 1_000,
              '''beta_schedule''': '''linear''',
              '''beta_start''': 0.0_00_85,
              '''beta_end''': 0.0_12,
              '''clip_sample''': False,
              '''set_alpha_to_one''': False,
              '''steps_offset''': 0,
              '''prediction_type''': '''epsilon''',
              '''thresholding''': False,
          }

          _a						=		DDIMScheduler(**__lowerCamelCase		)

          _a						=		{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''movq''': movq,
          }

          return components






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=0		)       ->    Optional[Any]:
          _a						=		floats_tensor((1, self.text_embedder_hidden_size)     ,							rng=random.Random(__lowerCamelCase		)		).to(__lowerCamelCase		)
          _a						=		floats_tensor((1, self.text_embedder_hidden_size)     ,							rng=random.Random(seed + 1		)		).to(
              __lowerCamelCase		)
          # create init_image
          _a						=		floats_tensor((1, 3, 64, 64)     ,							rng=random.Random(__lowerCamelCase		)		).to(__lowerCamelCase		)
          _a						=		image.cpu().permute(0     ,							2     ,							3     ,							1		)[0]
          _a						=		Image.fromarray(np.uinta(__lowerCamelCase		)		).convert("RGB"		).resize((256, 256)		)

          if str(__lowerCamelCase		).startswith("mps"		):
               _a						=		torch.manual_seed(__lowerCamelCase		)
          else:
               _a						=		torch.Generator(device=__lowerCamelCase		).manual_seed(__lowerCamelCase		)
          _a						=		{
              '''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		)       ->    List[str]:
          _a						=		'''cpu'''

          _a						=		self.get_dummy_components()

          _a						=		self.pipeline_class(**__lowerCamelCase		)
          _a						=		pipe.to(__lowerCamelCase		)

          pipe.set_progress_bar_config(disable=__lowerCamelCase		)

          _a						=		pipe(**self.get_dummy_inputs(__lowerCamelCase		)		)
          _a						=		output.images

          _a						=		pipe(
              **self.get_dummy_inputs(__lowerCamelCase		)     ,							return_dict=__lowerCamelCase     ,							)[0]

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

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

          _a						=		np.array(
              [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]		)
          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 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def  a_					(			self		)       ->    Tuple:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/kandinskyv22/kandinskyv22_img2img_frog.npy"		)

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

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

          _a						=		KandinskyVaaImgaImgPipeline.from_pretrained(
              "kandinsky-community/kandinsky-2-2-decoder"     ,							torch_dtype=torch.floataa		)
          _a						=		pipeline.to(__lowerCamelCase		)

          pipeline.set_progress_bar_config(disable=__lowerCamelCase		)

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

          _a						=		pipeline(
              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"     ,							)

          _a						=		output.images[0]

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

          assert_mean_pixel_difference(__lowerCamelCase     ,							__lowerCamelCase		)





'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def __UpperCamelCase   (			__lowerCamelCase		: BertModel      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     List[str]:







     '''simple docstring'''






     _a						=		("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     _a						=		(
         ("layer.", "layer_"),
         ("word_embeddings.weight", "word_embeddings"),
         ("position_embeddings.weight", "position_embeddings"),
         ("token_type_embeddings.weight", "token_type_embeddings"),
         (".", "/"),
         ("LayerNorm/weight", "LayerNorm/gamma"),
         ("LayerNorm/bias", "LayerNorm/beta"),
         ("weight", "kernel"),
     )

     if not os.path.isdir(__lowerCamelCase				):
          os.makedirs(__lowerCamelCase				)

     _a						=		model.state_dict()

     def to_tf_var_name(__lowerCamelCase		: str				):
          for patt, repl in iter(__lowerCamelCase				):
               _a						=		name.replace(__lowerCamelCase      ,  __lowerCamelCase				)
          return F"bert/{name}"

     def create_tf_var(__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: tf.Session				):
          _a						=		tf.dtypes.as_dtype(tensor.dtype				)
          _a						=		tf.get_variable(dtype=__lowerCamelCase      ,  shape=tensor.shape      ,  name=__lowerCamelCase      ,  initializer=tf.zeros_initializer()				)
          session.run(tf.variables_initializer([tf_var]				)				)
          session.run(__lowerCamelCase				)
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               _a						=		to_tf_var_name(__lowerCamelCase				)
               _a						=		state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose				):
                    _a						=		torch_tensor.T
               _a						=		create_tf_var(tensor=__lowerCamelCase      ,  name=__lowerCamelCase      ,  session=__lowerCamelCase				)
               tf.keras.backend.set_value(__lowerCamelCase      ,  __lowerCamelCase				)
               _a						=		session.run(__lowerCamelCase				)
               print(F"Successfully created {tf_name}: {np.allclose(__lowerCamelCase      ,  __lowerCamelCase				)}"				)

          _a						=		tf.train.Saver(tf.trainable_variables()				)
          saver.save(__lowerCamelCase      ,  os.path.join(__lowerCamelCase      ,  model_name.replace("-"      ,  "_"				) + ".ckpt"				)				)






def __UpperCamelCase   (			__lowerCamelCase		: str=None				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		argparse.ArgumentParser()
     parser.add_argument("--model_name"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="model name e.g. bert-base-uncased"				)
     parser.add_argument(
         "--cache_dir"      ,  type=__lowerCamelCase      ,  default=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory containing pytorch model"				)
     parser.add_argument("--pytorch_model_path"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="/path/to/<pytorch-model-name>.bin"				)
     parser.add_argument("--tf_cache_dir"      ,  type=__lowerCamelCase      ,  required=__lowerCamelCase      ,  help="Directory in which to save tensorflow model"				)
     _a						=		parser.parse_args(__lowerCamelCase				)

     _a						=		BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name      ,  state_dict=torch.load(args.pytorch_model_path				)      ,  cache_dir=args.cache_dir      ,  )

     convert_pytorch_checkpoint_to_tf(model=__lowerCamelCase      ,  ckpt_dir=args.tf_cache_dir      ,  model_name=args.model_name				)


if __name__ == "__main__":
   main()

