

"""simple docstring"""




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   __lowercase      (     __lowerCamelCase						,      unittest.TestCase       ):
  snake_case_												=							KandinskyVaaControlnetPipeline
  snake_case_												=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  snake_case_												=							["""image_embeds""", """negative_image_embeds""", """hint"""]
  snake_case_												=							[
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  snake_case_												=							False

  @property
  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   return 32

  @property
  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   return 32

  @property
  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   return self.time_input_dim

  @property
  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   return self.time_input_dim * 4

  @property
  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   return 100

  @property
  def  __lowercase							(	self		:      Any     ):






   '''simple docstring'''
   torch.manual_seed(0     )

   UpperCAmelCase__       :	Tuple        =       {
       """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,
   }

   UpperCAmelCase__       :	int        =       UNetaDConditionModel(**A     )
   return model

  @property
  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   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  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   torch.manual_seed(0     )
   UpperCAmelCase__       :	str        =       VQModel(**self.dummy_movq_kwargs     )
   return model

  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       self.dummy_unet
   UpperCAmelCase__       :	List[Any]        =       self.dummy_movq

   UpperCAmelCase__       :	List[Any]        =       DDIMScheduler(
       num_train_timesteps=1_000       ,beta_schedule="""linear"""       ,beta_start=0.0_0_0_8_5       ,beta_end=0.0_1_2       ,clip_sample=A       ,set_alpha_to_one=A       ,steps_offset=1       ,prediction_type="""epsilon"""       ,thresholding=A       ,)

   UpperCAmelCase__       :	Optional[Any]        =       {
       """unet""": unet,
       """scheduler""": scheduler,
       """movq""": movq,
   }
   return components

  def  __lowercase							(	self		:      str       ,A		:      Optional[Any]       ,A		:      Any=0     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       floats_tensor((1, self.text_embedder_hidden_size)       ,rng=random.Random(A     )     ).to(A     )
   UpperCAmelCase__       :	Union[str, Any]        =       floats_tensor((1, self.text_embedder_hidden_size)       ,rng=random.Random(seed + 1     )     ).to(
       A     )

   # create hint
   UpperCAmelCase__       :	int        =       floats_tensor((1, 3, 64, 64)       ,rng=random.Random(A     )     ).to(A     )

   if str(A     ).startswith("""mps"""     ):
    UpperCAmelCase__       :	Optional[int]        =       torch.manual_seed(A     )
   else:
    UpperCAmelCase__       :	Dict        =       torch.Generator(device=A     ).manual_seed(A     )
   UpperCAmelCase__       :	Dict        =       {
       """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  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       """cpu"""

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

   UpperCAmelCase__       :	Union[str, Any]        =       self.pipeline_class(**A     )
   UpperCAmelCase__       :	Optional[int]        =       pipe.to(A     )

   pipe.set_progress_bar_config(disable=A     )

   UpperCAmelCase__       :	Optional[int]        =       pipe(**self.get_dummy_inputs(A     )     )
   UpperCAmelCase__       :	Tuple        =       output.images

   UpperCAmelCase__       :	Dict        =       pipe(
       **self.get_dummy_inputs(A     )       ,return_dict=A       ,)[0]

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

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

   UpperCAmelCase__       :	Optional[int]        =       np.array(
       [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]     )

   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   __lowercase      (     unittest.TestCase       ):

  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""     )

   UpperCAmelCase__       :	int        =       load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinskyv22/hint_image_cat.png"""     )
   UpperCAmelCase__       :	int        =       torch.from_numpy(np.array(A     )     ).float() / 2_5_5.0
   UpperCAmelCase__       :	Union[str, Any]        =       hint.permute(2       ,0       ,1     ).unsqueeze(0     )

   UpperCAmelCase__       :	List[str]        =       KandinskyVaaPriorPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-2-prior"""       ,torch_dtype=torch.floataa     )
   pipe_prior.to(A     )

   UpperCAmelCase__       :	List[Any]        =       KandinskyVaaControlnetPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-2-controlnet-depth"""       ,torch_dtype=torch.floataa     )
   UpperCAmelCase__       :	int        =       pipeline.to(A     )
   pipeline.set_progress_bar_config(disable=A     )

   UpperCAmelCase__       :	Optional[Any]        =       """A robot, 4k photo"""

   UpperCAmelCase__       :	List[Any]        =       torch.Generator(device="""cuda"""     ).manual_seed(0     )
   UpperCAmelCase__ ,   UpperCAmelCase__       :	Tuple        =       pipe_prior(
       A       ,generator=A       ,num_inference_steps=5       ,negative_prompt=""""""       ,).to_tuple()

   UpperCAmelCase__       :	List[str]        =       torch.Generator(device="""cuda"""     ).manual_seed(0     )
   UpperCAmelCase__       :	int        =       pipeline(
       image_embeds=A       ,negative_image_embeds=A       ,hint=A       ,generator=A       ,num_inference_steps=100       ,output_type="""np"""       ,)

   UpperCAmelCase__       :	Any        =       output.images[0]

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

   assert_mean_pixel_difference(A       ,A     )












'''simple docstring'''

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowercase__		:     Dict			=							[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]







def    a__							(							lowercase   :	List[str] )						->						List[Any]:





     """simple docstring"""

     for pegasus_name, hf_name in PATTERNS:
          _UpperCamelCase							       =			k.replace(lowercase,   lowercase )
     return k







def    a__							(							lowercase   :	dict,   lowercase   :	dict )						->						PegasusForConditionalGeneration:





     """simple docstring"""

     _UpperCamelCase							       =			DEFAULTS.copy()
     cfg_kwargs.update(lowercase )
     _UpperCamelCase							       =			PegasusConfig(**lowercase )
     _UpperCamelCase							       =			PegasusForConditionalGeneration(lowercase )
     _UpperCamelCase							       =			torch_model.model.state_dict()
     _UpperCamelCase							       =			{}
     for k, v in tf_weights.items():
          _UpperCamelCase							       =			rename_state_dict_key(lowercase )
          if new_k not in sd:
               raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )

          if "dense" in k or "proj" in new_k:
               _UpperCamelCase							       =			v.T
          _UpperCamelCase							       =			torch.tensor(lowercase,   dtype=sd[new_k].dtype )
          assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
     # make sure embedding.padding_idx is respected
     _UpperCamelCase							       =			torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
     _UpperCamelCase							       =			mapping['''shared.weight''']
     _UpperCamelCase							       =			mapping['''shared.weight''']
     _UpperCamelCase							       =			{k: torch.zeros_like(lowercase ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
     mapping.update(**lowercase )
     _UpperCamelCase       ,							_UpperCamelCase							       =			torch_model.model.load_state_dict(lowercase,   strict=lowercase )
     _UpperCamelCase							       =			[
         k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
     ]
     assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
     assert extra == [], F"""no matches found for the following tf keys {extra}"""
     return torch_model







def    a__							(							lowercase   :	List[Any]="./ckpt/aeslc/model.ckpt-32000" )						->						Dict:





     """simple docstring"""

     _UpperCamelCase							       =			tf.train.list_variables(lowercase )
     _UpperCamelCase							       =			{}
     _UpperCamelCase							       =			['''Adafactor''', '''global_step''']
     for name, shape in tqdm(lowercase,   desc='''converting tf checkpoint to dict''' ):
          _UpperCamelCase							       =			any(pat in name for pat in ignore_name )
          if skip_key:
               continue
          _UpperCamelCase							       =			tf.train.load_variable(lowercase,   lowercase )
          _UpperCamelCase							       =			array
     return tf_weights







def    a__							(							lowercase   :	str,   lowercase   :	str )						->						Optional[int]:





     """simple docstring"""

     _UpperCamelCase							       =			Path(lowercase ).parent.name
     _UpperCamelCase							       =			task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
     _UpperCamelCase							       =			PegasusTokenizer.from_pretrained('''sshleifer/pegasus''',   model_max_length=lowercase )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(lowercase )

     # convert model
     _UpperCamelCase							       =			get_tf_weights_as_numpy(lowercase )
     _UpperCamelCase							       =			task_specific_params[F"""summarization_{dataset}"""]
     if dataset == "large":
          _UpperCamelCase							       =			task_specific_params
     _UpperCamelCase							       =			convert_pegasus(lowercase,   lowercase )
     torch_model.save_pretrained(lowercase )
     _UpperCamelCase							       =			torch_model.state_dict()
     sd.pop('''model.decoder.embed_positions.weight''' )
     sd.pop('''model.encoder.embed_positions.weight''' )
     torch.save(lowercase,   Path(lowercase ) / '''pytorch_model.bin''' )


if __name__ == "__main__":
   lowercase__		:     List[Any]			=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   lowercase__		:     Optional[int]			=							parser.parse_args()
   if args.save_dir is None:
      lowercase__		:     Tuple			=							Path(args.tf_ckpt_path).parent.name
      lowercase__		:     Optional[Any]			=							os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)



