





'''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


a__							:							int =[
    # 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 				lowercase__   (   __lowercase						:  Dict     ) ->      Tuple:



       """simple docstring"""






       for pegasus_name, hf_name in PATTERNS:
              __UpperCamelCase       =  k.replace(__lowercase				,  __lowercase     )
       return k
def 				lowercase__   (   __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 				lowercase__   (   __lowercase						:  Optional[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 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str     ) ->      Optional[Any]:



       """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__":
    a__							:							Union[str, 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.''')
    a__							:							int =parser.parse_args()
    if args.save_dir is None:
        a__							:							str =Path(args.tf_ckpt_path).parent.name
        a__							:							str =os.path.join('''pegasus''', dataset)
    convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   KandinskyVaaControlnetImgaImgPipeline
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   ["image_embeds", "negative_image_embeds", "image", "hint"]
					SCREAMING_SNAKE_CASE_		:				str							    =   ["image_embeds", "negative_image_embeds", "image", "hint"]
					SCREAMING_SNAKE_CASE_		:				Dict							    =   [
					    "generator",
					    "height",
					    "width",
					    "strength",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					SCREAMING_SNAKE_CASE_		:				Dict							    =   False




					@property
					def   A  (				self     :						Any   )  ->  Any:
							return 32




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




					@property
					def   A  (				self     :						Dict   )  ->  int:
							return self.time_input_dim




					@property
					def   A  (				self     :						Tuple   )  ->  str:
							return self.time_input_dim * 4




					@property
					def   A  (				self     :						Any   )  ->  str:
							return 1_00




					@property
					def   A  (				self     :						str   )  ->  List[str]:
							torch.manual_seed(0   )

							lowercase_ : List[Any]							       =   {
							    '''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,
							}

							lowercase_ : Dict							       =   UNetaDConditionModel(**A   )
							return model




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




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : Tuple							       =   self.dummy_unet
							lowercase_ : int							       =   self.dummy_movq

							lowercase_ : List[Any]							       =   {
							    '''num_train_timesteps''': 10_00,
							    '''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,
							}

							lowercase_ : str							       =   DDIMScheduler(**A   )

							lowercase_ : Tuple							       =   {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''movq''': movq,
							}

							return components




					def   A  (				self     :						Optional[int]    ,						A     :						int    ,						A     :						List[str]=0   )  ->  int:
							lowercase_ : str							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(A   )   ).to(A   )
							lowercase_ : Tuple							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(seed + 1   )   ).to(
							    A   )
							# create init_image
							lowercase_ : Union[str, Any]							       =   floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(A   )   ).to(A   )
							lowercase_ : Any							       =   image.cpu().permute(0    ,						2    ,						3    ,						1   )[0]
							lowercase_ : Optional[Any]							       =   Image.fromarray(np.uinta(A   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )
							# create hint
							lowercase_ : Optional[int]							       =   floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(A   )   ).to(A   )

							if str(A   ).startswith('''mps'''   ):
									lowercase_ : Optional[Any]							       =   torch.manual_seed(A   )
							else:
									lowercase_ : List[Any]							       =   torch.Generator(device=A   ).manual_seed(A   )
							lowercase_ : Dict							       =   {
							    '''image''': init_image,
							    '''image_embeds''': image_embeds,
							    '''negative_image_embeds''': negative_image_embeds,
							    '''hint''': hint,
							    '''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     :						Any   )  ->  List[Any]:
							lowercase_ : List[str]							       =   '''cpu'''

							lowercase_ : Any							       =   self.get_dummy_components()

							lowercase_ : Any							       =   self.pipeline_class(**A   )
							lowercase_ : int							       =   pipe.to(A   )

							pipe.set_progress_bar_config(disable=A   )

							lowercase_ : Dict							       =   pipe(**self.get_dummy_inputs(A   )   )
							lowercase_ : str							       =   output.images

							lowercase_ : int							       =   pipe(
							    **self.get_dummy_inputs(A   )    ,						return_dict=A    ,						)[0]

							lowercase_ : Dict							       =   image[0, -3:, -3:, -1]
							lowercase_ : Union[str, Any]							       =   image_from_tuple[0, -3:, -3:, -1]

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

							lowercase_ : List[str]							       =   np.array(
							    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]   )
							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 					_UpperCAmelCase       ( unittest.TestCase							):




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






					def   A  (				self     :						Any   )  ->  Optional[int]:
							lowercase_ : Dict							       =   load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''   )

							lowercase_ : Dict							       =   load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
							lowercase_ : Optional[int]							       =   init_image.resize((5_12, 5_12)   )

							lowercase_ : Dict							       =   load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/hint_image_cat.png'''   )
							lowercase_ : Optional[int]							       =   torch.from_numpy(np.array(A   )   ).float() / 255.0
							lowercase_ : Tuple							       =   hint.permute(2    ,						0    ,						1   ).unsqueeze(0   )

							lowercase_ : Optional[Any]							       =   '''A robot, 4k photo'''

							lowercase_ : Tuple							       =   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-prior'''    ,						torch_dtype=torch.floataa   )
							pipe_prior.to(A   )

							lowercase_ : Dict							       =   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''    ,						torch_dtype=torch.floataa   )
							lowercase_ : int							       =   pipeline.to(A   )

							pipeline.set_progress_bar_config(disable=A   )

							lowercase_ : Tuple							       =   torch.Generator(device='''cpu'''   ).manual_seed(0   )

							lowercase_       ,					lowercase_ : int							       =   pipe_prior(
							    A    ,						image=A    ,						strength=0.85    ,						generator=A    ,						negative_prompt=''''''    ,						).to_tuple()

							lowercase_ : str							       =   pipeline(
							    image=A    ,						image_embeds=A    ,						negative_image_embeds=A    ,						hint=A    ,						generator=A    ,						num_inference_steps=1_00    ,						height=5_12    ,						width=5_12    ,						strength=0.5    ,						output_type='''np'''    ,						)

							lowercase_ : Optional[Any]							       =   output.images[0]

							assert image.shape == (5_12, 5_12, 3)

							assert_mean_pixel_difference(A    ,						A   )



