






'''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			:    Tuple					  =[
    # 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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:






							for pegasus_name, hf_name in PATTERNS:
														lowerCamelCase__      :			Optional[Any] 						= k.replace(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE   )
							return k





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							lowerCamelCase__      :			Union[str, Any] 						= DEFAULTS.copy()
							cfg_kwargs.update(__SCREAMING_SNAKE_CASE   )
							lowerCamelCase__      :			Union[str, Any] 						= PegasusConfig(**__SCREAMING_SNAKE_CASE   )
							lowerCamelCase__      :			Any 						= PegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE   )
							lowerCamelCase__      :			Dict 						= torch_model.model.state_dict()
							lowerCamelCase__      :			Union[str, Any] 						= {}
							for k, v in tf_weights.items():
														lowerCamelCase__      :			List[str] 						= rename_state_dict_key(__SCREAMING_SNAKE_CASE   )
														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:
																					lowerCamelCase__      :			Dict 						= v.T
														lowerCamelCase__      :			Optional[Any] 						= torch.tensor(__SCREAMING_SNAKE_CASE       ,    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
							lowerCamelCase__      :			Optional[int] 						= torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]   )
							lowerCamelCase__      :			Union[str, Any] 						= mapping["shared.weight"]
							lowerCamelCase__      :			Optional[int] 						= mapping["shared.weight"]
							lowerCamelCase__      :			Optional[int] 						= {k: torch.zeros_like(__SCREAMING_SNAKE_CASE   ) for k, v in sd.items() if k.endswith("""bias"""   ) and k not in mapping}
							mapping.update(**__SCREAMING_SNAKE_CASE   )
							lowerCamelCase__      :			Optional[int] 						= torch_model.model.load_state_dict(__SCREAMING_SNAKE_CASE       ,    strict=__SCREAMING_SNAKE_CASE   )
							lowerCamelCase__      :			Union[str, Any] 						= [
							    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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase="./ckpt/aeslc/model.ckpt-32000"   )   -> str:
							lowerCamelCase__      :			Any 						= tf.train.list_variables(__SCREAMING_SNAKE_CASE   )
							lowerCamelCase__      :			Tuple 						= {}
							lowerCamelCase__      :			Optional[int] 						= ["Adafactor", "global_step"]
							for name, shape in tqdm(__SCREAMING_SNAKE_CASE       ,    desc="""converting tf checkpoint to dict"""   ):
														lowerCamelCase__      :			Optional[int] 						= any(pat in name for pat in ignore_name   )
														if skip_key:
																					continue
														lowerCamelCase__      :			Dict 						= tf.train.load_variable(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE   )
														lowerCamelCase__      :			Optional[Any] 						= array
							return tf_weights





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> List[Any]:
							lowerCamelCase__      :			Optional[int] 						= Path(__SCREAMING_SNAKE_CASE   ).parent.name
							lowerCamelCase__      :			Optional[Any] 						= task_specific_params[f'''summarization_{dataset}''']["max_position_embeddings"]
							lowerCamelCase__      :			int 						= PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""       ,    model_max_length=__SCREAMING_SNAKE_CASE   )
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(__SCREAMING_SNAKE_CASE   )

							# convert model
							lowerCamelCase__      :			Tuple 						= get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE   )
							lowerCamelCase__      :			List[Any] 						= task_specific_params[f'''summarization_{dataset}''']
							if dataset == "large":
														lowerCamelCase__      :			Optional[int] 						= task_specific_params
							lowerCamelCase__      :			Optional[int] 						= convert_pegasus(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE   )
							torch_model.save_pretrained(__SCREAMING_SNAKE_CASE   )
							lowerCamelCase__      :			int 						= torch_model.state_dict()
							sd.pop("""model.decoder.embed_positions.weight"""   )
							sd.pop("""model.encoder.embed_positions.weight"""   )
							torch.save(__SCREAMING_SNAKE_CASE       ,    Path(__SCREAMING_SNAKE_CASE   ) / """pytorch_model.bin"""   )


if __name__ == "__main__":
			_A			:    Optional[int]					  =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			:    Optional[Any]					  =parser.parse_args()
			if args.save_dir is None:
						_A			:    Tuple					  =Path(args.tf_ckpt_path).parent.name
						_A			:    Any					  =os.path.join('''pegasus''', dataset)
			convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









'''simple docstring'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

