
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											=    [
    # 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_	:     Optional[Any]				)			->						Dict:
       for pegasus_name, hf_name in PATTERNS:
              SCREAMING_SNAKE_CASE_							:				List[str]		=k.replace(UpperCAmelCase_			,							UpperCAmelCase_				)
       return k
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     dict			,							UpperCAmelCase_	:     dict				)			->						PegasusForConditionalGeneration:
       SCREAMING_SNAKE_CASE_							:				List[str]		=DEFAULTS.copy()
       cfg_kwargs.update(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=PegasusConfig(**UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				int		=PegasusForConditionalGeneration(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch_model.model.state_dict()
       SCREAMING_SNAKE_CASE_							:				List[str]		={}
       for k, v in tf_weights.items():
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=rename_state_dict_key(UpperCAmelCase_				)
              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:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=v.T
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.tensor(UpperCAmelCase_			,							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
       SCREAMING_SNAKE_CASE_							:				Dict		=torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]				)
       SCREAMING_SNAKE_CASE_							:				Any		=mapping['''shared.weight''']
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=mapping['''shared.weight''']
       SCREAMING_SNAKE_CASE_							:				Tuple		={k: torch.zeros_like(UpperCAmelCase_				) for k, v in sd.items() if k.endswith('''bias'''				) and k not in mapping}
       mapping.update(**UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=torch_model.model.load_state_dict(UpperCAmelCase_			,							strict=UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				str		=[
           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_	:     Union[str, Any]="./ckpt/aeslc/model.ckpt-32000"				)			->						Dict:
       SCREAMING_SNAKE_CASE_							:				Any		=tf.train.list_variables(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		={}
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=['''Adafactor''', '''global_step''']
       for name, shape in tqdm(UpperCAmelCase_			,							desc='''converting tf checkpoint to dict'''				):
              SCREAMING_SNAKE_CASE_							:				str		=any(pat in name for pat in ignore_name				)
              if skip_key:
                     continue
              SCREAMING_SNAKE_CASE_							:				Dict		=tf.train.load_variable(UpperCAmelCase_			,							UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Dict		=array
       return tf_weights
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     str				)			->						Union[str, Any]:
       # save tokenizer first
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=Path(UpperCAmelCase_				).parent.name
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=task_specific_params[f'summarization_{dataset}']['''max_position_embeddings''']
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''			,							model_max_length=UpperCAmelCase_				)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(UpperCAmelCase_				)

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


if __name__ == "__main__":
 _lowercase											=    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											=    parser.parse_args()
 if args.save_dir is None:
  _lowercase											=    Path(args.tf_ckpt_path).parent.name
  _lowercase											=    os.path.join("""pegasus""", dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DDIMParallelScheduler,)
       __lowerCamelCase				       =						(("eta", 0.0), ("num_inference_steps", 5_0))


       def 	_snake_case				(    self						,       **__A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		={
                  '''num_train_timesteps''': 1_000,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self						,       **__A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				str		=self.get_scheduler_config(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=10, 0.0

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_sample_deter

              scheduler.set_timesteps(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(__A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.step(__A						,       __A						,       __A						,       __A				).prev_sample

              return sample


       def 	_snake_case				(    self				)						->    Optional[Any]:
              for timesteps in [100, 500, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config(steps_offset=1				)
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A				)
              scheduler.set_timesteps(5				)
              assert torch.equal(scheduler.timesteps						,       torch.LongTensor([801, 601, 401, 201, 1]				)				)


       def 	_snake_case				(    self				)						->    Dict:
              for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]						,       [0.002, 0.02, 0.2, 2]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    str:
              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=__A				)


       def 	_snake_case				(    self				)						->    int:
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              self.check_over_configs(thresholding=__A				)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=__A						,       prediction_type=__A						,       sample_max_value=__A						,       )


       def 	_snake_case				(    self				)						->    Dict:
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=__A				)


       def 	_snake_case				(    self				)						->    Dict:
              for t, num_inference_steps in zip([1, 10, 50]						,       [10, 50, 500]				):
                     self.check_over_forward(time_step=__A						,       num_inference_steps=__A				)


       def 	_snake_case				(    self				)						->    int:
              for t, eta in zip([1, 10, 49]						,       [0.0, 0.5, 1.0]				):
                     self.check_over_forward(time_step=__A						,       eta=__A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(420						,       400				) - 0.14_771				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(980						,       960				) - 0.32_460				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(0						,       0				) - 0.0				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(487						,       486				) - 0.00_979				)				) < 1e-5
              assert torch.sum(torch.abs(scheduler._get_variance(999						,       998				) - 0.02				)				) < 1e-5


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				str		=scheduler_class(**__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=10, 0.0
              scheduler.set_timesteps(__A				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				str		=self.dummy_sample_deter
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter + 0.1
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_sample_deter - 0.1

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=samplea.shape[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.stack([samplea, samplea, samplea]						,       dim=0				)
              SCREAMING_SNAKE_CASE_							:				Any		=torch.arange(__A				)[0:3, None].repeat(1						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=model(samples.flatten(0						,       1				)						,       timesteps.flatten(0						,       1				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=scheduler.batch_step_no_noise(__A						,       timesteps.flatten(0						,       1				)						,       samples.flatten(0						,       1				)						,       __A				)

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
              assert abs(result_mean.item() - 0.4_982				) < 1e-3


       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 172.0_067				) < 1e-2
              assert abs(result_mean.item() - 0.223_967				) < 1e-3


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.full_loop(prediction_type='''v_prediction'''				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				str		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 52.5_302				) < 1e-2
              assert abs(result_mean.item() - 0.0_684				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.8_295				) < 1e-2
              assert abs(result_mean.item() - 0.1_951				) < 1e-3





       def 	_snake_case				(    self				)						->    Optional[Any]:
              # We specify different beta, so that the first alpha is 0.99
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.full_loop(set_alpha_to_one=__A						,       beta_start=0.01				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.mean(torch.abs(__A				)				)

              assert abs(result_sum.item() - 149.0_784				) < 1e-2
              assert abs(result_mean.item() - 0.1_941				) < 1e-3
