


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


UpperCAmelCase_   :				Optional[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  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Union[str, Any]:

       """simple docstring"""

       for pegasus_name, hf_name in PATTERNS:
              a_      :	Union[str, Any]							=						k.replace(__A						,					__A       )
       return k







def  SCREAMING_SNAKE_CASE_		(    __A				:    dict						,					__A				:    dict       )			->	PegasusForConditionalGeneration:

       """simple docstring"""

       a_      :	int							=						DEFAULTS.copy()
       cfg_kwargs.update(__A       )
       a_      :	List[str]							=						PegasusConfig(**__A       )
       a_      :	List[Any]							=						PegasusForConditionalGeneration(__A       )
       a_      :	List[str]							=						torch_model.model.state_dict()
       a_      :	Optional[int]							=						{}
       for k, v in tf_weights.items():
              a_      :	str							=						rename_state_dict_key(__A       )
              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:
                     a_      :	Union[str, Any]							=						v.T
              a_      :	List[str]							=						torch.tensor(__A						,					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
       a_      :	int							=						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]       )
       a_      :	Optional[Any]							=						mapping['shared.weight']
       a_      :	Any							=						mapping['shared.weight']
       a_      :	Optional[int]							=						{k: torch.zeros_like(__A       ) for k, v in sd.items() if k.endswith('bias'       ) and k not in mapping}
       mapping.update(**__A       )
       a_							,	a_      :	Optional[Any]							=						torch_model.model.load_state_dict(__A						,					strict=__A       )
       a_      :	Optional[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_		(    __A				:    Dict="./ckpt/aeslc/model.ckpt-32000"       )			->	Dict:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Any							=						Path(__A       ).parent.name
       a_      :	Dict							=						task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
       a_      :	Union[str, Any]							=						PegasusTokenizer.from_pretrained('sshleifer/pegasus'						,					model_max_length=__A       )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(__A       )

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


if __name__ == "__main__":
   UpperCAmelCase_   :				Dict						      = 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.')
   UpperCAmelCase_   :				str						      = parser.parse_args()
   if args.save_dir is None:
      UpperCAmelCase_   :				str						      = Path(args.tf_ckpt_path).parent.name
      UpperCAmelCase_   :				Optional[int]						      = 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 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[Any]       =		(DDIMParallelScheduler,)
      snake_case__       :  Union[str, Any]       =		(('''eta''', 0.0), ('''num_inference_steps''', 50))
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             a_      :	Union[str, Any]							=						{
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'clip_sample': True,
             }

             config.update(**SCREAMING_SNAKE_CASE__  )
             return config
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : Tuple  )	->    int:
             a_      :	Any							=						self.scheduler_classes[0]
             a_      :	str							=						self.get_scheduler_config(**SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	Optional[Any]							=						1_0, 0.0

             a_      :	str							=						self.dummy_model()
             a_      :	Dict							=						self.dummy_sample_deter

             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             for t in scheduler.timesteps:
                    a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[Any]							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ).prev_sample

             return sample
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[Any]:
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config(steps_offset=1  )
             a_      :	str							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )
             scheduler.set_timesteps(5  )
             assert torch.equal(scheduler.timesteps  ,							torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[str]:
             for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,							[0.002, 0.02, 0.2, 2]  ):
                    self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__  ,							beta_end=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Tuple:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Union[str, Any]:
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    int:
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__  )
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=SCREAMING_SNAKE_CASE__  ,							prediction_type=SCREAMING_SNAKE_CASE__  ,							sample_max_value=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             for t in [1, 1_0, 4_9]:
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for t, num_inference_steps in zip([1, 1_0, 5_0]  ,							[1_0, 5_0, 5_0_0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[Any]:
             for t, eta in zip([1, 1_0, 4_9]  ,							[0.0, 0.5, 1.0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							eta=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Union[str, Any]							=						self.scheduler_classes[0]
             a_      :	List[Any]							=						self.get_scheduler_config()
             a_      :	Dict							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_2_0  ,							4_0_0  ) - 0.14771  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_8_0  ,							9_6_0  ) - 0.32460  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0  ) - 0.0  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ,							4_8_6  ) - 0.00979  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ,							9_9_8  ) - 0.02  )  ) < 1E-5
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Dict:
             a_      :	Dict							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config()
             a_      :	List[str]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	int							=						1_0, 0.0
             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						self.dummy_model()
             a_      :	Optional[int]							=						self.dummy_sample_deter
             a_      :	Optional[Any]							=						self.dummy_sample_deter + 0.1
             a_      :	Any							=						self.dummy_sample_deter - 0.1

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

             a_      :	int							=						model(samples.flatten(0  ,							1  )  ,							timesteps.flatten(0  ,							1  )  )
             a_      :	Optional[Any]							=						scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__  ,							timesteps.flatten(0  ,							1  )  ,							samples.flatten(0  ,							1  )  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Optional[int]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 1147.7904  ) < 1E-2
             assert abs(result_mean.item() - 0.4982  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             a_      :	Tuple							=						self.full_loop()

             a_      :	List[str]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 172.0067  ) < 1E-2
             assert abs(result_mean.item() - 0.223967  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[str]:
             a_      :	Optional[int]							=						self.full_loop(prediction_type='v_prediction'  )

             a_      :	str							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Union[str, Any]							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 52.5302  ) < 1E-2
             assert abs(result_mean.item() - 0.0684  ) < 1E-3
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Dict							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	int							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.8295  ) < 1E-2
             assert abs(result_mean.item() - 0.1951  ) < 1E-3

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Union[str, Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Tuple							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	Dict							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

             assert abs(result_sum.item() - 149.0784  ) < 1E-2
             assert abs(result_mean.item() - 0.1941  ) < 1E-3


