






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class     _lowerCAmelCase(	UpperCAmelCase_							,     UpperCAmelCase_							,     UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Optional[int]									=[r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']





       @register_to_config
       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = 5_0_2_5_7  ,      _lowerCamelCase = 1_0_2_4  ,      _lowerCamelCase = 7_6_8  ,      _lowerCamelCase = 1_2  ,      _lowerCamelCase = 1_2  ,      _lowerCamelCase = None  ,      _lowerCamelCase = "gelu_new"  ,      _lowerCamelCase = 0.1  ,      _lowerCamelCase = 0.1  ,      _lowerCamelCase = 0.1  ,      _lowerCamelCase = 1e-5  ,      _lowerCamelCase = 0.0_2  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = False  ,      _lowerCamelCase = False  ,      ):
         super().__init__()

         UpperCamelCase_: str   				=    prefix_length

         if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
           raise ValueError(
               f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
               f''' `n_embd`: {n_embd} are not equal.'''							)

         UpperCamelCase_: Any   				=    prefix_inner_dim
         UpperCamelCase_: Dict   				=    prefix_hidden_dim

         UpperCamelCase_: int   				=    (
             nn.Linear(self.prefix_inner_dim  ,      self.prefix_hidden_dim							)
             if self.prefix_hidden_dim is not None
             else nn.Identity()
         )
         UpperCamelCase_: Tuple   				=    (
             nn.Linear(self.prefix_hidden_dim  ,      _lowerCamelCase							) if self.prefix_hidden_dim is not None else nn.Identity()
         )

         UpperCamelCase_: Union[str, Any]   				=    GPTaConfig(
             vocab_size=_lowerCamelCase  ,      n_positions=_lowerCamelCase  ,      n_embd=_lowerCamelCase  ,      n_layer=_lowerCamelCase  ,      n_head=_lowerCamelCase  ,      n_inner=_lowerCamelCase  ,      activation_function=_lowerCamelCase  ,      resid_pdrop=_lowerCamelCase  ,      embd_pdrop=_lowerCamelCase  ,      attn_pdrop=_lowerCamelCase  ,      layer_norm_epsilon=_lowerCamelCase  ,      initializer_range=_lowerCamelCase  ,      scale_attn_weights=_lowerCamelCase  ,      use_cache=_lowerCamelCase  ,      scale_attn_by_inverse_layer_idx=_lowerCamelCase  ,      reorder_and_upcast_attn=_lowerCamelCase  ,      )
         UpperCamelCase_: Optional[Any]   				=    GPTaLMHeadModel(_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      ):
         UpperCamelCase_: Any   				=    self.transformer.transformer.wte(_lowerCamelCase							)
         UpperCamelCase_: Any   				=    self.encode_prefix(_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    self.decode_prefix(_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    torch.cat((prefix_embeds, embedding_text)  ,      dim=1							)

         if labels is not None:
           UpperCamelCase_: Any   				=    self.get_dummy_token(input_ids.shape[0]  ,      input_ids.device							)
           UpperCamelCase_: str   				=    torch.cat((dummy_token, input_ids)  ,      dim=1							)
         UpperCamelCase_: int   				=    self.transformer(inputs_embeds=_lowerCamelCase  ,      labels=_lowerCamelCase  ,      attention_mask=_lowerCamelCase							)
         if self.prefix_hidden_dim is not None:
           return out, hidden
         else:
           return out





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         return torch.zeros(_lowerCamelCase  ,      self.prefix_length  ,      dtype=torch.intaa  ,      device=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.encode_prefix(_lowerCamelCase							)





       @torch.no_grad()
       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    torch.split(_lowerCamelCase  ,      1  ,      dim=0							)
         UpperCamelCase_: Optional[Any]   				=    []
         UpperCamelCase_: Optional[int]   				=    []
         for feature in features:
           UpperCamelCase_: Tuple   				=    self.decode_prefix(feature.to(_lowerCamelCase							)							)  # back to the clip feature
           # Only support beam search for now
           UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    self.generate_beam(
               input_embeds=_lowerCamelCase  ,      device=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase							)
           generated_tokens.append(output_tokens[0]							)
           generated_seq_lengths.append(seq_lengths[0]							)
         UpperCamelCase_: Any   				=    torch.stack(_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    torch.stack(_lowerCamelCase							)
         return generated_tokens, generated_seq_lengths





       @torch.no_grad()
       def       _a   ( self  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      _lowerCamelCase = 5  ,      _lowerCamelCase = 6_7  ,      _lowerCamelCase = 1.0  ,      _lowerCamelCase = None  ,      ):
         UpperCamelCase_: Dict   				=    eos_token_id
         UpperCamelCase_: Tuple   				=    None
         UpperCamelCase_: Optional[Any]   				=    None
         UpperCamelCase_: List[Any]   				=    torch.ones(_lowerCamelCase  ,      device=_lowerCamelCase  ,      dtype=torch.int							)
         UpperCamelCase_: int   				=    torch.zeros(_lowerCamelCase  ,      device=_lowerCamelCase  ,      dtype=torch.bool							)

         if input_embeds is not None:
           UpperCamelCase_: List[str]   				=    input_embeds
         else:
           UpperCamelCase_: str   				=    self.transformer.transformer.wte(_lowerCamelCase							)

         for i in range(_lowerCamelCase							):
           UpperCamelCase_: Dict   				=    self.transformer(inputs_embeds=_lowerCamelCase							)
           UpperCamelCase_: Any   				=    outputs.logits
           UpperCamelCase_: Optional[Any]   				=    logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
           UpperCamelCase_: str   				=    logits.softmax(-1							).log()

           if scores is None:
             UpperCamelCase_				,UpperCamelCase_: List[Any]   				=    logits.topk(_lowerCamelCase  ,      -1							)
             UpperCamelCase_: Union[str, Any]   				=    generated.expand(_lowerCamelCase  ,      *generated.shape[1:]							)
             UpperCamelCase_				,UpperCamelCase_: Union[str, Any]   				=    next_tokens.permute(1  ,      0							), scores.squeeze(0							)
             if tokens is None:
               UpperCamelCase_: Optional[Any]   				=    next_tokens
             else:
               UpperCamelCase_: Tuple   				=    tokens.expand(_lowerCamelCase  ,      *tokens.shape[1:]							)
               UpperCamelCase_: Optional[int]   				=    torch.cat((tokens, next_tokens)  ,      dim=1							)
           else:
             UpperCamelCase_: Tuple   				=    -float(np.inf							)
             UpperCamelCase_: Tuple   				=    0
             UpperCamelCase_: Optional[Any]   				=    scores[:, None] + logits
             seq_lengths[~is_stopped] += 1
             UpperCamelCase_: Optional[int]   				=    scores_sum / seq_lengths[:, None]
             UpperCamelCase_				,UpperCamelCase_: str   				=    scores_sum_average.view(-1							).topk(_lowerCamelCase  ,      -1							)
             UpperCamelCase_: List[str]   				=    next_tokens // scores_sum.shape[1]
             UpperCamelCase_: Union[str, Any]   				=    seq_lengths[next_tokens_source]
             UpperCamelCase_: int   				=    next_tokens % scores_sum.shape[1]
             UpperCamelCase_: Optional[Any]   				=    next_tokens.unsqueeze(1							)
             UpperCamelCase_: Dict   				=    tokens[next_tokens_source]
             UpperCamelCase_: List[Any]   				=    torch.cat((tokens, next_tokens)  ,      dim=1							)
             UpperCamelCase_: List[Any]   				=    generated[next_tokens_source]
             UpperCamelCase_: Union[str, Any]   				=    scores_sum_average * seq_lengths
             UpperCamelCase_: List[Any]   				=    is_stopped[next_tokens_source]

           UpperCamelCase_: Tuple   				=    self.transformer.transformer.wte(next_tokens.squeeze()							).view(generated.shape[0]  ,      1  ,      -1							)
           UpperCamelCase_: int   				=    torch.cat((generated, next_token_embed)  ,      dim=1							)
           UpperCamelCase_: Dict   				=    is_stopped + next_tokens.eq(_lowerCamelCase							).squeeze()
           if is_stopped.all():
             break

         UpperCamelCase_: Optional[Any]   				=    scores / seq_lengths
         UpperCamelCase_: str   				=    scores.argsort(descending=_lowerCamelCase							)
         # tokens tensors are already padded to max_seq_length
         UpperCamelCase_: Dict   				=    [tokens[i] for i in order]
         UpperCamelCase_: Optional[int]   				=    torch.stack(_lowerCamelCase  ,      dim=0							)
         UpperCamelCase_: Optional[Any]   				=    torch.tensor([seq_lengths[i] for i in order]  ,      dtype=seq_lengths.dtype							)
         return output_texts, seq_lengths







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   snake_case (UpperCAmelCase__  )	->    str:
  for pegasus_name, hf_name in PATTERNS:
    UpperCamelCase_: List[str]   				=    k.replace(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return k

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  # save tokenizer first
  UpperCamelCase_: Any   				=    Path(UpperCAmelCase__  ).parent.name
  UpperCamelCase_: Tuple   				=    task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
  UpperCamelCase_: Optional[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
  UpperCamelCase_: Optional[Any]   				=    get_tf_weights_as_numpy(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    task_specific_params[F'''summarization_{dataset}''']
  if dataset == "large":
    UpperCamelCase_: Union[str, Any]   				=    task_specific_params
  UpperCamelCase_: Tuple   				=    convert_pegasus(UpperCAmelCase__     ,   UpperCAmelCase__  )
  torch_model.save_pretrained(UpperCAmelCase__  )
  UpperCamelCase_: 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__":
   A_		:		str		     =			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_		:		Union[str, Any]		     =			Path(args.tf_ckpt_path).parent.name
      A_		:		Optional[Any]		     =			os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)