



'''simple docstring'''





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__lowerCamelCase         =			[
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
__lowerCamelCase         =			[
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

__lowerCamelCase         =			(
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

__lowerCamelCase         =			(
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

__lowerCamelCase         =			[
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]







def    _snake_case (       __snake_case					,  __snake_case    )     ->     List[Any]:






      '''simple docstring'''







      for tf_name, hf_name in patterns:
            UpperCAmelCase_  :	List[str]				      =		k.replace(__snake_case					,  __snake_case    )
      return k







def    _snake_case (       __snake_case					,  __snake_case    )     ->     BigBirdPegasusForConditionalGeneration:






      '''simple docstring'''







      UpperCAmelCase_  :	int				      =		BigBirdPegasusConfig(**__snake_case    )
      UpperCAmelCase_  :	str				      =		BigBirdPegasusForConditionalGeneration(__snake_case    )
      UpperCAmelCase_  :	Optional[int]				      =		torch_model.state_dict()
      UpperCAmelCase_  :	List[str]				      =		{}

      # separating decoder weights
      UpperCAmelCase_  :	List[str]				      =		{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"    )}
      UpperCAmelCase_  :	Tuple				      =		{k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"    )}

      for k, v in tqdm(decoder_weights.items()					,  "tf -> hf conversion"    ):
            UpperCAmelCase_  :	List[Any]				      =		[k.endswith(__snake_case    ) for ending in KEYS_TO_IGNORE]
            if any(__snake_case    ):
                  continue
            UpperCAmelCase_  :	Optional[Any]				      =		DECODER_PATTERNS
            UpperCAmelCase_  :	Any				      =		rename_state_dict_key(__snake_case					,  __snake_case    )
            if new_k not in state_dict:
                  raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""    )
            if any(True if i in k else False for i in ["dense", "query", "key", "value"]    ):
                  UpperCAmelCase_  :	int				      =		v.T
            UpperCAmelCase_  :	Dict				      =		torch.from_numpy(__snake_case    )
            assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

      for k, v in tqdm(remaining_weights.items()					,  "tf -> hf conversion"    ):
            UpperCAmelCase_  :	Optional[Any]				      =		[k.endswith(__snake_case    ) for ending in KEYS_TO_IGNORE]
            if any(__snake_case    ):
                  continue
            UpperCAmelCase_  :	Tuple				      =		REMAINING_PATTERNS
            UpperCAmelCase_  :	int				      =		rename_state_dict_key(__snake_case					,  __snake_case    )
            if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
                  raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""    )
            if any(True if i in k else False for i in ["dense", "query", "key", "value"]    ):
                  UpperCAmelCase_  :	Union[str, Any]				      =		v.T
            UpperCAmelCase_  :	List[str]				      =		torch.from_numpy(__snake_case    )
            if k != "pegasus/embeddings/position_embeddings":
                  assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

      UpperCAmelCase_  :	List[Any]				      =		mapping["model.embed_positions.weight"]
      UpperCAmelCase_  :	Dict				      =		mapping.pop("model.embed_positions.weight"    )
      UpperCAmelCase_  :	Dict				      =		torch_model.load_state_dict(__snake_case					,  strict=__snake_case    )
      UpperCAmelCase_  :	List[str]				      =		[
          k
          for k in missing
          if k
          not in [
              "final_logits_bias",
              "model.encoder.embed_tokens.weight",
              "model.decoder.embed_tokens.weight",
              "lm_head.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 (       __snake_case    )     ->     Dict:






      '''simple docstring'''







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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     str:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		get_tf_weights_as_numpy(__snake_case    )
      UpperCAmelCase_  :	Any				      =		convert_bigbird_pegasus(__snake_case					,  __snake_case    )
      torch_model.save_pretrained(__snake_case    )


if __name__ == "__main__":
   __lowerCamelCase         =			argparse.ArgumentParser()
   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.''')
   __lowerCamelCase         =			parser.parse_args()
   __lowerCamelCase         =			{}
   convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput


@dataclass
class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				1



       @register_to_config
       def __init__(     self     ,lowercase = 2000     ,lowercase = 0.15     ,lowercase = 0.01     ,lowercase = 1348.0     ,lowercase = 1E-5     ,lowercase = 1     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		sigma_max

             # setable values
             UpperCAmelCase_  :	Optional[int]				      =		None

             self.set_sigmas(lowercase     ,lowercase     ,lowercase     ,lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             return sample



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

             UpperCAmelCase_  :	List[Any]				      =		torch.linspace(1     ,lowercase     ,lowercase     ,device=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		sigma_min if sigma_min is not None else self.config.sigma_min
             UpperCAmelCase_  :	int				      =		sigma_max if sigma_max is not None else self.config.sigma_max
             UpperCAmelCase_  :	Union[str, Any]				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
             if self.timesteps is None:
                   self.set_timesteps(lowercase     ,lowercase)

             UpperCAmelCase_  :	Union[str, Any]				      =		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
             UpperCAmelCase_  :	Optional[int]				      =		torch.exp(torch.linspace(math.log(lowercase)     ,math.log(lowercase)     ,lowercase))
             UpperCAmelCase_  :	Any				      =		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             return torch.where(
                 timesteps == 0     ,torch.zeros_like(t.to(timesteps.device))     ,self.discrete_sigmas[timesteps - 1].to(timesteps.device)     ,)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = True     ,):

             """simple docstring"""





             if self.timesteps is None:
                   raise ValueError(
                       "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

             UpperCAmelCase_  :	Optional[int]				      =		timestep * torch.ones(
                 sample.shape[0]     ,device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
             UpperCAmelCase_  :	Tuple				      =		(timestep * (len(self.timesteps) - 1)).long()

             # mps requires indices to be in the same device, so we use cpu as is the default with cuda
             UpperCAmelCase_  :	Optional[int]				      =		timesteps.to(self.discrete_sigmas.device)

             UpperCAmelCase_  :	Optional[Any]				      =		self.discrete_sigmas[timesteps].to(sample.device)
             UpperCAmelCase_  :	Optional[Any]				      =		self.get_adjacent_sigma(lowercase     ,lowercase).to(sample.device)
             UpperCAmelCase_  :	Any				      =		torch.zeros_like(lowercase)
             UpperCAmelCase_  :	Dict				      =		(sigma**2 - adjacent_sigma**2) ** 0.5

             # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
             # also equation 47 shows the analog from SDE models to ancestral sampling methods
             UpperCAmelCase_  :	Dict				      =		diffusion.flatten()
             while len(diffusion.shape) < len(sample.shape):
                   UpperCAmelCase_  :	List[str]				      =		diffusion.unsqueeze(-1)
             UpperCAmelCase_  :	List[Any]				      =		drift - diffusion**2 * model_output

             #  equation 6: sample noise for the diffusion term of
             UpperCAmelCase_  :	Union[str, Any]				      =		randn_tensor(
                 sample.shape     ,layout=sample.layout     ,generator=lowercase     ,device=sample.device     ,dtype=sample.dtype)
             UpperCAmelCase_  :	Any				      =		sample - drift  # subtract because `dt` is a small negative timestep
             # TODO is the variable diffusion the correct scaling term for the noise?
             UpperCAmelCase_  :	Tuple				      =		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

             if not return_dict:
                   return (prev_sample, prev_sample_mean)

             return SdeVeOutput(prev_sample=lowercase     ,prev_sample_mean=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = True     ,):

             """simple docstring"""





             if self.timesteps is None:
                   raise ValueError(
                       "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

             # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
             # sample noise for correction
             UpperCAmelCase_  :	int				      =		randn_tensor(sample.shape     ,layout=sample.layout     ,generator=lowercase).to(sample.device)

             # compute step size from the model_output, the noise, and the snr
             UpperCAmelCase_  :	Union[str, Any]				      =		torch.norm(model_output.reshape(model_output.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	Optional[Any]				      =		torch.norm(noise.reshape(noise.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	List[Any]				      =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
             UpperCAmelCase_  :	Optional[Any]				      =		step_size * torch.ones(sample.shape[0]).to(sample.device)
             # self.repeat_scalar(step_size, sample.shape[0])

             # compute corrected sample: model_output term and noise term
             UpperCAmelCase_  :	Any				      =		step_size.flatten()
             while len(step_size.shape) < len(sample.shape):
                   UpperCAmelCase_  :	Tuple				      =		step_size.unsqueeze(-1)
             UpperCAmelCase_  :	Dict				      =		sample + step_size * model_output
             UpperCAmelCase_  :	int				      =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

             if not return_dict:
                   return (prev_sample,)

             return SchedulerOutput(prev_sample=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		timesteps.to(original_samples.device)
             UpperCAmelCase_  :	List[str]				      =		self.discrete_sigmas.to(original_samples.device)[timesteps]
             UpperCAmelCase_  :	Tuple				      =		(
                 noise * sigmas[:, None, None, None]
                 if noise is not None
                 else torch.randn_like(lowercase) * sigmas[:, None, None, None]
             )
             UpperCAmelCase_  :	Tuple				      =		noise + original_samples
             return noisy_samples



       def __len__(     self):

             """simple docstring"""





             return self.config.num_train_timesteps
