

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__UpperCAmelCase								=			(
    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
)

__UpperCAmelCase								=			(
    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
)

__UpperCAmelCase								=			[
    '''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_				(__A					:						Optional[int] ,       __A					:						List[Any]					)      ->     Optional[Any]:
   for tf_name, hf_name in patterns:
      __lowerCAmelCase  :      List[str]         =						k.replace(_a ,       _a					)
   return k





def      snake_case_				(__A					:						dict ,       __A					:						dict					)      ->     Optional[Any]:
   __lowerCAmelCase  :      Tuple         =						BigBirdPegasusConfig(**_a					)
   __lowerCAmelCase  :      Any         =						BigBirdPegasusForConditionalGeneration(_a					)
   __lowerCAmelCase  :      int         =						torch_model.state_dict()
   __lowerCAmelCase  :      Any         =						{}

   # separating decoder weights
   __lowerCAmelCase  :      List[str]         =						{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""					)}
   __lowerCAmelCase  :      Any         =						{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"""					):
      __lowerCAmelCase  :      str         =						[k.endswith(_a					) for ending in KEYS_TO_IGNORE]
      if any(_a					):
         continue
      __lowerCAmelCase  :      Any         =						DECODER_PATTERNS
      __lowerCAmelCase  :      Optional[Any]         =						rename_state_dict_key(_a ,       _a					)
      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"""]					):
         __lowerCAmelCase  :      str         =						v.T
      __lowerCAmelCase  :      Optional[int]         =						torch.from_numpy(_a					)
      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"""					):
      __lowerCAmelCase  :      Optional[int]         =						[k.endswith(_a					) for ending in KEYS_TO_IGNORE]
      if any(_a					):
         continue
      __lowerCAmelCase  :      List[Any]         =						REMAINING_PATTERNS
      __lowerCAmelCase  :      str         =						rename_state_dict_key(_a ,       _a					)
      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"""]					):
         __lowerCAmelCase  :      Dict         =						v.T
      __lowerCAmelCase  :      Union[str, Any]         =						torch.from_numpy(_a					)
      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}'''

   __lowerCAmelCase  :      Optional[int]         =						mapping["model.embed_positions.weight"]
   __lowerCAmelCase  :      str         =						mapping.pop("""model.embed_positions.weight"""					)
   __lowerCAmelCase  :      Optional[int]         =						torch_model.load_state_dict(_a ,       strict=_a					)
   __lowerCAmelCase  :      Union[str, Any]         =						[
       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_				(__A					:						Optional[int]					)      ->     List[str]:
   __lowerCAmelCase  :      Any         =						tf.train.list_variables(_a					)
   __lowerCAmelCase  :      int         =						{}
   __lowerCAmelCase  :      Dict         =						["global_step"]
   for name, shape in tqdm(_a ,       desc="""converting tf checkpoint to dict"""					):
      __lowerCAmelCase  :      str         =						any(pat in name for pat in ignore_name					)
      if skip_key:
         continue
      __lowerCAmelCase  :      str         =						tf.train.load_variable(_a ,       _a					)
      __lowerCAmelCase  :      Dict         =						array
   return tf_weights





def      snake_case_				(__A					:						str ,       __A					:						str ,       __A					:						dict					)      ->     Union[str, Any]:
   __lowerCAmelCase  :      Optional[Any]         =						get_tf_weights_as_numpy(_a					)
   __lowerCAmelCase  :      List[str]         =						convert_bigbird_pegasus(_a ,       _a					)
   torch_model.save_pretrained(_a					)


if __name__ == "__main__":
   __UpperCAmelCase								=			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.""")
   __UpperCAmelCase								=			parser.parse_args()
   __UpperCAmelCase								=			{}
   convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)





import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				()      ->     str:
   # Get the sagemaker specific mp parameters from smp_options variable.
   __lowerCAmelCase  :      List[Any]    =						os.getenv("""SM_HP_MP_PARAMETERS""" ,       """{}"""					)
   try:
      # Parse it and check the field "partitions" is included, it is required for model parallel.
      __lowerCAmelCase  :      Any    =						json.loads(__A					)
      if "partitions" not in smp_options:
         return False
   except json.JSONDecodeError:
      return False

   # Get the sagemaker specific framework parameters from mpi_options variable.
   __lowerCAmelCase  :      Any    =						os.getenv("""SM_FRAMEWORK_PARAMS""" ,       """{}"""					)
   try:
      # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
      __lowerCAmelCase  :      Union[str, Any]    =						json.loads(__A					)
      if not mpi_options.get("""sagemaker_mpi_enabled""" ,       __A					):
         return False
   except json.JSONDecodeError:
      return False
   # Lastly, check if the `smdistributed` module is present.
   return importlib.util.find_spec("""smdistributed"""					) is not None


if is_sagemaker_model_parallel_available():
   import smdistributed.modelparallel.torch as smp

   smp.init()



@dataclass
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										=field(
      default=""       ,							metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}       ,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[int]:






     """simple docstring"""







     super().__post_init__()
     warnings.warn(
         """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
         """`TrainingArguments` instead."""	,							lowerCAmelCase	,							)







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     "torch.device":






     """simple docstring"""







     logger.info("""PyTorch: setting up devices"""   )
     if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
        logger.warning(
            """torch.distributed process group is initialized, but local_rank == -1. """
            """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""   )
     if self.no_cuda:
        __lowerCAmelCase  :      Optional[Any]    =						torch.device("""cpu"""   )
        __lowerCAmelCase  :      Union[str, Any]    =						0
     elif is_sagemaker_model_parallel_available():
        __lowerCAmelCase  :      int    =						smp.local_rank()
        __lowerCAmelCase  :      str    =						torch.device("""cuda"""	,							lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						1
     elif is_sagemaker_dp_enabled():
        import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

        torch.distributed.init_process_group(backend="""smddp"""	,							timeout=self.ddp_timeout_delta   )
        __lowerCAmelCase  :      List[str]    =						int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""   )   )
        __lowerCAmelCase  :      Tuple    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      Optional[int]    =						1
     elif self.local_rank == -1:
        # if n_gpu is > 1 we'll use nn.DataParallel.
        # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
        # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
        # trigger an error that a device index is missing. Index 0 takes into account the
        # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
        # will use the first GPU in that env, i.e. GPU#1
        __lowerCAmelCase  :      List[Any]    =						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )
        # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
        # the default value.
        __lowerCAmelCase  :      Optional[int]    =						torch.cuda.device_count()
     else:
        # Here, we'll use torch.distributed.
        # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
        if not torch.distributed.is_initialized():
           torch.distributed.init_process_group(backend="""nccl"""	,							timeout=self.ddp_timeout_delta   )
        __lowerCAmelCase  :      List[str]    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      int    =						1

     if device.type == "cuda":
        torch.cuda.set_device(lowerCAmelCase   )

     return device







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     if is_sagemaker_model_parallel_available():
        return smp.dp_size()

     return super().world_size







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     return not is_sagemaker_model_parallel_available()







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     return False


