



"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__A   :							Optional[int]										=   logging.get_logger(__name__)
class 			_a :
			"""simple docstring"""
			UpperCamelCase__			      =	42
			UpperCamelCase__			      =	None



			@staticmethod
			def  lowercase__						(		)->Optional[int]:
							raise NotImplementedError



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    str				,   **__UpperCamelCase     :    Any   )->List[Any]:
							raise NotImplementedError



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    Tuple   )->Tuple:
							raise NotImplementedError



			def  lowercase__						(		self     :    Any   )->Union[str, Any]:
							if not self.is_available():
											raise RuntimeError(
											    F'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'   )



			@classmethod
			def  lowercase__						(		cls     :    Any   )->Union[str, Any]:
							return F'`pip install {cls.pip_package or cls.name}`'



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""optuna"""



			@staticmethod
			def  lowercase__						(		)->Optional[Any]:
							return is_optuna_available()



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    str				,   **__UpperCamelCase     :    Tuple   )->Optional[Any]:
							return run_hp_search_optuna(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    Union[str, Any]   )->Optional[Any]:
							return default_hp_space_optuna(__UpperCamelCase   )



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""ray"""
			UpperCamelCase__			      =	"""'ray[tune]'"""



			@staticmethod
			def  lowercase__						(		)->int:
							return is_ray_available()



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    str				,   **__UpperCamelCase     :    Any   )->Optional[Any]:
							return run_hp_search_ray(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    Optional[int]   )->List[Any]:
							return default_hp_space_ray(__UpperCamelCase   )



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""sigopt"""



			@staticmethod
			def  lowercase__						(		)->int:
							return is_sigopt_available()



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    str				,   **__UpperCamelCase     :    Tuple   )->Any:
							return run_hp_search_sigopt(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    Optional[int]   )->Union[str, Any]:
							return default_hp_space_sigopt(__UpperCamelCase   )



class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""wandb"""



			@staticmethod
			def  lowercase__						(		)->Union[str, Any]:
							return is_wandb_available()



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    str				,   **__UpperCamelCase     :    List[Any]   )->List[Any]:
							return run_hp_search_wandb(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Any   )->Union[str, Any]:
							return default_hp_space_wandb(__UpperCamelCase   )



__A   :							Optional[int]										=   {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
				if len(_SCREAMING_SNAKE_CASE				) > 0:
								_UpperCAmelCase					=    available_backends[0].name
								if len(_SCREAMING_SNAKE_CASE				) > 1:
												logger.info(
												    f'{len(_SCREAMING_SNAKE_CASE				)} hyperparameter search backends available. Using {name} as the default.'				)
								return name
				raise RuntimeError(
				    '''No hyperparameter search backend available.\n'''
				    + '''\n'''.join(
				        f' - To install {backend.name} run {backend.pip_install()}'
				        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()				)				)








"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A   :							Tuple										=   (
    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
)

__A   :							Optional[int]										=   (
    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
)

__A   :							Union[str, Any]										=   [
    "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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				for tf_name, hf_name in patterns:
								_UpperCAmelCase					=    k.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				return k







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						dict					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    torch_model.state_dict()
				_UpperCAmelCase					=    {}

				# separating decoder weights
				_UpperCAmelCase					=    {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''				)}
				_UpperCAmelCase					=    {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					=    [k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    DECODER_PATTERNS
								_UpperCAmelCase					=    rename_state_dict_key(_SCREAMING_SNAKE_CASE					,     _SCREAMING_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					=    v.T
								_UpperCAmelCase					=    torch.from_numpy(_SCREAMING_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					=    [k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    REMAINING_PATTERNS
								_UpperCAmelCase					=    rename_state_dict_key(_SCREAMING_SNAKE_CASE					,     _SCREAMING_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					=    v.T
								_UpperCAmelCase					=    torch.from_numpy(_SCREAMING_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					=    mapping['''model.embed_positions.weight''']
				_UpperCAmelCase					=    mapping.pop('''model.embed_positions.weight'''				)
				_UpperCAmelCase		,						_UpperCAmelCase					=    torch_model.load_state_dict(_SCREAMING_SNAKE_CASE					,     strict=_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    [
				    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				_UpperCAmelCase					=    tf.train.list_variables(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    {}
				_UpperCAmelCase					=    ['''global_step''']
				for name, shape in tqdm(_SCREAMING_SNAKE_CASE					,     desc='''converting tf checkpoint to dict'''				):
								_UpperCAmelCase					=    any(pat in name for pat in ignore_name				)
								if skip_key:
												continue
								_UpperCAmelCase					=    tf.train.load_variable(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    array
				return tf_weights







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				torch_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


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



