


'''simple docstring'''







import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A				:					Any    						=			(
    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				:					Dict    						=			(
    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				:					int    						=			[
    '''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 				lowerCAmelCase__	(    lowerCamelCase					:      Dict  ,lowerCamelCase					:      Tuple       ):
       for tf_name, hf_name in patterns:
              _A					:     Tuple     = k.replace(lowerCamelCase  ,lowerCamelCase       )
       return k






def 				lowerCAmelCase__	(    lowerCamelCase					:      dict  ,lowerCamelCase					:      dict       ):
       _A					:     str     = BigBirdPegasusConfig(**lowerCamelCase       )
       _A					:     Tuple     = BigBirdPegasusForConditionalGeneration(lowerCamelCase       )
       _A					:     int     = torch_model.state_dict()
       _A					:     Union[str, Any]     = {}

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

       _A					:     Any     = mapping['model.embed_positions.weight']
       _A					:     Optional[Any]     = mapping.pop('model.embed_positions.weight'       )
       _A  ,			_A					:     Optional[int]     = torch_model.load_state_dict(lowerCamelCase  ,strict=lowerCamelCase       )
       _A					:     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 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       _A					:     str     = tf.train.list_variables(lowerCamelCase       )
       _A					:     Tuple     = {}
       _A					:     Tuple     = ['global_step']
       for name, shape in tqdm(lowerCamelCase  ,desc='converting tf checkpoint to dict'       ):
              _A					:     Optional[Any]     = any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              _A					:     List[str]     = tf.train.load_variable(lowerCamelCase  ,lowerCamelCase       )
              _A					:     Tuple     = array
       return tf_weights






def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      str  ,lowerCamelCase					:      dict       ):
       _A					:     Tuple     = get_tf_weights_as_numpy(lowerCamelCase       )
       _A					:     List[Any]     = convert_bigbird_pegasus(lowerCamelCase  ,lowerCamelCase       )
       torch_model.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
   A				:					int    						=			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				:					Optional[Any]    						=			parser.parse_args()
   A				:					Optional[int]    						=			{}
   convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)










'''simple docstring'''







import operator as op


A				:					List[str]    						=			'''scaler.pt'''
A				:					Optional[int]    						=			'''pytorch_model'''
A				:					Union[str, Any]    						=			'''random_states'''
A				:					List[Any]    						=			'''optimizer'''
A				:					Optional[Any]    						=			'''scheduler'''
A				:					Optional[int]    						=			'''pytorch_model.bin'''
A				:					List[str]    						=			'''pytorch_model.bin.index.json'''
A				:					List[Any]    						=			'''model.safetensors'''
A				:					int    						=			'''model.safetensors.index.json'''
A				:					List[str]    						=			'''1.10.2'''
A				:					str    						=			'''py38'''
A				:					int    						=			'''4.17.0'''
A				:					Tuple    						=			['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
A				:					Union[str, Any]    						=			['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
A				:					Optional[Any]    						=			['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
A				:					Dict    						=			['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
A				:					Any    						=			['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
A				:					Tuple    						=			'''2.0.1'''
A				:					List[str]    						=			['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
A				:					Union[str, Any]    						=			['''default''', '''reduce-overhead''', '''max-autotune''']

A				:					List[Any]    						=			{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
A				:					Tuple    						=			[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

A				:					List[str]    						=			['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
A				:					Any    						=			['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']






