





'''simple docstring'''







import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A         =				(
    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         =				(
    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         =				[
    '''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 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						Any							,	lowerCAmelCase__  :						Any)      ->    Optional[Any]:

     '''simple docstring'''




     for tf_name, hf_name in patterns:
          _lowercase				:   str	    =				k.replace(lowerCAmelCase__							,	lowerCAmelCase__)
     return k

def 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						int							,	lowerCAmelCase__  :						str)      ->    BigBirdPegasusForConditionalGeneration:

     '''simple docstring'''




     _lowercase				:   List[str]	    =				BigBirdPegasusConfig(**lowerCAmelCase__)
     _lowercase				:   List[Any]	    =				BigBirdPegasusForConditionalGeneration(lowerCAmelCase__)
     _lowercase				:   List[str]	    =				torch_model.state_dict()
     _lowercase				:   int	    =				{}

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

     _lowercase				:   str	    =				mapping["""model.embed_positions.weight"""]
     _lowercase				:   str	    =				mapping.pop('model.embed_positions.weight')
     _lowercase				:   Union[str, Any]	    =				torch_model.load_state_dict(lowerCAmelCase__							,	strict=lowerCAmelCase__)
     _lowercase				:   int	    =				[
         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 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						List[Any])      ->    Dict:

     '''simple docstring'''




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

def 		SCREAMING_SNAKE_CASE   (       lowerCAmelCase__  :						List[str]							,	lowerCAmelCase__  :						str							,	lowerCAmelCase__  :						Dict)      ->    Union[str, Any]:

     '''simple docstring'''




     _lowercase				:   Union[str, Any]	    =				get_tf_weights_as_numpy(lowerCAmelCase__)
     _lowercase				:   Optional[Any]	    =				convert_bigbird_pegasus(lowerCAmelCase__							,	lowerCAmelCase__)
     torch_model.save_pretrained(lowerCAmelCase__)


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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							=				logging.get_logger(__name__)

UpperCAmelCase_							=				{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Optional[int]       =	"""canine"""






		def __init__(							self,   snake_case__=7_68,   snake_case__=12,   snake_case__=12,   snake_case__=30_72,   snake_case__="gelu",   snake_case__=0.1,   snake_case__=0.1,   snake_case__=1_63_84,   snake_case__=16,   snake_case__=0.02,   snake_case__=1E-12,   snake_case__=0,   snake_case__=0XE_0_0_0,   snake_case__=0XE_0_0_1,   snake_case__=4,   snake_case__=4,   snake_case__=8,   snake_case__=1_63_84,   snake_case__=1_28,   **snake_case__,   )       -> Optional[int]:






									"""simple docstring"""
									super().__init__(pad_token_id=snake_case__,   bos_token_id=snake_case__,   eos_token_id=snake_case__,   **snake_case__  )

									lowercase_	:   int  				=     max_position_embeddings
									lowercase_	:   Union[str, Any]  				=     hidden_size
									lowercase_	:   Optional[Any]  				=     num_hidden_layers
									lowercase_	:   str  				=     num_attention_heads
									lowercase_	:   List[Any]  				=     intermediate_size
									lowercase_	:   Optional[Any]  				=     hidden_act
									lowercase_	:   List[str]  				=     hidden_dropout_prob
									lowercase_	:   Any  				=     attention_probs_dropout_prob
									lowercase_	:   Union[str, Any]  				=     initializer_range
									lowercase_	:   Optional[Any]  				=     type_vocab_size
									lowercase_	:   Dict  				=     layer_norm_eps

									# Character config:
									lowercase_	:   Optional[int]  				=     downsampling_rate
									lowercase_	:   List[str]  				=     upsampling_kernel_size
									lowercase_	:   int  				=     num_hash_functions
									lowercase_	:   List[str]  				=     num_hash_buckets
									lowercase_	:   List[str]  				=     local_transformer_stride