


'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
      from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


A__		:					Tuple												=  logging.get_logger(__name__)

A__		:					Union[str, Any]												=  {
    """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/resolve/main/config.json""",
    """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/config.json""",
    """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json""",
    """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json""",
    """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/config.json""",
    """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json""",
}





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Optional[int]							     =			'bloom'
    lowerCamelCase  :					int							     =			['past_key_values']
    lowerCamelCase  :					Any							     =			{
        'num_hidden_layers': 'n_layer',
        'num_attention_heads': 'n_head',
    }







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=25_08_80			,     SCREAMING_SNAKE_CASE_=64			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=8			,     SCREAMING_SNAKE_CASE_=1E-5			,     SCREAMING_SNAKE_CASE_=0.0_2			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=1			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=0.0			,     SCREAMING_SNAKE_CASE_=0.0			,     SCREAMING_SNAKE_CASE_=1			,     SCREAMING_SNAKE_CASE_=False			,     **SCREAMING_SNAKE_CASE_			,     )				->     Any:
         __lowerCamelCase   :	List[str]								=		vocab_size
         # Backward compatibility with n_embed kwarg
         __lowerCamelCase   :	Tuple								=		kwargs.pop('n_embed'			,     SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		hidden_size if n_embed is None else n_embed
         __lowerCamelCase   :	str								=		n_layer
         __lowerCamelCase   :	Any								=		n_head
         __lowerCamelCase   :	Dict								=		layer_norm_epsilon
         __lowerCamelCase   :	List[str]								=		initializer_range
         __lowerCamelCase   :	Dict								=		use_cache
         __lowerCamelCase   :	Any								=		pretraining_tp
         __lowerCamelCase   :	str								=		apply_residual_connection_post_layernorm
         __lowerCamelCase   :	Tuple								=		hidden_dropout
         __lowerCamelCase   :	Optional[Any]								=		attention_dropout

         __lowerCamelCase   :	str								=		bos_token_id
         __lowerCamelCase   :	str								=		eos_token_id
         __lowerCamelCase   :	Optional[int]								=		slow_but_exact

         super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_			,     eos_token_id=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)







class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Dict							     =			version.parse('1.12'  )







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = "default"			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = False			,     )				->     int:
         super().__init__(SCREAMING_SNAKE_CASE_			,     task=SCREAMING_SNAKE_CASE_			,     patching_specs=SCREAMING_SNAKE_CASE_			,     use_past=SCREAMING_SNAKE_CASE_	)
         if not getattr(self._config			,     'pad_token_id'			,     SCREAMING_SNAKE_CASE_	):
              # TODO: how to do that better?
              __lowerCamelCase   :	Optional[int]								=		0







    @property
    def       lowercase_				(							self	)				->     Mapping[str, Mapping[int, str]]:
         __lowerCamelCase   :	Any								=		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}	)
         if self.use_past:
              # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
              self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_			,     direction='inputs'			,     inverted_values_shape=SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Optional[int]								=		{0: 'batch', 1: 'past_sequence + sequence'}
         else:
              __lowerCamelCase   :	Optional[Any]								=		{0: 'batch', 1: 'sequence'}

         return common_inputs







    @property
    def       lowercase_				(							self	)				->     int:
         return self._config.n_layer







    @property
    def       lowercase_				(							self	)				->     int:
         return self._config.n_head







    @property
    def       lowercase_				(							self	)				->     float:
         return 1E-3







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = -1			,     SCREAMING_SNAKE_CASE_ = -1			,     SCREAMING_SNAKE_CASE_ = False			,     SCREAMING_SNAKE_CASE_ = None			,     )				->     Mapping[str, Any]:
         __lowerCamelCase   :	List[str]								=		super(SCREAMING_SNAKE_CASE_			,     self	).generate_dummy_inputs(
             SCREAMING_SNAKE_CASE_			,     batch_size=SCREAMING_SNAKE_CASE_			,     seq_length=SCREAMING_SNAKE_CASE_			,     is_pair=SCREAMING_SNAKE_CASE_			,     framework=SCREAMING_SNAKE_CASE_	)

         # We need to order the input in the way they appears in the forward()
         __lowerCamelCase   :	Optional[int]								=		OrderedDict({'input_ids': common_inputs['input_ids']}	)

         # Need to add the past_keys
         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
              else:
                   import torch

                   __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		common_inputs['input_ids'].shape
                   # Not using the same length for past_key_values
                   __lowerCamelCase   :	Optional[Any]								=		seqlen + 2
                   __lowerCamelCase   :	Optional[int]								=		self._config.hidden_size // self.num_attention_heads
                   __lowerCamelCase   :	Optional[int]								=		(
                       batch * self.num_attention_heads,
                       head_dim,
                       past_key_values_length,
                   )
                   __lowerCamelCase   :	Tuple								=		(
                       batch * self.num_attention_heads,
                       past_key_values_length,
                       head_dim,
                   )
                   __lowerCamelCase   :	Any								=		[
                       (torch.zeros(SCREAMING_SNAKE_CASE_	), torch.zeros(SCREAMING_SNAKE_CASE_	)) for _ in range(self.num_layers	)
                   ]

         __lowerCamelCase   :	str								=		common_inputs['attention_mask']
         if self.use_past:
              __lowerCamelCase   :	Dict								=		ordered_inputs['attention_mask'].dtype
              __lowerCamelCase   :	List[str]								=		torch.cat(
                  [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     dtype=SCREAMING_SNAKE_CASE_	)]			,     dim=1	)

         return ordered_inputs







    @property
    def       lowercase_				(							self	)				->     int:
         return 13










'''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__		:					Dict												=  [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

A__		:					int												=  (
    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__		:					Tuple												=  (
    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__		:					Tuple												=  [
    """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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any							)     ->		List[str]:
     for tf_name, hf_name in patterns:
          __lowerCamelCase   :	Optional[int]								=		k.replace(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return k



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				dict      ,    UpperCAmelCase_    :				dict							)     ->		BigBirdPegasusForConditionalGeneration:
     __lowerCamelCase   :	int								=		BigBirdPegasusConfig(**UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		BigBirdPegasusForConditionalGeneration(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		torch_model.state_dict()
     __lowerCamelCase   :	Tuple								=		{}

     # separating decoder weights
     __lowerCamelCase   :	Dict								=		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'							)}
     __lowerCamelCase   :	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'							):
          __lowerCamelCase   :	Tuple								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Tuple								=		DECODER_PATTERNS
          __lowerCamelCase   :	Optional[int]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          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   :	Union[str, Any]								=		v.T
          __lowerCamelCase   :	str								=		torch.from_numpy(UpperCAmelCase_							)
          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[Any]								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Dict								=		REMAINING_PATTERNS
          __lowerCamelCase   :	List[str]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          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   :	List[str]								=		v.T
          __lowerCamelCase   :	List[str]								=		torch.from_numpy(UpperCAmelCase_							)
          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   :	Any								=		mapping['model.embed_positions.weight']
     __lowerCamelCase   :	Union[str, Any]								=		mapping.pop('model.embed_positions.weight'							)
     __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		torch_model.load_state_dict(UpperCAmelCase_      ,    strict=UpperCAmelCase_							)
     __lowerCamelCase   :	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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     __lowerCamelCase   :	int								=		tf.train.list_variables(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		{}
     __lowerCamelCase   :	List[Any]								=		['global_step']
     for name, shape in tqdm(UpperCAmelCase_      ,    desc='converting tf checkpoint to dict'							):
          __lowerCamelCase   :	Any								=		any(pat in name for pat in ignore_name							)
          if skip_key:
               continue
          __lowerCamelCase   :	Dict								=		tf.train.load_variable(UpperCAmelCase_      ,    UpperCAmelCase_							)
          __lowerCamelCase   :	List[Any]								=		array
     return tf_weights



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				dict							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		get_tf_weights_as_numpy(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		convert_bigbird_pegasus(UpperCAmelCase_      ,    UpperCAmelCase_							)
     torch_model.save_pretrained(UpperCAmelCase_							)


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






