


'''simple docstring'''



from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
      import tensorflow as tf

      from ..tf_utils import stable_softmax


if is_torch_available():
      import torch


A__		:					int												=  logging.get_logger(__name__)





@add_end_docstrings(
    _UpperCAmelCase					,		r'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '					,		)
class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     np.ndarray:
         if self.framework == "tf":
              __lowerCamelCase   :	int								=		tf.where(input_ids == self.tokenizer.mask_token_id	).numpy()
         elif self.framework == "pt":
              __lowerCamelCase   :	Union[str, Any]								=		torch.nonzero(input_ids == self.tokenizer.mask_token_id			,     as_tuple=SCREAMING_SNAKE_CASE_	)
         else:
              raise ValueError('Unsupported framework'	)
         return masked_index







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     np.ndarray:
         __lowerCamelCase   :	List[Any]								=		self.get_masked_index(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Tuple								=		np.prod(masked_index.shape	)
         if numel < 1:
              raise PipelineException(
                  'fill-mask'			,     self.model.base_model_prefix			,     f'No mask_token ({self.tokenizer.mask_token}) found on the input'			,     )







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Tuple:
         if isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	):
              for model_input in model_inputs:
                   self._ensure_exactly_one_mask_token(model_input['input_ids'][0]	)
         else:
              for input_ids in model_inputs["input_ids"]:
                   self._ensure_exactly_one_mask_token(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None			,     **SCREAMING_SNAKE_CASE_	)				->     Dict[str, GenericTensor]:
         if return_tensors is None:
              __lowerCamelCase   :	Union[str, Any]								=		self.framework
         __lowerCamelCase   :	Union[str, Any]								=		self.tokenizer(SCREAMING_SNAKE_CASE_			,     return_tensors=SCREAMING_SNAKE_CASE_	)
         self.ensure_exactly_one_mask_token(SCREAMING_SNAKE_CASE_	)
         return model_inputs







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Any:
         __lowerCamelCase   :	Tuple								=		self.model(**SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		model_inputs['input_ids']
         return model_outputs







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=5			,     SCREAMING_SNAKE_CASE_=None	)				->     List[Any]:
         # Cap top_k if there are targets
         if target_ids is not None and target_ids.shape[0] < top_k:
              __lowerCamelCase   :	Any								=		target_ids.shape[0]
         __lowerCamelCase   :	Any								=		model_outputs['input_ids'][0]
         __lowerCamelCase   :	str								=		model_outputs['logits']

         if self.framework == "tf":
              __lowerCamelCase   :	Optional[Any]								=		tf.where(input_ids == self.tokenizer.mask_token_id	).numpy()[:, 0]

              __lowerCamelCase   :	Dict								=		outputs.numpy()

              __lowerCamelCase   :	Any								=		outputs[0, masked_index, :]
              __lowerCamelCase   :	int								=		stable_softmax(SCREAMING_SNAKE_CASE_			,     axis=-1	)
              if target_ids is not None:
                   __lowerCamelCase   :	int								=		tf.gather_nd(tf.squeeze(SCREAMING_SNAKE_CASE_			,     0	)			,     target_ids.reshape(-1			,     1	)	)
                   __lowerCamelCase   :	List[Any]								=		tf.expand_dims(SCREAMING_SNAKE_CASE_			,     0	)

              __lowerCamelCase   :	str								=		tf.math.top_k(SCREAMING_SNAKE_CASE_			,     k=SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		topk.values.numpy(), topk.indices.numpy()
         else:
              __lowerCamelCase   :	Dict								=		torch.nonzero(input_ids == self.tokenizer.mask_token_id			,     as_tuple=SCREAMING_SNAKE_CASE_	).squeeze(-1	)
              # Fill mask pipeline supports only one ${mask_token} per sample

              __lowerCamelCase   :	Tuple								=		outputs[0, masked_index, :]
              __lowerCamelCase   :	Optional[int]								=		logits.softmax(dim=-1	)
              if target_ids is not None:
                   __lowerCamelCase   :	Any								=		probs[..., target_ids]

              __lowerCamelCase				,       __lowerCamelCase   :	Any								=		probs.topk(SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Dict								=		[]
         __lowerCamelCase   :	Union[str, Any]								=		values.shape[0] == 1
         for i, (_values, _predictions) in enumerate(zip(values.tolist()			,     predictions.tolist()	)	):
              __lowerCamelCase   :	Optional[int]								=		[]
              for v, p in zip(_values			,     _predictions	):
                   # Copy is important since we're going to modify this array in place
                   __lowerCamelCase   :	int								=		input_ids.numpy().copy()
                   if target_ids is not None:
                        __lowerCamelCase   :	List[Any]								=		target_ids[p].tolist()

                   __lowerCamelCase   :	Dict								=		p
                   # Filter padding out:
                   __lowerCamelCase   :	Optional[int]								=		tokens[np.where(tokens != self.tokenizer.pad_token_id	)]
                   # Originally we skip special tokens to give readable output.
                   # For multi masks though, the other [MASK] would be removed otherwise
                   # making the output look odd, so we add them back
                   __lowerCamelCase   :	Dict								=		self.tokenizer.decode(SCREAMING_SNAKE_CASE_			,     skip_special_tokens=SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	Dict								=		{'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]	), 'sequence': sequence}
                   row.append(SCREAMING_SNAKE_CASE_	)
              result.append(SCREAMING_SNAKE_CASE_	)
         if single_mask:
              return result[0]
         return result







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     Union[str, Any]:
         if isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	):
              __lowerCamelCase   :	int								=		[targets]
         try:
              __lowerCamelCase   :	List[Any]								=		self.tokenizer.get_vocab()
         except Exception:
              __lowerCamelCase   :	str								=		{}
         __lowerCamelCase   :	Optional[Any]								=		[]
         for target in targets:
              __lowerCamelCase   :	int								=		vocab.get(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
              if id_ is None:
                   __lowerCamelCase   :	Dict								=		self.tokenizer(
                       SCREAMING_SNAKE_CASE_			,     add_special_tokens=SCREAMING_SNAKE_CASE_			,     return_attention_mask=SCREAMING_SNAKE_CASE_			,     return_token_type_ids=SCREAMING_SNAKE_CASE_			,     max_length=1			,     truncation=SCREAMING_SNAKE_CASE_			,     )['input_ids']
                   if len(SCREAMING_SNAKE_CASE_	) == 0:
                        logger.warning(
                            f'The specified target token `{target}` does not exist in the model vocabulary. '
                            'We cannot replace it with anything meaningful, ignoring it'	)
                        continue
                   __lowerCamelCase   :	List[str]								=		input_ids[0]
                   # XXX: If users encounter this pass
                   # it becomes pretty slow, so let's make sure
                   # The warning enables them to fix the input to
                   # get faster performance.
                   logger.warning(
                       f'The specified target token `{target}` does not exist in the model vocabulary. '
                       f'Replacing with `{self.tokenizer.convert_ids_to_tokens(id_	)}`.'	)
              target_ids.append(id_	)
         __lowerCamelCase   :	Dict								=		list(set(SCREAMING_SNAKE_CASE_	)	)
         if len(SCREAMING_SNAKE_CASE_	) == 0:
              raise ValueError('At least one target must be provided when passed.'	)
         __lowerCamelCase   :	List[Any]								=		np.array(SCREAMING_SNAKE_CASE_	)
         return target_ids







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None	)				->     str:
         __lowerCamelCase   :	Tuple								=		{}

         if targets is not None:
              __lowerCamelCase   :	str								=		self.get_target_ids(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Dict								=		target_ids

         if top_k is not None:
              __lowerCamelCase   :	Any								=		top_k

         if self.tokenizer.mask_token_id is None:
              raise PipelineException(
                  'fill-mask'			,     self.model.base_model_prefix			,     'The tokenizer does not define a `mask_token`.'	)
         return {}, {}, postprocess_params







    def __call__(							self			,     SCREAMING_SNAKE_CASE_			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)				->     Union[str, Any]:
         __lowerCamelCase   :	str								=		super().__call__(SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)
         if isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) and len(SCREAMING_SNAKE_CASE_	) == 1:
              return outputs[0]
         return outputs










'''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)






