


'''simple docstring'''





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowerCamelCase												=						(
    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
)

__lowerCamelCase												=						(
    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
)

__lowerCamelCase												=						[
    '''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        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      List[Any]						):
   for tf_name, hf_name in patterns:
      UpperCAmelCase__	:str      =      k.replace(UpperCamelCase_,   UpperCamelCase_						)
   return k


def        a__     (					UpperCamelCase_						:      dict,   UpperCamelCase_						:      dict						):
   UpperCAmelCase__	:Tuple      =      BigBirdPegasusConfig(**UpperCamelCase_						)
   UpperCAmelCase__	:List[str]      =      BigBirdPegasusForConditionalGeneration(UpperCamelCase_						)
   UpperCAmelCase__	:Dict      =      torch_model.state_dict()
   UpperCAmelCase__	:Union[str, Any]      =      {}

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

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


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      str,   UpperCamelCase_						:      dict						):
   UpperCAmelCase__	:Dict      =      get_tf_weights_as_numpy(UpperCamelCase_						)
   UpperCAmelCase__	:int      =      convert_bigbird_pegasus(UpperCamelCase_,   UpperCamelCase_						)
   torch_model.save_pretrained(UpperCamelCase_						)


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







'''simple docstring'''





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	int = 1_6			,       __lowerCamelCase							:	int = 8_8			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "geglu"			,       __lowerCamelCase							:	Optional[int] = None			,       ):
    super().__init__()
    UpperCAmelCase__	:Optional[int]      =      nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=__lowerCamelCase			,       attention_head_dim=__lowerCamelCase			,       in_channels=__lowerCamelCase			,       num_layers=__lowerCamelCase			,       dropout=__lowerCamelCase			,       norm_num_groups=__lowerCamelCase			,       cross_attention_dim=__lowerCamelCase			,       attention_bias=__lowerCamelCase			,       sample_size=__lowerCamelCase			,       num_vector_embeds=__lowerCamelCase			,       activation_fn=__lowerCamelCase			,       num_embeds_ada_norm=__lowerCamelCase			,       )
            for _ in range(2 )
        ] )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase__	:Tuple      =      0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase__	:int      =      [7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase__	:Any      =      [1, 0]





 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:Optional[int]      =      hidden_states

    UpperCAmelCase__	:Dict      =      []
    UpperCAmelCase__	:Tuple      =      0
    # attention_mask is not used yet
    for i in range(2 ):
       # for each of the two transformers, pass the corresponding condition tokens
       UpperCAmelCase__	:List[Any]      =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       UpperCAmelCase__	:Dict      =      self.transformer_index_for_condition[i]
       UpperCAmelCase__	:Tuple      =      self.transformers[transformer_index](
           __lowerCamelCase			,       encoder_hidden_states=__lowerCamelCase			,       timestep=__lowerCamelCase			,       cross_attention_kwargs=__lowerCamelCase			,       return_dict=__lowerCamelCase			,       )[0]
       encoded_states.append(encoded_state - input_states )
       tokens_start += self.condition_lengths[i]

    UpperCAmelCase__	:Optional[int]      =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase__	:Optional[Any]      =      output_states + input_states

    if not return_dict:
       return (output_states,)

    return TransformeraDModelOutput(sample=__lowerCamelCase )



