




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


_lowerCamelCase	:		Optional[Any]							       =   [
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
_lowerCamelCase	:		Optional[int]							       =   [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

_lowerCamelCase	:		List[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
)

_lowerCamelCase	:		Optional[int]							       =   (
    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	:		Dict							       =   [
    """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				(       lowercase_ ,				lowercase_						)   ->       List[Any]:



       """simple docstring"""

       for tf_name, hf_name in patterns:
              A__       =    k.replace(lowercase_ ,				lowercase_						)
       return k







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       BigBirdPegasusForConditionalGeneration:



       """simple docstring"""

       A__       =    BigBirdPegasusConfig(**lowercase_						)
       A__       =    BigBirdPegasusForConditionalGeneration(lowercase_						)
       A__       =    torch_model.state_dict()
       A__       =    {}

       # separating decoder weights
       A__       =    {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''						)}
       A__       =    {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__       =    [k.endswith(lowercase_						) for ending in KEYS_TO_IGNORE]
              if any(lowercase_						):
                     continue
              A__       =    DECODER_PATTERNS
              A__       =    rename_state_dict_key(lowercase_ ,				lowercase_						)
              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__       =    v.T
              A__       =    torch.from_numpy(lowercase_						)
              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__       =    [k.endswith(lowercase_						) for ending in KEYS_TO_IGNORE]
              if any(lowercase_						):
                     continue
              A__       =    REMAINING_PATTERNS
              A__       =    rename_state_dict_key(lowercase_ ,				lowercase_						)
              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__       =    v.T
              A__       =    torch.from_numpy(lowercase_						)
              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__       =    mapping['''model.embed_positions.weight''']
       A__       =    mapping.pop('''model.embed_positions.weight'''						)
       A__ ,							A__       =    torch_model.load_state_dict(lowercase_ ,				strict=lowercase_						)
       A__       =    [
           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				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       A__       =    tf.train.list_variables(lowercase_						)
       A__       =    {}
       A__       =    ['''global_step''']
       for name, shape in tqdm(lowercase_ ,				desc='''converting tf checkpoint to dict'''						):
              A__       =    any(pat in name for pat in ignore_name						)
              if skip_key:
                     continue
              A__       =    tf.train.load_variable(lowercase_ ,				lowercase_						)
              A__       =    array
       return tf_weights







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       Any:



       """simple docstring"""

       A__       =    get_tf_weights_as_numpy(lowercase_						)
       A__       =    convert_bigbird_pegasus(lowercase_ ,				lowercase_						)
       torch_model.save_pretrained(lowercase_						)


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








from ...processing_utils import ProcessorMixin


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''SpeechT5FeatureExtractor'''
    UpperCAmelCase__							  =      '''SpeechT5Tokenizer'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Tuple)						->Union[str, Any]:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__					, UpperCAmelCase__)


    def __call__(			self      :	Dict					, *UpperCAmelCase__      :	List[str]					, **UpperCAmelCase__      :	Any)						->Optional[Any]:





           '''simple docstring'''

           A__       =    kwargs.pop('''audio'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''audio_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''sampling_rate'''					, UpperCAmelCase__)

           if audio is not None and text is not None:
                  raise ValueError(
                      '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?''')
           if audio_target is not None and text_target is not None:
                  raise ValueError(
                      '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?''')
           if audio is None and audio_target is None and text is None and text_target is None:
                  raise ValueError(
                      '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.''')

           if audio is not None:
                  A__       =    self.feature_extractor(UpperCAmelCase__					, *UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, **UpperCAmelCase__)
           elif text is not None:
                  A__       =    self.tokenizer(UpperCAmelCase__					, **UpperCAmelCase__)
           else:
                  A__       =    None

           if audio_target is not None:
                  A__       =    self.feature_extractor(audio_target=UpperCAmelCase__					, *UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, **UpperCAmelCase__)
                  A__       =    targets['''input_values''']
           elif text_target is not None:
                  A__       =    self.tokenizer(UpperCAmelCase__					, **UpperCAmelCase__)
                  A__       =    targets['''input_ids''']
           else:
                  A__       =    None

           if inputs is None:
                  return targets

           if targets is not None:
                  A__       =    labels

                  A__       =    targets.get('''attention_mask''')
                  if decoder_attention_mask is not None:
                         A__       =    decoder_attention_mask

           return inputs


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	Tuple					, **UpperCAmelCase__      :	int)						->Optional[int]:





           '''simple docstring'''

           A__       =    kwargs.pop('''input_values'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''input_ids'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''labels'''					, UpperCAmelCase__)

           if input_values is not None and input_ids is not None:
                  raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.''')
           if input_values is None and input_ids is None and labels is None:
                  raise ValueError(
                      '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.''')

           if input_values is not None:
                  A__       =    self.feature_extractor.pad(UpperCAmelCase__					, *UpperCAmelCase__					, **UpperCAmelCase__)
           elif input_ids is not None:
                  A__       =    self.tokenizer.pad(UpperCAmelCase__					, **UpperCAmelCase__)
           else:
                  A__       =    None

           if labels is not None:
                  if "input_ids" in labels or (isinstance(UpperCAmelCase__					, UpperCAmelCase__) and "input_ids" in labels[0]):
                         A__       =    self.tokenizer.pad(UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    targets['''input_ids''']
                  else:
                         A__       =    self.feature_extractor.feature_size
                         A__       =    self.feature_extractor.num_mel_bins
                         A__       =    self.feature_extractor.pad(UpperCAmelCase__					, *UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    feature_size_hack
                         A__       =    targets['''input_values''']
           else:
                  A__       =    None

           if inputs is None:
                  return targets

           if targets is not None:
                  A__       =    labels

                  A__       =    targets.get('''attention_mask''')
                  if decoder_attention_mask is not None:
                         A__       =    decoder_attention_mask

           return inputs


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, *UpperCAmelCase__      :	Dict					, **UpperCAmelCase__      :	Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           return self.tokenizer.batch_decode(*UpperCAmelCase__					, **UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Union[str, Any])						->Dict:





           '''simple docstring'''

           return self.tokenizer.decode(*UpperCAmelCase__					, **UpperCAmelCase__)


