



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    _snake_case (       __snake_case					,  __snake_case    )     ->     List[Any]:






      '''simple docstring'''







      for tf_name, hf_name in patterns:
            UpperCAmelCase_  :	List[str]				      =		k.replace(__snake_case					,  __snake_case    )
      return k







def    _snake_case (       __snake_case					,  __snake_case    )     ->     BigBirdPegasusForConditionalGeneration:






      '''simple docstring'''







      UpperCAmelCase_  :	int				      =		BigBirdPegasusConfig(**__snake_case    )
      UpperCAmelCase_  :	str				      =		BigBirdPegasusForConditionalGeneration(__snake_case    )
      UpperCAmelCase_  :	Optional[int]				      =		torch_model.state_dict()
      UpperCAmelCase_  :	List[str]				      =		{}

      # separating decoder weights
      UpperCAmelCase_  :	List[str]				      =		{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"    )}
      UpperCAmelCase_  :	Tuple				      =		{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_  :	List[Any]				      =		[k.endswith(__snake_case    ) for ending in KEYS_TO_IGNORE]
            if any(__snake_case    ):
                  continue
            UpperCAmelCase_  :	Optional[Any]				      =		DECODER_PATTERNS
            UpperCAmelCase_  :	Any				      =		rename_state_dict_key(__snake_case					,  __snake_case    )
            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_  :	int				      =		v.T
            UpperCAmelCase_  :	Dict				      =		torch.from_numpy(__snake_case    )
            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(__snake_case    ) for ending in KEYS_TO_IGNORE]
            if any(__snake_case    ):
                  continue
            UpperCAmelCase_  :	Tuple				      =		REMAINING_PATTERNS
            UpperCAmelCase_  :	int				      =		rename_state_dict_key(__snake_case					,  __snake_case    )
            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_  :	Union[str, Any]				      =		v.T
            UpperCAmelCase_  :	List[str]				      =		torch.from_numpy(__snake_case    )
            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_  :	Dict				      =		mapping.pop("model.embed_positions.weight"    )
      UpperCAmelCase_      ,      UpperCAmelCase_  :	Dict				      =		torch_model.load_state_dict(__snake_case					,  strict=__snake_case    )
      UpperCAmelCase_  :	List[str]				      =		[
          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    _snake_case (       __snake_case    )     ->     Dict:






      '''simple docstring'''







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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     str:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		get_tf_weights_as_numpy(__snake_case    )
      UpperCAmelCase_  :	Any				      =		convert_bigbird_pegasus(__snake_case					,  __snake_case    )
      torch_model.save_pretrained(__snake_case    )


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)





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


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""trocr"""
       _lowerCamelCase			    =				["""past_key_values"""]
       _lowerCamelCase			    =				{
           """num_attention_heads""": """decoder_attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """decoder_layers""",
       }



       def __init__(     self     ,lowercase=50265     ,lowercase=1024     ,lowercase=12     ,lowercase=16     ,lowercase=4096     ,lowercase="gelu"     ,lowercase=512     ,lowercase=0.1     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=2     ,lowercase=0.02     ,lowercase=0.0     ,lowercase=True     ,lowercase=False     ,lowercase=True     ,lowercase=True     ,lowercase=1     ,lowercase=0     ,lowercase=2     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		vocab_size
             UpperCAmelCase_  :	Tuple				      =		d_model
             UpperCAmelCase_  :	List[str]				      =		decoder_layers
             UpperCAmelCase_  :	Union[str, Any]				      =		decoder_attention_heads
             UpperCAmelCase_  :	Optional[Any]				      =		decoder_ffn_dim
             UpperCAmelCase_  :	Tuple				      =		activation_function
             UpperCAmelCase_  :	List[Any]				      =		max_position_embeddings
             UpperCAmelCase_  :	List[Any]				      =		dropout
             UpperCAmelCase_  :	List[str]				      =		attention_dropout
             UpperCAmelCase_  :	Union[str, Any]				      =		activation_dropout
             UpperCAmelCase_  :	Optional[int]				      =		init_std
             UpperCAmelCase_  :	Dict				      =		decoder_layerdrop
             UpperCAmelCase_  :	List[str]				      =		use_cache
             UpperCAmelCase_  :	Dict				      =		scale_embedding
             UpperCAmelCase_  :	Dict				      =		use_learned_position_embeddings
             UpperCAmelCase_  :	Optional[Any]				      =		layernorm_embedding

             super().__init__(
                 pad_token_id=lowercase     ,bos_token_id=lowercase     ,eos_token_id=lowercase     ,decoder_start_token_id=lowercase     ,**lowercase     ,)
