


"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowerCamelCase      :Optional[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[Any]	    =		(
    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      :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   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						List[str]    )   ->      Optional[Any]:
      for tf_name, hf_name in patterns:
            lowerCamelCase					:					Optional[Any]        =	k.replace(_UpperCAmelCase			,				_UpperCAmelCase    )
      return k




def   snake_case		(     UpperCamelCase__       :						Any			,				UpperCamelCase__       :						Optional[int]    )   ->      List[Any]:
      lowerCamelCase					:					Optional[int]        =	BigBirdPegasusConfig(**_UpperCAmelCase    )
      lowerCamelCase					:					List[Any]        =	BigBirdPegasusForConditionalGeneration(_UpperCAmelCase    )
      lowerCamelCase					:					Any        =	torch_model.state_dict()
      lowerCamelCase					:					int        =	{}

      # separating decoder weights
      lowerCamelCase					:					Optional[Any]        =	{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""    )}
      lowerCamelCase					:					int        =	{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					:					Any        =	[k.endswith(_UpperCAmelCase    ) for ending in KEYS_TO_IGNORE]
            if any(_UpperCAmelCase    ):
                  continue
            lowerCamelCase					:					List[str]        =	DECODER_PATTERNS
            lowerCamelCase					:					Union[str, Any]        =	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					:					Tuple        =	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					:					str        =	[k.endswith(_UpperCAmelCase    ) for ending in KEYS_TO_IGNORE]
            if any(_UpperCAmelCase    ):
                  continue
            lowerCamelCase					:					str        =	REMAINING_PATTERNS
            lowerCamelCase					:					Union[str, Any]        =	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					:					int        =	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					:					Union[str, Any]        =	mapping["""model.embed_positions.weight"""]
      lowerCamelCase					:					str        =	mapping.pop("""model.embed_positions.weight"""    )
      lowerCamelCase     ,			lowerCamelCase					:					int        =	torch_model.load_state_dict(_UpperCAmelCase			,				strict=_UpperCAmelCase    )
      lowerCamelCase					:					Optional[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   snake_case		(     UpperCamelCase__       :						List[Any]    )   ->      Dict:
      lowerCamelCase					:					List[str]        =	tf.train.list_variables(_UpperCAmelCase    )
      lowerCamelCase					:					Optional[Any]        =	{}
      lowerCamelCase					:					List[str]        =	["""global_step"""]
      for name, shape in tqdm(_UpperCAmelCase			,				desc="""converting tf checkpoint to dict"""    ):
            lowerCamelCase					:					Dict        =	any(pat in name for pat in ignore_name    )
            if skip_key:
                  continue
            lowerCamelCase					:					Optional[int]        =	tf.train.load_variable(_UpperCAmelCase			,				_UpperCAmelCase    )
            lowerCamelCase					:					int        =	array
      return tf_weights




def   snake_case		(     UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Dict    )   ->      Dict:
      lowerCamelCase					:					str        =	get_tf_weights_as_numpy(_UpperCAmelCase    )
      lowerCamelCase					:					Optional[Any]        =	convert_bigbird_pegasus(_UpperCAmelCase			,				_UpperCAmelCase    )
      torch_model.save_pretrained(_UpperCAmelCase    )


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








"""simple docstring"""


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


__lowerCamelCase      :Tuple	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													='''glpn'''

     def __init__(	self:						Dict     ,		__a:						List[str]=3     ,		__a:						Optional[int]=4     ,		__a:						Dict=[2, 2, 2, 2]     ,		__a:						str=[8, 4, 2, 1]     ,		__a:						Optional[int]=[32, 64, 160, 256]     ,		__a:						Dict=[7, 3, 3, 3]     ,		__a:						Dict=[4, 2, 2, 2]     ,		__a:						Optional[Any]=[1, 2, 5, 8]     ,		__a:						Tuple=[4, 4, 4, 4]     ,		__a:						int="gelu"     ,		__a:						Union[str, Any]=0.0     ,		__a:						str=0.0     ,		__a:						Union[str, Any]=0.02     ,		__a:						str=0.1     ,		__a:						Union[str, Any]=1e-6     ,		__a:						Any=64     ,		__a:						Dict=10     ,		__a:						Union[str, Any]=-1     ,		**__a:						Optional[Any]     ,		)->    Dict:
           super().__init__(**__a	)

           lowerCamelCase					:					Dict        =	num_channels
           lowerCamelCase					:					Any        =	num_encoder_blocks
           lowerCamelCase					:					Dict        =	depths
           lowerCamelCase					:					List[str]        =	sr_ratios
           lowerCamelCase					:					Dict        =	hidden_sizes
           lowerCamelCase					:					Tuple        =	patch_sizes
           lowerCamelCase					:					Optional[int]        =	strides
           lowerCamelCase					:					Optional[Any]        =	mlp_ratios
           lowerCamelCase					:					Union[str, Any]        =	num_attention_heads
           lowerCamelCase					:					List[str]        =	hidden_act
           lowerCamelCase					:					Any        =	hidden_dropout_prob
           lowerCamelCase					:					Optional[int]        =	attention_probs_dropout_prob
           lowerCamelCase					:					List[Any]        =	initializer_range
           lowerCamelCase					:					Dict        =	drop_path_rate
           lowerCamelCase					:					Any        =	layer_norm_eps
           lowerCamelCase					:					Optional[Any]        =	decoder_hidden_size
           lowerCamelCase					:					Tuple        =	max_depth
           lowerCamelCase					:					Optional[Any]        =	head_in_index




