import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

a_    					=       (
    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_    					=       (
    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_    					=       [
    '''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    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     List[str]	):



 for tf_name, hf_name in patterns:
  UpperCamelCase_					:						int =					k.replace(lowerCamelCase  ,  lowerCamelCase	)
 return k





def    __lowercase     (						lowerCamelCase					:     dict  ,  lowerCamelCase					:     dict	):
 UpperCamelCase_					:						Any =					BigBirdPegasusConfig(**lowerCamelCase	)
 UpperCamelCase_					:						List[str] =					BigBirdPegasusForConditionalGeneration(lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					torch_model.state_dict()
 UpperCamelCase_					:						str =					{}

 # separating decoder weights
 UpperCamelCase_					:						Optional[Any] =					{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'	)}
 UpperCamelCase_					:						Union[str, 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_					:						Optional[Any] =					[k.endswith(lowerCamelCase	) for ending in KEYS_TO_IGNORE]
  if any(lowerCamelCase	):
   continue
  UpperCamelCase_					:						Any =					DECODER_PATTERNS
  UpperCamelCase_					:						str =					rename_state_dict_key(lowerCamelCase  ,  lowerCamelCase	)
  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_					:						List[Any] =					v.T
  UpperCamelCase_					:						List[str] =					torch.from_numpy(lowerCamelCase	)
  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_					:						int =					[k.endswith(lowerCamelCase	) for ending in KEYS_TO_IGNORE]
  if any(lowerCamelCase	):
   continue
  UpperCamelCase_					:						Tuple =					REMAINING_PATTERNS
  UpperCamelCase_					:						int =					rename_state_dict_key(lowerCamelCase  ,  lowerCamelCase	)
  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_					:						Union[str, Any] =					torch.from_numpy(lowerCamelCase	)
  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_					:						Dict =					mapping["""model.embed_positions.weight"""]
 UpperCamelCase_					:						int =					mapping.pop('model.embed_positions.weight'	)
 UpperCamelCase_					:						Optional[Any] =					torch_model.load_state_dict(lowerCamelCase  ,  strict=lowerCamelCase	)
 UpperCamelCase_					:						Dict =					[
     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    __lowercase     (						lowerCamelCase					:     Tuple	):
 UpperCamelCase_					:						Optional[Any] =					tf.train.list_variables(lowerCamelCase	)
 UpperCamelCase_					:						str =					{}
 UpperCamelCase_					:						Optional[int] =					["""global_step"""]
 for name, shape in tqdm(lowerCamelCase  ,  desc='converting tf checkpoint to dict'	):
  UpperCamelCase_					:						Dict =					any(pat in name for pat in ignore_name	)
  if skip_key:
   continue
  UpperCamelCase_					:						Any =					tf.train.load_variable(lowerCamelCase  ,  lowerCamelCase	)
  UpperCamelCase_					:						Tuple =					array
 return tf_weights





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     dict	):
 UpperCamelCase_					:						List[Any] =					get_tf_weights_as_numpy(lowerCamelCase	)
 UpperCamelCase_					:						List[str] =					convert_bigbird_pegasus(lowerCamelCase  ,  lowerCamelCase	)
 torch_model.save_pretrained(lowerCamelCase	)


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











from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
	from .camera import create_pan_cameras
	from .pipeline_shap_e import ShapEPipeline
	from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
	from .renderer import (
	    BoundingBoxVolume,
	    ImportanceRaySampler,
	    MLPNeRFModelOutput,
	    MLPNeRSTFModel,
	    ShapEParamsProjModel,
	    ShapERenderer,
	    StratifiedRaySampler,
	    VoidNeRFModel,
	)
