

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowercase_   							=  (
    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
)

lowercase_   							=  (
    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
)

lowercase_   							=  [
    '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 (     A__      :  Optional[Any] ,  A__      :  Optional[int]	)       -> int:





      """simple docstring"""



      for tf_name, hf_name in patterns:
            _lowercase      			=k.replace(A__ ,  A__	)
      return k
def 	a (     A__      :  dict ,  A__      :  dict	)       -> BigBirdPegasusForConditionalGeneration:





      """simple docstring"""



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

      # separating decoder weights
      _lowercase      			={k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'	)}
      _lowercase      			={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'	):
            _lowercase      			=[k.endswith(A__	) for ending in KEYS_TO_IGNORE]
            if any(A__	):
                  continue
            _lowercase      			=DECODER_PATTERNS
            _lowercase      			=rename_state_dict_key(A__ ,  A__	)
            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']	):
                  _lowercase      			=v.T
            _lowercase      			=torch.from_numpy(A__	)
            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'	):
            _lowercase      			=[k.endswith(A__	) for ending in KEYS_TO_IGNORE]
            if any(A__	):
                  continue
            _lowercase      			=REMAINING_PATTERNS
            _lowercase      			=rename_state_dict_key(A__ ,  A__	)
            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']	):
                  _lowercase      			=v.T
            _lowercase      			=torch.from_numpy(A__	)
            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}'''

      _lowercase      			=mapping['model.embed_positions.weight']
      _lowercase      			=mapping.pop('model.embed_positions.weight'	)
      _lowercase			,			_lowercase      			=torch_model.load_state_dict(A__ ,  strict=A__	)
      _lowercase      			=[
          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 (     A__      :  Tuple	)       -> Dict:





      """simple docstring"""



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





      """simple docstring"""



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


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






from __future__ import annotations






def 					UpperCamelCase   (      __magic_name__	:				list[int]	)  ->    list[int]:  # This function is recursive
		"""simple docstring"""




		lowercase__	     =						len(__magic_name__	)
		# If the array contains only one element, we return it (it's the stop condition of
		# recursion)
		if array_length <= 1:
				return array
				# Else
		lowercase__	     =						array[0]
		lowercase__	     =						False
		lowercase__	     =						1
		lowercase__	     =						[]
		while not is_found and i < array_length:
				if array[i] < pivot:
						lowercase__	     =						True
						lowercase__	     =						[element for element in array[i:] if element >= array[i]]
						lowercase__	     =						longest_subsequence(__magic_name__	)
						if len(__magic_name__	) > len(__magic_name__	):
								lowercase__	     =						temp_array
				else:
						i += 1

		lowercase__	     =						[element for element in array[1:] if element >= pivot]
		lowercase__	     =						[pivot, *longest_subsequence(__magic_name__	)]
		if len(__magic_name__	) > len(__magic_name__	):
				return temp_array
		else:
				return longest_subseq


if __name__ == "__main__":
	import doctest

	doctest.testmod()

