


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A			:						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
)

A			:						str        =  (
    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			:						str        =  [
    '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 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Optional[int]	)  ->    int:
		"""simple docstring"""




		for tf_name, hf_name in patterns:
				lowercase__	     =						k.replace(__magic_name__       ,       __magic_name__	)
		return k






def 					UpperCamelCase   (      __magic_name__	:				dict       ,       __magic_name__	:				dict	)  ->    BigBirdPegasusForConditionalGeneration:
		"""simple docstring"""




		lowercase__	     =						BigBirdPegasusConfig(**__magic_name__	)
		lowercase__	     =						BigBirdPegasusForConditionalGeneration(__magic_name__	)
		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(__magic_name__	) for ending in KEYS_TO_IGNORE]
				if any(__magic_name__	):
						continue
				lowercase__	     =						DECODER_PATTERNS
				lowercase__	     =						rename_state_dict_key(__magic_name__       ,       __magic_name__	)
				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(__magic_name__	)
				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(__magic_name__	) for ending in KEYS_TO_IGNORE]
				if any(__magic_name__	):
						continue
				lowercase__	     =						REMAINING_PATTERNS
				lowercase__	     =						rename_state_dict_key(__magic_name__       ,       __magic_name__	)
				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(__magic_name__	)
				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(__magic_name__       ,       strict=__magic_name__	)
		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 					UpperCamelCase   (      __magic_name__	:				Tuple	)  ->    Dict:
		"""simple docstring"""




		lowercase__	     =						tf.train.list_variables(__magic_name__	)
		lowercase__	     =						{}
		lowercase__	     =						["""global_step"""]
		for name, shape in tqdm(__magic_name__       ,       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(__magic_name__       ,       __magic_name__	)
				lowercase__	     =						array
		return tf_weights






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str       ,       __magic_name__	:				dict	)  ->    int:
		"""simple docstring"""




		lowercase__	     =						get_tf_weights_as_numpy(__magic_name__	)
		lowercase__	     =						convert_bigbird_pegasus(__magic_name__       ,       __magic_name__	)
		torch_model.save_pretrained(__magic_name__	)


if __name__ == "__main__":
	A			:						Union[str, 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.')
	A			:						Any        =  parser.parse_args()
	A			:						Optional[Any]        =  {}
	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()

