

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A_	:			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
)

A_	:			Dict  	=      (
    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_	:			int  	=      [
    '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 (lowercase_:			List[Any]   ,				lowercase_:			List[str] )					->				Optional[int]:
	for tf_name, hf_name in patterns:
		A__							:   List[Any]					       =		k.replace(lowercase_   ,				lowercase_ )
	return k

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			dict )					->				BigBirdPegasusForConditionalGeneration:
	A__							:   str					       =		BigBirdPegasusConfig(**lowercase_ )
	A__							:   List[Any]					       =		BigBirdPegasusForConditionalGeneration(lowercase_ )
	A__							:   Optional[Any]					       =		torch_model.state_dict()
	A__							:   Tuple					       =		{}

	# separating decoder weights
	A__							:   str					       =		{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder""" )}
	A__							:   Dict					       =		{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""" ):
		A__							:   Tuple					       =		[k.endswith(lowercase_ ) for ending in KEYS_TO_IGNORE]
		if any(lowercase_ ):
			continue
		A__							:   Optional[Any]					       =		DECODER_PATTERNS
		A__							:   Dict					       =		rename_state_dict_key(lowercase_   ,				lowercase_ )
		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"""] ):
			A__							:   Tuple					       =		v.T
		A__							:   int					       =		torch.from_numpy(lowercase_ )
		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""" ):
		A__							:   Union[str, Any]					       =		[k.endswith(lowercase_ ) for ending in KEYS_TO_IGNORE]
		if any(lowercase_ ):
			continue
		A__							:   Optional[Any]					       =		REMAINING_PATTERNS
		A__							:   Optional[int]					       =		rename_state_dict_key(lowercase_   ,				lowercase_ )
		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"""] ):
			A__							:   int					       =		v.T
		A__							:   Optional[Any]					       =		torch.from_numpy(lowercase_ )
		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}"""

	A__							:   Optional[Any]					       =		mapping["""model.embed_positions.weight"""]
	A__							:   List[Any]					       =		mapping.pop("""model.embed_positions.weight""" )
	A__       ,       A__							:   Optional[int]					       =		torch_model.load_state_dict(lowercase_   ,				strict=lowercase_ )
	A__							:   Tuple					       =		[
	    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 (lowercase_:			int )					->				Dict:
	A__							:   Any					       =		tf.train.list_variables(lowercase_ )
	A__							:   List[Any]					       =		{}
	A__							:   int					       =		["""global_step"""]
	for name, shape in tqdm(lowercase_   ,				desc="""converting tf checkpoint to dict""" ):
		A__							:   Optional[Any]					       =		any(pat in name for pat in ignore_name )
		if skip_key:
			continue
		A__							:   Optional[Any]					       =		tf.train.load_variable(lowercase_   ,				lowercase_ )
		A__							:   str					       =		array
	return tf_weights

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict )					->				Optional[Any]:
	A__							:   Optional[Any]					       =		get_tf_weights_as_numpy(lowercase_ )
	A__							:   Optional[Any]					       =		convert_bigbird_pegasus(lowercase_   ,				lowercase_ )
	torch_model.save_pretrained(lowercase_ )


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_	:			List[Any]  	=      parser.parse_args()
	A_	:			Optional[int]  	=      {}
	convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






import argparse
from collections import defaultdict

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[int]   ,				lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			Any )					->				int:
	A__							:   Optional[Any]					       =		f"""{file}_{class_name}_{test_name}"""
	done_test[_id] += 1

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   Union[str, Any]					       =		f.readlines()

	A__							:   str					       =		f"""class {class_name}("""
	A__							:   Optional[Any]					       =		f"""{4 * ' '}def {test_name}("""
	A__							:   Union[str, Any]					       =		f"""{8 * ' '}{correct_line.split()[0]}"""
	A__							:   Optional[int]					       =		f"""{16 * ' '}{correct_line.split()[0]}"""
	A__							:   int					       =		False
	A__							:   str					       =		False
	A__							:   Tuple					       =		False
	A__							:   Optional[int]					       =		False
	A__							:   Optional[Any]					       =		0
	A__							:   Dict					       =		0

	A__							:   List[str]					       =		[]
	for line in lines:
		if line.startswith(lowercase_ ):
			A__							:   Dict					       =		True
		elif in_class and line.startswith(lowercase_ ):
			A__							:   Optional[Any]					       =		True
		elif in_class and in_func and (line.startswith(lowercase_ ) or line.startswith(lowercase_ )):
			A__							:   Tuple					       =		len(line.split(correct_line.split()[0] )[0] )
			count += 1

			if count == done_test[_id]:
				A__							:   Any					       =		True

		if in_class and in_func and in_line:
			if ")" not in line:
				continue
			else:
				A__							:   Dict					       =		True

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f"""{spaces * ' '}{correct_line}""" )
			A__							:   List[str]					       =		False
		else:
			new_lines.append(lowercase_ )

	with open(lowercase_   ,				"""w""" ) as f:
		for line in new_lines:
			f.write(lowercase_ )

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Optional[Any]=None )					->				Any:
	if fail is not None:
		with open(lowercase_   ,				"""r""" ) as f:
			A__							:   Dict					       =		{l.strip() for l in f.readlines()}
	else:
		A__							:   List[str]					       =		None

	with open(lowercase_   ,				"""r""" ) as f:
		A__							:   int					       =		f.readlines()

	A__							:   Union[str, Any]					       =		defaultdict(lowercase_ )
	for line in correct_lines:
		A__       ,       A__       ,       A__       ,       A__							:   Optional[int]					       =		line.split(""";""" )
		if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
			overwrite_file(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	A_	:			Optional[Any]  	=      argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	A_	:			Optional[Any]  	=      parser.parse_args()

	main(args.correct_filename, args.fail_filename)



