
"""simple docstring"""


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 					_snake_case (						snake_case__      :   Optional[Any]		,					snake_case__      :   List[str]   ):
	for tf_name, hf_name in patterns:
		A        =      k.replace(snake_case__		,					snake_case__   )
	return k






def 					_snake_case (						snake_case__      :   dict		,					snake_case__      :   dict   ):
	A        =      BigBirdPegasusConfig(**snake_case__   )
	A        =      BigBirdPegasusForConditionalGeneration(snake_case__   )
	A        =      torch_model.state_dict()
	A        =      {}

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






def 					_snake_case (						snake_case__      :   str		,					snake_case__      :   str		,					snake_case__      :   dict   ):
	A        =      get_tf_weights_as_numpy(snake_case__   )
	A        =      convert_bigbird_pegasus(snake_case__		,					snake_case__   )
	torch_model.save_pretrained(snake_case__   )


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)

"""simple docstring"""





import argparse
from collections import defaultdict







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    f"""{file}_{class_name}_{test_name}"""
   done_test[_id] += 1

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					List[Any]			     =    f.readlines()

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

   UpperCAmelCase_					:					int			     =    []
   for line in lines:
      if line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Tuple			     =    True
      elif in_class and line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Optional[int]			     =    True
      elif in_class and in_func and (line.startswith(__lowerCamelCase  ) or line.startswith(__lowerCamelCase  )):
         UpperCAmelCase_					:					Any			     =    len(line.split(correct_line.split()[0]  )[0]  )
         count += 1

         if count == done_test[_id]:
            UpperCAmelCase_					:					Union[str, Any]			     =    True

      if in_class and in_func and in_line:
         if ")" not in line:
            continue
         else:
            UpperCAmelCase_					:					Any			     =    True

      if in_class and in_func and in_line and insert_line:
         new_lines.append(f"""{spaces * " "}{correct_line}"""  )
         UpperCAmelCase_					:					int			     =    False
      else:
         new_lines.append(__lowerCamelCase  )

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







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=None  ):
   if fail is not None:
      with open(__lowerCamelCase,		"r"  ) as f:
         UpperCAmelCase_					:					Tuple			     =    {l.strip() for l in f.readlines()}
   else:
      UpperCAmelCase_					:					str			     =    None

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   UpperCAmelCase_					:					Any			     =    defaultdict(__lowerCamelCase  )
   for line in correct_lines:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    line.split(";"  )
      if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
         overwrite_file(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							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		       =							parser.parse_args()

    main(args.correct_filename, args.fail_filename)


