






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__      :     Tuple       =					logging.get_logger(__name__)

A__      :     Any       =					{
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			"""visual_bert"""




				def __init__(	self		:       int,    lowerCamelCase		:       List[str]=30_522,    lowerCamelCase		:       List[Any]=768,    lowerCamelCase		:       Optional[Any]=512,    lowerCamelCase		:       Union[str, Any]=12,    lowerCamelCase		:       str=12,    lowerCamelCase		:       List[str]=3_072,    lowerCamelCase		:       Optional[int]="gelu",    lowerCamelCase		:       Optional[Any]=0.1,    lowerCamelCase		:       List[Any]=0.1,    lowerCamelCase		:       Optional[Any]=512,    lowerCamelCase		:       List[Any]=2,    lowerCamelCase		:       Optional[Any]=0.02,    lowerCamelCase		:       Tuple=1E-12,    lowerCamelCase		:       Union[str, Any]=False,    lowerCamelCase		:       Any=True,    lowerCamelCase		:       str=1,    lowerCamelCase		:       Dict=0,    lowerCamelCase		:       str=2,    **lowerCamelCase		:       str,    ):

								'''simple docstring'''
								super().__init__(pad_token_id=lowerCamelCase,    bos_token_id=lowerCamelCase,    eos_token_id=lowerCamelCase,    **lowerCamelCase    )

								lowercase__  					=     vocab_size
								lowercase__  					=     max_position_embeddings
								lowercase__  					=     hidden_size
								lowercase__  					=     visual_embedding_dim
								lowercase__  					=     num_hidden_layers
								lowercase__  					=     num_attention_heads
								lowercase__  					=     intermediate_size
								lowercase__  					=     hidden_act
								lowercase__  					=     hidden_dropout_prob
								lowercase__  					=     attention_probs_dropout_prob
								lowercase__  					=     initializer_range
								lowercase__  					=     type_vocab_size
								lowercase__  					=     layer_norm_eps
								lowercase__  					=     bypass_transformer
								lowercase__  					=     special_visual_initialize












import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

A__      :     List[str]       =					(
    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__      :     List[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__      :     Dict       =					[
    '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     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				for tf_name, hf_name in patterns:
								lowercase__  					=     k.replace(lowerCamelCase_						,  lowerCamelCase_							)
				return k






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     BigBirdPegasusConfig(**lowerCamelCase_							)
				lowercase__  					=     BigBirdPegasusForConditionalGeneration(lowerCamelCase_							)
				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(lowerCamelCase_							) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase_							):
												continue
								lowercase__  					=     DECODER_PATTERNS
								lowercase__  					=     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''']							):
												lowercase__  					=     v.T
								lowercase__  					=     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'''							):
								lowercase__  					=     [k.endswith(lowerCamelCase_							) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase_							):
												continue
								lowercase__  					=     REMAINING_PATTERNS
								lowercase__  					=     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''']							):
												lowercase__  					=     v.T
								lowercase__  					=     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}"""

				lowercase__  					=     mapping['''model.embed_positions.weight''']
				lowercase__  					=     mapping.pop('''model.embed_positions.weight'''							)
				lowercase__					,		lowercase__  					=     torch_model.load_state_dict(lowerCamelCase_						,  strict=lowerCamelCase_							)
				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     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     tf.train.list_variables(lowerCamelCase_							)
				lowercase__  					=     {}
				lowercase__  					=     ['''global_step''']
				for name, shape in tqdm(lowerCamelCase_						,  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(lowerCamelCase_						,  lowerCamelCase_							)
								lowercase__  					=     array
				return tf_weights






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     get_tf_weights_as_numpy(lowerCamelCase_							)
				lowercase__  					=     convert_bigbird_pegasus(lowerCamelCase_						,  lowerCamelCase_							)
				torch_model.save_pretrained(lowerCamelCase_							)


if __name__ == "__main__":
							A__      :     Optional[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__      :     Tuple       =					parser.parse_args()
							A__      :     int       =					{}
							convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




