import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCamelCase_    :		int							=	(
    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
)

lowerCamelCase_    :		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
)

lowerCamelCase_    :		List[Any]							=	[
    """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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					for tf_name, hf_name in patterns:
										__a		 =       k.replace(__lowerCamelCase				,    __lowerCamelCase						)
					return k
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       BigBirdPegasusConfig(**__lowerCamelCase						)
					__a		 =       BigBirdPegasusForConditionalGeneration(__lowerCamelCase						)
					__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(__lowerCamelCase						) for ending in KEYS_TO_IGNORE]
										if any(__lowerCamelCase						):
															continue
										__a		 =       DECODER_PATTERNS
										__a		 =       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']						):
															__a		 =       v.T
										__a		 =       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'						):
										__a		 =       [k.endswith(__lowerCamelCase						) for ending in KEYS_TO_IGNORE]
										if any(__lowerCamelCase						):
															continue
										__a		 =       REMAINING_PATTERNS
										__a		 =       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']						):
															__a		 =       v.T
										__a		 =       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}'''

					__a		 =       mapping['model.embed_positions.weight']
					__a		 =       mapping.pop('model.embed_positions.weight'						)
					__a ,				__a		 =       torch_model.load_state_dict(__lowerCamelCase				,    strict=__lowerCamelCase						)
					__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 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       tf.train.list_variables(__lowerCamelCase						)
					__a		 =       {}
					__a		 =       ['global_step']
					for name, shape in tqdm(__lowerCamelCase				,    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(__lowerCamelCase				,    __lowerCamelCase						)
										__a		 =       array
					return tf_weights
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       get_tf_weights_as_numpy(__lowerCamelCase						)
					__a		 =       convert_bigbird_pegasus(__lowerCamelCase				,    __lowerCamelCase						)
					torch_model.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
	lowerCamelCase_    :		List[str]							=	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.""")
	lowerCamelCase_    :		Dict							=	parser.parse_args()
	lowerCamelCase_    :		Optional[Any]							=	{}
	convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase_    :		Optional[Any]							=	{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	[
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




