






"""simple docstring"""
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCamelCase_							      = (
    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_							      = (
    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_							      = [
    '''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 (  A__      ,A__		):
			for tf_name, hf_name in patterns:
						UpperCAmelCase_	:					Tuple     	=   k.replace(A__      ,A__		)
			return k

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   BigBirdPegasusConfig(**A__		)
			UpperCAmelCase_	:					List[Any]     	=   BigBirdPegasusForConditionalGeneration(A__		)
			UpperCAmelCase_	:					List[str]     	=   torch_model.state_dict()
			UpperCAmelCase_	:					Any     	=   {}

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

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

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   get_tf_weights_as_numpy(A__		)
			UpperCAmelCase_	:					Union[str, Any]     	=   convert_bigbird_pegasus(A__      ,A__		)
			torch_model.save_pretrained(A__		)


if __name__ == "__main__":
			lowerCamelCase_							      = 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_							      = parser.parse_args()
			lowerCamelCase_							      = {}
			convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)











'''simple docstring'''



import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

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


__UpperCamelCase   :							int         =   logging.get_logger(__name__)

__UpperCamelCase   :							int         =   {
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align_text_model"




      def __init__(	self			, lowerCamelCase=3_0522			, lowerCamelCase=768			, lowerCamelCase=12			, lowerCamelCase=12			, lowerCamelCase=3072			, lowerCamelCase="gelu"			, lowerCamelCase=0.1			, lowerCamelCase=0.1			, lowerCamelCase=512			, lowerCamelCase=2			, lowerCamelCase=0.02			, lowerCamelCase=1e-12			, lowerCamelCase=0			, lowerCamelCase="absolute"			, lowerCamelCase=True			, **lowerCamelCase			, )				->List[Any]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         __a					  =     vocab_size
         __a					  =     hidden_size
         __a					  =     num_hidden_layers
         __a					  =     num_attention_heads
         __a					  =     hidden_act
         __a					  =     intermediate_size
         __a					  =     hidden_dropout_prob
         __a					  =     attention_probs_dropout_prob
         __a					  =     max_position_embeddings
         __a					  =     type_vocab_size
         __a					  =     initializer_range
         __a					  =     layer_norm_eps
         __a					  =     position_embedding_type
         __a					  =     use_cache
         __a					  =     pad_token_id






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the text config dict if we are loading from AlignConfig
         if config_dict.get('model_type'   ) == "align":
            __a					  =     config_dict['text_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align_vision_model"




      def __init__(	self			, lowerCamelCase = 3			, lowerCamelCase = 600			, lowerCamelCase = 2.0			, lowerCamelCase = 3.1			, lowerCamelCase = 8			, lowerCamelCase = [3, 3, 5, 3, 5, 5, 3]			, lowerCamelCase = [32, 16, 24, 40, 80, 112, 192]			, lowerCamelCase = [16, 24, 40, 80, 112, 192, 320]			, lowerCamelCase = []			, lowerCamelCase = [1, 2, 2, 2, 1, 2, 1]			, lowerCamelCase = [1, 2, 2, 3, 3, 4, 1]			, lowerCamelCase = [1, 6, 6, 6, 6, 6, 6]			, lowerCamelCase = 0.25			, lowerCamelCase = "swish"			, lowerCamelCase = 2560			, lowerCamelCase = "mean"			, lowerCamelCase = 0.02			, lowerCamelCase = 0.001			, lowerCamelCase = 0.99			, lowerCamelCase = 0.2			, **lowerCamelCase			, )				->Dict:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         __a					  =     num_channels
         __a					  =     image_size
         __a					  =     width_coefficient
         __a					  =     depth_coefficient
         __a					  =     depth_divisor
         __a					  =     kernel_sizes
         __a					  =     in_channels
         __a					  =     out_channels
         __a					  =     depthwise_padding
         __a					  =     strides
         __a					  =     num_block_repeats
         __a					  =     expand_ratios
         __a					  =     squeeze_expansion_ratio
         __a					  =     hidden_act
         __a					  =     hidden_dim
         __a					  =     pooling_type
         __a					  =     initializer_range
         __a					  =     batch_norm_eps
         __a					  =     batch_norm_momentum
         __a					  =     drop_connect_rate
         __a					  =     sum(lowerCamelCase   ) * 4






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the vision config dict if we are loading from AlignConfig
         if config_dict.get('model_type'   ) == "align":
            __a					  =     config_dict['vision_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align"
      __a	       =True




      def __init__(	self			, lowerCamelCase=None			, lowerCamelCase=None			, lowerCamelCase=640			, lowerCamelCase=1.0			, lowerCamelCase=0.02			, **lowerCamelCase			, )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         if text_config is None:
            __a					  =     {}
            logger.info('text_config is None. Initializing the AlignTextConfig with default values.'   )

         if vision_config is None:
            __a					  =     {}
            logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'   )

         __a					  =     AlignTextConfig(**lowerCamelCase   )
         __a					  =     AlignVisionConfig(**lowerCamelCase   )

         __a					  =     projection_dim
         __a					  =     temperature_init_value
         __a					  =     initializer_range




      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, lowerCamelCase			, **lowerCamelCase   )				->int:



         '''simple docstring'''
         return cls(text_config=text_config.to_dict()			, vision_config=vision_config.to_dict()			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     copy.deepcopy(self.__dict__   )
         __a					  =     self.text_config.to_dict()
         __a					  =     self.vision_config.to_dict()
         __a					  =     self.__class__.model_type
         return output