




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__a							     =					(
    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							     =					(
    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							     =					[
    "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__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     for tf_name, hf_name in patterns:
          UpperCAmelCase_     :				int  =     k.replace(lowerCAmelCase__					,		lowerCAmelCase__ )
     return k







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     BigBirdPegasusConfig(**lowerCAmelCase__ )
     UpperCAmelCase_     :				Tuple  =     BigBirdPegasusForConditionalGeneration(lowerCAmelCase__ )
     UpperCAmelCase_     :				Optional[Any]  =     torch_model.state_dict()
     UpperCAmelCase_     :				Tuple  =     {}

     # separating decoder weights
     UpperCAmelCase_     :				Optional[Any]  =     {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
     UpperCAmelCase_     :				int  =     {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(lowerCAmelCase__ ) for ending in KEYS_TO_IGNORE]
          if any(lowerCAmelCase__ ):
               continue
          UpperCAmelCase_     :				Any  =     DECODER_PATTERNS
          UpperCAmelCase_     :				str  =     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'''] ):
               UpperCAmelCase_     :				int  =     v.T
          UpperCAmelCase_     :				Tuple  =     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''' ):
          UpperCAmelCase_     :				Any  =     [k.endswith(lowerCAmelCase__ ) for ending in KEYS_TO_IGNORE]
          if any(lowerCAmelCase__ ):
               continue
          UpperCAmelCase_     :				Optional[Any]  =     REMAINING_PATTERNS
          UpperCAmelCase_     :				Tuple  =     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'''] ):
               UpperCAmelCase_     :				int  =     v.T
          UpperCAmelCase_     :				str  =     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}'''

     UpperCAmelCase_     :				Dict  =     mapping['''model.embed_positions.weight''']
     UpperCAmelCase_     :				List[str]  =     mapping.pop('''model.embed_positions.weight''' )
     UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     torch_model.load_state_dict(lowerCAmelCase__					,		strict=lowerCAmelCase__ )
     UpperCAmelCase_     :				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 lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[str]  =     tf.train.list_variables(lowerCAmelCase__ )
     UpperCAmelCase_     :				List[Any]  =     {}
     UpperCAmelCase_     :				Tuple  =     ['''global_step''']
     for name, shape in tqdm(lowerCAmelCase__					,		desc='''converting tf checkpoint to dict''' ):
          UpperCAmelCase_     :				List[str]  =     any(pat in name for pat in ignore_name )
          if skip_key:
               continue
          UpperCAmelCase_     :				Dict  =     tf.train.load_variable(lowerCAmelCase__					,		lowerCAmelCase__ )
          UpperCAmelCase_     :				Union[str, Any]  =     array
     return tf_weights







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     get_tf_weights_as_numpy(lowerCAmelCase__ )
     UpperCAmelCase_     :				List[Any]  =     convert_bigbird_pegasus(lowerCAmelCase__					,		lowerCAmelCase__ )
     torch_model.save_pretrained(lowerCAmelCase__ )


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


'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""image_processor""", """tokenizer"""]
				_a    	=     """CLIPImageProcessor"""
				_a    	=     ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")



				def __init__(    self,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.',     _lowercase,     )
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('feature_extractor'       )

											SCREAMING_SNAKE_CASE_				=				image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'       )
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'       )

											super().__init__(_lowercase,     _lowercase       )



				def __call__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							str:

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'       )

											if text is not None:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if images is not None:
																		SCREAMING_SNAKE_CASE_				=				self.image_processor(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if text is not None and images is not None:
																		SCREAMING_SNAKE_CASE_				=				image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_lowercase       ),     tensor_type=_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											return self.tokenizer.batch_decode(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[Any]:
											return self.tokenizer.decode(*_lowercase,     **_lowercase       )





				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.model_input_names
											SCREAMING_SNAKE_CASE_				=				self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





