


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase__  :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
)

lowerCAmelCase__  :Optional[int]             =							(
    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__  :Tuple             =							[
    '''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__			(      a__:		Any      ,  a__:		Optional[Any]	)  ->		List[str]:


       '''simple docstring'''





       for tf_name, hf_name in patterns:
              _UpperCAmelCase					=      k.replace(a__      ,  a__	)
       return k

def 	lowerCAmelCase__			(      a__:		dict      ,  a__:		dict	)  ->		BigBirdPegasusForConditionalGeneration:


       '''simple docstring'''





       _UpperCAmelCase					=      BigBirdPegasusConfig(**a__	)
       _UpperCAmelCase					=      BigBirdPegasusForConditionalGeneration(a__	)
       _UpperCAmelCase					=      torch_model.state_dict()
       _UpperCAmelCase					=      {}

       # separating decoder weights
       _UpperCAmelCase					=      {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'	)}
       _UpperCAmelCase					=      {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					=      [k.endswith(a__	) for ending in KEYS_TO_IGNORE]
              if any(a__	):
                     continue
              _UpperCAmelCase					=      DECODER_PATTERNS
              _UpperCAmelCase					=      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					=      v.T
              _UpperCAmelCase					=      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					=      [k.endswith(a__	) for ending in KEYS_TO_IGNORE]
              if any(a__	):
                     continue
              _UpperCAmelCase					=      REMAINING_PATTERNS
              _UpperCAmelCase					=      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					=      v.T
              _UpperCAmelCase					=      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					=      mapping['model.embed_positions.weight']
       _UpperCAmelCase					=      mapping.pop('model.embed_positions.weight'	)
       _UpperCAmelCase		,	_UpperCAmelCase					=      torch_model.load_state_dict(a__      ,  strict=a__	)
       _UpperCAmelCase					=      [
           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__			(      a__:		Tuple	)  ->		Dict:


       '''simple docstring'''





       _UpperCAmelCase					=      tf.train.list_variables(a__	)
       _UpperCAmelCase					=      {}
       _UpperCAmelCase					=      ['global_step']
       for name, shape in tqdm(a__      ,  desc='converting tf checkpoint to dict'	):
              _UpperCAmelCase					=      any(pat in name for pat in ignore_name	)
              if skip_key:
                     continue
              _UpperCAmelCase					=      tf.train.load_variable(a__      ,  a__	)
              _UpperCAmelCase					=      array
       return tf_weights

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		dict	)  ->		List[str]:


       '''simple docstring'''





       _UpperCAmelCase					=      get_tf_weights_as_numpy(a__	)
       _UpperCAmelCase					=      convert_bigbird_pegasus(a__      ,  a__	)
       torch_model.save_pretrained(a__	)


if __name__ == "__main__":
  lowerCAmelCase__  :Union[str, 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.''')
  lowerCAmelCase__  :Optional[Any]             =							parser.parse_args()
  lowerCAmelCase__  :str             =							{}
  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_torch_available,
    is_vision_available,
)


lowerCAmelCase__  :Tuple             =							{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							['''BeitFeatureExtractor''']
		lowerCAmelCase__  :Optional[Any]             =							['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :int             =							[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Tuple             =							[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__  :Dict             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
