






'''simple docstring'''






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        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       str  )    -> Dict:







   """simple docstring"""
   for tf_name, hf_name in patterns:
      __snake_case				:      List[Any]     							= k.replace(A  ,		A  )
   return k




def        _SCREAMING_SNAKE_CASE  (				A  :       dict  ,		A  :       dict  )    -> BigBirdPegasusForConditionalGeneration:







   """simple docstring"""
   __snake_case				:      Tuple     							= BigBirdPegasusConfig(**A  )
   __snake_case				:      Dict     							= BigBirdPegasusForConditionalGeneration(A  )
   __snake_case				:      int     							= torch_model.state_dict()
   __snake_case				:      str     							= {}

   # separating decoder weights
   __snake_case				:      str     							= {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'  )}
   __snake_case				:      str     							= {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'  ):
      __snake_case				:      Tuple     							= [k.endswith(A  ) for ending in KEYS_TO_IGNORE]
      if any(A  ):
         continue
      __snake_case				:      str     							= DECODER_PATTERNS
      __snake_case				:      Optional[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']  ):
         __snake_case				:      Optional[Any]     							= v.T
      __snake_case				:      Dict     							= 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'  ):
      __snake_case				:      List[str]     							= [k.endswith(A  ) for ending in KEYS_TO_IGNORE]
      if any(A  ):
         continue
      __snake_case				:      List[str]     							= REMAINING_PATTERNS
      __snake_case				:      List[str]     							= 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']  ):
         __snake_case				:      Union[str, Any]     							= v.T
      __snake_case				:      Dict     							= 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}"""

   __snake_case				:      Optional[int]     							= mapping['model.embed_positions.weight']
   __snake_case				:      Tuple     							= mapping.pop('model.embed_positions.weight'  )
   __snake_case							,__snake_case				:      Optional[int]     							= torch_model.load_state_dict(A  ,		strict=A  )
   __snake_case				:      Optional[int]     							= [
       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        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= tf.train.list_variables(A  )
   __snake_case				:      Optional[int]     							= {}
   __snake_case				:      int     							= ['global_step']
   for name, shape in tqdm(A  ,		desc='converting tf checkpoint to dict'  ):
      __snake_case				:      str     							= any(pat in name for pat in ignore_name  )
      if skip_key:
         continue
      __snake_case				:      List[str]     							= tf.train.load_variable(A  ,		A  )
      __snake_case				:      Union[str, Any]     							= array
   return tf_weights




def        _SCREAMING_SNAKE_CASE  (				A  :       str  ,		A  :       str  ,		A  :       dict  )    -> int:







   """simple docstring"""
   __snake_case				:      List[Any]     							= get_tf_weights_as_numpy(A  )
   __snake_case				:      List[Any]     							= convert_bigbird_pegasus(A  ,		A  )
   torch_model.save_pretrained(A  )


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 contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
      from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
      from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
      from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__A    	=   DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__A    	=   '''main'''
# Default branch name
__A    	=   '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
__A    	=   '''aaaaaaa'''
# This commit does not exist, so we should 404.

__A    	=   '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
__A    	=   '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''




@contextlib.contextmanager
def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:







   """simple docstring"""
   print('Welcome!'  )
   yield
   print('Bye!'  )




@contextlib.contextmanager
def        _SCREAMING_SNAKE_CASE  (				)    -> Optional[Any]:







   """simple docstring"""
   print('Bonjour!'  )
   yield
   print('Au revoir!'  )




class a_    (    unittest.TestCase							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      assert transformers.__spec__ is not None
      assert importlib.util.find_spec('transformers') is not None




class a_    (    unittest.TestCase							):







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      int:
      """simple docstring"""






      with ContextManagers([]):
         print('Transformers are awesome!')
      # The print statement adds a new line at the end of the output
      self.assertEqual(mock_stdout.getvalue()					,   'Transformers are awesome!\n')







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      List[str]:
      """simple docstring"""






      with ContextManagers([context_en()]):
         print('Transformers are awesome!')
      # The output should be wrapped with an English welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()					,   'Welcome!\nTransformers are awesome!\nBye!\n')







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Tuple:
      """simple docstring"""






      with ContextManagers([context_fr(), context_en()]):
         print('Transformers are awesome!')
      # The output should be wrapped with an English and French welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()					,   'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n')







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   ['labels'])
      self.assertEqual(find_labels(__a)					,   ['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(__a)					,   ['start_positions', 'end_positions'])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   ['labels'])







   @require_tf
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   ['labels'])
      self.assertEqual(find_labels(__a)					,   ['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(__a)					,   ['start_positions', 'end_positions'])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   ['labels'])







   @require_flax
   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   [])
      self.assertEqual(find_labels(__a)					,   [])
      self.assertEqual(find_labels(__a)					,   [])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   [])