






"""simple docstring"""



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_UpperCamelCase		:  Dict						=		(
    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
)

_UpperCamelCase		:  Tuple						=		(
    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
)

_UpperCamelCase		:  int						=		[
    '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 (    __snake_case			:  List[Any]		,					__snake_case			:  List[Any]    ):





  '''simple docstring'''





  for tf_name, hf_name in patterns:
    lowercase							    =       k.replace(lowerCamelCase_		,					lowerCamelCase_    )
  return k
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  lowercase							    =       BigBirdPegasusConfig(**lowerCamelCase_    )
  lowercase							    =       BigBirdPegasusForConditionalGeneration(lowerCamelCase_    )
  lowercase							    =       torch_model.state_dict()
  lowercase							    =       {}

  # separating decoder weights
  lowercase							    =       {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'    )}
  lowercase							    =       {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'    ):
    lowercase							    =       [k.endswith(lowerCamelCase_    ) for ending in KEYS_TO_IGNORE]
    if any(lowerCamelCase_    ):
      continue
    lowercase							    =       DECODER_PATTERNS
    lowercase							    =       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']    ):
      lowercase							    =       v.T
    lowercase							    =       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'    ):
    lowercase							    =       [k.endswith(lowerCamelCase_    ) for ending in KEYS_TO_IGNORE]
    if any(lowerCamelCase_    ):
      continue
    lowercase							    =       REMAINING_PATTERNS
    lowercase							    =       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']    ):
      lowercase							    =       v.T
    lowercase							    =       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}'

  lowercase							    =       mapping['model.embed_positions.weight']
  lowercase							    =       mapping.pop('model.embed_positions.weight'    )
  lowercase    ,     lowercase							    =       torch_model.load_state_dict(lowerCamelCase_		,					strict=lowerCamelCase_    )
  lowercase							    =       [
      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 (    __snake_case			:  Dict    ):





  '''simple docstring'''





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





  '''simple docstring'''





  lowercase							    =       get_tf_weights_as_numpy(lowerCamelCase_    )
  lowercase							    =       convert_bigbird_pegasus(lowerCamelCase_		,					lowerCamelCase_    )
  torch_model.save_pretrained(lowerCamelCase_    )


if __name__ == "__main__":
    _UpperCamelCase		:  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.')
    _UpperCamelCase		:  Tuple						=		parser.parse_args()
    _UpperCamelCase		:  Optional[int]						=		{}
    convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)










import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class       _UpperCAmelCase					(       A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       CTRLTokenizer
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowercase__    =			['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
								lowercase__    =			dict(zip(lowerCamelCase,  range(len(lowerCamelCase  )  )  )  )
								lowercase__    =			['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
								lowercase__    =			{'''unk_token''': '''<unk>'''}

								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''vocab_file''']  )
								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write(json.dumps(lowerCamelCase  ) + '''\n'''  )
								with open(self.merges_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write('''\n'''.join(lowerCamelCase  )  )




				def 				lowercase__   (     self						:    Union[str, Any],  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								kwargs.update(self.special_tokens_map  )
								return CTRLTokenizer.from_pretrained(self.tmpdirname,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt react readapt apt'''
								return input_text, output_text




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			CTRLTokenizer(self.vocab_file,  self.merges_file,  **self.special_tokens_map  )
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
								lowercase__    =			tokenizer.tokenize(lowerCamelCase  )
								self.assertListEqual(lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			tokens + [tokenizer.unk_token]

								lowercase__    =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase  ),  lowerCamelCase  )




