





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 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Optional[int]:






				"""simple docstring"""


				for tf_name, hf_name in patterns:
								snake_case_		:       Optional[int]           =    k.replace(_UpperCamelCase					,							_UpperCamelCase					)
				return k






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		BigBirdPegasusForConditionalGeneration:






				"""simple docstring"""


				snake_case_		:       Tuple           =    BigBirdPegasusConfig(**_UpperCamelCase					)
				snake_case_		:       List[str]           =    BigBirdPegasusForConditionalGeneration(_UpperCamelCase					)
				snake_case_		:       Tuple           =    torch_model.state_dict()
				snake_case_		:       Tuple           =    {}

				# separating decoder weights
				snake_case_		:       Dict           =    {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''					)}
				snake_case_		:       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'''					):
								snake_case_		:       Optional[int]           =    [k.endswith(_UpperCamelCase					) for ending in KEYS_TO_IGNORE]
								if any(_UpperCamelCase					):
												continue
								snake_case_		:       str           =    DECODER_PATTERNS
								snake_case_		:       Optional[int]           =    rename_state_dict_key(_UpperCamelCase					,							_UpperCamelCase					)
								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_		:       List[str]           =    v.T
								snake_case_		:       Dict           =    torch.from_numpy(_UpperCamelCase					)
								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_		:       Union[str, Any]           =    [k.endswith(_UpperCamelCase					) for ending in KEYS_TO_IGNORE]
								if any(_UpperCamelCase					):
												continue
								snake_case_		:       Optional[Any]           =    REMAINING_PATTERNS
								snake_case_		:       int           =    rename_state_dict_key(_UpperCamelCase					,							_UpperCamelCase					)
								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_		:       List[str]           =    v.T
								snake_case_		:       Dict           =    torch.from_numpy(_UpperCamelCase					)
								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_		:       Dict           =    mapping['''model.embed_positions.weight''']
				snake_case_		:       Tuple           =    mapping.pop('''model.embed_positions.weight'''					)
				snake_case_     ,				snake_case_		:       Union[str, Any]           =    torch_model.load_state_dict(_UpperCamelCase					,							strict=_UpperCamelCase					)
				snake_case_		:       Union[str, 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_		(							_UpperCamelCase					)						->		Dict:






				"""simple docstring"""


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






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					)						->		int:






				"""simple docstring"""


				snake_case_		:       List[Any]           =    get_tf_weights_as_numpy(_UpperCamelCase					)
				snake_case_		:       Optional[Any]           =    convert_bigbird_pegasus(_UpperCamelCase					,							_UpperCamelCase					)
				torch_model.save_pretrained(_UpperCamelCase					)


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"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MgpstrModel""",
      """MgpstrPreTrainedModel""",
      """MgpstrForSceneTextRecognition""",
  ]

if TYPE_CHECKING:
  from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
  from .processing_mgp_str import MgpstrProcessor
  from .tokenization_mgp_str import MgpstrTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mgp_str import (
        MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
        MgpstrForSceneTextRecognition,
        MgpstrModel,
        MgpstrPreTrainedModel,
    )
else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
