



'''simple docstring'''





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowercase_									=    (
    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
)

lowercase_									=    (
    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
)

lowercase_									=    [
    "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    ,      __A):



     """simple docstring"""



     for tf_name, hf_name in patterns:
          _a           =    k.replace(__A    ,      __A)
     return k




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    BigBirdPegasusConfig(**__A)
     _a           =    BigBirdPegasusForConditionalGeneration(__A)
     _a           =    torch_model.state_dict()
     _a           =    {}

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

     _a           =    mapping['''model.embed_positions.weight''']
     _a           =    mapping.pop('''model.embed_positions.weight''')
     _a    ,       _a           =    torch_model.load_state_dict(__A    ,      strict=__A)
     _a           =    [
         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):



     """simple docstring"""



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




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    get_tf_weights_as_numpy(__A)
     _a           =    convert_bigbird_pegasus(__A    ,      __A)
     torch_model.save_pretrained(__A)


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








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_									=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    ["BlipImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    [
      "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BlipModel",
      "BlipPreTrainedModel",
      "BlipForConditionalGeneration",
      "BlipForQuestionAnswering",
      "BlipVisionModel",
      "BlipTextModel",
      "BlipForImageTextRetrieval",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=    [
      "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFBlipModel",
      "TFBlipPreTrainedModel",
      "TFBlipForConditionalGeneration",
      "TFBlipForQuestionAnswering",
      "TFBlipVisionModel",
      "TFBlipTextModel",
      "TFBlipForImageTextRetrieval",
  ]

if TYPE_CHECKING:
  from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
  from .processing_blip import BlipProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_blip import BlipImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip import (
        BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipForConditionalGeneration,
        BlipForImageTextRetrieval,
        BlipForQuestionAnswering,
        BlipModel,
        BlipPreTrainedModel,
        BlipTextModel,
        BlipVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blip import (
        TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBlipForConditionalGeneration,
        TFBlipForImageTextRetrieval,
        TFBlipForQuestionAnswering,
        TFBlipModel,
        TFBlipPreTrainedModel,
        TFBlipTextModel,
        TFBlipVisionModel,
    )

else:
  import sys

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



