import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def       lowerCAmelCase_     (		lowercase:							Any  ,					lowercase:							List[str]  ,					lowercase:							Any  ,					lowercase:							Union[str, Any]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= s.rsplit(lowercase  ,					lowercase		)
 return new.join(lowercase		)




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				List[Any]:







 '''simple docstring'''
 # encoder.embeddings are double copied in original FLAVA
 return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()		)




def       lowerCAmelCase_     (		lowercase:							Dict		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= {}

 _UpperCamelCase:     Tuple							= ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
 for key, value in state_dict.items():
  for group_key in group_keys:
   if group_key in key:
    _UpperCamelCase:     Any							= key.replace(F"""{group_key}."""  ,					F"""{group_key}.group."""		)

  if "res_path" in key:
   _UpperCamelCase:     int							= key.replace('''res_path.'''  ,					'''res_path.path.'''		)

  if key.endswith('''.w'''		):
   _UpperCamelCase:     List[str]							= rreplace(lowercase  ,					'''.w'''  ,					'''.weight'''  ,					1		)
  if key.endswith('''.b'''		):
   _UpperCamelCase:     List[str]							= rreplace(lowercase  ,					'''.b'''  ,					'''.bias'''  ,					1		)

  _UpperCamelCase:     Optional[Any]							= value.float()

 return upgrade




@torch.no_grad()
def       lowerCAmelCase_     (		lowercase:							Union[str, Any]  ,					lowercase:							Dict  ,					lowercase:							List[str]=None  ,					lowercase:							List[str]=True		)    ->				Any:







 '''simple docstring'''
 from dall_e import Encoder

 _UpperCamelCase:     Any							= Encoder()
 if os.path.exists(lowercase		):
  _UpperCamelCase:     Union[str, Any]							= torch.load(lowercase		)
 else:
  _UpperCamelCase:     Union[str, Any]							= torch.hub.load_state_dict_from_url(lowercase		)

 if isinstance(lowercase  ,					lowercase		):
  _UpperCamelCase:     str							= ckpt.state_dict()
 encoder.load_state_dict(lowercase		)

 if config_path is not None:
  _UpperCamelCase:     List[Any]							= FlavaImageCodebookConfig.from_pretrained(lowercase		)
 else:
  _UpperCamelCase:     int							= FlavaImageCodebookConfig()

 _UpperCamelCase:     Dict							= FlavaImageCodebook(lowercase		).eval()
 _UpperCamelCase:     str							= encoder.state_dict()

 _UpperCamelCase:     Union[str, Any]							= upgrade_state_dict(lowercase		)
 hf_model.load_state_dict(lowercase		)
 _UpperCamelCase:     Union[str, Any]							= hf_model.state_dict()
 _UpperCamelCase:     Optional[Any]							= count_parameters(lowercase		)
 _UpperCamelCase:     str							= count_parameters(lowercase		)

 assert torch.allclose(lowercase  ,					lowercase  ,					atol=1E-3		)

 if save_checkpoint:
  hf_model.save_pretrained(lowercase		)
 else:
  return hf_state_dict


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  UpperCAmelCase_			    =							parser.parse_args()

  convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''XLNetTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLNetForMultipleChoice''',
      '''XLNetForQuestionAnswering''',
      '''XLNetForQuestionAnsweringSimple''',
      '''XLNetForSequenceClassification''',
      '''XLNetForTokenClassification''',
      '''XLNetLMHeadModel''',
      '''XLNetModel''',
      '''XLNetPreTrainedModel''',
      '''load_tf_weights_in_xlnet''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLNetForMultipleChoice''',
      '''TFXLNetForQuestionAnsweringSimple''',
      '''TFXLNetForSequenceClassification''',
      '''TFXLNetForTokenClassification''',
      '''TFXLNetLMHeadModel''',
      '''TFXLNetMainLayer''',
      '''TFXLNetModel''',
      '''TFXLNetPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_			    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)