






"""simple docstring"""







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A )     ->       Tuple:
				lowercase    :      Optional[Any]       = s.rsplit(_A  ,				_A )
				return new.join(_A )


def      UpperCamelCase       ( _A )     ->       str:
				# 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      UpperCamelCase       ( _A )     ->       Tuple:
				lowercase    :      List[Any]       = {}

				lowercase    :      str       = ["""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:
																lowercase    :      Dict       = key.replace(F"""{group_key}."""  ,				F"""{group_key}.group.""" )

								if "res_path" in key:
												lowercase    :      List[str]       = key.replace("""res_path."""  ,				"""res_path.path.""" )

								if key.endswith(""".w""" ):
												lowercase    :      str       = rreplace(_A  ,				""".w"""  ,				""".weight"""  ,				1 )
								if key.endswith(""".b""" ):
												lowercase    :      Union[str, Any]       = rreplace(_A  ,				""".b"""  ,				""".bias"""  ,				1 )

								lowercase    :      Tuple       = value.float()

				return upgrade


@torch.no_grad()
def      UpperCamelCase       ( _A  ,				_A  ,				_A=None  ,				_A=True )     ->       Optional[int]:
				from dall_e import Encoder

				lowercase    :      str       = Encoder()
				if os.path.exists(_A ):
								lowercase    :      Dict       = torch.load(_A )
				else:
								lowercase    :      str       = torch.hub.load_state_dict_from_url(_A )

				if isinstance(_A  ,				_A ):
								lowercase    :      List[str]       = ckpt.state_dict()
				encoder.load_state_dict(_A )

				if config_path is not None:
								lowercase    :      List[str]       = FlavaImageCodebookConfig.from_pretrained(_A )
				else:
								lowercase    :      Optional[Any]       = FlavaImageCodebookConfig()

				lowercase    :      Optional[Any]       = FlavaImageCodebook(_A ).eval()
				lowercase    :      Dict       = encoder.state_dict()

				lowercase    :      List[str]       = upgrade_state_dict(_A )
				hf_model.load_state_dict(_A )
				lowercase    :      List[str]       = hf_model.state_dict()
				lowercase    :      Optional[int]       = count_parameters(_A )
				lowercase    :      int       = count_parameters(_A )

				assert torch.allclose(_A  ,				_A  ,				atol=1e-3 )

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


if __name__ == "__main__":
				_lowerCAmelCase            =	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')
				_lowerCAmelCase            =	parser.parse_args()

				convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








"""simple docstring"""







from typing import TYPE_CHECKING

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


_lowerCAmelCase            =	{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

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