'''simple docstring'''




import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint






def 							snake_case__				(     _A:   Any					)       ->      int:

		'''simple docstring'''


		return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()					)






def 							snake_case__				(     _A:   Any ,  _A:   Optional[Any]					)       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    {}

		for key, value in state_dict.items():
				if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
						continue

				lowerCAmelCase     =    key.replace("""heads.cmd.mim_head.cls.predictions""" ,  """mmm_image_head"""					)
				lowerCAmelCase     =    key.replace("""heads.cmd.mlm_head.cls.predictions""" ,  """mmm_text_head"""					)
				lowerCAmelCase     =    key.replace("""heads.cmd.itm_head.cls""" ,  """itm_head"""					)
				lowerCAmelCase     =    key.replace("""heads.cmd.itm_head.pooler""" ,  """itm_head.pooler"""					)
				lowerCAmelCase     =    key.replace("""heads.cmd.clip_head.logit_scale""" ,  """flava.logit_scale"""					)
				lowerCAmelCase     =    key.replace("""heads.fairseq_mlm.cls.predictions""" ,  """mlm_head"""					)
				lowerCAmelCase     =    key.replace("""heads.imagenet.mim_head.cls.predictions""" ,  """mim_head"""					)
				lowerCAmelCase     =    key.replace("""mm_text_projection""" ,  """flava.text_to_mm_projection"""					)
				lowerCAmelCase     =    key.replace("""mm_image_projection""" ,  """flava.image_to_mm_projection"""					)
				lowerCAmelCase     =    key.replace("""image_encoder.module""" ,  """flava.image_model"""					)
				lowerCAmelCase     =    key.replace("""text_encoder.module""" ,  """flava.text_model"""					)
				lowerCAmelCase     =    key.replace("""mm_encoder.module.encoder.cls_token""" ,  """flava.multimodal_model.cls_token"""					)
				lowerCAmelCase     =    key.replace("""mm_encoder.module""" ,  """flava.multimodal_model"""					)
				lowerCAmelCase     =    key.replace("""text_projection""" ,  """flava.text_projection"""					)
				lowerCAmelCase     =    key.replace("""image_projection""" ,  """flava.image_projection"""					)

				lowerCAmelCase     =    value.float()

		for key, value in codebook_state_dict.items():
				lowerCAmelCase     =    value

		return upgrade






@torch.no_grad()
def 							snake_case__				(     _A:   Dict ,  _A:   Union[str, Any] ,  _A:   Tuple ,  _A:   int=None					)       ->      Optional[int]:

		'''simple docstring'''


		if config_path is not None:
				lowerCAmelCase     =    FlavaConfig.from_pretrained(_A					)
		else:
				lowerCAmelCase     =    FlavaConfig()

		lowerCAmelCase     =    FlavaForPreTraining(_A					).eval()

		lowerCAmelCase     =    convert_dalle_checkpoint(_A ,  _A ,  save_checkpoint=_A					)

		if os.path.exists(_A					):
				lowerCAmelCase     =    torch.load(_A ,  map_location="""cpu"""					)
		else:
				lowerCAmelCase     =    torch.hub.load_state_dict_from_url(_A ,  map_location="""cpu"""					)

		lowerCAmelCase     =    upgrade_state_dict(_A ,  _A					)
		hf_model.load_state_dict(_A					)
		lowerCAmelCase     =    hf_model.state_dict()
		lowerCAmelCase     =    count_parameters(_A					)
		lowerCAmelCase     =    count_parameters(_A					) + count_parameters(_A					)

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

		hf_model.save_pretrained(_A					)


if __name__ == "__main__":
						__lowercase        =    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('''--codebook_path''', default=None, type=str, help='''Path to flava codebook checkpoint''')
						parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						__lowercase        =    parser.parse_args()

						convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase        =    logging.get_logger(__name__)

__lowercase        =    {
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			'''mobilenet_v1'''



			def __init__(  self			,       __lowerCAmelCase=3			,       __lowerCAmelCase=224			,       __lowerCAmelCase=1.0			,       __lowerCAmelCase=8			,       __lowerCAmelCase="relu6"			,       __lowerCAmelCase=True			,       __lowerCAmelCase=0.999			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=0.001			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)

					if depth_multiplier <= 0:
							raise ValueError("""depth_multiplier must be greater than zero.""")

					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    depth_multiplier
					lowerCAmelCase     =    min_depth
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    tf_padding
					lowerCAmelCase     =    classifier_dropout_prob
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    layer_norm_eps







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			version.parse('''1.11'''      )



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return OrderedDict([("""pixel_values""", {0: """batch"""})])



			@property
			def 				a_			(  self):




					"""simple docstring"""


					if self.task == "image-classification":
							return OrderedDict([("""logits""", {0: """batch"""})])
					else:
							return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})])







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 1E-4




