



import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)		->				str:
		UpperCAmelCase_						       =					s.rsplit(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)
		return new.join(__SCREAMING_SNAKE_CASE			)







def snake_case__	(      __SCREAMING_SNAKE_CASE			)		->				Optional[int]:
		# 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 snake_case__	(      __SCREAMING_SNAKE_CASE			)		->				Optional[int]:
		UpperCAmelCase_						       =					{}

		UpperCAmelCase_						       =					["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_						       =					key.replace(f'''{group_key}.'''      ,  f'''{group_key}.group.'''			)

				if "res_path" in key:
						UpperCAmelCase_						       =					key.replace("res_path."      ,  "res_path.path."			)

				if key.endswith(".w"			):
						UpperCAmelCase_						       =					rreplace(__SCREAMING_SNAKE_CASE      ,  ".w"      ,  ".weight"      ,  1			)
				if key.endswith(".b"			):
						UpperCAmelCase_						       =					rreplace(__SCREAMING_SNAKE_CASE      ,  ".b"      ,  ".bias"      ,  1			)

				UpperCAmelCase_						       =					value.float()

		return upgrade







@torch.no_grad()
def snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE=None      ,  __SCREAMING_SNAKE_CASE=True			)		->				List[Any]:
		from dall_e import Encoder

		UpperCAmelCase_						       =					Encoder()
		if os.path.exists(__SCREAMING_SNAKE_CASE			):
				UpperCAmelCase_						       =					torch.load(__SCREAMING_SNAKE_CASE			)
		else:
				UpperCAmelCase_						       =					torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE			)

		if isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			):
				UpperCAmelCase_						       =					ckpt.state_dict()
		encoder.load_state_dict(__SCREAMING_SNAKE_CASE			)

		if config_path is not None:
				UpperCAmelCase_						       =					FlavaImageCodebookConfig.from_pretrained(__SCREAMING_SNAKE_CASE			)
		else:
				UpperCAmelCase_						       =					FlavaImageCodebookConfig()

		UpperCAmelCase_						       =					FlavaImageCodebook(__SCREAMING_SNAKE_CASE			).eval()
		UpperCAmelCase_						       =					encoder.state_dict()

		UpperCAmelCase_						       =					upgrade_state_dict(__SCREAMING_SNAKE_CASE			)
		hf_model.load_state_dict(__SCREAMING_SNAKE_CASE			)
		UpperCAmelCase_						       =					hf_model.state_dict()
		UpperCAmelCase_						       =					count_parameters(__SCREAMING_SNAKE_CASE			)
		UpperCAmelCase_						       =					count_parameters(__SCREAMING_SNAKE_CASE			)

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

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE  					=    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")
			SCREAMING_SNAKE_CASE  					=    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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     Any									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)