






"""simple docstring"""

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def _lowerCAmelCase	(  __lowerCamelCase:Optional[Any] ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Tuple   ):

						'''simple docstring'''






						__magic_name__            =     OmegaConf.load(__lowerCamelCase   )
						__magic_name__            =     torch.load(__lowerCamelCase ,						map_location="cpu"   )["model"]
						__magic_name__            =     list(state_dict.keys()   )

						# extract state_dict for VQVAE
						__magic_name__            =     {}
						__magic_name__            =     "first_stage_model."
						for key in keys:
												if key.startswith(__lowerCamelCase   ):
																		__magic_name__            =     state_dict[key]

    # extract state_dict for UNetLDM
						__magic_name__            =     {}
						__magic_name__            =     "model.diffusion_model."
						for key in keys:
												if key.startswith(__lowerCamelCase   ):
																		__magic_name__            =     state_dict[key]

						__magic_name__            =     config.model.params.first_stage_config.params
						__magic_name__            =     config.model.params.unet_config.params

						__magic_name__            =     VQModel(**__lowerCamelCase   ).eval()
						vqvae.load_state_dict(__lowerCamelCase   )

						__magic_name__            =     UNetLDMModel(**__lowerCamelCase   ).eval()
						unet.load_state_dict(__lowerCamelCase   )

						__magic_name__            =     DDIMScheduler(
						    timesteps=config.model.params.timesteps ,						beta_schedule="scaled_linear" ,						beta_start=config.model.params.linear_start ,						beta_end=config.model.params.linear_end ,						clip_sample=__lowerCamelCase ,						)

						__magic_name__            =     LDMPipeline(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )
						pipeline.save_pretrained(__lowerCamelCase   )


if __name__ == "__main__":
			lowercase    			=       argparse.ArgumentParser()
			parser.add_argument('''--checkpoint_path''', type=str, required=True)
			parser.add_argument('''--config_path''', type=str, required=True)
			parser.add_argument('''--output_path''', type=str, required=True)
			lowercase    			=       parser.parse_args()

			convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)











"""simple docstring"""

from typing import TYPE_CHECKING

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


lowercase    			=       {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

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



